Metrics that Matter: Dating & Community Edition
Lakshith Dinesh
Updated on: Dec 26, 2025
Most dating app teams can tell you exactly how many downloads they drove last month and which profiles get the most right-swipes. Ask them which marketing channel actually delivers users who match, message, and stick around past Week 2, or what their true CAC looks like when you account for ghost users, and you'll get a long pause.
The metrics that separate growing dating platforms from ones burning cash aren't the vanity numbers in investor updates. They're the network health indicators, match quality metrics, and gender-balanced cohorts that show whether your community actually functions when new users join.
This guide covers the acquisition, matching, engagement, and monetization metrics that mobile-first dating and community apps track to build sustainable networks instead of hollow user bases.
Why Dating App Metrics Are Different
Dating and community apps face a measurement challenge that eCommerce and fintech don't: your product only works if the network works. A new user is worthless if there's nobody to talk to when they open the app.
The difference between vanity metrics and actionable metrics is brutal here:
Vanity metrics hide problems:
Total registered users (90% might be inactive)
Profile views (views without matches mean nothing)
Messages sent (if nobody replies, engagement is broken)
Actionable metrics reveal network health:
Active user ratio by geography and gender
Match rate and conversation start rate by cohort
Time to first quality match by acquisition source
True CAC for users who actually engage
Gender ratio balance and quality distribution
Dating attribution is uniquely complex because quality matters more than quantity, gender balance is critical, geographic density drives value, and your best users leave when they find partners (intentional churn).
Customer Acquisition Cost: Install vs Engaged User
CAC is total marketing spend divided by engaged users acquired, not installs, not registrations, but users who complete profiles, browse, match, and message.
If you spent ₹5 lakhs and drove 2,000 installs but only 600 became active participants, your true CAC is ₹833, not ₹250.
The Install vs Engaged User Gap
Most dating apps track CAC as "cost per install." This hides catastrophic inefficiency.
You might drive 20,000 installs at ₹40 each (₹8 lakh spend), but if only 3,000 complete profiles, 1,500 actively browse, and 600 actually match and message, your engaged-user CAC is ₹1,333, 33x higher than the install number suggests.
Track both:
Install-level CAC: What you pay per download
Engaged-user CAC: What you pay for network participants
The gap reveals your onboarding efficiency. If Meta brings installs at ₹40 but only 8% engage while Google costs ₹120 but 45% engage, Google is actually cheaper (₹267 vs ₹500 per engaged user).
Benchmarks by app type:
Mass-market dating: ₹500-1,200 per engaged user
Niche communities: ₹800-2,000 per engaged user
Premium platforms: ₹2,000-5,000 per engaged user
Match Rate: The Metric That Reveals Everything
Match rate is the percentage of active users who achieve at least one match within 7 days. This single metric exposes whether your network works.
Track conversion at every stage:
Install-to-profile-creation: 60-75% target
Profile-to-active-browsing: 50-70% target
Browse-to-match: 30-60% within 7 days
Match-to-conversation: 40-60% target
Conversation-to-depth (3+ messages): 30-50% target
Why Match Rates Vary Wildly
Match rates aren't uniform. They vary by:
Gender: Men see 5-20% match rates; women see 40-80%
Geography: Metro cities see higher rates due to density
Profile quality: Verified, complete profiles match 3-5x more
Acquisition source: Referral users match better than paid ad users
A platform-wide "40% match rate" might hide that 80% of users (men in Tier 2 cities) see 15% rates while 20% (women in metros) see 70% rates. Blended metrics lie.
Time to First Match
Benchmarks by app type:
Swipe-based apps: 1-3 days for quality users
Algorithm-heavy apps: 3-7 days
Niche communities: 7-14 days
Users who don't match within their expected timeframe churn fast. If your typical time is 4 days but a user hits Day 6 with zero matches, churn risk spikes to 80%+.
Gender Balance: The Network Health Killer
Gender ratio is the distribution of active users who actually browse, match, and message, not registered users.
If you have 100,000 registered users split 60/40 male/female, but only 40,000 are active at 75/25 male/female, your active ratio is 3:1, not 1.5:1. Your network is more imbalanced than registration numbers suggest.
Why Imbalance Creates Death Spirals
10:1 male-to-female ratio:
Women get overwhelmed (100+ matches per day)
Women become hyper-selective or disengage
Men see 5% match rates and churn
Platform reputation degrades
Healthy ratios:
Mainstream dating: 1.2:1 to 1.8:1 male-to-female
Friendship/networking: 1:1 ideal
Track active gender ratio by geography, age group, and acquisition channel. If Meta brings 80% male users while Instagram influencers bring 60% female users, adjust budget allocation to balance the network, not maximize installs.
Message Response Rate and Conversation Depth
Message response rate is the percentage of first messages that receive replies. Conversation depth measures messages exchanged per conversation.
Response rates vary by:
Women responding to men: 30-50%
Men responding to women: 70-90%
Generic openers ("Hey"): 10-25%
Personalised messages: 50-70%
Within first hour: 60-80%
After 48 hours: 15-30%
Conversation depth levels:
1-2 messages: Superficial, likely fizzles
3-10 messages: Moderate interest
10+ messages: Strong engagement
Multi-day conversations: Premium behaviour
If 70% of matches never get past 2 messages, you have a discovery problem. Users match on photos but aren't compatible.
Profile Completion and Verification
Profile completion rate measures users who fill out detailed profiles beyond basic info. Verification rate tracks identity verification adoption.
Impact of complete profiles:
Basic info only: Baseline
Bio and interests: +40% match rate
Detailed preferences: +25% match rate
Verification: +60% trust signal, +35% match rate
If 60% of users have incomplete profiles, 60% of your network isn't fully functional.
Verified users see:
50-80% higher match rates
40-60% higher message response rates
3-5x lower report/block rates
2-3x higher subscription conversion
Track verification by acquisition channel. Referral users verify at 70%+; paid ad users verify at 20-30%.
Monetisation: Subscriptions and Features
Dating apps monetise through subscriptions, à la carte features (boosts, super likes), or hybrid models.
Free-to-Paid Conversion
Benchmarks:
Mainstream dating apps: 3-8% conversion
Niche premium platforms: 8-15% conversion
Conversion varies by:
Match success: Users who match convert at 3-5x the rate
Time in app: Conversion peaks Week 2-4
Gender: Men traditionally convert at 2-3x the rate
ARPU and ARPPU
ARPU (all users):
Freemium dating: ₹50-150 per monthly active user
Premium dating: ₹200-500 per monthly active user
ARPPU (paying users only):
Subscription-focused: ₹800-2,000/month
IAP-heavy (boosts, likes): ₹400-1,200/month
Users who match regularly are 3-5x more likely to pay. Users with zero matches in two weeks almost never convert.
Customer Lifetime Value and Intentional Churn
LTV for dating apps is complicated by intentional churn, users who find partners and leave.
Basic formula: monthly price (or ARPU) × customer lifespan. If subscribers pay ₹500/month for 8 months, LTV is ₹4,000.
But dating apps face unique churn:
Good churn (5-15%): Found partners, achieved goals
Bad churn (30-40%): Never matched, found better alternatives
You can't stop intentional churn without undermining your value proposition. But you can:
Increase pre-churn monetization
Win back churned users when relationships end
Cross-sell into friendship or social features
Track LTV by acquisition source, matching success, geography, and verification status. Referral users have 2-3x higher LTV.
The CAC to LTV Ratio
The CAC:LTV ratio tells you whether you're acquiring users profitably while accounting for inevitable churn.
If CAC is ₹1,200 and LTV is ₹2,400, you have 1:2, barely covers platform costs. If LTV is ₹6,000, you have 1:5, now you can scale.
Critical nuance: Track CAC:LTV separately for engaged users versus total installs. If blended CAC:LTV is 1:3 but engaged-user CAC:LTV is 1:6, you're profitable on participants but losing money on ghosts. The solution isn't cutting spend; it's improving conversion from install to engagement.
Geographic Density and Network Effects
Dating apps are local networks. A user in Jaipur needs matches in Jaipur, not Mumbai. Geographic density determines network health.
High density (10,000+ active users per city):
Match rates 2-3x higher
Users see fresh profiles daily
Self-sustaining network
Low density (<1,000 active users):
Match rates 50-70% lower
Users exhaust pool in days
Constant new acquisition required
Don't expand geographically until you've saturated core markets. If you have 100,000 users spread across 500 cities, you don't have one functional network, you have 500 thin networks where users churn due to lack of matches.
Attribution Metrics for Quality vs Quantity
Dating apps face a unique attribution challenge: channels that drive the most installs often deliver the lowest-quality users.
Example:
Meta broad targeting: 10,000 installs, ₹40 CPI, 8% match rate
Google search intent: 2,000 installs, ₹120 CPI, 45% match rate
Blended metrics show Meta is "3x cheaper." But engaged-user CAC:
Meta: ₹40 ÷ 8% = ₹500 per engaged user
Google: ₹120 ÷ 45% = ₹267 per engaged user
Google is 47% cheaper when accounting for quality. You can't see this without unified attribution tracking installs AND engagement events by source.
Return on Ad Spend by Channel
For dating apps, ROAS must account for:
Conversion lag (users match for weeks, then subscribe)
Gender imbalance (channels bringing 90% men break networks)
Match success correlation (channels bringing users who match drive higher LTV)
Track ROAS plus network contribution metrics, gender balance, match rates, engagement depth, so you optimize for long-term network health, not just short-term revenue.
Mobile measurement partners like Linkrunner unify attribution data from Meta, Google, TikTok, and influencer partnerships in one dashboard. You see which channels drive installs AND which drive engaged users without reconciling five different tools.
Trust, Safety, and Moderation Metrics
Fake profile rate: <2% healthy; 5-10% correlates with 20-30% churn increase; >10% triggers reputation damage.
Report and block rates:
Report rate: <1% of active users per week
Block rate: 2-5% of active users per week
High rates signal harassment, fake profiles slipping through, or poor matching algorithms.
Track these by acquisition channel. If Meta-acquired profiles have 8% fake rate while referral profiles have <1%, targeting is too broad.
Building Your Analytics Stack
Most dating teams have fragmented tools: Firebase for app, backend for matches, payment processors for revenue, moderation tools for safety. Hours wasted reconciling data.
A unified stack connects:
Attribution: Track source quality (CAC + match rates)
Network metrics: Profile completion, gender balance, density
Engagement: Match rates, message depth, session patterns
Revenue: Subscriptions, feature purchases, LTV by source
Safety: Fake profiles, reports, verification rates
Linkrunner unifies attribution, deep linking, and engagement analytics so you see CAC, match rates, ROAS, and LTV by channel in one dashboard. No more reconciling screenshots from Meta, Google, and backend systems. Request a demo to see how dating apps track what matters in real time.
Track What Matters and Build Network Effects That Last
Tracking the right metrics, engaged-user CAC, match rate by segment, gender balance, conversation depth, LTV, lets you build sustainable networks instead of hollow user bases. Dating platforms need unified attribution to see the full picture of network health.
Linkrunner auto-surfaces campaigns that bring low-quality users and suggests where to reallocate budget for better network balance. Request a demo to see how we help dating and community apps scale profitably.
FAQs About Dating & Community App Metrics
What are the most important metrics for dating apps?
Engaged-user CAC (not install CAC), match rate by segment, gender ratio balance, message response rate, time to first match, and LTV accounting for intentional churn. Each ties directly to network health and helps you decide where to spend.
How do I track attribution when success means users leave?
Separate intentional churn (found partners) from bad churn (didn't match). Track engagement patterns before churn to identify success. Build win-back campaigns for users who churned successfully, relationships end, and they'll re-enter the market.
What is a healthy match rate for dating apps?
Varies by gender and platform. Women typically see 40-80% match rates; men see 5-20% within 7 days. The key is maintaining balance so both sides have good experiences and tracking match rates by acquisition channel to identify quality sources.
How often should dating apps review their metrics?
Review gender balance, match rates, and engaged-user CAC daily or weekly to catch network health issues early. Review churn patterns, LTV by cohort, and geographic density monthly. Monitor fake profile rates and safety metrics continuously.
Why do some marketing channels bring users who don't match?
Channels optimised for volume (broad Meta targeting, viral TikTok) bring curious but uncommitted users outside your core demographic. Channels optimized for intent (Google search, referrals, niche influencers) bring users who match your network and engage. Track install-to-match rate by channel to identify quality sources.
