How to Actually Grow a Dating Business
Stop building features. Start fixing your funnel.
So, you already have your own dating app or site. It’s live on the App Store and Google Play. You have some sign-ups, maybe even a few purchases.
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:
Traffic: Did fewer users log in today? (Data check: No, DAU is stable).
Tech: Is the “Like” button broken? (Data check: No, API logs are clean).
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:
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.
Retention Deep Dive: What is cohort analysis?
The Tracking Plan: Which specific events you must track to build these dashboards and visualize your OMTM.