You're spending ₹3 lakh per month driving installs to your dating app. Last week, you hit 50,000 installs. Your board asks the question that matters: "Which campaigns are actually driving matches and subscriptions?"

You open three dashboards. Meta shows install data. Google Analytics shows sessions. Your in-app events show profile completions. But you can't connect spend to the one metric that matters for dating apps: quality matches that lead to engagement.

This is the dating app measurement problem. You're tracking activity, not relationship formation.

Why Generic Event Tracking Fails for Dating Apps (The Engagement Quality Problem)

Most mobile app event frameworks measure task completion. A fintech app tracks account creation and first deposit. An eCommerce app tracks cart additions and purchases. These apps have clear conversion funnels with definable end states.

Dating and community apps work differently. The core value is not a transaction, it's relationship formation. A user might complete 100 actions (profile views, likes, messages sent) but create zero meaningful connections. Another user might complete 10 actions and form three lasting relationships.

Generic engagement metrics hide quality problems. If you're measuring "messages sent" without measuring "messages replied to", you're counting spam. If you're tracking "matches made" without tracking "conversations started", you're counting dead leads. If you're optimising for installs without tracking match quality, you're buying users who degrade the experience for everyone else.

The financial impact is measurable. Across attribution audits for dating apps spending ₹2-8 lakh monthly on paid acquisition, we consistently see 30-45% of install volume coming from campaigns that drive low match rates and high early churn. Without event-level tracking tied to attribution, these campaigns continue running because they look profitable at the install level.

This guide identifies seven events that reveal match quality, engagement depth, and monetisation potential from day one. Implement these events before scaling spend, and you'll know which campaigns drive real users instead of profile collectors.

Event #1: Profile Completed (Activation Checkpoint)

What It Measures

Profile Completed tracks when a user adds enough information (photos, bio, preferences) to become discoverable and matchable. This is your activation event because an incomplete profile cannot generate value.

Why It Matters

Install-to-profile completion rate separates curious browsers from serious users. Users who complete profiles typically show 3-5× higher D7 retention compared to users who install but never finish setup.

This metric also reveals campaign quality differences. A campaign driving 1,000 installs with 15% profile completion is substantially weaker than a campaign driving 500 installs with 60% profile completion. Without tracking this event, both campaigns look equally valuable at the install level.

Implementation Details

Event Name: profile_completed

When to Fire: When user submits the final step of profile creation (typically after adding required photos + bio + basic preferences)

Parameters to Track:

• completion_time_seconds: Time from install to profile completion

• photo_count: Number of photos uploaded

• bio_length: Character count of bio text

• preferences_set: Boolean indicating whether match preferences were configured

Campaign-Level Insight

Connect this event to attribution to identify which channels drive users who actually complete setup. Dating apps typically see:

• Instagram campaigns: 35-50% profile completion

• Google UAC: 25-40% profile completion

• TikTok campaigns: 20-35% profile completion

• Influencer referrals: 45-65% profile completion

If your Google campaigns show 18% profile completion while Instagram shows 42%, you need to investigate creative quality and targeting, not scale Google spend.

Event #2: First Match/Connection (Core Value Delivered)

What It Measures

First Match tracks when a user successfully matches with another user for the first time. This is your "aha moment" because users who experience a match understand the app's value proposition.

Why It Matters

Time-to-first-match predicts long-term retention. Users who match within 24 hours show 4-6× higher D30 retention compared to users who take 3+ days to match. Users who never match typically churn within 5-7 days.

This event also exposes supply-side quality problems. If your install volume grows but time-to-first-match degrades, you're adding users who can't form matches (wrong demographics, inactive users, or quality issues).

Implementation Details

Event Name: first_match

When to Fire: When the match is confirmed (both users have expressed mutual interest)

Parameters to Track:

• hours_since_install: Time from install to first match

• profiles_viewed: Number of profiles viewed before matching

• match_source: How the match occurred (swipe, search, algorithm suggestion)

Benchmarking Match Rates

Healthy dating apps typically see:

• 40-60% of users match within 48 hours

• 60-75% of users match within 7 days

• Time-to-first-match median: 8-18 hours

If your median time-to-first-match exceeds 24 hours, you have a supply-side quality or algorithm issue, not a marketing problem.

Event #3: First Message Sent (Engagement Initiation)

What It Measures

First Message Sent tracks when a user initiates a conversation with a match. This separates users who match passively from users who actively engage.

Why It Matters

Match-to-message conversion reveals engagement intent. Users who send a first message within 12 hours of matching show 5-8× higher probability of forming multi-day conversations compared to users who wait 24+ hours.

This metric also identifies dormant matches. If 60% of matches never result in a sent message, your matching algorithm may be generating low-quality connections.

Implementation Details

Event Name: first_message_sent

When to Fire: When user sends their first message to any match

Parameters to Track:

• hours_since_match: Time from match to first message

• message_length: Character count of first message

• match_count_at_send: Number of existing matches when first message was sent

Campaign Quality Signal

Connect first message rates to acquisition channels. Dating apps typically see:

• High-intent channels (paid search): 50-70% of matches send first message

• Social discovery (Instagram, TikTok): 35-55% of matches send first message

• Generic awareness campaigns: 20-40% of matches send first message

If a campaign drives matches but low message initiation rates, the users are browsing, not seriously engaging.

Event #4: Message Reply Received (Quality Match Signal)

What It Measures

Message Reply Received tracks when a user receives a reply to a message they sent. This confirms mutual engagement and indicates a quality match.

Why It Matters

Reply rate is your strongest proxy for match quality. A 60% reply rate indicates healthy two-sided engagement. A 20% reply rate indicates users are sending low-quality messages or matching with inactive profiles.

This event also reveals gender balance and engagement asymmetries. If male users receive 15% reply rates while female users receive 75% reply rates, you have a supply-side imbalance problem.

Implementation Details

Event Name: message_reply_received

When to Fire: When user receives a reply to any message they sent

Parameters to Track:

• hours_to_reply: Time from message sent to reply received

• conversation_starter: Boolean indicating if this was the first exchange in the match

• reply_length: Character count of received reply

Identifying Problem Campaigns

Low reply rates by acquisition channel indicate quality issues:

• Reply rate below 30%: Campaign may be driving spammers or low-intent users

• Reply rate 30-50%: Acceptable engagement, monitor for degradation

• Reply rate 50-70%: Healthy engagement indicating quality user acquisition

• Reply rate above 70%: Excellent engagement, scale this channel

Without tracking reply rates by channel, you can't distinguish campaigns that drive engaged users from campaigns that drive profile collectors.

Event #5: Premium Subscription Started (Monetisation Conversion)

What It Measures

Premium Subscription Started tracks when a user begins a paid subscription (typically unlocking features like unlimited swipes, priority visibility, or advanced filters).

Why It Matters

Install-to-subscription conversion reveals true revenue potential by channel. A campaign driving 5,000 installs with 0.8% subscription rate generates ₹40,000 revenue (assuming ₹1,000 average subscription value). A campaign driving 2,000 installs with 2.5% subscription rate generates ₹50,000 revenue despite half the volume.

Without connecting subscriptions to acquisition source, you optimise for install volume instead of revenue per install.

Implementation Details

Event Name: subscription_started

When to Fire: When user completes payment for any subscription tier (monthly, quarterly, annual)

Parameters to Track:

• days_since_install: Time from install to subscription

• subscription_tier: Which plan was purchased (basic, premium, platinum)

• subscription_period: Billing cycle (monthly, annual)

• subscription_value: Revenue amount in local currency

• matches_before_subscribe: Number of matches at subscription time

• messages_before_subscribe: Number of messages sent before subscribing

Attribution Window Considerations

Dating apps typically see subscriptions occur:

• 15-25% within first 24 hours (users who match quickly and want more features)

• 35-50% within 3-7 days (users who hit free tier limits)

• 25-35% within 7-14 days (users who form relationships and want premium features)

Use a 14-day attribution window minimum for subscription attribution. A 7-day window undercounts late converters.

Event #6: Profile Reported/Blocked (Safety & Quality Signal)

What It Measures

Profile Reported/Blocked tracks when users report profiles for inappropriate behaviour, fake accounts, or harassment. This is your platform safety and quality monitoring event.

Why It Matters

High report rates by acquisition channel indicate quality control problems. If 8% of users from a specific campaign get reported within 7 days compared to 1.5% platform average, that campaign is driving problem users who degrade experience for legitimate members.

This metric also protects brand reputation. Dating apps face reputational risk from scammers, catfishers, and harassment. Tracking reports by acquisition source helps you block problem channels before they damage retention across the platform.

Implementation Details

Event Name: profile_reported or profile_blocked

When to Fire: When any user reports or blocks another user's profile

Parameters to Track:

• report_reason: Why the profile was reported (spam, harassment, fake account, inappropriate content)

• days_since_target_install: How long the reported user has been on platform

• reporter_match_status: Whether reporter had matched with reported user

Quality Thresholds

Healthy dating apps typically see:

• 1-3% of profiles receive a report within first 30 days

• 0.3-0.8% of profiles get blocked

• Report rates spike in first 48 hours as new users encounter the platform

If a campaign shows 6%+ report rates within 7 days, pause that campaign and investigate targeting quality.

Event #7: App Opened 5+ Days in Week (Habit Formation)

What It Measures

App Opened 5+ Days in Week (also called Weekly Active Days or WAD) tracks how many unique days in a 7-day period a user opens your app. This reveals habit formation.

Why It Matters

Weekly active days predict long-term retention better than raw session counts. A user who opens the app 15 times across 3 days is less engaged than a user who opens it 8 times across 6 days. The second user has formed a daily habit.

This metric also reveals channel quality differences. Users from high-intent channels (search, referrals) typically form habits faster than users from interruptive channels (display ads, some social).

Implementation Details

Event Name: weekly_active_5plus

When to Fire: When a user opens the app on their 5th unique day within any rolling 7-day window

Parameters to Track:

• weeks_since_install: Which week after install the 5+ day threshold was achieved

• matches_at_milestone: Number of active matches when habit formed

• messages_at_milestone: Number of messages sent when habit formed

Benchmarking Active Days

Healthy dating apps typically see:

• 25-40% of users achieve 5+ active days in week 1

• 15-25% maintain 5+ active days in week 4

• Users who hit 5+ days in week 1 show 6-8× higher D90 retention

Connect weekly active days to acquisition channel to identify which campaigns drive habitual users versus one-time browsers.

Attribution Windows for Dating Apps: Why 7-Day Windows Capture Initial Engagement

Dating apps have compressed engagement cycles compared to other verticals. Users form opinions about the app within 48-72 hours (can I match? Do people reply? Is this worth my time?).

This means shorter attribution windows accurately capture campaign effectiveness:

D1 Metrics (First 24 hours):

• Install to profile completion rate

• Time to first match

• Onboarding friction points

D3 Metrics (First 3 days):

• Match rate and match quality

• Message send and reply rates

• Initial monetisation signals

D7 Metrics (First 7 days):

• Habit formation (weekly active days)

• Subscription conversion

• Platform safety signals (reports, blocks)

D14-D30 Metrics (Long-term quality):

• Subscription renewal rates

• Multi-week retention

• Relationship formation indicators

Use D3 and D7 metrics for campaign optimization decisions. Use D14-D30 metrics for channel-level investment decisions.

Implementation Playbook: Dating App Event Setup in Week One

Step 1: Define Event Specifications (Day 1-2)

Create an event specification document covering:

• Exact event names (use consistent naming: snake_case, no spaces)

• When each event fires (specific user actions that trigger the event)

• Required parameters for each event

• Optional parameters that add context

Example specification:

Step 2: Implement SDK Events (Day 3-5)

Work with your mobile engineering team to implement event tracking in your app's codebase. Use your MMP's SDK to send events with proper parameter formatting.

Example implementation (React Native with Linkrunner SDK):

import Linkrunner from '@linkrunner/react-native-sdk';

// When user completes profile
const onProfileCompleted = (userData) => {
  Linkrunner.trackEvent('profile_completed', {
    completion_time_seconds: userData.setupDuration,
    photo_count: userData.photos.length,
    bio_length: userData.bio.length,
    preferences_set: userData.hasSetPreferences
  });
};

Step 3: Validate Event Delivery (Day 5-6)

Before running paid campaigns, validate that events are firing correctly:

1. Test each event manually: Complete the user action that should trigger each event

2. Check MMP dashboard: Verify events appear within 1-2 minutes

3. Verify parameters: Confirm parameter values match expected data

4. Test attribution flow: Install via a test campaign link, complete events, confirm attribution appears

Step 4: Connect Events to Ad Networks (Day 7)

Configure postback events to Meta, Google, and TikTok so their algorithms optimise toward your quality events:

High Priority Postbacks (send immediately):

• profile_completed: Helps algorithms find users who complete setup

• first_match: Signals users experiencing core value

• subscription_started: Revenue event for value-based bidding

Medium Priority Postbacks (send after validation):

• first_message_sent: Engagement depth signal

• message_reply_received: Match quality signal

Without postbacks, Meta optimises for installs. With quality event postbacks, Meta optimises for users who match and engage.

Step 5: Build Reporting Dashboard (Ongoing)

Create a dashboard showing event progression by acquisition channel:

This dashboard reveals:

• Meta drives highest profile completion and match rates (quality targeting)

• Google drives acceptable engagement at moderate cost

• TikTok drives lower quality users (25% lower completion, 3× higher cost per subscription)

Without this event progression view, all three channels appear similar at the install level.

How Linkrunner Helps Dating Apps Track Quality Events

Platforms like Linkrunner provide dating and community apps with unified event tracking and attribution in a single implementation. Rather than juggling separate tools for deep linking, attribution, and event analytics, Linkrunner connects acquisition spend to every event in the user journey.

For dating apps specifically, Linkrunner enables:

• Event-level attribution showing which campaigns drive matches versus profile collectors

• Quality cohort analysis comparing D1/D3/D7 engagement by acquisition channel

• Automated postback configuration sending quality signals (matches, messages, subscriptions) to Meta/Google/TikTok for algorithm optimisation

• Real-time dashboards showing profile completion rates, match rates, and subscription conversions by channel without spreadsheet exports

• Fraud detection blocking install farms and click spam before they degrade match quality metrics

Starting at ₹0.80 per attributed install with a 3,000 install/month free tier, dating apps can implement full event tracking without the ₹2-8 lakh monthly costs of legacy MMPs.

Key Takeaways

Dating and community apps need different event tracking than transactional apps. Track these seven events from day one:

1. Profile Completed: Activation checkpoint separating browsers from serious users

2. First Match: Core value delivered, predicts long-term retention

3. First Message Sent: Engagement initiation revealing match quality

4. Message Reply Received: Strongest proxy for two-sided engagement

5. Premium Subscription Started: Monetisation event for revenue attribution

6. Profile Reported/Blocked: Safety and quality monitoring by channel

7. App Opened 5+ Days/Week: Habit formation predicting 90-day retention

Connect these events to attribution to identify which campaigns drive quality matches and subscriptions instead of optimising for install volume that churns in 48 hours.

For dating apps ready to implement quality-based attribution, request a demo from Linkrunner to see how unified event tracking and attribution can identify your highest-quality acquisition channels in the first week.