Now, it is time to bring it all together and talk about the variable that bankrupts 90% of dating startups in their first year: Marketing Costs.

Let’s revisit the Golden Formula of the dating business:

Expected Revenue = (New Buyers × LTV) – Marketing Costs

Why is it that when we talk about “marketing costs” in a scalable business, we almost exclusively mean paid traffic (Performance Marketing)? Because businesses require predictability. This formula only works when you know with absolute certainty: if I invest $1 today, I am guaranteed to get $2 tomorrow. Only paid channels provide that level of control.

But before we dive into the brutal mathematics of media buying, we need to establish one critical baseline.

The Foundation: The Cold Start Problem Must Be Solved

Paid traffic is not a magic wand you wave over an empty app. Think of it as a pure multiplier. You do not apply a multiplier to zero; you apply it to an ecosystem that is already working.

For this article, we are assuming you have already solved the “Cold Start Problem.” You have basic liquidity, your ecosystem is alive, and new users are hitting their “Aha-Moment” (receiving their first like or message) organically.

(Note: If your product is still a ghost town, stop right here. Paid ads will kill your startup. Focus on building your initial base for free first).

The Organic Trap: Great for Starting, Terrible for Scaling

How did you acquire those first few hundred users? Most likely, you relied on guerrilla tactics and organic channels:

• SEO and ASO (App Store Optimization)

• Content Marketing (YouTube, TikTok, Instagram Reels)

• Community Building (Facebook groups, WhatsApp chats, Reddit)

• Offline Events (Local parties, speed dating)

These channels are fantastic for getting off the ground. But they share a fatal flaw: they do not scale predictably.

• SEO and ASO (The Glass Ceiling): These channels rely on Demand Capturing. You are capturing people who are already searching for “vegan dating in London.” That yields a hyper-targeted audience, but if only 500 people search for that a month, you hit a glass ceiling immediately.

• YouTube and TikTok (The Viral Lottery): Virality is amazing, but it is a lottery. You cannot walk into a Monday meeting and say, “We need 10,000 paying users in New York by Friday; let’s shoot a viral video.” The algorithm does not work for you.

• Communities and Offline (Manual Labor): Hustling in Facebook groups is grinding, manual labor. It simply does not allow for exponential growth.

To control your growth with a budget slider, you have to step into the open market of paid advertising.

The Proving Ground: What to Do With Your First $500 in Revenue?

Let’s say your organic traffic is flowing, and the app is generating a modest $500 to $1,000 a month. You might think, “Great! I’ll take $3,000, dump it into Facebook Ads, and 5x my revenue!”

Stop. Organic traffic is warm and forgiving. If your free users coming from highly specific search terms are converting into paying subscribers at a 1% rate, cold traffic from Facebook will likely convert at 0.1%. You will burn through your runway in a weekend.

This is exactly where the concept of the Growth Loop comes into play. Let’s look at our formula again:

Expected Revenue = (New Buyers × LTV) – Marketing Costs

For your business to scale, your Expected Revenue must be strictly greater than zero (> 0). That positive difference is your true margin. In a sustainable business, you take that margin and immediately reinvest it back into buying more ads. This creates a compounding Growth Loop where the business systematically funds its own escalating turnover.

But—and this is the critical catch—this loop only spins if your product actually converts. It is not just about buying high-quality traffic; the app itself must be a ruthless conversion machine that reliably extracts LTV from subscribers so that your Expected Revenue stays above zero. Only then do you have the margin to feed back into the ad networks.

Throughout my 15+ years in this industry—building platforms like SkaDate and observing the inner workings of giants like Seeking, Meetville, and Dil Mil—I’ve seen this exact scenario play out constantly. The core of my growth work with existing dating projects is turning the product into a conversion machine before we open the floodgates to expensive ad networks.

Your organic traffic is your testing ground. Before buying a single click, you must extract maximum value from your current base and patch the holes in your funnel:

1. Stop Building Random Features; Fix the Funnel: Break down your “New Buyers” into two highly specific metrics: Paywall View Rate (the percentage of users who actually see your premium offer) and Purchase Conversion (the percentage of those who buy). As a former UX engineer, I can tell you that fixing the friction on these two screens will yield higher returns than any new matching algorithm.

2. Engineer Motivation: To maximize buyers, you must remove friction during the profile setup phase, but drastically spike the user’s motivation right before the paywall. Make staying on the “free tier” feel frustratingly inefficient.

3. Patch the Leaky Bucket: There is zero point in buying expensive traffic if users churn on Day 1 before experiencing the core value of the app. Rely on hard cohort analysis—tracking specific groups of users over time—not just blind faith in your product.

Only when your product is efficiently converting free organic signups into paid subscriptions are you actually ready for Performance Marketing.

Entering a Different World: Your Guide to the Ad Market

Media buying for dating apps is nothing like running ads for a local bakery or a standard SaaS tool. If you are new to this, here is your step-by-step operational manual.

Step 1: Face Control

Dating is considered a “Restricted Category” across all major ad networks. You cannot just attach a credit card and launch a campaign. Before creating a single ad, you must apply for a specific Dating Certificate from Meta, Google, or TikTok.

Human moderators will review your website, privacy policy, age gates (strictly 18+), and user moderation mechanics. If you try to run dating ads without being on the official White List, your entire Business Manager account will receive an instant, permanent ban. Start this compliance process a month before your planned launch.

Step 2: Platform Choice and Why Meta Rules Dating

Historically, Meta (Facebook and Instagram) captures about 80% of dating ad budgets. Why not Google Search?

Because Google is Demand Capturing (catching existing intent). Meta is Demand Generation. A user scrolling Instagram is looking at memes; they aren’t actively trying to find a date. Our job is to interrupt their scrolling with a high-emotion video and generate the desire to download the app right now.

Furthermore, Meta has an absolute monopoly on one vital data point: the Relationship Status profile field. Neither TikTok nor Google has a database of billions of people who have voluntarily labeled themselves as “Single.” Targeting only single users means you don’t waste a single cent showing ads to married couples. Your Click-Through Rate (CTR) spikes, and your acquisition costs plummet.

Step 3: How the Algorithm Actually Works

Beginners think media buying is about manually guessing and selecting user interests like “single” or “likes dating.” Today, the AI does all the heavy lifting via Smart Bidding.

But the AI is blind until you train it. Here is exactly how it works:

You install a tracking code (an SDK or Facebook Pixel) into your app. This tracker acts as a direct communication line back to Facebook. When someone downloads your app, it sends a signal: “Event: Install.” But the magic happens deeper in the funnel. When a user finally swipes their card to buy a premium subscription, the tracker sends the ultimate signal: “Event: Purchase.” The Smart Bidding algorithm takes that specific buyer, analyzes thousands of their hidden data points (what they click, when they are active, what other apps they use), and automatically hunts for their behavioral “lookalikes” across the entire platform.

You are essentially saying to the machine: “Here is exactly what a paying customer looks like; go find me a thousand more just like them.” You do not manually find the users; you feed the algorithm purchase data so it can find them for you.

Step 4: The Creative Testing Pipeline

Because we are interrupting people, our only weapon is the “creative” (the ad itself). In dating, this almost exclusively means UGC (User-Generated Content)—videos that look like raw, authentic selfie-cam footage.

Creatives suffer from “ad fatigue” quickly, so you need a constant assembly line. Before you spend a dime, open the Meta Ads Library (Facebook’s free public database of active ads). Type in competitors like Tinder, Bumble, or Hinge, and study exactly what video ads they are actively paying to run right now.

Use a two-stage launch strategy:

• Test for Installs (Cheap Data): Launch 5-10 different videos optimized purely for “App Installs.” The goal isn’t profitability yet; it’s to cheaply discover which video stops the scroll and gets clicks (highest CTR).

• Optimize for Purchases (The Money Maker): Take the winning video and launch your core campaign. Now, command the Smart Bidding algorithm to find only users likely to trigger the “Purchase” event. The AI will squeeze maximum paying customers out of your proven ad.

Step 5: The Margin Battle

If you send Facebook traffic directly to the App Store or Google Play, you are bleeding money.

• The Baseline Fix: Apple and Google take a standard 30% cut of every transaction. Apply for the Small Business Program immediately. Until you hit $1 million in annual revenue, they drop the fee to 15%. This 15% margin swing is life or death for early startups.

• The Pro Move (Web2App): When Apple introduced iOS 14.5 (App Tracking Transparency), they severely restricted Facebook’s ability to track what users do inside your app. To bypass both the 30% store tax and the tracking blackout, industry leaders use Web-to-App funnels. The user clicks the ad, lands on a mobile website, completes an onboarding quiz, buys the subscription via Stripe (which only charges a 3% processing fee), and only then is directed to download the app.

Step 6: The Price of Admission

Here is the hardest truth about Smart Bidding: the machine demands volume to learn. To exit the “learning phase” and stabilize, Facebook’s algorithm requires a minimum of 50 “Purchase” events per week per campaign.

Let’s do the math. If a registration costs you $5, and your purchase conversion rate is a healthy 5%, you need 20 registrations to get one paying user ($5 × 20 = $100). That is a $100 Cost Acquisition Cost (CAC).

To get your 50 purchases a week, you must spend $5,000 weekly ($100 × 50) just to train the algorithm properly. Launching a $300 “test” campaign is practically a charitable donation to Mark Zuckerberg.

Death by Cash Flow and the Savior of Blended CAC

Once your campaigns are running, your entire existence revolves around CAC—the exact dollar amount it costs to buy one paying subscriber.

Let’s say in the US market, a high-quality registration costs $7. At a 5% conversion rate, your CAC is $140. If your LTV (Lifetime Value) is only $40, you are losing $100 per user. Why do companies still play this game? Two reasons:

1. Blended CAC (The Halo Effect): When you buy one user for $140 via Meta, they do not arrive in a vacuum. Their download pushes your app higher in the App Store rankings, they tell their single friends, and they trigger organic virality. That one paid user might bring in 3 free organic users. Suddenly, your total acquisition cost (Blended CAC) drops from a ruinous $140 to a sustainable $35. Your organic foundation subsidizes your paid scaling.

2. The Payback Period & Growth Tools: Even if your LTV is a fantastic $150, you can still go bankrupt. If you pay Facebook $140 today, but collect that $150 from the user in $25 monthly increments, you will run out of cash before month three. Worse, Apple holds your App Store revenue for up to 45 days before paying it out.

   If you are aggressively feeding your Growth Loop, a 45-day delay is a death sentence. This is why founders use revenue-based financing tools like Braavo (Braavo Capital). These platforms connect to your App Store and advance your incoming revenue to you by the next week. You bypass Apple’s 45-day wait, ensuring you always have immediate cash to reinvest into tomorrow’s ads.

The Ultimate Marketing Paradox

The great secret that performance agencies won’t tell you is this: You cannot win a marketing war with marketing alone.

Traffic costs have a market floor dictated by the auction. The only mathematically viable way to make the formula Revenue = (New Buyers × LTV) – Marketing Costs work is to relentlessly improve your product. The better your onboarding converts free traffic into upfront cash, the more aggressively you can spin your Growth Loop and outbid your competitors. Product dictates marketing, not the other way around.

In my next article, I’ll show you how to tie all of this together. I’ll share my exact playbook for stepping in as a Growth Partner for dating apps, absorbing the heavy lifting of “product mathematics” so founders can focus entirely on vision and scale.

Why Build From Scratch?

You just learned the brutal math of Blended CAC and Growth Loops. To make this math work and actually scale your ads, your app cannot be a leaky bucket. It must be a ruthless conversion machine.

Building a high-converting onboarding funnel, testing optimal paywall placements, and coding complex monetization mechanics like “Super Like” or “See Who Liked Me” from scratch will drain your runway before you even launch your first campaign.

This is why we built SkaDate.

We provide a battle-tested dating engine engineered specifically for maximum subscriber conversion. We have already done the heavy lifting—from frictionless registration flows to aggressive paywall triggers and out-of-the-box premium features. You do not need to hire a dev team to reinvent the wheel.

Stop treating your MVP like an expensive IT research project. Buy the engine, launch faster, and deploy your capital where it actually scales your business: buying traffic and feeding your Growth Loop.

Ready to skip the development nightmare?

Discuss Your Project with Me

You focus on finding the users; we’ll make sure the technology keeps them there.

The dating industry is not a monolith. The Hinge audience, looking for “the one,” is radically different from college students on Tinder looking for a weekend adventure. The matriarchy of Bumble feels nothing like the conservative ecosystem of Plenty of Fish. Niche apps like BLK or Chispa create entirely unique cultural micro-climates.

These are different products, different brands, and completely different people with different goals.

But if you look “under the hood” of their financial reports, you will see a startling picture.

Take the two giants of the market:

• Match Group (owners of Tinder, Hinge, OkCupid, Match, BLK, Chispa): Market Cap ~$7.36B.

• Bumble Inc. (Badoo, Bumble): Market Cap ~$781.5M.

Combined, this is an empire worth over $8 billion.

And the most amazing thing about this machine is that 90% of all that money is generated by the exact same three features.

I’ve conducted deep product analysis on the market flagships: from mass-market Tinder to “ethical” Bumble, from marriage-oriented Hinge to ethnic niches. Despite different designs, philosophies, and demographics, they all exploit the same fundamental human vulnerabilities: Impatience, Ego, and Fear.

In this article, we will dissect the “Holy Trinity” of dating monetization. We aren’t talking about abstractions. We are breaking down the concrete mechanics of three buttons that uphold the economy of love in the 21st century:

1. See Who Liked You (Selling Certainty).

2. Super Like (Selling Dominance and Visibility).

3. Unlimited Swipes (Selling Air).

Welcome to the real world of the dating business.

1. The Anatomy of “See Who Liked You”

To understand how this function generates billions, you first need to understand the basic architecture of any swipe-based dating app. If you’ve never used Tinder, imagine the app is divided into two distinct “rooms.”

The Architecture: Two Rooms

Room #1: The Stack (The Feed)

This is the main screen where the work happens. You see profile cards one by one.

• Swipe Right: “Like.”

• Swipe Left: “Pass.”

• The Blind Spot: In this feed, you see people, but you don’t know if they have liked you or not. To you, every card looks the same.

Room #2: The “Likes You” Grid (The Secret Room)

This is the backlog where the profiles of everyone who has already swiped right on you are stored.

• In the Free Version: The door to this room is transparent but frosted. You see that people are there (a “99+ Likes” badge) and you see blurred silhouettes. But you cannot see faces or names.

The Fundamental Problem: “The Tragedy of the Left Swipe”

In the free version, the user is forced to play a game of Minesweeper in Room #1.

Imagine this scenario: You are shown a card of a girl. She is cute, but not perfect. You hesitate... and you swipe left.

The Problem: In that moment, you may have made a fatal error. Perhaps that girl had already liked you. By swiping left, you destroyed the Match. You will never know that you just missed out on a person who was genuinely interested in you.

This is Information Asymmetry, and it is exactly where Match Group prints money.

The Mechanics of Pain: Abundance vs. Scarcity

Users live in two different realities, but the solution (buying a subscription) is the same for both.

Problem A: “The Curse of Abundance” (Typically Women)

For a popular woman, the Feed is an endless stream. She has 1,000+ likes sitting in her “Secret Room.”

• The Pain: If she wants to find a date right now, she has to spend hours scrolling through the general feed, hoping to stumble upon one of the men who already liked her. It’s like finding a needle in a haystack.

• The Solution: She buys Gold, opens the “Likes You” list, and picks the best candidate in 60 seconds.

Problem B: “FOMO” (Typically Men)

For the average guy, every like is precious.

• The Pain (Fear Of Missing Out): He is tormented by paranoia: “What if that blonde I just swiped left on actually liked me? I just killed my own chance!”

• The Solution: He buys Gold to insure his risks. He wants to see who liked him so he doesn’t accidentally reject an interested woman.

Product Metaphor: Cassette vs. Spotify

As a Product Manager, I explain the difference between the free and paid versions through the evolution of music.

• Free Version = The Cassette Tape (Sequential Access). You are forced to “listen” (swipe) through everything in order. You waste time fast-forwarding through songs you don’t like. You never know if the next track will be a hit (a Match). You are working blindly.

• Paid Version = Spotify / CD (Random Access). The “See Who Liked You” function gives you a dashboard. You go into the list, see everyone who wants you, and you press “Play” (Match) on the ones you like.

The Verdict: We are selling Certainty and Time. We move dating from a Probabilistic model (”maybe I’ll meet someone”) to a Deterministic one (”here is the list, choose one”). Without a subscription, you are a slave to the algorithm. With a subscription, you are the master of the data.

2. The Anatomy of “The Super Like”

To understand why this button generates hundreds of millions of dollars, we need to take off the rose-colored glasses and look at the Fundamental Imbalance of the market.

The Problem: Invisibility and Inflation

In the free version of a dating app, the average man is invisible.

• The Male Strategy (”Spray and Pray”): Men tend to swipe right on 40–60% of all women. This creates massive outbound noise.

• The Female Reality (Overload): A verified girl in a major city has a Stack clogged with thousands of profiles.

• The Tragedy of the Ordinary Like: When you send a standard like, you get in the back of the line. You might be number 3,548 in her queue. She physically cannot swipe enough to reach you. Even if you are the ideal candidate, your like drowns in inflation. She will never see your card.

The Solution: You need a tool to elbow your way to the front.

How It Works: Blindness vs. Insight

Scenario A: Ordinary Like (The Blind Game)

Even if she eventually reaches your card, she doesn’t know if you liked her.

• Risk: She hesitates and swipes left.

• Result: Match destroyed. Mutual attraction killed by algorithmic blindness.

Scenario B: Super Like (Cards on the Table)

A Super Like changes the UI physics. When the algorithm serves her your card, it looks different.

• She sees it immediately: The card is highlighted blue, has a star, or says “Super Liked You.”

• She knows BEFORE she decides: The moment she looks at your photo, she knows you have already shown strong interest—and spent money to do so.

• Result: She makes a decision with her eyes open. The risk of accidentally swiping left on an interested person vanishes.

Technical Mechanics: Queue Injection

Many founders think a Super Like is just a sticker. It’s not. The primary value you are selling is Priority.

1. Queue Jumping: The backend takes your card and physically moves it from position #3,548 to position #1.

2. Impressions: You are buying a guaranteed view. Next time she opens the app, you are the first thing she sees.

3. Visibility: You penetrate “banner blindness” with visual dominance (blue borders, animations).

The Economics of “Costly Signaling”

Why is the conversion rate of a Super Like 3x higher than a regular like? In economics, this is called Costly Signaling.

Signals that cost nothing (regular likes) are ignored because they are cheap to produce.

• Inflation: “Spray and Pray” devalues the regular like.

• Value: A Super Like costs real money or is strictly rationed.

• The Message: The blue star says: “I chose you specifically out of thousands. I am not a bot. I spent resources on you.”

Pricing Strategy: Controlling the Supply

Match Group defends the value of this signal by keeping the price high and the supply low.

1. The Subscription Hook (”Use it or Lose it”) Subscribers get a “drip feed” of Super Likes to keep them logging in weekly.

• Tinder Gold: 2 Free Super Likes per week.

• Tinder Platinum: 3 Free Super Likes per week.

• The Catch: They do not rollover. If you don’t use them, they burn.

2. A La Carte Pricing (The Revenue Driver) If you want more than your weekly ration, you have to pay a premium. The current US pricing tiers are aggressive:

• 3 Super Likes: ~$9.99 (~$3.33 / unit)

• 15 Super Likes: ~$35.00 (~$2.33 / unit)

• 30 Super Likes: ~$60.00 (~$2.00 / unit)

The Psychological Anchor: By setting the base price at ~$3.33—roughly the cost of a cup of coffee—the app converts a “mindless digital action” into a considered investment. You don’t spam a button that costs $3 to press. You use it only when it matters.

3. The Anatomy of “Unlimited Swipes”

What we are selling: Air, an “All-You-Can-Eat Buffet,” and the Right to be Wrong.

This feature is the foundation of the basic paid tier. To understand its value, you have to ask a counter-intuitive question: “Why limit swipes in the first place?”

After all, a swipe is a digital action. It costs the company nothing. The servers won’t melt down if a user swipes 1,000 times instead of 100.

The answer is cynical: The swipe limit is an artificial scarcity upon which the entire economy of the app rests.

Why Limits Exist: Inventory Management

We limit free users (usually ~100 likes per 12 hours) not because we are greedy, but to save the ecosystem from burning out.

1. Inventory Management

Dating is a business with finite inventory. Unlike TikTok, where content is infinite, the number of active single people within 10 miles of Walnut Creek is strictly limited.

• The “Vacuum” Scenario: An active male user can physically evaluate 2,000 profiles in an evening. In a mid-sized city, he will “burn through” the entire database of active women in 48 hours.

• Churn Risk: On day 3, he opens the app, sees the “No new people around” screen, decides the app is dead, and deletes it.

• The Fix: We ration profiles. We artificially stretch consumption so the user comes back for 30 days, rather than eating everything in two.

2. The Psychological Trap: “The Hot State Paywall”

The limit doesn’t hit randomly. The algorithm blocks you exactly when you are most engaged.

• You are in a “Flow” state. You’ve caught a rhythm. You see the girl of your dreams on the screen... and BAM!

• “Out of Likes. Want to like her? Pay up.”

• This is selling at peak demand. It is physically painful for a human to stop when they are “heated up” with dopamine.

The Hidden Mechanic: The Noise Generator (Cross-Side Network Effect)

And now, the key insight you won’t read in standard blogs.

Why does Tinder sell “Unlimited Swipes” in its cheapest subscription tier? Why not make this an elite feature?

Because users with unlimited swipes are the unpaid workforce of Tinder.

1. Notification Generation: A man with unlimited swipes starts liking thousands of women.

2. Creating Hype: These likes trigger push notifications for women: “Someone likes you!” This brings women back into the app (Retention).

3. Selling Gold: The woman logs in, sees “99+ Likes,” and feels overwhelmed. To sort through the noise, she is forced to buy Tinder Gold (See Feature #1).

The Brutal Truth: By selling cheap “Unlimited Swipes” to one group (men), the app uses their labor to create an “Abundance Problem” for the other group (women), forcing the latter to buy expensive filters.

The guy with the unlimited subscription thinks he bought an advantage for himself. In reality, he paid to become a traffic generator for the platform.

Summary

Unlimited Swipes is the sale of air. The application takes an artificial restriction—which it created itself to protect its own economy—and sells the removal of that restriction as a premium feature. It is a tax on impatience and the primary tool for pumping activity into the ecosystem.

Conclusion

Let’s be honest: The $8 billion empire of Match Group wasn’t built on “better matching algorithms” or “true love.” It was built on these three buttons.

Impatience. Ego. Fear.

These are the raw materials of the dating business. If you are building a dating app in 2026, you don’t need to reinvent the wheel. You don’t need to dream up some complex, never-before-seen monetization model. You simply need to give your users the tools they are already willing to pay for.

The market has already been trained. Users expect to pay for certainty (”Who Liked Me”), for priority (”Super Like”), and for freedom (”Unlimited Swipes”).

The only question left is: Are you going to spend the next 12 months trying to code these psychological triggers from scratch, or are you going to start generating revenue on Day 1?

Why Build From Scratch?

You are entering a market where speed and execution are everything. Your job as a founder is to focus on User Acquisition and Unit Economics, not on debugging the backend logic of a “queue injection” for a Super Like feature.

Coding these monetization layers is deceptively difficult. You have to handle the blurred image layering, the database queries for “who liked me” (which are resource-heavy), and the precise countdown timers for swipe limits.

This is why we built SkaDate.

We have already engineered these exact revenue generators. You don’t need to hire a dev team to build them; you just need to activate them.

The SkaDate engine offers ready-to-deploy plugins for the “Holy Trinity” of monetization:

• “See Who Liked You” Plugin: Out-of-the-box functionality to blur profiles and upsell the “reveal” to your users.

• “Super Like” Plugin: Native logic that highlights profiles and pushes them to the top of the matching queue.

• “Swipe Volume Control” Plugin: Flexible settings to limit free users and instantly unlock “Unlimited Swipes” for premium subscribers.

Stop treating your MVP like a research project. Buy the engine, install the plugins, and launch a product that is ready to monetize from the first download.

Ready to skip the development nightmare?

Discuss Your Project with Me

You focus on finding the users; we’ll make sure the technology keeps them there.

The dating industry has fundamentally changed. The era of “growth at all costs” is dead and buried. As I mentioned in my previous post, Paid Ads Are Suicide, trying to solve your growth problems by simply throwing money at them is now a death sentence for startups.

Let’s look at the brutal reality of the numbers. I’ve run these calculations hundreds of times across products like Seeking, Dil Mil, and now with SkaDate clients.

In the US market, the Cost Per Action (CPA) for a single registration via Meta (Facebook/Instagram) or TikTok currently hovers between $5 and $10.

But here is the catch: a registration is not money in the bank. In the mass dating market, the average conversion rate from a free user to a paid subscriber is roughly 5% (and for brand new products without liquidity, it’s often much lower).

Let’s do the math:

• To get 1 paying client, you need to acquire 20 free registrations (at a 5% conversion rate).

• If you pay $7 per registration, your real Customer Acquisition Cost (CAC) is $140 ($7 × 20).

Now, look at your revenue. The average monthly subscription price in mass dating is $30–$40.

The math is cruel: You are spending $140 to earn $30.

You are losing $110 on every single customer you acquire. This model is fundamentally broken if the user only pays once.

The only way to survive in this new era of efficiency is Retention. If you cannot keep a user active and paying for months, your unit economics will never balance.

Chapter 1: What is Retention, Really?

Many founders have a simplistic view of retention. They ask, “Did the user open the app again?” But in the dating industry, that definition is too shallow.

Retention is a measure of the value a user gets from your product over time.

If a user opens your app, stares at a blank screen, doesn’t like anyone, and sends no messages, that is not retention. That is “Zombie Traffic.” True retention isn’t built on “App Opens”; it is built on performing a Core Action. In dating, that Core Action is a conversation.

The “Leaky Bucket” Economy

Think of your product as a bucket. Marketing is the hose pouring water (users) into it. Retention is the structural integrity of that bucket.

• The Cold Start Problem: In a new app with an empty database, the average subscriber Lifespan often collapses to just 1 month. The user enters, sees no one nearby, gets no matches, and churns immediately.

• The Mature Product: In a product with liquidity and established retention loops, the average Lifespan is 3–4 months.

Let’s revisit our formula:

• The Leaky Bucket Scenario: You spend $140 to acquire a customer. They stay 1 month ($40 revenue). Net Loss: -$100.

• The Retention Scenario: You extend the user’s life to 4 months ($40 × 4 = $160 revenue). Net Profit: +$20.

Retention is the only lever that transforms a bleeding startup into a profitable business.

Chapter 2: The Reforge Methodology and Engagement Loops

You cannot simply order a user to “Stay!” Retention is the result of carefully engineered product loops. In my article How to Actually Grow a Dating Business I broke down the user journey using the Reforge framework.

To build retention, a user must pass through specific, sequential stages:

Setup → Aha → Habit → Engagement.

2.1. The Setup Moment (Foundation)

Just because a user downloaded the app doesn’t mean you’ve won. The Setup Moment is when the user has done the bare minimum required to actually receive value. In dating, this means:

• A completed profile.

• An uploaded photo (that passed moderation).

• Approval by your trust & safety team.

If a user doesn’t complete the Setup, they technically cannot be retained because they are invisible to the rest of the network.

2.2. The Aha Moment (First Value)

Retention is born the moment a user gets a result. In dating, the Aha Moment is the first conversation.

Notice I didn’t say “Match.” Matches are vanity metrics—they feel good but often lead nowhere. A dialogue is value. If a user chats with at least one person within their first week, their probability of retention skyrockets.

2.3. The Habit Moment (Cementing Behavior)

To turn a one-time action into a routine, you need repetition. The Habit Moment occurs when a user has engaged meaningfully—for example, chatting with 10 different people in their first week. At this point, checking your app becomes part of their daily ritual, like checking email or Instagram.

How it Works: The Engagement Loop

To move users through these stages, we design loops based on behavioral psychology:

1. Trigger: A push notification (”You have a new like” or “Someone messaged you”).

2. Action: The user opens the app, likes back, and matches with someone or replies to a message.

3. Variable Reward: They receive a new match or a like at a random time.

4. Investment: They reply to the message. By replying, they are “investing” in the platform and creating a trigger for their next visit (waiting for a reply).

> Note: Building effective Engagement Loops (often called Habit Loops) is a complex engineering and psychological task. It’s not just about spamming push notifications; it’s about designing behavior. I will dedicate a future deep-dive article solely to constructing these loops from scratch.

Chapter 3: Cohort Analysis

How do you know if your Engagement Loops are working?

Do not look at general top-line charts like DAU (Daily Active Users) or MAU (Monthly Active Users). They lie to you. They blend your loyal, old users with the flood of new users who are churning immediately, hiding the true health of your product.

You need Cohort Analysis.

What is a Cohort?

A cohort is a group of users who entered your product during the same time period (usually grouped by week or month). We track the behavior of this specific group over time, isolated from everyone else.

Why does this matter?

Imagine you launched a buggy version of your app in January, and a fixed version in February. If you look at total retention, you see an “average.” But Cohort Analysis reveals the truth:

• January Cohort: By Month 3, only 2% remain.

• February Cohort: By Month 3, 15% remain.

This proves your product changes worked. Without cohorts, you are flying blind.

Chapter 4: Practice in Mixpanel

As a product expert, I don’t trust intuition; I trust data. In analytics tools like Mixpanel or Amplitude, there are two specific reports you must monitor daily.

4.1. The Retention Report (Linear Retention)

This chart answers the question: "What percentage of users from a specific cohort came back to the product N days/weeks later?"

Figure 1: The Linear Retention Report in Mixpanel.

This chart tracks users who performed “Join Completed” and returned to do “Any Event.”

How to Read This Chart (Step-by-Step)

If you are new to analytics, this heatmap can look intimidating. Let’s break it down column by column to understand exactly what you are looking at.

1. The Left Column: Weekly Cohorts

   Look at the list of dates on the far left (Aug 4, Aug 11, etc.). These are your Cohorts. Each row represents a specific group of real people who triggered the “Join Completed” event (registered) during that specific 7-day period. We track them separately so we can see if your product is getting better or worse week over week.

2. The Columns: Day 1, Day 2...

   The columns to the right show what percentage of those specific people came back N days later.

   • Day 1: This is the most critical column. It shows the percentage of users who registered and then returned the very next day.

   • Weighted Average: The top row (purple) summarizes the average performance across all the weeks listed below. In this example, the Weighted Average for Day 1 is 40.61%. This means that out of everyone who signed up in this period, roughly 40% opened the app again the next day.

3. The Curve: The Flattening (Day 30)

   Follow the data to the right. In the first few days, the numbers drop fast—this is natural. But look at Day 30. The curve stops crashing. It becomes parallel to the X-axis, stabilizing around 8–10%. This stabilization is what we call “The Flattening.”

The Verdict: Is This Good?

To understand if these numbers are a success or a failure, we need to look at industry benchmarks.

• The “Cold Start” Benchmark: For a brand new dating app with zero liquidity (an empty database), Day 1 Retention is usually around 10–15%. Users enter, see nobody, and leave.

• The “Mature Product” Benchmark: Established giants with massive user bases and high liquidity often see Day 1 Retention hit 50% or higher.

Conclusion for Figure 1: Based on these benchmarks, the chart above is excellent.

1. High Liquidity: The Day 1 retention of 40.61% proves that new users are finding immediate value (likely chatting with existing users). The “Empty Room” problem is solved.

2. Product-Market Fit: Because the curve flattens at Day 30 and does not hit zero, we can confidently say Product-Market Fit has been achieved. We have found a core group of users who stick around.

4.2. Frequency of Use (The Power User Curve)

The Linear Retention report we just looked at tells us if a user came back. But it doesn’t tell us how addicted they are.

You can have high retention but low engagement. For example, a user might open your app once a week just to check if they have notifications, but never actually talk to anyone. We call these users “Zombies.” They look alive in your DAU stats, but they generate zero value.

To spot this, we need the Frequency Report (often called the Power User Curve or L30).

What is this chart? Instead of showing when users return, this chart shows the intensity of their usage over a 30-day window.

• X-Axis: The number of days a user performed an action (from 1 to 30).

• Y-Axis: The percentage of your user base.

Ideally, you want a “Smile Graph”—high on the left (new users) and high on the right (addicted power users).

Figure 2: The Frequency Report (L30) for "Message Sent"

Now, let’s analyze the real-world report in Figure 2. This chart tracks the frequency of the “Message Sent” event.

This is a classic example of a product that has retention, but no habit.

1. The “Tourist” Cliff (Left Side): Look at the start of the curve.

   • 18.86% of users sent messages on 2 days out of the month.

   • By 4 days, this drops to 7.4%.

   • This indicates that users are coming in, chatting for a weekend (like tourists), and then losing interest.

2. The “Power User” Void (Right Side):

   • Look at the tail of the graph (Days 20–30). The line is flatlining near 0%.

   • This means almost nobody is chatting daily. You have no core group of addicted users.

3. The Verdict:

   • If this were a healthy dating app, you would see an uptick on the right side (the “Smile”).

   • The fact that this chart dies out completely on the right suggests your Engagement Loops are broken. Users do not feel the psychological need to check their messages every day.

Summary: The “Zombie Product” Trap

Why is it critical to look at both charts?

If you only looked at the Retention Report (Figure 1), you would pop the champagne. You have 40% retention! You have Product-Market Fit! You might think you are ready to scale.

But if you overlay the Frequency Report (Figure 2), you see the grim reality. Yes, users are coming back, but they are not engaging. They are “Zombies”—walking around your app, looking at profiles, but not talking.

The Golden Rule of Analytics:

• Linear Retention measures if your product is valid (Do they delete it?).

• Frequency measures if your product is monetizable (Do they use it enough to pay?).

You cannot build a billion-dollar business on Retention alone. You need the Flattening in Chart 1 and the Smile in Chart 2. Until you have both, your unit economics will remain under pressure.

Implementation Note: Don’t Code This From Scratch

Setting up these specific events (Join Completed, Message Sent, Subscription Started) incorrectly is the #1 reason founders get bad data.

If you are using SkaDate to launch your product, we have already done this work for you. Our platform has a native integration with Mixpanel. You don’t need to hire developers to map these events—just enable the plugin, and your dashboard will automatically start populating with the exact charts I showed above from Day 1.

Conclusion

In 2026, the unit economics of the dating industry are no longer about finding cheap traffic. That ship has sailed. The game is now entirely about the battle for LTV (Lifetime Value).

• Your Formula: Expected Income = (New Buyers x LTV) - Marketing Costs.

• Your Lever: Retention, which multiplies LTV.

• Your Tool: Engagement Loops (Setup → Aha → Habit) that engineer user behavior.

• Your Validation: Cohort Analysis in Mixpanel.

Stop building features based on guesses. Start measuring retention by cohorts and optimizing the user’s path from registration to that first dialogue. That is the only way to make the math work.

Why Build From Scratch?

You are already fighting a war for Retention and LTV. Setting up monetization logic, fighting SMS fraud, and optimizing the onboarding flow is hard enough without having to code the entire platform yourself.

This is why we built SkaDate.

We provide the complete technical engine—native mobile apps, flexible paywalls, and robust anti-spam tools—ready from Day 1.

Ready to skip the development nightmare?

Discuss Your Project with Me

You focus on finding the users; we’ll make sure the technology keeps them there.

But let’s be honest: most founders underestimate just how brutal this math actually is.

When you launch a dating app, your database is empty. New users sign up, see an empty feed, and leave immediately. This is the classic “Empty Bar” syndrome.

Dating is a hyper-local marketplace. It is significantly harder to scale than an e-commerce marketplace like Amazon or Etsy. For Amazon, a seller in Texas can ship a book to a buyer in New York. In dating, your supply (users) must be physically near your demand (other users), and they must match specific criteria (age, gender, intent).

If you don’t solve the liquidity problem (having enough people in one specific area), your product fails. Period.

Many founders think, “I’ll just buy ads.” Let’s look at the unit economics and see why that is a suicide mission for a bootstrap startup.

The Math of Failure: Why You Can’t Buy Your Way Out

Let’s look at the funnel. In the US market, a Paid Registration (via Meta Ads, Google, or TikTok) will cost you anywhere from $5 to $10.

But a registration is not a paying customer.

• Average conversion from Reg to Subscriber: ~5% (For a new, unoptimized product, it’s usually lower).

• The Multiplier: To get 1 subscriber, you need 20 registrations.

Let’s do the math:

It costs you roughly $140 to acquire one paying subscriber.

Now, look at your revenue.

• Retention: The average dating user lifecycle is 3–4 months. For a new app with a “thin” database, it’s likely 1 month. They will sign up, look around, maybe pay, realize there are no dates, and cancel immediately.

• Pricing: Unless you are a niche “Sugar” site like Seeking (where LTV is high), you are likely charging $30–40/month.

The Result: You pay $140 to make $30.

You are losing $110 on every customer. You cannot scale this. You will burn through your seed round before you get your first 1,000 active users.

To solve the Cold Start Problem, you need an “Unfair Advantage”—a channel that delivers users for free (or very cheaply) to offset the paid acquisition costs.

Let’s look at how the “Old Guard” solved this. While the specific tactics have changed, the principles of finding a viral loop remain the same.

Case Study 1: Plenty of Fish (The “Craigslist” SEO Strategy)

Markus Frind, the founder of Plenty of Fish (POF), famously ran the company with almost no employees for years. While competitors had massive teams, Markus was alone in his apartment, managing millions of users.

How? By mastering SEO (Search Engine Optimization).

POF was the Craigslist of dating. It was ugly, utilitarian, and raw. At Meetville (my previous company), we were obsessed with sleek UI/UX. Back in 2010, we used to look at POF and spit on the design. We would say, “What is this horror? How can anyone use this?”

But while we were polishing pixels, Markus was printing money. He understood that users didn’t care about pretty buttons; they cared about results.

The Strategy: Radical Openness

At the time, most dating sites (like Match.com) were “walled gardens.” You couldn’t see anything unless you registered. Google’s bots cannot index content behind a login wall, so those sites were invisible to search engines.

Markus made profiles public.

• If a user named “Mike” created a profile in Vancouver, POF created a public page for him.

• When someone searched for “Men in Vancouver,” POF ranked #1.

The Business Model: Arbitrage

Markus acquired users for $0 (via SEO) and monetized them for pennies (via Google AdSense). Since his cost was zero, his profit margin was 100%. In 2015, this strategy paid off when Match Group acquired Plenty of Fish for $575 million.

• Read more: Match Group Buys PlentyOfFish for $575M

The Modern Problem: AI and “Zero-Click” Search

Today, you cannot just copy POF’s strategy. Why? Because Google is changing. With the rise of AI (like ChatGPT and Google’s AI Overviews), users often get answers directly on the search page without clicking a link. The era of “10 blue links” is fading.

To grow via SEO in 2026, you need Programmatic SEO.

What is Programmatic SEO? Instead of manually writing one blog post at a time, you use code and data to generate thousands of high-quality landing pages automatically.

• Example: Instead of writing one article about “Dating in California,” you programmatically generate pages for “Best Date Spots in Walnut Creek,” “Best Date Spots in Oakland,” “Best Date Spots in Palo Alto,” etc.

You create thousands of “doors” for Google to find, capturing “long-tail” traffic that is less competitive. This is the only way to replicate POF’s scale in the modern era.

Case Study 2: Badoo (The Viral Graph)

Badoo was founded by Andrey Andreev, the product genius behind Mamba. When he expanded Badoo globally, he didn’t rely on SEO. He hacked the Facebook Social Graph.

To understand Badoo’s success, you first have to understand the specific loophole in how the internet worked in 2010.

The “Open Door” (Permission to Spam)

Today, if an app wants to post to your social media, it has to ask you for permission every single time. But in 2010, Facebook had an Open Graph API that worked very differently.

The API allowed apps to publish posts to your friends’ walls automatically.

Once you clicked “Allow” just one time, the app had the keys to your account. It could post messages to the News Feeds of all your friends without asking you again. Imagine this scenario today: Imagine if your friend could post a photo or a status update on your Instagram profile without even telling you. That is exactly what the API allowed.

It was the “Wild West.” This lack of friction was the key to their growth.

The Viral Loop

Badoo built a mechanism to exploit this “auto-posting” capability perfectly. They created a quiz app that lived inside Facebook.

Here is the exact Viral Loop that generated millions of users:

1. The Trigger: You are using the app and you answer a question about one of your friends.

   • Example: “Will your friend John help an old lady to cross the road?”

   • Your Answer: “Yes.”

2. The Auto-Post (Crucial Step): Badoo immediately publishes a post to John’s personal feed:

   • The Message: “Your friend answered a question about you: ‘Will you help an old lady to cross the road?’ Click here to see the answer.”

   • The Multiplier: Because this is posted on John’s wall, all of John’s friends see this note too. This is where the visibility explodes.

3. The Click: John (or one of his curious friends) clicks the link to see the answer.

4. The Gate: A popup appears: “To enter Badoo and see the answer, you must grant access to your profile.” John agrees and enters the app.

5. The Viral Cost: Now the app tells John: “To see the answer, you need to score 10 points. To score 1 point, you need to answer a question about YOUR friend.”

6. The Loop: John answers a question about his friend Mike... and the cycle repeats instantly.

The Billion-Dollar Checkbox: This is what the interface looked like at Badoo’s peak. Notice the tiny, pre-checked box at the bottom: “Publish on your friend’s wall.” This subtle UI detail meant that every interaction automatically spammed a friend’s feed, driving the K-Factor through the roof.

The End of the Era (Autumn 2012): When Facebook restricted the Open Graph API, Badoo was forced to change the mechanics. Notice the shift from "Friend's wall" to “Publish on your own wall.” This small text change effectively killed the viral loop and brought their explosive growth to a halt.

The Metric: K-Factor In growth marketing, we measure virality using the K-Factor.

• If K = 1, every new user brings in exactly one other user.

• If K > 1, growth is exponential.

• Badoo’s K-Factor was roughly 10. Every single person who joined brought in ten more people.

This allowed Badoo to gather hundreds of millions of users faster than any company in history. Eventually, Facebook realized these apps were spamming users and shut down the API, but Badoo had already won.

• Deep Dive: I wrote a detailed breakdown of this era on Medium: Boosting the audience from 12M to 375M: Myth or Reality?

Case Study 3: “Are You Interested?” (The Rise and The Crash)

While Badoo was huge, another app called “Are You Interested?” (AYI) mastered this Facebook era so well that they actually went public based on this strategy alone.

AYI was built by Cliff Lerner. His strategy was to gamify dating notifications. They would send you a notification saying: “2 of your friends have a crush on you! Click to see who.” Of course, to see who, you had to invite more friends. They optimized every pixel of their notifications to maximize the Click-Through Rate (CTR).

The IPO and The Crash

The parent company, SNAP Interactive, was publicly traded. When their Facebook strategy started working, the stock went crazy.

• The Start: The stock was a “penny stock,” trading for around $0.20 - $0.25.

• The Peak: In late 2010, after announcing massive user growth from Facebook, the stock surged 2,000% in just a few days, hitting over $4.00. The market cap exploded overnight.

• The Crash: However, building a business on someone else’s land (Facebook) is dangerous. When Facebook changed their algorithms and shut down the viral channels, the free traffic stopped. The stock eventually lost over 90% of its value, crashing back down to pennies.

Cliff Lerner documented this entire rollercoaster in his book, “Explosive Growth.” It is a must-read for any founder to understand how to engineer viral loops, and the dangers of relying on a single platform.

• Recommended Reading: “Explosive Growth” by Cliff Lerner - Buy on Amazon

Case Study 4: Tinder (The Physical “Hack”)

Tinder didn’t use SEO or Facebook spam. They solved the problem physically.

Most dating apps fail because they try to launch “everywhere” at once. Having 1,000 users spread across the entire USA is useless—they will never match with each other. But having 1,000 users in one university dorm is a massive success. This is called Liquidity.

The Strategy: The USC Party Tinder was incubated inside Match Group, but they acted like a scrappy startup. The team, led by Whitney Wolfe Herd (who later founded Bumble with Andrey Andreev from Badoo), went to the University of Southern California (USC).

• Supply First: Whitney went to the sororities first. She convinced the influential girls on campus to download the app.

• Demand Second: Then she went to the fraternities. She told the guys, “All the cute girls from the sororities are on this app.” Naturally, the guys downloaded it immediately.

The Execution: They threw a massive launch party at a fraternity house.

• The Ticket: There was a bouncer at the door. To get into the party, you didn’t pay cash. You had to show the bouncer your phone with the Tinder app installed.

• The Result: 500 students entered the party. They were all young, single, attractive, and—most importantly—in the same room.

The Psychological Effect Once inside, students opened the app and saw the faces of the people standing right next to them. This created instant Social Proof. It wasn’t a “creepy internet thing”; it was something the “cool kids” at the party were doing. Matches happened instantly. The “Cold Start” problem for USC was solved in one night. Then, they simply moved to the next campus and repeated the process.

Read my full analysis of Tinder’s start here: The Foundation - Solving the Cold Start Problem

So, what now?

The “Old Guard” had their unfair advantages. What is yours?

In my next article, I will move away from history and look at the Modern Playbook. We will analyze dating apps that launched in the last 2–3 years and successfully cracked the Cold Start Problem without bankrupting themselves on Meta Ads.

Stay tuned.

Why Build From Scratch?

As you just read, solving the Cold Start Problem requires 100% of your focus—and your budget—on marketing.

Developing a sophisticated dating engine from scratch will cost you tens of thousands of dollars before you even launch. Why burn your runway reinventing the wheel?

That is our territory.

SkaDate provides you with a battle-tested engine that is ready to generate revenue from Day 1.

We don’t just give you code; we provide the proven business models, premium features, and monetization logic that successful dating apps rely on.

We save you months of development time and huge capital. You focus on the growth strategy; we provide the engine to power it.

Ready to launch with a pro-level product?

Discuss Your Project with Me

You focus on finding the users; we’ll make sure the technology is ready to monetize them.

The first rule of implementation is simple: You cannot grow what you cannot measure. If you aren’t tracking the right metrics, you aren’t managing your product—you’re just guessing.

To make this manageable, we break the dating app ecosystem into two global funnels:

1. The Value Funnel: This measures how much value users get from your product. In dating, value equals human connection (dates, chats). The more value users receive, the longer they stay. This directly drives LTV (Lifetime Value) because retention is the baseline for monetization.

2. The Revenue Funnel: This measures how effectively you convert free users into paid subscribers. It determines your Number of First Buyers.

If we look at the fundamental Unit Economics formula:

The Value Funnel drives the LTV, and the Revenue Funnel drives the First Buyers.

Today, we are focusing exclusively on the Value Funnel. We will break it down into five critical steps:

Sign up → Setup → Aha → Habit → Engagement

1. Sign Up

The goal: Get them through the door.

We define Sign Up as the moment a user completes the entire registration process—entering data, uploading photos, answering profile questions—and actually lands inside the app’s main interface.

Here, we track our first critical conversion metric, C1 (Visitor to Registration).

To calculate this, divide the number of users who completed registration by the number of users who opened the first screen of the app.

Tracking with Mixpanel

A quick side note for non-technical founders: We track this using Product Analytics tools like Mixpanel. Unlike Google Analytics (which tracks traffic), Mixpanel tracks Events (actions users take) and User Properties (attributes like age, gender, subscription status). Check out this comparison between Mixpanel and Google Analytics.

If you are using the SkaDate platform, our Premium plugin automatically sends these events to Mixpanel. You can read more about our plugin in this article - How Mixpanel Product Analytics Can Unlock Your Dating App Growth Potential.

To optimize your Sign Up flow, you need a Funnel Report. You cannot just look at the start and end; you need to see every hurdle in between. In our architecture, we track:

1. Onboarding Create Account Clicked: User taps the button on the landing screen.

2. Sign Up Completed: User inputs email/password or authenticates via SSO (Google/Apple).

3. Profile Step Completed: User fills in text fields (name, age, etc.).

4. Photo Step Completed: User uploads media.

5. Join Completed: The user is fully registered and lands on the discovery screen.

Here is a sample Mixpanel report built using the events above:

Analyzing the Data

When you look at this report in Mixpanel, you are looking for two things:

1. Overall Conversion: (e.g., 72.93%). If 100 people start, how many finish?

2. Time to Convert: This is often overlooked. If the gap between Sign Up Completed and Profile Step Completed is 168 seconds, that’s nearly three minutes of friction.

Founder Tip: Your goal is to increase the conversion % and decrease the time. High friction during registration is the silent killer of paid acquisition campaigns. You can also segment this data: Do iOS users convert faster than Android users? Do men drop off at the photo step more than women?

2. Setup

The goal: Ready to mingle.

Let’s define what the Setup Moment actually is. The Setup Moment is considered complete when the user has performed the minimum necessary actions required for the product to work for them.

In a dating app, this means two things must happen:

1. The User’s Job: They must fill out their profile and upload photos. (Usually, they do this during the Sign-Up flow).

2. The Platform’s Job: The profile must be verified and approved.

You cannot consider a user “Set Up” just because they finished registration. If their photos are pending or their bio is empty, the product does not work for them yet because they are invisible to others.

This brings us to Moderation. Moderators must review not just the photos, but also open-ended text fields like “About Me” or Profile Prompts.

• Photos: Users might upload inappropriate content that violates store policies.

• Text: Users often use the “About Me” section for spam, ads, or offensive content.

Until this content is vetted, the user cannot be shown in the discovery pool. In SkaDate, we track the event Avatar Approved.

This is your operational bottleneck. If a user registers on Friday night but isn’t approved until Monday morning, they are “invisible inventory.” They can’t get likes, they can’t get matches, and they certainly won’t feel any value.

Below is the funnel showing the path from Registration to Approval.

Looking at the chart above, you can see that the average approval time is 10.3 hours. This is a major issue because during these 10 hours, the user is completely invisible in the catalog. Other people cannot see them, cannot like them, and cannot match with them. We clearly need to accelerate this process—ideally reducing it to minutes—to prevent that user from churning before they even start.

3. The “Aha!” Moment

The goal: Proof of value.

First, let’s define it. The Aha! Moment is the precise instant a new user experiences the core value of your product for the first time. It’s the moment the lightbulb turns on, and they stop wondering “Why am I here?” and start thinking “I get it.”

In a dating app, the Aha! Moment isn’t when a user sees a pretty profile. It is when they get a reply to a message. That is the moment they realize: “This works. There are real people here.”

But you need to understand: if you have a Freemium app and a business model like Tinder, users can usually only chat for free if they have Liked each other and formed a Match.

Therefore, it is important to track the intermediate events: specifically, that the user is Liking other profiles, and that the user is forming Matches with them.

So, I recommend building a funnel consisting of these events:

1. Join Completed

2. Profile Liked (User takes action)

3. Profiles Matched (Mutual interest)

4. Conversation Started (First move)

5. Conversation Replied (The Aha! Moment)

Your job is to widen this funnel. If users are registering but not Liking anyone, you likely have an Inventory problem (an empty database or bad search results). If they are Liking but not Matching, you have a Liquidity problem (inactive users or poor matching algorithms). And if they are Matching but not talking, you face a Psychological barrier—users are often too shy to initiate or simply don't know what to say.

4. Habit

The goal: Retention.

Once a user experiences “Aha!”, we need them to build a habit. A habit means they use the product automatically—checking for matches while waiting for coffee or lying in bed.

How do we define a Habit Moment?

Slack defines a habit as: “The user exchanged at least one message 4 days out of their first 7 days.”

In dating, we can use a similar metric. We want to track users who engage in a core action (messaging or swiping) repeatedly within their first week.

By the way, Mixpanel has a chart that allows you to see very interesting information regarding this: specifically, how many days within a single week users performed a certain action (for example, sending a message).

For example, looking at this data, we see that 24% of users sent messages on at least 2 days in a week. However, only 0.14% communicated on the site every single day.

I would recommend tracking this Habit Moment with a separate Mixpanel event. For example, as soon as a user sends a message on their 4th day out of 7, we fire the event Habit Moment Happened. This allows you to track the conversion rate and time-to-conversion from Join Completed to this specific Habit Moment Happened.

5. Engagement

The goal: Liquidity and Depth.

Many founders mistake DAU (Daily Active Users) for Engagement. They think, “People are opening the app, so we are good.”

This is wrong. In dating, a user can open the app, swipe 100 times, get zero matches, and leave angry. That user is an “Active User,” but they are actually churning.

Following the Reforge methodology, we measure Engagement based on Value, not just activity. We look at this on three levels:

Level 1: Total Engagement (Volume)

This is the pulse of your marketplace. We track:

• Messages Sent (Total)

• Conversations Started (Total)

Red Flag: If your Registrations are skyrocketing but your Messages Sent is flat, you are acquiring low-quality traffic. You have users, but you don’t have liquidity.

Level 2: Engagement Per Active User (Density)

We need to know how effective the average user is.

Metric: Average Conversations Started per User (Total Conversations / WAU).

If last month your average user started 3 chats a week, and this month it’s only 1, your product experience is diluting. This often happens when you scale marketing too fast and bring in “tourists” rather than serious daters.

Level 3: Segmentation (The Power User Curve)

Never look at “average” users alone. Split them into buckets using a Frequency Report (often called the L30 or Power User Curve):

1. Casual Users (”Tourists”): Log in rarely, minimal action.

2. Core Users: Regular usage, consistent swiping. The foundation of your retention.

3. Power Users (”Whales”): These users send dozens of messages a day.

Industry Insight: In dating apps, Power Users are critical because they generate the liquidity for everyone else. They are the ones initiating chats and keeping the ecosystem alive. You need to monitor this segment closely—if they burn out, your ecosystem dies.

Summary

You now have the framework for the Value Funnel:

Sign up → Setup → Aha → Habit → Engagement

You have the events to track in Mixpanel. Now you can find your bottleneck:

• Losing people at photo upload? Fix your Setup UI.

• People register but nobody talks? Fix your Liquidity (Aha Moment).

• People talk for two days and leave? Fix your Habit loops.

But remember, making users happy is only half the battle. We are running a business. In the next article, we will tackle the Revenue Funnel—how to turn this engagement into money, optimize paywalls, and maximize LTV.

Stay tuned.

Why Build From Scratch?

Implementing the Value Funnel strategy we just discussed requires more than just a good idea - it requires a sophisticated technical foundation.

Coding every single event, integrating SDKs, and setting up the logic to track “Habit Moments” or “Conversion Time” is a massive development drain. Most founders skip it to save time, and then they launch blind.

This is exactly why we built SkaDate.

We don’t just give you a dating app script; we give you observability out of the box. The Mixpanel integration, the event tracking, and the data structures I described in this article are already built into our core. You don’t need to spend weeks coding analytics. You just turn it on, look at the dashboards, and start optimizing your growth immediately.

Stop guessing. Start measuring.

Discuss Your Project with Me

But you’re stuck at a plateau. There is no growth. Registrations aren’t climbing, revenue isn’t increasing, and you don’t have the budget to pour into paid acquisition. You feel your hands tied, and you just want to give up.

However, you keep building new features, believing they will fix everything: “I’ll just ship this one feature, then that one, and then the money will start rolling in!” That’s what you tell yourself.

You spend more money and time. The features go live. Revenue stays flat. You get frustrated. Then, you repeat the cycle. This goes on for years.

Let me give it to you straight: You have fallen into the classic “Build Trap”.

This is when you measure product success by the quantity of features released, rather than whether those features actually solve real problems for your users and your business.

This is even more painful for entrepreneurs investing their own hard-earned cash. You are spending your blood, sweat, and tears releasing new features, yet the business isn’t growing. Worse yet, you don’t even know how these features are being used. Who is actually using them? What percentage of people even know they exist? I call this the “Blind Build Trap”.

The problem isn’t that you aren’t doing enough. The problem is that you don’t know why you are doing it or what it influences.

The Way Out: Goals Over Features

The good news is that there is a way out of this trap. It doesn’t start with a new feature, a redesign, or an ad campaign. It starts with properly formulated product and business goals.

It’s easy to say “set goals”, but what kind? Ideally, we move away from generic wishes like “I want growth”, “I want more money”, or “I want more users”. These are impossible to manage directly, and they lead you right back to developing random features.

To set the right goals, we need to understand the dating business on a deeper level.

I have written previously about the primary formula for any dating business -

Here is the formula:

Expected Income = (Number of New Buyers × LTV) – Marketing Costs

This formula ties marketing and product together:

• Number of New Buyers: How well your product converts sign-ups into customers.

• LTV (Lifetime Value): How much you earn from one customer over their entire “life” in the app.

• Marketing Costs: Your acquisition spend.

We say “Expected Income” because people don’t always buy immediately after registration, and ideally, they buy more than once.

Imagine a user buys a one-month subscription but then realizes your user base is small or full of spammers (or worse, thinks you created fake profiles yourself) and cancels. You get only one payment.

But, if you solve the Cold Start Problem and the user receives tangible value after subscribing, they will stay for a second cycle, maybe a third. In this case, our Expected Income includes all these payments thanks to the LTV parameter.

This formula sheds light on the economics, but it isn’t fully actionable yet. Goals based on it—like “Increase LTV”—are still too abstract.

To make them practical, we need to go one level deeper: User Behavior.

The formula explains where the money comes from, but it doesn’t answer why some users convert and stay while others leave. To answer this, we need to map the complete user journey, specifically including the stage where they actually experience the product:

Ad Click → App Download → Registration → Onboarding (Setup) → Activation (Aha Moment) → Freemium Usage (Habit + Engagement) → Paywall View → Purchase → Repeat Purchase

To manage this journey effectively, we must look at it through two lenses simultaneously: Value (why they stay) and Money (why they pay).

1. The Value Lens: Retention & Engagement

This lens corresponds directly to the LTV component of our income formula.

It’s simple math: You cannot have high Lifetime Value if the user’s “Life” in the app is short. Higher retention and deeper engagement mean the user stays longer, creating more payment cycles and driving LTV up. Conversely, if the user doesn’t find value immediately, they won’t stay, and LTV collapses to zero.

I prefer using the Reforge methodology (from their Retention + Engagement program), which helps us understand if we are successfully keeping the user. They highlight these key stages:

Sign up → Setup → Aha → Habit → Engagement

• Sign up: The user registered. They haven’t received value yet.

• Setup: They performed the minimum actions to make the product work (filled out their profile, added photos, were approved by moderators, and became discoverable and able to connect with other users).

• Aha: The key moment when the user actually feels the value (received a meaningful reply).

• Habit: The user formed a habit after interacting with several people.

• Engagement: They constantly return to the product to chat and date.

Now, let’s see how adopting this framework shifts our focus from simply shipping features to actually solving user problems. Let’s apply this to a specific feature, like Video Chat.

The goal is not to “build a video chat feature.” The goal is to remove the friction between the stages (Sign up → Setup → Aha → Habit → Engagement):

• If users drop off at Setup, building a cool “video chat” is useless - they aren’t even completing their profiles! You need to fix the onboarding flow.

• If users finish Setup but don’t reach Aha (no matches/replies), you need to fix the matching algorithm or user liquidity, not change the color of the buttons.

• If they reach Aha but don’t form a Habit, you need re-engagement mechanics (push notifications, digests).

• But if users are engaged but leaving the app to talk on WhatsApp, then you build Video Chat — to keep that value inside your product (Engagement stage).

We stop guessing and start building specific paths to move the user from one stage to the next.

2. The Money Lens: Decomposing “New Buyers”

Delivering value keeps them in the app, but you still need to get paid. Many founders treat monetization as a passive event. It isn’t.

We need to look at the Number of New Buyers part of our formula and decompose it. It is a structural funnel:

New Buyers = (Registrations × Paywall View Rate) × Purchase Conversion

This formula reveals that “Monetization” isn’t a single task. It consists of two distinct levers that you must optimize separately:

A. Paywall View Rate (Visibility)

You can’t sell what people can’t see.

• Subscription: Do they see the offer during onboarding? On a new session start?

• Feature Unlocks: Think about Tinder’s “See Who Liked You”. This is a massive revenue driver. If you hide this feature deep in a menu, your Paywall View Rate is near zero. If you show a blurred photo teaser at the start of the session, and on a separate tab, and in the Messages section, your View Rate skyrockets.

• Consumables: Think about “Super Likes,” “Boosts”, etc. Are these offers visible on the swipe card or in other places?

B. Purchase Conversion (Effectiveness)

Of the people who clicked “See Who Liked Me” or tried to send a “Super Like,” how many actually paid?

• If 100% of users see the “Who Liked You” teaser, but only 0.1% buy, your value proposition or pricing is off.

• If 5% see it, but 20% buy, you have a visibility problem. You need to show that teaser more aggressively.

The Lesson: Don’t just “add payments.” Analyze specifically: Are users seeing the paid features? And if they see them, are they converting?

The Metrics Tree: Connecting Value and Money

Now we combine these two views into a Metrics Tree. This prevents you from drowning in data by organizing metrics into a logical hierarchy of Outputs (Results) and Inputs (Levers).

You start at the top and drill down to find the root cause:

Level 1: The Output (Business Health)

At the top is Net Revenue. This is your root metric, but it’s a lagging indicator. You can’t impact it directly; it merely tells you what happened in the past.

Level 2: The Drivers (Strategic Focus)

To move Revenue, you break it down into its core components:

• Monetization Velocity: New Buyers (How well you convert demand).

• Retention Strength: LTV & Churn (How much value users get).

Level 3: The Inputs (Product Levers)

This is where the “Value Lens” takes over. These are leading indicators—user behaviors you can actually influence through product changes:

• To improve New Buyers: Look at Paywall Visibility and Registration-to-Purchase Conversion. Registration-to-Purchase Conversion can be decomposed into these specific sub-metrics:

  • 1. Onboarding Completion (Inventory Generation): If a user doesn’t upload a photo during onboarding, they won’t get any Likes. If they have 0 Likes, the “Who Liked Me” screen is empty. There is nothing to sell.

  • 2. Notification Deliverability (The Hook): Most Likes happen when the user is offline. You need to bring them back. If your Push/Email Delivery Rate is low (or going to Spam), or your Open Rate is low (boring copy), the user never returns to the app to see the blurred photo.

  • 3. Teaser Visibility (In-App Paywall View): Once they are in the app, do they see the teaser? Is there a “Red Dot” badge on the tab? Is the blurred photo visible on the main screen?

  • 4. Close Rate (The Offer): When they tap the blurred photo, how many actually pay? This tests your pricing and value proposition.

• To improve LTV: Look at Activation and Habit Formation.

  • Why aren’t they retaining? → Maybe the “Aha Moment” is broken or there is technical friction (e.g., transactional emails are going to Spam instead of Inbox).

  • Why no “Aha Moment”? → Check Liquidity Metrics: Visit-to-Like Rate, Match Rate, and Reply Rate.

  • Let’s decompose the “Reply Rate”. If users match but don’t talk, they churn. Why is the Reply Rate low?

    • 1. Notification Speed & Deliverability: Speed is critical in chat. If a push notification arrives 5 minutes late (or goes to spam), the “emotional moment” is lost, and the user might have already closed the app.

    • 2. Opening Message Quality (The “Hi” Problem): Analyze the content. If 80% of first messages are just “Hey”, the Reply Rate will be low.

    • 3. Profile Trust Signals: Does the sender look real? Users often ignore messages from profiles with 1 photo or no bio, fearing bots or scammers.

    • 4. Active Status: Was the message sent to a user who hasn’t logged in for 30 days? (This is an Inventory currency problem—you are showing inactive users).

Real-World Example: The “Who Likes Me” Trap Imagine your overall Subscription Conversion drops by 10%. You drill down and see the drop is specifically coming from your top revenue source: the “See Who Likes You” paywall trigger.

Your first instinct might be to change the text on the button or lower the price. But the Metrics Tree forces you to look deeper to find where the problem is actually buried:

• Trace it down: Users are hitting the paywall, but the Close Rate has dropped. Why?

• Dig deeper: You investigate the “Inventory” behind the paywall. You discover that the average number of “Incoming Likes” per user has dropped significantly. Instead of seeing “You have 10 new likes,” users now see “You have 1 new like.” The curiosity gap has collapsed.

• Hypothesize & Analyze: Why did the Like Volume drop? You generate three hypotheses:

  1. Traffic: Did fewer users log in today? (Data check: No, DAU is stable).

  2. Tech: Is the “Like” button broken? (Data check: No, API logs are clean).

  3. Relevance: Are users seeing people they don’t like? (Data check: Yes. The “Swipe Left” rate has spiked by 15%).

• The Root Cause: The issue isn’t the paywall price—it’s the Feed Algorithm. You are showing users profiles that they don’t find attractive.

• Result: You improve feed relevance → Global Like Rate goes up → Users see “You have 15 new likes” → Curiosity peaks → Subscription Conversion recovers.

You stop worrying about the top-level number and focus on the specific input metric (Swipe Left Rate) that is actually broken.

OMTM: Focus on One Lever

Once the Metrics Tree is built, you will see problems everywhere. However, you cannot fix everything at once. You must select your OMTM (One Metric That Matters). This is the single specific metric the team will focus on for the next quarter.

1. How to Select the OMTM (The Bottleneck Principle)

You look at your tree and identify the “Constraint”—the step in the funnel with the biggest drop-off or the lowest performance compared to benchmarks.

• Example: If your Registration-to-Purchase conversion is healthy, but your Day-1 Retention is terrible, it often means users aren’t finding anyone attractive to interact with. They browse but don’t swipe right and don’t get matches as a result. You shouldn’t waste time A/B testing the paywall or chat features. Your OMTM becomes Signup-to-Match Rate.

2. From Metric to Action: The Hypothesis Loop

Once the OMTM is set (e.g., Signup-to-Match Rate), you switch from “Analysis Mode” to “Execution Mode.” You don’t just stare at the metric; you launch a process to move it:

• Generate Ideas: Brainstorm specific features or changes that could impact this metric.

  • Idea A: Show the most attractive users first.

  • Idea B: Improve the default sorting (Active users first).

  • Idea C: Increase the like limit from 10 to 20.

• Prioritize (ICE Framework): To avoid chaos, evaluate every idea using the ICE Score:

  • Impact: How much will this move the OMTM? (1–10)

  • Confidence: How sure are we that it will work? (1–10)

  • Ease: How easy is it to build? (1–10)

• Execute: Pick the ideas with the highest ICE score and run them as experiments (A/B tests).

• Analyze & Decide (The Critical Step): An experiment is useless without a decision. Look at the data and take action:

  • Ship: If the hypothesis is confirmed and the metric went up — roll it out to 100% of users.

  • Kill: If the metric didn’t move (or dropped) — delete the code. Do not keep “zombie features” that didn’t prove their value. They only add technical debt.

  • Learn: Even a failed test is a success if you learned why users didn’t engage. Use this insight to update your Hypothesis List.

This turns your strategy into a clear, repeatable pipeline: Tree → Bottleneck → OMTM → Ideas → ICE → Experiment → Analysis (Ship or Kill)

What to Do Next: Your Growth Algorithm

If your dating app isn’t growing, stop shipping random features. You cannot code your way out of a strategy problem. Instead, follow this diagnostic loop:

1. Audit the Journey (Two Tracks)

Map out your user flows specifically for Value (Sign up → Setup → Match → Chat) and Money (Registration → Paywall Trigger → Purchase). Do not mix them yet.

2. Build the Metrics Tree

Stop looking at “Revenue.” Connect your bank account to user behavior.

• Revenue (Output) depends on Purchases.

• Purchases depend on Paywall Views.

• Paywall Views depend on Matches (if you use a “Who Likes Me” model).

• Matches depend on Likes (Input).

3. Locate the Leak (The Bottleneck)

Look at the data. Where is the biggest drop-off relative to benchmarks?

• Is it a Money Problem? (Users see value but the price/offer is wrong).

• Is it a Value Problem? (Users aren’t swiping, matching, or chatting).

• Crucial Check: Is it a Technical Problem? (Are emails hitting spam? Is the chat server laggy?)

4. Pick Your OMTM

Select the one metric that represents that bottleneck.

• Example: “We will ignore Revenue for this quarter and focus 100% of our energy on increasing Signup-to-Match Rate.”

5. Run the ICE Cycle

Don’t just “redesign.”

• Brainstorm 10 ideas to move that OMTM.

• Score them (Impact, Confidence, Ease).

• Implement the winner.

• Analyze & Iterate: Look at the data. Did the metric move? Regardless of the outcome, extract the lesson (”Why did users behave this way?”). Use this insight to refine your understanding of the user and sharpen your ideas for the next cycle.

Why This Strategy Fails Without Analytics

Everything discussed above, the Metrics Tree, the OMTM, the ICE framework, is impossible to execute on “gut feeling.”

Unless you can measure:

• The exact drop-off rate during Setup,

• The metric Signup-to-Match Rate

• The visibility of the Who Liked You paywall,

• The percentage of users reaching the Aha Moment (First Chat),

• The actual conversion to Paid Subscriber,

...you are not managing the product—you are just believing in it.

The logical next step is Product Analytics. Tools like Mixpanel, Amplitude, or PostHog allow you to see the real user path, build these funnels, and manage through hard data, not guesses.

In the next articles, I will move from strategy to implementation:

1. Conversion Mechanics: How to measure the Sign-up to Purchase, Signup-to-Match Rate rates correctly and identify exactly where users drop off in the funnel.

2. Retention Deep Dive: What is cohort analysis?

3. The Tracking Plan: Which specific events you must track to build these dashboards and visualize your OMTM.

Expected Income = (Number of New Buyers × LTV) – Marketing Costs

Last time, we examined LTV in detail in this article:

Today, we will talk about the Number of New Buyers.

Generating the Energy

You launched a Facebook campaign.

In the video banner, for instance, you show a successful man: you show how he cooks, how he goes to the gym, and how he walks his dog on the beach.

If a user is allergic to dogs, they obviously won’t click on this banner. But let’s assume they have no allergies and no life partner—they are single. And the time has come for them to find a partner.

They are tired of endlessly scrolling through their Instagram feed, and they are frankly sick of the photos of their mega-successful acquaintances. The moment is right, and with great hope, they click on this banner.

The banner created a vision of a desired goal; the user is excited and wants to find a partner right now.

Of course, they won’t land on a date with this handsome man from the banner immediately after the click. But the banner and the image of the goal have charged them with energy, and they are ready to overcome almost any difficulty. Thank this banner for that energy; we will still need it in the future.

The Harsh Reality

Next, the harsh reality begins. The user lands on your app listing in the App Store (or Google Play).

Also, Web2App funnels have become very popular recently: this is when you send users from an ad to your web application, where they undergo registration, fill out a profile, buy a subscription, and only after that proceed to the listing to download the app from the App Store or Google Play.

Why do this and complicate the path for users?

Because you pay a 15% to 30% commission to Apple and Google on every transaction.

• If you have a new app and you earn up to $1 million per year, you will give away 15% (as a member of the Small Business Program).

• If you exceed this threshold, you will start paying 30%.

  I sincerely wish for you to exceed this threshold as soon as possible.

So, when a user pays not through Apple/Google, but enters their card details and pays via a Payment Gateway (for example, Stripe), the commission in the US is only 2.9% + $0.30, which is much less than 20-30%. That is exactly why Web2App funnels have acquired such popularity. There are even ready-made funnel builders that allow you to create them without developers. I will review them in detail in a separate article.

By the way, many beginners think that Web2App is technically difficult. In reality, there is no “magic”: the user registers and pays on the mobile site, and then downloads the app and simply logs in with the same email and password. Your database sees the “Premium” status and opens access. This is a simple synchronization available to any development team.

Important Note on Payments in the US

Recently, following the legal battles between Epic Games and Apple, developers in the US were allowed to place links to external payment methods directly inside the app (you might have seen a “Pay via Credit Card” button in Tinder).

Does this solve the problem? No. Apple still charges a commission on these transactions (around 27%), plus it requires complex reporting.

The Verdict: For a startup, a Web2App funnel is still the superior strategy. Don’t try to cheat the system from inside the app; it’s better to lead the user to the website from the start.

ASO and Installation

Next, let’s talk about how the user looks at screenshots and how important they are, as well as the icon and reviews — nobody reads the app description, so it is important to convey the essence through screenshots and headers. Screenshots and icons are so important that Apple/Google created a mechanism for A/B testing screenshots and icons.

Then, the user downloads the app.

Verification

Next, as a rule, is SMS verification. It helps to filter out lazy scammers, serving as a minimum defense. But you must be aware of two critical threats:

1. SMS Pumping Attacks (Toll Fraud). This is a nightmare scenario where fraudsters use bots to trigger thousands of SMS verification codes to premium-rate numbers that they control (usually in high-cost regions like Indonesia or parts of Africa). The math is scary: You pay Twilio/Telesign $0.10 per SMS. The fraudster gets a kickback of $0.03 from the corrupt telecom provider. They run a bot for one night, sending 100,000 requests. You wake up to a $10,000 bill, and they walk away with $3,000 profit. This is a topic for a separate article, but you need to set rate limits immediately.

2. Disposable Phone Numbers. These are temporary virtual numbers (often VoIP or SIM farms) that anyone can rent for minutes to receive a code and then discard.

This means SMS is not a 100% protection against scammers.

The Next Step — Get the User’s Email

This is critically important because email is our main tool for Retention (returning users to the app).

How exactly to get it? The classic “Email + Password” combo is already outdated and considered unsafe. Users often set weak passwords, making them vulnerable to Account Takeover (ATO) through brute-force attacks.

Therefore, I strongly recommend using Email OTP (One-Time Password). This is a “passwordless” method: the user simply enters their email, a 4-digit code is sent to them, they enter it, and they are in. No password needs to be remembered, and it cannot be stolen.

Also, add Google SSO and Apple SSO. This is even faster for the user, and for you, it is a huge plus that you do not need to verify the email — Google and Apple have already done this for you; you get a guaranteed valid email.

Important advice: Do not make a login via Facebook. Trust in it has fallen, and people are afraid to share their data through this social network.

Date of Birth

Next, we request the Date of Birth. We categorically must not let in users under 18 years old. However, the strictness of the check directly depends on your niche.

1. Mainstream Dating Apps (Tinder, Bumble): For ordinary dating apps hosted in the App Store and Google Play, a standard DOB Picker (selecting the date of birth in the interface) is currently enough. At the moment, this is enough to comply with store policies, although requirements are gradually tightening.

2. Adult Industry: Such projects are usually banned in mobile stores and live exclusively on the Web. And here, legislation (especially in the US and EU) requires strict Age Assurance. You cannot simply trust the entered date — it is illegal. You are obliged to use advanced mechanisms:

   1. Selfie verification with AI age estimation (liveness check).

   2. Full document check (Government ID verification).

      This topic is a legal and technical minefield. In the future, I will make a separate big article about age verification, where we will consider all the pitfalls.

Filling the Profile: Energy vs. Friction

Next, we ask the user to fill out various important fields, for example: Height, Education, Job, Smoking habits, etc.

By the way, what do you think: if we don’t make these fields mandatory and request them only after the user has gotten inside the app — will they fill them out?

Actually, as my experience and other apps like Tinder, Hinge, and others show — people willingly fill out mandatory fields during registration that can be entered quickly (dropdown-based). But if you make them optional, people do not fill them out.

The secret is that during registration, people are excited and they have that energy which was created by your marketing and that video banner. And this energy, these expectations, become the fuel that, despite the friction, moves the user through the many steps of registration.

But if you make these fields optional, users get inside the app, and many of them then simply leave and do not return.

Retention Day 1 after registration for new dating apps is 10-20%, meaning 80-90% of users registered and left. And if they didn’t fill out the profile, nothing remains of them in your catalog. But if they filled out the profile but churned, then later some other user might write to this user and resurrect them.

And what do you think, what percentage of people will fill out the “About Me” field during registration if it is optional? And is it worth making photos mandatory? How many photos to request? And how does profile completeness and the presence of photos affect the reply rate? I will publish a separate article on all these questions.

The Setup Moment

Okay, the user filled out the profile, uploaded a photo, what next? Next, they get inside the app. What do they do? They look around — look at other people’s profiles, like others, and so on.

But do other people see this user? No, because moderators haven’t checked the profile yet; what if there is nudity in the photo? We cannot show it to other people, and we cannot deliver any types of messages from this user to others until they are approved.

As a rule, this moment when the user can use the product and has already performed the necessary settings is called the Setup Moment. In our case, the user must have a filled profile, they must have at least 1 photo, and they must be approved by moderators (meaning a human/system must check their profile data and their photos).

Sounds expensive? Don’t be scared. You don’t need to hire a staff of moderators from the start. In the beginning, you can (and should) do this yourself to feel the audience. And a bit later, plug in AI solutions (like AWS Rekognition), which cost pennies and automatically filter 95% of forbidden content.

Switching Modes: From Funnel to Product

Our hero has overcome all barriers: downloaded the app, passed SMS verification, completed the profile, and was approved.

They are inside. Phew.

This means we have successfully closed two stages at once: Sign Up Moment and Setup Moment (the user is technically ready to use the product).

Here we need to pause and change optics.

Until this moment, we looked at the process through the eyes of a Marketer and a Developer. Now we must put on the glasses of a Product Manager.

Why? After all, we are talking about money (Number of New Buyers), why not simply ask for payment right now?

Because in Dating Apps, Money is a consequence of Value. If we try to sell a subscription to a user who simply filled out a profile but hasn’t yet understood why they are here, we will burn the budget.

To make our business model work, it remains for us to lead the user through the next two steps:

1. Aha Moment (First Value). The moment when the user for the first time gets a real result and thinks: “Oh, this is what I need!”. It is exactly this realization that forms their willingness to pay.

2. Habit Moment (Habit). The moment when using the app becomes part of their routine. This guarantees that they will not just make a one-time purchase, but will attach to us and pay again and again while this habit is active.

But to understand when exactly these moments occur, we first need to define the global meaning of our app for the user.

Global Goal (NSM)

The ultimate goal of any dating app is to create a happy couple. That is the “Product Vision.”

But as a business, we have a problem: We cannot track happiness. It happens offline, away from our servers.

To find a metric we can track, we need to reverse-engineer the “Happy Path” of a user. Let’s trace the steps backward from the goal:

1. Happy Couple (The Goal)

   • Can we measure this? No.

2. Offline Dates

   • Can we measure this? Generally, no. We cannot stand next to them with a candle to see if they actually met at Starbucks.

3. Exchange of Contacts (Phone number / Instagram / FaceTime)

   • Can we measure this? YES. This is a digital action inside our chat.

4. Active Dialogue

   • Can we measure this? YES.

The “Offline Blindness” Problem

Most apps go blind the moment users leave the chat. Exception: Hinge is trying to solve this. They built a feature called “We Met.” After an exchange of phone numbers, the app eventually asks: “Did you go on a date?”

If the user clicks “Yes,” Hinge asks a follow-up quality question: “Is she/he the type of person you’d see again?”

It is a brilliant attempt to feed offline data back into their online algorithm.

However, for most apps, we cannot rely on users to report back. We have to trust the data we have.

That is why our North Star Metric—the most reliable “thermometer” of success—is the step just before the offline date:

NSM = The number of dialogues that result in a Contact Exchange.

The Aha Moment

In product analytics, the next big moment after Setup is called the Aha Moment. This is the moment when the user experiences the core value prop for the first time, meaning in our case, when their first dialog took place.

In our case with dating, the Aha Moment is when the user has spoken with at least one person within 1 week after registration.

Examples of Aha Moments for other products:

• Airbnb: Booked night turns out well.

• Pinterest: I’ve found something cool in an interesting feed.

The Habit Moment

The next important moment is Habit. This is the moment when a habit appears in the user around this core value prop. In our case, this is the dialogs with other users. That is, when a user has spoken with, for example, 10 people within 1 week after registration. This means that the user has developed a habit, and they will remain with us with a very high probability and will use our product further.

Examples of Habit Moments from other spheres:

• Pinterest: “I find cool things on Pinterest every week.”

• Airbnb: Booking second stay within one year.

The Engagement Moment

The next stage is the Engagement Moment — when the user has not just formed a habit, but continues to actively use the app. Moreover, they do not just come to the app, but perform the core action often, which in our case is participation in chats. And we will measure engagement in dating with such metrics:

• Daily Active Chatters / Weekly Active Chatters. The meaning of the metric is we want people ideally to come to us every day and talk to at least someone. This is an indicator that measures the frequency of engagement.

Examples of how Engagement Frequency is measured for other products:

• Airbnb: Frequency - QAG/YAG (Quarterly active guests / Yearly active guests).

• Pinterest: Frequency - DAR/WAR (Daily Active Pins/Repins / Weekly Active Pins/Repins).

The Finale: Synchronizing Value and Revenue

We examined the user’s value track with you, but where is the Number of New Buyers here? Well, first I wanted to show how the user receives value from dating apps.

And now that value is received, is it time for us to earn on this?

Here we approach the main question: how to synchronize Value and Revenue?

The main decision that a founder must make: where exactly to place the Paywall relative to the Aha Moment?

If you ask for money too early, when there is no trust yet — the user will leave. If too late — you will leave money on the table.

Let’s compare how market leaders do this.

Two Dominant Monetization Models

1. The Tinder Model (Freemium): Payment as an Accelerator and Competitive Advantage

In this model, the Aha Moment (dialogue) is technically free. Two users can match and start corresponding without spending a penny.

Why then do they pay?

• They pay for Speed (Tinder Gold): Swiping at random is work. Guessing “did they like me or not” is stress. By buying access to the “Who Likes You” list, the user cuts a corner: they immediately see those who have already shown interest. This is the monetization of impatience and curiosity.

• They pay for Visibility (Tinder Platinum): The market is oversaturated. Beautiful girls simply do not see the likes of ordinary users in the pile. By buying Platinum, a man pays for his like to be shown first (Priority Likes). This is already the monetization of competition.

The essence of the strategy: Value is available for free, but efficiency costs money.

2. The Seeking Model (Paywall): Payment as a Pass

In niche apps or models with high intention, the Aha Moment is strictly behind a paywall.

• You can look at profiles, you can add them to favorites, but you physically cannot enter into a dialogue without payment.

• The essence of the strategy: We monetize access. The user cannot experience the Aha Moment (dialogue) until they become a buyer.

• In this model, Purchase is a prerequisite for receiving value.

Main Metric: Reg-to-Paid Conversion

Regardless of which model you choose, for the variable Number of New Buyers in our formula, one metric is critically important: Registration-to-Purchase Conversion.

This is the bridge between the world of product and the world of finance.

• User’s view: “I register -> Fill out profile (Setup) -> See value -> Pay”.

• Business view: “Traffic -> Setup -> First purchase -> LTV”.

To maximize the quantity of new buyers, you must minimize friction at the Setup stage (make entry simple), but maximize motivation before the Paywall (make being in a free status “painful” or ineffective).

Summary

Let’s look at our formula one more time:

Expected Income = (Number of New Buyers × LTV) – Marketing Costs

In this article, we analyzed the first variable — Number of New Buyers.

We found out that this is not simply “buying clicks” on Facebook. This is a complex chain:

1. Funnel: Efficient delivery of the user from advertising to download (preferably via Web2App to save 30%).

2. Setup: Guiding the user through verification and filling out the profile on the energy created by marketing.

3. Trigger: Installing the paywall exactly where the user’s motivation reaches a peak — whether it is a desire to cut the path (as in Tinder) or get access (as in Seeking).

What’s Next? We have already covered LTV in Part 1. In the final article of this series, we will tackle the last variable of our equation — Marketing Costs. We will discuss how to lower your CAC (Customer Acquisition Cost), how to test creatives efficiently, and how to scale without burning your budget.

Why Build From Scratch?

Setting up monetization logic, fighting SMS fraud, and optimizing the onboarding flow is hard enough without having to code the entire platform yourself.

This is why we built SkaDate.

We provide the complete technical engine—native mobile apps, flexible paywalls, and robust anti-spam tools—ready from Day 1. Ready to skip the development nightmare?

Discuss Your Project with Me

You focus on finding the users; we’ll make sure the technology keeps them there.

In this article, we are going to dissect a product like Tinder to understand exactly what a modern dating app is made of.

Imagine Tinder. You’ve likely used it. Let’s freeze it in time for a moment. Freeze the product and the traffic—no new users arriving, no one leaving. Now, let’s look at it under a microscope to see the infrastructure required to keep it alive.

We see thousands of users currently active inside the ecosystem. However, we see them accessing the product through different platforms:

• Native Mobile Apps: Android and iPhone apps.

• Desktop Web: The browser version on a computer.

• PWA (Progressive Web App): The mobile browser experience that behaves like an app.

Let’s break down why these distinctions matter for a founder.

The Platforms: Native vs. Desktop vs. PWA

Native Apps (iOS/Android)

With native apps, the math is simple: these platforms are much stickier and typically drive 3.5X higher LTV (Lifetime Value) compared to the mobile web. The primary reason is push notifications. They allow you to activate and retain users effectively—bringing them back into the app repeatedly. We will discuss the mechanics of this later.

Desktop Web

Users open dating sites on desktop when they are in a calmer, more deliberate context: at home, on a laptop, checking email, or clicking a link from a notification. The larger screen fundamentally changes behavior. It facilitates reading profiles, viewing photos in detail, and—crucially—typing long-form messages.

Desktop users swipe less and chat more. This platform isn’t about rapid discovery; it’s about deepening interactions. Therefore, a desktop version complements mobile apps by driving up engagement metrics and conversation quality, even if it represents a smaller slice of your total traffic.

PWA (Progressive Web App)

A PWA is essentially a website that acts like an app.

Here is how it works: A user opens your site in a mobile browser (via a link, search, or ad). They register and start using the product. Then, you prompt them to “Add to Home Screen.” Once installed, the PWA opens in its own window without the browser interface and, crucially, can receive push notifications.

The PWA Revolution

The watershed moment came in 2023 when Apple added support for web push notifications in Safari on iOS. Before this, PWAs were a compromise. Now, they are a legitimate retention channel comparable to native apps. A PWA can live on the user’s home screen and ping them with “You have a match” notifications—a feature critical for high-frequency apps like dating.

If you look at the bigger picture, this is part of a platform war. Google has historically bet on the open web, where products reach users directly. Apple acts as a gatekeeper, controlling access via the App Store. Apple’s support for Safari push notifications wasn’t altruism; it was a forced move in this competition.

Why PWA Matters for Niche Founders

For certain niches, a PWA isn’t just an alternative—it’s the only option. Categories like Sugar Dating, Adult, or other “sensitive” verticals face constant friction with the App Store and Google Play: rejection during review, inconsistent moderation, and the looming risk of getting de-platformed overnight.

In these high-risk verticals, a PWA allows you to own your distribution. You exist outside the store ecosystems while retaining a mobile-first experience and the ability to re-engage users via push.

(Note: I will write a separate deep-dive article on the technical implementation of PWAs, including installation prompts and notification logic.)

The Traffic: Acquisition vs. Re-engagement

Let’s look at how those users inside our “frozen” Tinder got there.

Acquisition (New Users)

Some clicked an ad banner on Instagram, downloaded the app, and are currently onboarding. Others searched “dating apps” in the App Store—this is your organic search traffic (ASO).

Retention (Returning Users)

But how did the existing users get back into the app right now?

• Some received a transactional push notification: “You have a new like” or “You have a match.”

• Some are replying to an ongoing conversation.

• Others hadn’t opened the app in weeks and received a marketing push designed to trigger FOMO (Fear Of Missing Out), such as:

  • “Swipe Surge is live. Let others know what you’re down for. Tap to join now!”

  • “And the award for the most anticipated return to Tinder goes to Alexander.”

  • “We’ve got new people popping up every day, but it’s not the same without you...”

Email as a Retention Channel

Email works similarly but serves a different context. The same triggers—matches, likes, messages—can be sent via email, specifically targeting users who haven’t opened the app recently or are desktop-heavy users. Email is often used for “softer” re-engagement. It doesn’t demand immediate attention like a phone vibration, but it gently reminds the user that activity is happening waiting for them.

The Engine: Lifecycle Marketing

All notifications in a dating product generally fall into two buckets:

1. Event-Based (Transactional): Triggered by a specific product action (a like, a match, a message). Users expect these. They rarely perceive them as spam. These notifications drive the “Aha-moment” and facilitate actual dates.

2. Lifecycle (Marketing): Dependent on the user’s state, not a specific event. Is the user dormant? Did they stall during registration? Did they stop replying? These are used to resurrect users. They are powerful but dangerous; if you send them too often or without context, you will burn out your audience.

To manage this, you must understand that a user is not static. They move through a User Lifecycle:

• Onboarding: Just registered, looking around.

• Active: Swiping, chatting daily.

• Fading: Hasn’t opened the app in 3 days.

• Dormant: Gone for 30+ days.

A signal that helps a new user (e.g., “Complete your profile to get 5x more matches”) is annoying noise to an active user. This is why Lifecycle Marketing exists. It determines the right message, channel, and time for every user segment.

The Tech Stack

In practice, dating products rely on specific tools to handle this orchestration:

• Push Notifications: Firebase or OneSignal. They handle the delivery of native, web, and PWA notifications triggered by product events.

• Transactional Email: AWS SES or SendGrid. The priority here is deliverability—ensuring the “Verify your email” or “You have a message” email actually hits the inbox, not the spam folder.

• Marketing Email: Mailchimp, Customer.io, or Iterable. These allow you to build logic chains (e.g., “If user doesn’t open app for 7 days -> Send email”).

• Orchestration (Enterprise Level): Mature products use Customer Engagement Platforms like Braze, CleverTap, or OneSignal. These tools unify everything. They allow you to manage push, email, and in-app messages from a single dashboard, segmenting users by behavior without needing constant developer intervention.

The Gatekeepers: Moderation and Content Safety

Let’s go back to the user who just finished registration. They can swipe, but we cannot show their profile to others yet. Why? Because they might have uploaded explicit content, hate speech, or solicitation ads in their “About Me” section.

Moderation is the immune system of any product reliant on User Generated Content (UGC). In dating, UGC includes photos, videos, audio, and free-text bios.

Historically, we used classic Computer Vision AI to detect nudity in photos (via AWS Rekognition API or other API). Today, thanks to LLMs (Large Language Models) like Gemini, ChatGPT, and Claude, we can also automate text moderation with incredible nuance. The machine can now understand intent. It can tell the difference between a romantic poem and a cleverly disguised solicitation for prostitution. Today, content moderation can be almost entirely automated.

The Trust Ladder: Verification

Another critical layer is Verification. A “Verified” badge is a psychological trigger; it signals safety, which increases the Reply Rate.

However, verification isn’t a single feature; it’s a ladder of friction vs. trust:

1. Email Verification: The baseline. Prevents typos, but offers zero protection against scammers. Bots can create thousands of emails instantly.

2. SMS Verification: Adds cost to the attacker. While disposable numbers exist, they cost money and vary by country. It’s a barrier, not a wall.

3. Liveness Check: This is not just asking the user to upload a video, which can easily be faked. It’s active liveness detection (powered by tools like AWS Rekognition). Here is how it works:

   1. The Challenge: The user sees a specific prompt on their screen, such as fitting their face into an oval or watching a sequence of flashing colored lights.

   2. Real-Time Analysis: As they react, the AI analyzes the video stream in real-time. It looks for 3D depth and subtle skin reflections that only occur on a live human face.

   3. Spoof Detection: This process instantly detects if someone is holding up a phone with a video playing (a “replay attack”), wearing a realistic mask, or using deepfake software.

   If the “live” person passes this test, we then use Face Matching to confirm they are the same person shown in their static profile photos.

   The best part? It’s cheap. The cost of a single liveness check is negligible—often just pennies. This means we can perform this verification at our own expense. We don’t need to charge the user or put this behind a paywall;

4. ID Verification: The user scans a government ID. This is Liveness + Identity Confirmation. It offers high trust but high friction (users are hesitant to share IDs). Often reserved for Premium tiers.

   While market prices for ID verification are dropping, it remains an order of magnitude more expensive than a standard Liveness + Face Comparison check. Because of this higher unit cost, you rarely want to run this on every single free user.

   However, it serves a specific purpose that AI cannot fully solve: Age Verification. ID scanning is the only reliable way to validate a user’s exact age with legal certainty. If your product requires strict age compliance (e.g., ensuring 18+ users), this step is mandatory, regardless of the cost.

5. Background Check: This involves checking public records for criminal history. Unlike liveness checks (which cost pennies), a background check costs real money—typically $10–$30 each. You cannot subsidize this without destroying your unit economics; users usually pay for this themselves before a date.

   Crucially, this is a US-centric feature. In the US, criminal records are public products you can buy via API. In Europe (GDPR), this data is private state property, making commercial background checks legally impossible.

Founder’s Advice: Don’t Overbuild Too Early. Your job is to choose the rung on the ladder that balances safety with growth. But here is a warning: if you are just launching, do not over-engineer this. Heavy verification creates friction, and friction kills growth. You need liquidity (users) before you need a fortress.

Look at Tinder. Even today, with unlimited resources, they do not make email or liveness verification mandatory for new users. They know that every extra step during onboarding drops conversion. Start with low friction to get traction, then tighten the screws as you scale.

The Safety Net: Support

Once you are in production, Customer Support stops being an option and becomes a system requirement. Users will write in about billing errors, harassment reports, bans, or emotional disputes.

Do not build this yourself. Use Zendesk or Freshdesk.

Crucially, do not view Support merely as a “Cost Center” (a department that only spends money). In dating, Support is a retention tool. Speed and quality of response define your brand’s trustworthiness. One mishandled ticket regarding harassment can cost you a user forever—and potentially trigger a PR crisis.

The Money: Payments Infrastructure

Finally, the layer that keeps the lights on.

Historically, mobile dating apps offloaded this headache to the App Store and Google Play. You paid a “tax” (30% commission, or 15% on your first $1M/year), and in exchange, Apple and Google handled the taxes, the refunds, and the fraud.

The landscape is shifting. In the US, Apple now allows alternative billing systems. This makes third-party processors like Stripe or Adyen attractive.

• App Store: ~30% fee. Handles everything.

• Stripe: ~2.9% + $0.30 fee. You handle everything.

The lower fee is tempting, but it comes with Merchant of Record responsibilities. If you use Stripe, you are responsible for refunds, chargebacks (when a user disputes a charge with their bank), and fraud detection. In dating, chargebacks are common—often due to “buyer’s remorse” or emotional reactions to a bad date. If your chargeback rate gets too high, Visa/Mastercard can blacklist you. Moving off-store saves money, but requires a dedicated team to manage the financial risk.

Conclusion: Don’t Reinvent the Wheel

If there is one takeaway from dissecting the anatomy of a giant like Tinder, it is this: a modern dating product is a complex organism.

It requires native mobile apps, a PWA for retention, sophisticated lifecycle marketing tools, AI moderation, and a robust payment infrastructure. Building this “skeleton” from scratch can take years and hundreds of thousands of dollars.

This is the trap many founders fall into. They burn their entire budget building the infrastructure—the login systems, the chat servers, the payment gateways—and have nothing left for marketing. By the time they launch, they are exhausted and broke.

This is exactly why we built SkaDate.

We have spent years building and refining this anatomy so you don’t have to.

• Need a PWA that supports push notifications on iOS? We have it.

• Need native iOS and Android apps? We have them.

• Need integrated payment gateways and anti-spam tools? It’s already there.

Your job as a founder is to provide the soul of the product: the brand, the niche, the marketing, and the community. Let us provide the body.

If you are ready to launch a product that has all these professional layers from Day 1—without the development nightmare—let’s talk.

Discuss Your Project with Me

You focus on finding the users; we’ll make sure the technology keeps them there.

It is time to dive into the most critical topic for any entrepreneur planning to launch a dating business. Since many readers of this newsletter are also Skadate clients—who bought our engine and now want to grow their profits—this article is for you, too.

This post will give you a clear formula for growth. It will direct your efforts—and your hard-earned capital—toward the specific levers that lead to scalable revenue.

The Golden Formula of the Dating Business

Expected Income = (Number of New Buyers × LTV) – Marketing Costs

This formula calculates your expected income per unit of time—for example, per week. You spend money to acquire users; a percentage of them become buyers; those buyers generate a certain amount of cash over their “lifetime” in the app.

Naturally, we want Expected Income to be greater than zero. Otherwise, what is the point of spending more on advertising than you earn?

Of course, you may have to work “in the red” for a while. Uber burned cash for years before generating an operating profit. But if you want to build a scalable business, sooner or later you must reach a positive Expected Income. To scale, you need to increase marketing spend, and how can you increase spend if you have no profit?

Do not forget: we are talking about dating. The “virality” and “word-of-mouth” growth that everyone loves doesn’t work very well here. People rarely brag to their friends about the new dating app they just joined. So, one way or another, you will have to pay for new users. And it is much healthier to pay for them out of profits rather than borrowed funds.

Sure, there are VC investments, but investors are not fools. They won’t give money to a business that cannot generate profit. Yes, there are “Seed” stage investors when you just have a pile of dreams and maybe no product yet. But you will still have to spend that seed money on turning the startup into a business and getting your Expected Income above zero.

Let’s look at the formula components closer.

1. Marketing Costs

Let’s take a typical day. You spent $X on advertising today—for example, in Meta Ads (Facebook and Instagram) and Google Ads (Search Ads + Performance Max). As a result, Y people clicked on your banner.

That settles Marketing Costs. In reality, traffic acquisition is 70% of success, not 50%, for any dating app. I will dedicate a separate article to this topic later.

2. Number of First Sales

Some users will come from the ad to your listing in the App Store/Google Play. Some will download the app, some will finish registration, some will do something important inside (like liking another user or writing a message). And a very small fraction will buy and bring you revenue.

Essentially, users need to walk a specific “path of success” that leads them from registration to purchase. You can imagine this path as a bridge connecting the promises you made to people on the Instagram banner and your actual product, which must fulfill those promises.

The most important components of this path are Onboarding and Activation.

• Onboarding: You likely understand this because everyone has flown on a plane. In dating apps, onboarding includes registration, filling out the profile, uploading photos, etc.

• Activation: This is a newer concept. It essentially means the user received value from your product for the first time. In the case of a plane, it might be the first successful flight. In the case of Airbnb, the first successful booking.

This topic is critically important because this path leads your users to the purchase.

The Airline Analogy: In the case of flights, you are unlikely to change your mind about flying after buying the ticket or demand a refund (and as an airline, you basically earned the money the moment you sold the ticket).

The Dating Reality: In a dating app, users have 1,001 reasons to step off this successful path and never make a purchase.

And don’t forget the refund rate. This “path” deserves a whole cycle of articles where we will analyze the elements, the metrics we use to measure the stages, and the typical problems that force users to drop out halfway—or even earlier. Our task is to engineer a path that guides the user from registration to the first purchase, and from the first purchase to the maximum number of repeat purchases. In exchange, we must provide real value and utility to the user.

3. LTV (Lifetime Value)

Today, however, we will focus on LTV. Let’s go!

So, Y people clicked, Z people downloaded, registered, liked others, and someone got their first match. What happens next depends heavily on your app’s business model.

• Pay-to-communicate: A requirement to buy a subscription to send an outgoing message.

• Credits: Buying packs of credits for specific actions.

• Freemium: The model used by all popular apps (Tinder, Hinge, Bumble, etc.).

The essence of the Freemium model is that the most important action—the ability to communicate with another person—is free. It’s true: if you match with someone on Tinder, you can chat for free. But if you are impatient, or a business person who values their time, you will buy a subscription to see who liked you.

By the way, have you noticed that Tinder doesn’t immediately show the people who liked you in your card stack? Do you know why? If not, definitely follow this newsletter—in the future, I will do a deconstruction of popular (and not so popular) dating apps to explain these mechanics.

Incidentally, apps where you must buy a subscription to start chatting still exist—for example, Seeking.com. And we won’t overlook the new class of dating apps where people talk to LLM bots, like EVA AI.

I have analyzed all these Business Models in detail in this article -

So, a user buys something in your app. Perhaps an impatient user wanted to see who liked them. Another user in a different app wanted to write a message. A third wanted to buy credits to unlock private photos. There is a huge variety of premium features hidden behind a Paywall.

A joyous event has occurred—a reward for all your long labor in launching and tuning your dating app: The user bought.

Now, a completely new stage in this user’s life begins. They finally get access to all paid features. By the way, some apps (and this is becoming a trend) are creating multiple subscription tiers: two or even three, like in Tinder (Plus, Gold, Platinum). This helps raise revenue, and we will discuss “tiers” in a separate article.

After the purchase, the user starts using your product to the fullest and extracting the value you built into the subscription.

The Lifespan Factor

Don’t forget that the user came to you with a specific goal. The classic case: a user looks for a partner. Ideally, they will stop using your app as soon as they find one. Therefore, such a user has an average “lifespan” in the app. The sum that one such user brings is what we call LTV (Lifetime Value).

This metric is calculated in dollars. It is one of the most important metrics for any business, online or offline. Imagine the LTV of a car manufacturer whose loyal customers might buy only that brand of car for their entire lives.

In our case:

LTV = ARPPU (Average Revenue Per Paying User) × Lifespan

This means: how much the user pays us on average per month, multiplied by the number of months until they stop making one-time purchases or cancel their subscription.

Industry Insight: In some dating niches, users return to the site to search for partners multiple times—or let’s say, much more often than in “Vanilla dating” (standard romance apps). This is common on sites like Seeking.com (the sugar daddy industry).

The conclusion is simple: the longer a user remains a paying user, the more you earn. Simple.

4. Why Users Leave (The LTV Killers)

It would be great if all users canceled subscriptions only after finding partners. The harsh truth is that there is a huge pile of other reasons why people stop paying you much earlier. We will analyze these reasons in the future, but here are a few examples.

The Cold Start Problem

You just launched your dating site, and you have very few users. A subscriber might quickly view all available users in a week and that’s it—there is no point in remaining on the site. They cancel.

Of course, you know where this problem comes from. Any dating app is essentially a marketplace with two sides: Demand and Supply. At launch, the sharpest problem is always the “Cold Start.”

I wrote a detailed guide on how to solve the Cold Start Problem here -

Scammers

Another reason why users either don’t buy at all or cancel immediately is scammers. There are many varieties of scammers in dating (I will write a separate article about them). But today, what matters is that they try to move communication off your site to an external platform (Telegram or WhatsApp) as quickly as possible.

• The nuance: Scammers usually register as women.

• The behavior: They usually write the first message.

All of this causes strong suspicion in most users, but unfortunately, some fall for it and become victims.

How does this affect LTV? Simple. People don’t trust a site where they meet scammers. They buy less. Also, a user might buy a subscription just to read a message from a girl, only to find out she is a scammer.

Guess what the user thinks? I’ll tell you a secret: users often think the site owners specifically created these profiles to force them to buy subscriptions. However, the harsh truth is that these scammers register themselves. Young sites that haven’t implemented protection tools like Photo or Liveness verification are especially vulnerable. I will definitely write a separate article on how to build such protection with minimal resources.

5. Refunds, Chargebacks, and VAMP

It is one thing if a user simply cancels. But some people demand a Refund.

The worst part isn’t that you lose the revenue—it’s that your app can be removed from the App Store. Apple automatically tracks the refund rate for your app, and if it goes over the limits, your app can simply be deleted.

Then there are payment gateways, which you connect to accept payments bypassing the App Store/Google Play (to lower the commission from 30% to 3-5% via a web app). Here we are talking about Chargeback Rate.

A chargeback is essentially when your user demands money back through their bank, not directly from you. If you have too many returns and your chargeback rate exceeds the limits, you can be banned from accepting card payments entirely.

The VAMP Warning

For example, Visa has a special monitoring program—VAMP (Visa Acquirer Monitoring Program)—which sets these limits.

• From June 1, 2025, Visa began counting both chargebacks and fraud transactions in a single indicator, setting a new risk threshold of 2.2%.

• From April 2026, they are lowering it to 1.5%.

Because of this, it is now much easier for dating site owners to fall into the “risk zone,” even if their chargebacks are within the normal range. I will write separately about VAMP and the upcoming limit changes in the future.

Did you know? Apple recently allowed developers in the US to accept credit cards directly inside apps. You can look at how this is implemented in Tinder, Hinge, and Bumble.

Conclusion

We have now dissected the LTV component of our core formula. We know how to calculate it, why it matters, and what kills it—from scammers and the “Cold Start” problem to technical threats like chargebacks.

But a high LTV is meaningless if you have zero users entering the funnel.

In the next article, we will tackle the second critical variable: Number of New Buyers. We will break down the exact path a user takes to become a customer—starting from your App Store or Google Play listing, moving through the onboarding process, and finally reaching that crucial first purchase.

Why Build From Scratch?

Maximizing LTV and fighting scammers is hard enough without having to code the entire platform yourself.

This is why we built Skadate.

We provide the complete technical engine—native apps, payments, and monetization—ready from Day 1. Ready to skip the development nightmare?

Discuss Your Project with Me

You focus on finding the users; we’ll make sure the technology keeps them there.

If you have ever built a standard business—an agency, a SaaS tool, a blog, or a YouTube channel—you know the typical playbook: start small, buy some Facebook ads, drive traffic, make a sale, get feedback, improve the product, and repeat. It’s a linear path.

In the dating industry, this logic does not work.

Dating isn’t just a software product; it is a network connecting people. Until that network exists, the product has zero value. A single user in a dating app gets nothing out of it. Even a hundred users might feel zero value if they are different ages, live in different neighborhoods, or log in at different times.

This is why every new dating project faces the same enemy: The Cold Start Problem—the challenge of igniting a network from absolute zero.

Understanding Network Effects (It’s Not Just Buzzwords)

Most founders have heard the phrase “network effects,” but few understand that in dating, this isn’t just a “nice to have.” It is the fundamental physics determining whether your startup lives or dies.

A Network Effect occurs when a product becomes more valuable to every user as the number of other users increases.

• WhatsApp: If all your friends are there, it’s essential. If no one is there, it’s a useless utility.

• Uber: More drivers mean faster pickups; more passengers mean higher earnings for drivers. It’s a flywheel.

• Airbnb: More listings mean better choices for guests; more guests mean better occupancy for hosts.

The “Double Coincidence of Wants” in Dating

However, dating network effects are significantly harder to achieve than Uber or WhatsApp. In dating, “more users” does not automatically equal “better product.”

Value in a dating app only appears when there are specific people who meet a rigorous set of criteria:

1. Correct Demographics: The right gender and age preference.

2. Hyper-Local: They must be in the same specific city or neighborhood.

3. Active: They must be online recently (ghost profiles kill retention).

4. Mutual Interest: Crucially—and this is unique to dating—they must also like you back.

Therefore, the dating network effect isn’t just about volume. It’s about Liquidity—having enough of the right people active at the same time to create matches.

The “Just Buy Traffic” Trap: The Brutal Math

I see this constantly at SkaDate. A founder says: “I’ll just buy traffic on Facebook, Instagram, or TikTok. We’ll quickly gather a user base in one city, then expand to the next.”

It sounds logical until you run the unit economics. Let’s look at the real numbers for a new dating app launching in the US today.

• CPI (Cost Per Install): ~$4 (up to $8 for niche apps). This is standard for a new app on Meta/Google/TikTok with no brand recognition and no organic lift.

• Conversion to Subscriber: ~5%. For new projects, it’s often lower because the database is small (”ghost town” effect), and you likely haven’t hired a Head of Growth or optimized your lifecycle marketing (emails, pushes) yet.

The Unit Economics Nightmare

Let’s do the math on your CAC (Customer Acquisition Cost):

$4 (CPI)÷0.05 (Conversion)=$80

It costs you $80 to acquire one paying subscriber via paid ads.

Now, consider LTV (Lifetime Value). The average subscriber retention in dating is often just one month. Why? Because users subscribe, look around, realize the database is small/low quality, get very few matches, and churn immediately.

If your CAC is $80, you need to charge $80/month just to break even.

The Market Reality:

• The average dating subscription is $20–$40.

• Charging $80 for a brand-new app with no trust is impossible.

• Even the giants (Tinder, Bumble) charge less because they survive on scale, not high margins per user.

The Verdict on Performance Marketing

Performance marketing cannot solve the Cold Start Problem.

The economy doesn’t balance. You need a high price to cover ad costs, but you can’t charge a high price without a high-quality user base, which you don’t have yet.

Field Note: Paid ads work after the network is alive—when organic K-factor kicks in, retention stabilizes, and users are getting matches. Using ads to start the fire is like trying to boil the ocean with a matchstick.

Case Study: How Tinder Actually Solved the Cold Start

When people talk about Tinder’s explosion, they usually credit the “Swipe” UI.

Let’s be clear: The Swipe was a product innovation, not a marketing one. Product alone does not acquire users.

(Side note: The swipe was vital because it reduced rejection anxiety. Unlike Match.com or POF where you cold-messaged people, the Swipe forced a “Double Opt-In,” ensuring that when you messaged someone, you already knew they were interested. That raised reply rates dramatically.)

But the marketing breakthrough—the way they solved the Cold Start Problem—was purely offline.

The Campus Strategy

Tinder didn’t try to launch “in the USA.” They launched campus by campus. Universities are the perfect petri dish for dating networks:

• High density of young people.

• High social activity.

• Shared context (same school, same age).

Tinder realized they didn’t need to build a new network from scratch; they simply needed to digitize a network that already existed offline.

The “Gating” Tactic

Tinder didn’t recruit users one by one. They recruited clusters.

1. They threw parties at USC and other campuses.

2. The Price of Admission: You had to show the bouncer that you had Tinder downloaded on your phone.

3. Hundreds of students downloaded it simultaneously at the door.

4. When they opened the app inside the party, they saw the faces of the people standing right next to them.

This created instant density. Users got matches in the first 10 minutes. The product felt “alive” immediately. This is something Facebook Ads can never replicate.

Solving the Gender Balance (The Sorority Hack)

Every dating app founder fears the “Sausage Fest” scenario: 80% men, 20% women.

Tinder solved this by leveraging the social hierarchy of Greek Life:

1. They pitched to Sororities (female houses) first. They got the key influencers and socialites on the app.

2. Then, they went to the Fraternities (male houses).

3. The pitch to the guys was easy: “All the cute girls from the sororities are already on this app.”

The guys downloaded it en masse. Because the supply (women) was already there, the demand (men) was satisfied, and the ecosystem flourished.

Tinder conquered one campus, creating a functional mini-network. Then they moved to the next. Eventually, as students visited friends at other colleges, the “atomic networks” began to merge, and organic growth took over.

Actionable Advice for Founders

If you are launching a dating business today, accept the reality: You cannot afford to scale with paid ads on Day 1.

Your Cost Per Subscription will be too high, your conversion too low, and your churn too fast.

The Solution:

Stop looking at Facebook Ads Manager. Start looking at Communities.

You need to replicate the Tinder model of digitizing an existing offline network where trust and connections already exist.

Where to find your first 1,000 users:

• Offline Events: Parties, mixers, speed dating.

• Micro-Communities: Coworking spaces, local running clubs, language schools.

• Niche Interests: Conventions, hobby groups, local subreddits.

You need to acquire users in batches, not individuals, to ensure that when they open the app, they see people they might actually want to date.

What’s Next?

In the next article, I will break down a case study of a dating project that launched in January 2024 and solved the Cold Start Problem using a completely different channel: YouTube.

Stay tuned.

Why Build From Scratch?

As you just read, solving the Cold Start Problem is the hardest battle in the dating industry. It requires 100% of your focus on marketing and community. You cannot win this battle if you are distracted by product development or managing servers.

That is our territory.

This is exactly why we built SkaDate.

We are the product experts. We provide you with a battle-tested technical engine—native apps, payments, and monetization—perfected over years of development.

You focus on igniting the network; we’ll provide the professional infrastructure to support it.

Ready to launch with a pro-level product?

Discuss Your Project with Me

You focus on finding the users; we’ll make sure the technology keeps them there.

“What is the point of launching a dating app now? There is already a site for every possible niche.”

I hear this constantly. It is healthy skepticism, but it is based on a false assumption: that the market is frozen in time. In reality, online dating is one of the most dynamic markets in the digital world. Society changes, relationship norms shift, and new identities emerge. As life scenarios evolve, they create vacuums for new niches and formats.

Here is why new niches are still opening up—and how to validate them before you burn cash on development.

Why New Niches Keep Appearing

In recent years, the landscape of modern relationships has shifted dramatically. Entire groups of people who previously weren’t visible—or weren’t ready to be open about their preferences—are now looking for specific platforms.

We are seeing traction in:

• Gender identity: Trans and gender-diverse users who often feel unsafe on mainstream apps.

• Relationship structures: Polyamory, Ethical Non-Monogamy (ENM), and open relationships.

• Lifestyle choices: Specific dynamics like “trad wife” or “stay-at-home girlfriend” scenarios.

• Diaspora communities: South Asians in the US/UK (what we targeted with Dil Mil), Russian speakers in Europe, etc.

• Hyper-specific interests: Pickleball is taking over the world right now—dating apps centered around courts and matches are popping up fast.

But the most significant shift isn’t demographic—it’s technological.

The New Frontier: Generative AI

We have already seen the rise of AI Companions (such as Replika and EVA AI). These products address loneliness by providing an AI partner available 24/7. While they solve the “Cold Start” problem (the AI is always there), they often struggle with long-term retention once the novelty wears off.

However, the technology is now evolving from companionship to efficiency.

The Next Wave: AI Agents & RAG (Ditto.ai)

A new wave of startups, like Ditto.ai, uses Generative AI not to be your date, but to find one for you.

This relies on a tech stack called RAG (Retrieval-Augmented Generation). Unlike a standard chatbot that just makes things up, RAG gives the AI a “memory file” of your specific data.

Apps like Ditto use this to learn your voice, your schedule, and your specific taste, and then go out and talk to other people’s AI agents for you. They simulate the “talking stage”—scheduling the date and vetting the match—so you don’t have to swipe or chat. You just get a calendar invite.

This is a massive blue ocean. It solves “Dating App Fatigue” by removing the manual labor entirely.

Part 1: How to Analyze an Existing Niche (The Operator’s Framework)

You don’t need me to list every possible niche for you. You can ask Gemini or ChatGPT, “What are the current segments in online dating?” and get a list in seconds.

The real value I can offer you—based on my time scaling Seeking, Dil Mil, and advising founders at SkaDate—is a method to validate those ideas.

Before you write a line of code or hire a designer, use this 6-step framework to see if the economics actually make sense.

Step 1. Keyword Analysis: What are people actually typing?

Surveys lie. Google search bars don’t. We need to look at two things:

1. Volume: How many people are searching for this per month?

2. Keyword Difficulty (KD): How hard is it to rank for these words?

I use Google Ads Keyword Planner to gauge the raw size of the interest.

Resource: Back in 2021, we conducted a massive analysis of 1,855 dating niche keywords. It’s a map of how users formulate their desires. You can view the raw volume data here:

👉 Dating Niche Keywords Spreadsheet

Step 2. SEO Competition: Ubersuggest vs. Ahrefs

When checking competitors, you need the right tool.

• For a quick glance: Ubersuggest. It helps answer the simple question: “Is there any movement here at all?”

• For a deep dive: Ahrefs. This is the gold standard because Ahrefs has its own “crawler”—a bot that scans the entire internet just like Google does.

⚠️ The “Global Trap” (Liquidity Warning)

Be careful with the numbers. If you see 10,000 searches for “Knitting Dating,” that sounds great. But if those searches are spread evenly across the world, you have a problem. Validate the niche locally first, or choose a “Virtual-Only” niche.

Step 3. The Money Check: SensorTower

A website might look popular, but is it making money?

For mobile apps, SensorTower is my go-to. It models revenue by country and platform.

Understanding the Metrics:

• Downloads: How many new people install the app.

• Revenue: How much money the app makes through the app store.

• ARPU (Average Revenue Per User): This is critical. It tells you how much a single user is worth to the business.

Operator’s Tip: From my experience, SensorTower’s revenue estimates are often conservative. I usually multiply their revenue number by x2 to get closer to the real picture.

Why? SensorTower only sees transactions that go through the App Store or Google Play. It misses Stripe/Web payments, which smart founders use to avoid the 30% commission Apple and Google charge.

Step 4. Web Traffic: SimilarWeb

Don’t ignore the web. Even mobile-first products often have significant web traffic for SEO and acquisition. SimilarWeb will show you:

• Traffic Sources: Are they buying users (Display Ads) or getting them for free (Direct/SEO)?

• Stability: If traffic has been steady for years, the niche is healthy.

Step 5. Trend Spotting: Google Trends

Before falling in love with your idea, check the timeline on Google Trends.

• Is interest in “polyamory” rising or plateauing?

• Is the “trad wife” discourse spiking or dying out?

You want to enter a market where the curve is pointing up or holding a high plateau.

Recommendation: Subscribe to Global Dating Insights. It’s the daily digest I read to see where the industry money is moving.

Step 6. Ad Intelligence: Are the Unit Economics Working?

This is the most critical step. If competitors are spending money on ads, it is a very good sign. It means their Unit Economics are working.

The “Niche Pricing” Rule:

In a small niche, you cannot charge $5/month. You won’t have the volume of users to support the business. Successful niche apps usually have a High LTV (Lifetime Value) model—charging $30, $40, or even $50/month.

How to spy on competitor ads (Step-by-Step):

We use the Facebook Ads Library to see exactly how competitors are selling.

1. Go to: Facebook Ads Library

2. Select Country: Choose a specific country (e.g., USA) or “All”.

3. Search: Type the brand or app name (e.g., “Bumble”, “Hinge”, “Muzmatch”, “Dil Mil”).

4. Analyze: Facebook will show you all active campaigns.

Look for longevity: If an ad has been running for 3+ months, it means it is profitable. The LTV is covering the ad costs.

Part 2: The Innovator’s Path (Validating “Blue Oceans”)

The 6-step framework above is perfect for existing demand (e.g., “Christian Dating”). But what if you are building something completely new, like Ditto.ai?

If you type “AI agent to date for me” into Google, the volume will be zero. People don’t search for products they don’t know exist yet. In this case, standard SEO tools are useless.

You must validate in two phases: Qualitative (talking) and Quantitative (testing).

Phase 1: The “Therapist” Interviews (Qualitative)

Before you spend a dollar on ads, you need to verify the pain point. Find 10-20 singles who are currently using Tinder/Bumble and interview them.

• Don’t ask: “Would you use an AI agent?” (They will politely say yes).

• Do ask: “Tell me about your last month on dating apps.” “How do you feel when you open the app?” “How much time do you spend swiping vs. actually going on dates?”

You are looking for “Swipe Fatigue” and “Burnout.” If their stories are full of frustration, exhaustion, and “dating feels like a second job,” you have found your problem. Innovation solves pain, not just features.

Phase 2: The Smoke Test (Quantitative)

Once you have confirmed the pain in interviews, prove it with data.

• The Setup: Spend $200 on Facebook/TikTok ads targeting that exact pain point (e.g., “Stop Swiping forever”).

• The Destination: Send them to a simple landing page explaining your new concept.

• The Metric: Measure the Click-Through Rate (CTR) and Email Signups.

If people are clicking and giving you their email for a product that doesn’t exist yet, that is your validation.

Reality Check: Before You Launch

Even if the numbers look good, there are two operational hurdles you must consider in 2025.

1. Apple is the Gatekeeper

While Google Play is relatively open, Apple has become extremely strict. They have a specific rule (Guideline 4.3) regarding “Spam” and template apps.

If you build a generic clone that just swaps the logo and color scheme, Apple will likely reject it. Your niche product needs unique functionality or a very distinct UI to pass review.

2. The Cold Start Problem

Validating demand is one thing; getting users to actually stay is another.

If the first 1,000 users sign up but find nobody to talk to, they will leave immediately. It’s not just about acquisition; it’s about liquidity—ensuring that when a user logs in, they see active profiles and get a response.

I will dedicate a full separate article to solving the “Cold Start Problem,” as it deserves its own deep dive.

Summary & Next Steps

The market isn’t dead; it’s just fragmented. To succeed, follow the framework:

• Identify a shift in culture (AI, legal changes, new identities).

• Verify density (or use the “Smoke Test” method for innovative ideas).

• Verify money via SensorTower (remember the x2 rule).

What now?

The classic mistake is spending $50k and six months coding an MVP just to test these hypotheses. Your goal is to test the market, not your ability to write code.

This is exactly why we built SkaDate.

We provide a flexible, launch-ready engine that allows you to enter your niche immediately. Instead of burning your budget on development, you can use it to acquire users and validate your idea.

Discuss Your Project with Me

You focus on capturing the market; we’ll provide the technology to power it.

Ready to launch your niche MVP?

What is a Business Model?

Simply put, a business model is the way your product makes money. It answers two very straightforward questions:

1. How do we deliver value to our users?

2. What do those users pay us for?

In the dating industry, the choice of business model dictates everything:

• What users can do for free inside the app.

• What they must pay for, and the size of your future revenue.

• The level of trust users will have in the platform.

• The speed at which you can launch the product.

• The business’s risks and its long-term stability.

• Your operating costs and even your company’s reputation.

It even determines whether you can get into the App Store at all. Getting approved by Apple is much harder now; since 2018, they consider the dating category extremely saturated and vet new apps with heavy scrutiny. (I’ll cover this in a separate post.)

Today, let’s break down the 5 primary business models in modern online dating. Each has its own pros, cons, risks, and ideal use case.

1. The Freemium Model (Tinder, Bumble, Hinge)

This model allows 100% of users to use the core product for free. The most critical action in any dating app (after finding someone you like) is exchanging messages to arrange a date and move the interaction offline (IRL).

In Tinder, if you like someone and they like you back, you have a Match, and you can chat for free forever. Liking and matching—the core value proposition—is always free for everyone.

These applications make money on boosters and premium features that make the online dating process more effective or efficient. The most profitable feature is usually the ability to see who has already liked you.

The apps deliberately hide the faces of people who have liked you. They also don’t show those profiles at the top of your deck by default. This is strategic: it encourages you to swipe on new profiles, which generates more likes for others, making them want to see who liked them. Imagine the efficiency if you immediately saw your matches and could skip straight to the chat. It saves time but drastically reduces the number of total likes you might send out, which reduces overall engagement. This feature would be especially helpful for women, who typically receive significantly more likes from men.

Another common booster is unlimited likes. Limiting a user to, say, 10 likes per day reduces the number of likes in the system but increases user engagement (they must return daily) and forces more thoughtful profile selection. I’ll dedicate an entire article to the typical boosters used across the industry.

The main advantage of the Freemium model is that your active user base grows organically: 100% of users can get real value completely for free.

Advantages

• Ability for organic growth and virality

• Maximum user trust

• Minimal risk of Apple/Google blocks

• Good chance to build a recognizable brand

Disadvantages

• Dependency on the network effect: no users = no value

• Requires large marketing budgets to scale initially

• LTV is lower than in more aggressive models

2. Subscription for Access to Messaging (Seeking, eHarmony)

In this model, the user must buy a subscription to be able to send messages to others. Years ago, Match.com used this, requiring both parties to have an active subscription. Post-Tinder, user expectations changed dramatically, and Match Group eventually removed paid subscriptions from Match.com.

Case Study: Seeking.com (Niche Access)

Seeking.com is the leader in the Sugar Daddy niche (which they pioneered in 2006). In their model, the Sugar Daddies pay, and the Sugar Babies use the site for free.

Seeking is essentially selling access to a specific audience, not just the ability to chat. Users could look for partners on regular (”vanilla”) dating apps, but the efficiency would be much lower because they’d need to first establish their intentions. On Seeking, everyone shares the same dating intention, eliminating the need for the “are you looking for sugar?” vetting phase.

The paywall on Seeking also acts as a filter and a marker of seriousness. Not everyone is willing to spend, for example, $375 for a Diamond 30-Day subscription. The pricing is part of the positioning.

Furthermore, the swiping mechanic itself would be redundant on Seeking because, generally speaking, Sugar Babies are open to communicating with most Sugar Daddies and reply readily. Moreover, it is standard practice in this niche for Sugar Babies to message first.

This behavior contrasts sharply with the mass market. Bumble made a strong USP out of a similar idea—after a match, only the woman could send the first message—and used it to differentiate its brand from Tinder. This was a powerful move. However, they later abandoned this rule and allowed men to message first. Why? In Western culture, the expectation that the man makes the first move still dominates, and for many women, initiating contact is socially uncomfortable. Consequently, a significant portion of matches on Bumble “stalled” without a conversation starting, which worsened the product’s economics (by reducing conversion from match to chat) and eventually led to the abandonment of the rule.

I will write a separate deep-dive on the economics of this sugar daddy/sigar baby niche after my time at Seeking.

Case Study: eHarmony (Quality & Intent Filter)

eHarmony is the philosophical opposite of Tinder. The company bets on deep psychological compatibility and long-term relationships. Most people come to eHarmony with serious intent—to find a spouse.

The service is based on its own scientifically developed compatibility model, assessing values, communication style, empathy, and stability. After taking a detailed test, the system selects pairs with the highest probability of a successful union. Swiping mechanics are unnecessary here; users don’t browse hundreds of profiles—they receive a concierge list of pre-vetted matches.

In this context, the paywall is not a barrier; it’s a filter against non-serious users, spammers, casual chat, and “just trying it out” users.

A crucial insight: This model not only improves the quality of the user base but also strengthens the product itself. When people pay for a serious service, they behave more responsibly and are more invested in the dating process. This creates a completely different atmosphere—less chaos, fewer random contacts, and more respectful, conscious communication. This is why eHarmony has one of the highest marriage rates in the industry; the product is optimized for results, not just engagement. (I’ll touch on the dopamine loops of Tinder in another post.)

Advantages

• High LTV and stable subscription revenue

• Paywall filters non-serious users and improves user base quality

• More respectful, conscious communication atmosphere

Disadvantages

• High barrier to entry: a portion of users leave before paying

• Requires strong, niche positioning to justify the cost

• Lower virality: subscription reduces organic spread

3. Credits and Pay-Per-Message (iDates, Dating.com)

In this model, users purchase virtual credits that are then deducted for sending messages. The chat is not just a communication tool; it becomes the primary revenue stream around which the entire product economy is built.

In more aggressive implementations, a portion of the female accounts may be supported by chat agents who are paid to stimulate male users to continue the conversation—and thus buy more credits.

A notable example of this model’s philosophy comes from iDates, which states clearly in its footer: “All chat partners are virtual profiles moderated by exciting real people. Real meetings with them are not possible.” The service openly informs the user that real dates are off the table; the conversation is the product.

Why does this model work?

It’s a misconception that only older users fall for this. Many paying customers know about Tinder but dislike the swipe model: the high competition, low response rate, and emotional uncertainty. In credit models, they receive instant attention, a guaranteed response, and an emotional feeling of involvement—things often missing in freemium dating. They pay, not out of ignorance, but because these services fulfill their emotional needs faster and easier.

Psychologically, this model thrives on the “small cost” effect: each action is inexpensive on its own, and the user doesn’t immediately grasp the total expenditure, which is what drives very high monetization. One active male user can generate tens or even hundreds of dollars in an evening. These “whales” form a disproportionately large part of the total revenue, resulting in an extremely high LTV rarely achievable in other models.

However, high revenue comes with severe downsides. Many users realize too late how much they’ve spent, feel deceived, and initiate chargebacks. This creates constant tension with payment processors, increases the risk of platform blocking, and requires a heavy operational infrastructure: extensive moderation, anti-fraud measures, operator control, and legal protection. The reputation quickly deteriorates, forcing many companies to change domains and brand names to escape the negative publicity.

Advantages

• Very high LTV and revenue per active user

Disadvantages

• Low user trust, high risk of chargebacks and complaints

• Poor reputation: low ratings, negative reviews, need to change domains

• High operational complexity: agents, anti-fraud, legal risks

4. AI Partner Dating (Replika, EVA AI)

This is the newest, fastest-growing business model, sitting at the intersection of dating, loneliness, and generative AI. In traditional dating, we connect real people; in AI dating, the product is the conversational partner.

You can easily find these apps in the stores using queries like “AI girlfriend” or “AI friend.

Though apps like Replika or EVA AI don’t always position themselves as dating apps, they compete directly with the industry by offering users a sense of attention, validation, stability, and emotional intimacy. And yes, a major part of the monetization is built around users satisfying their fantasies, which is now supported by LLMs generating high-quality photos and videos upon request.

Why the recent explosion?

• The Rise of Loneliness: In AI dating, the bot is available 24/7, answers instantly, is always attentive and polite, and never “ghosts.” You don’t compete with hundreds of other users.

• Zero Fear of Rejection: It’s emotionally easier for people to communicate when there’s no risk of being rejected, mocked, or simply uninteresting.

• Personalization Effect: The model adapts to the user’s personality, remembers details, and reinforces the illusion of “chemistry,” creating a feeling of a real relationship.

Monetization is based on “payment for emotional intimacy”:

• Generating intimate photos and videos on demand.

• Purchasing a “premium” personality (more romantic, caring, or playful versions of the AI).

These apps show high engagement in the first few days but often bore users quickly because the interaction remains predictable and simulated. Users experiment briefly, lose interest, and rarely pay long-term, resulting in low retention and low LTV. Ultimately, it’s more of an emotional “casual product” than a sustainable dating business.

Advantages

• No Cold Start Problem: The AI is always available, no need for a user base.

• Low operational risk: No moderation of live users, fewer complaints, minimal chargebacks.

• Scalability: A single AI partner can “manage” millions of dialogues.

Disadvantages

• High competition (easy to launch because there is no Cold Start problem here)

• App Store Limitations: Apple and Google strictly regulate 18+ content.

• Ethical Risks: Addiction, emotional attachment, and regulatory complexity.

5. White-Label Dating with Shared Database (HubPeople.ai)

This model involves launching your dating site on a single technical platform that shares a common user database. A partner creates a simple landing page and drives traffic to it. Everything else—the product, the matching, the database—is handled by the platform. You receive a percentage of all payments made by your users (e.g., 70% to you, 30% to the platform).

This setup promises fast launch, no need for your own user base (no cold start problem), and seems convenient for partners.

The Fundamental Flaw: You own neither the users nor the code nor the product. All user data, all profiles, all messages—they belong to the platform, not you.

A key example is WhiteLabelDating.com, once a major player. It operated on this model for many years but eventually closed, and thousands of partners lost their sites and the entire business they had built. Because the database was not theirs and the code/infrastructure was platform-controlled, people were simply left with nothing.

As CEO of SkaDate, I always advise that white-label is only suitable as a temporary experiment to test a niche. It should never be viewed as a strategic product. The WhiteLabelDating history is a stark lesson: without owning the user base and the code, you are not building a business—you are merely renting it. And the lease can end, leaving you empty-handed.

Advantages

• Fastest launch without development costs

• No cold start problem (users are immediately available)

Disadvantages

• You do not own the database; you lose all users if the platform closes.

• Impossible to create a unique product: all sites share the same database and mechanics.

• No code, no infrastructure, no ability to migrate—your project vanishes with the platform.

Conclusion: Your Model is Your Foundation

In dating, the business model is the foundation that dictates everything: growth rate, user trust, launch complexity, monetization, and even your chance of passing App Store moderation.

• Freemium suits those ready to build a massive product with a long-term horizon and invest heavily in the network effect.

• The Subscription for Access model only works when the audience’s value is demonstrably higher than average (like on Seeking or eHarmony), and users understand exactly what they are paying for: access to a specific type of person and a higher quality, more serious dating experience.

• Credit models offer the highest revenue but come with major reputational risks and severe operational complexity.

• AI Dating is an emotional toy that users burn out on quickly, not a sustainable business.

• White-label platforms are great for niche testing, but do not create a real product—without owning the database and code, you are merely renting your business.

The correct business model isn’t just a way to make money. It is a strategic decision that determines whether your product can grow, retain users, pass moderation, and generate stable profit. Choose your model not by potential earnings, but by the unique value you are creating, who it is for, and how truly unique that value proposition is.

Why Build From Scratch?

Choosing the right business model is critical, but coding it is a nightmare. Different models require completely different technical architectures—credit systems, subscription tiers, paywalls, or AI integrations.

This is exactly why we built SkaDate.

We don’t force you into a box. Our engine is flexible enough to power any of these models. Whether you need a Freemium Tinder-clone, a high-end Subscription site like Seeking, or a Credit-based niche app, we have the features ready to go.

Discuss Your Project with Me

You choose the strategy; we provide the engine to execute it.