3.1 First 1,000 Users Strategy3.2 Growth Loop Mechanics3.3 Referral Engine3.4 Influencer Strategy3.5 Digital Acquisition Engine3.5.A Paid Acquisition — Ad Testing Framework and CAC Ladder3.5.B WOM Engineering System — Full Operational Manual3.6 8-Stage Growth Funnel3.6.A Growth Funnel — Drop-off Analysis and Debugging3.7 CAC Model & Budget3.8 Cohort Analysis & Retention Model
Understanding where users drop out of the funnel is more important than understanding where they enter. Each stage has a target conversion rate and a set of root causes for drop-off. When a stage underperforms, the corresponding debugging protocol activates.
Funnel Stage Analysis
Stage 1 → 2: Exposure to Waitlist/Landing (Target: 5–8%)
| Drop-off Cause | Diagnosis | Fix |
|---|---|---|
| Ad creative not compelling | CTR below 1% | Test 3 new creative concepts. Use authentic UGC over polished ads. |
| Wrong audience targeting | High CPM, low CTR | Narrow to Bangkok 24–35, relationship-minded. Exclude broad interests. |
| Landing page not converting | High bounce rate | Simplify landing page. One CTA: "Join the waitlist." Remove all friction. |
| Brand not credible | Low organic search | PR push: 2 media placements before next paid campaign. |
Stage 2 → 3: Landing to Install (Target: 30–40%)
| Drop-off Cause | Diagnosis | Fix |
|---|---|---|
| App store listing weak | Low conversion from store page | Improve screenshots, add video preview, update description. |
| Friction in download process | High drop-off at store | Ensure iOS and Android links work. Test on multiple devices. |
| Trust signals missing | Users hesitant | Add "Verified users only" and "Female-first safety" to store listing. |
Stage 3 → 4: Install to Profile Complete (Target: 60–70%)
| Drop-off Cause | Diagnosis | Fix |
|---|---|---|
| Onboarding too long | High drop-off at step 3+ | Reduce onboarding to 3 screens maximum. Photo + 2 prompts. |
| Photo upload friction | Drop-off at photo step | Add "Upload from camera roll" as default. Remove selfie requirement. |
| Prompts too complex | Drop-off at prompt step | Simplify prompts. Offer 10 pre-written options. |
| No immediate value signal | Users don't see why to complete | Show "3 people near you are waiting to match" during onboarding. |
Stage 4 → 5: Profile to First Match (Target: 70–80%)
| Drop-off Cause | Diagnosis | Fix |
|---|---|---|
| Low user density | No relevant matches nearby | Manual curation: founder personally seeds 3 matches within 24h. |
| Algorithm quality poor | Irrelevant match suggestions | Review algorithm: prioritise recency, location, and mutual interests. |
| Gender imbalance | Too many male profiles, few female | Activate Level 2 Liquidity War Plan immediately. |
| Slow match delivery | Users wait 48h+ for first match | Manual curation until algorithm density is sufficient. |
Stage 5 → 6: Match to Event Booking (Target: 30–40%, Phase 3+ only)
| Drop-off Cause | Diagnosis | Fix |
|---|---|---|
| Event not visible in app | Users don't know events exist | Add event discovery to home screen. Push notification for nearby events. |
| Event price too high | High abandonment at checkout | Test lower price point. Add "bring a friend free" offer. |
| Event format not appealing | Low click-through on event cards | Test different formats: speed dating vs. mixer vs. workshop. |
| Users prefer to meet 1:1 | Low event booking despite matches | Add 1:1 experience suggestions alongside group events. |
Stage 6 → 7: Booking to Attendance (Target: 80–90%)
| Drop-off Cause | Diagnosis | Fix |
|---|---|---|
| No-show rate above 20% | Users book but don't attend | Add no-show fee ($15). Send 3 reminder messages. Require card on file. |
| Last-minute cancellations | Cancellations within 24h | Activate waitlist immediately. Reduce event size rather than cancel. |
| Wrong day/time | Low attendance for specific slots | Test different days. Thursdays and Saturdays perform best in Bangkok. |
Stage 7 → 8: Attendance to Referral (Target: 30–40%)
| Drop-off Cause | Diagnosis | Fix |
|---|---|---|
| No story prompt | Users don't share spontaneously | Activate post-event story prompt 2 hours after event ends. |
| Referral reward not compelling | Low referral rate despite prompt | Test higher reward: 2 months premium for referrer + 1 month for referee. |
| Attendees don't know how to share | Low social post rate | Create pre-written Instagram caption + hashtag. One-tap share. |
| Event experience mediocre | NPS below 50 | Fix the event before fixing the referral mechanic. |
Growth Debugging Protocol
When any funnel stage drops below warning threshold for 7 consecutive days:
- Identify the stage: Which specific stage is underperforming?
- Diagnose the cause: Use the table above to identify the most likely root cause.
- Execute the fix: Implement the corresponding fix within 48 hours.
- Measure the result: Check the metric again after 5 days.
- Escalate if needed: If the fix does not work, try the next most likely cause.
DEBUGGING RULE: Fix one thing at a time. If you change multiple variables simultaneously, you cannot identify what worked. Isolate each fix and measure before moving to the next.