How do you pinpoint the exact drop-off in onboarding without app releases?

Analytics & Insights
1

I got tired of guessing why people disappear between click and pay. Moving onboarding + paywall to the web let me test fast and isolate the exact step that bleeds. What helped most:

  • Instrument every step with a consistent schema: step_name, step_index, variant_id, offer_id, price_id, trial_length, device, country, utm_source/medium/campaign, session_id, user_id (if known).
  • Track time_on_step, scroll_depth, errors, rage_clicks, and whether the next step was reached.
  • Split checkout into clear events: paywall_view, payment_intent_start, payment_intent_error (with reason), payment_success, refund_requested.
  • Add a light exit-intent survey on the paywall with 3–5 reasons (price, unclear value, signup friction, payment trust, other). Keep it optional.
  • Run small, quick tests: change step order, remove one field, tweak trial length, swap copy, add a screenshot carousel, adjust price anchors. Ship one variable per test.
  • Compare cohorts by traffic source and creative. If meta_video_A has normal CTR but unusual drop at payment_intent_start, that’s a messaging mismatch, not a broken form.
  • Validate “false drops” with session replays or synthetic flows when a metric spikes.

The biggest unlock for me was tying time_on_step + exit reason to the exact variant. That pointed at a single confusing benefits slide, not the paywall. Fixing that lifted click-through to paywall by ~9% without touching price.

What events or tiny tests actually surfaced your worst drop-off, and how did you verify it wasn’t just noise?

2

I keep it simple.

One schema for all steps. Track step_index, time_on_step, next_step_reached, and error_reason. Add exit poll on paywall.

I test one change for 48 hours and move on. If I need new steps, I use Web2Wave’s generator to output JSON and plug it in. Fast iterations beat perfect tracking.

3

I start with an A/A to set a baseline, then flip one variable.

Web2Wave lets me swap copy, order, and offers on the web so the app updates instantly. I segment by utm + variant + step_index and watch time_on_step spikes. If one creative cohort stalls on the benefits slide, I change that slide only.

4

Log each step with the same fields and watch time on step.

Add a short exit poll at the paywall. Price vs trust vs confusion shows up fast.

If one source drops only at payment start, fix the messaging, not the form.

5

Time on step exposes the leak fast

6

Define events before testing. Use fields like step_name, step_index, variant_id, offer_id, price_id, trial_days, utm_source, utm_campaign, country, and device. Capture payment_intent_error with a normalized error_reason. Then bucket by step_index and variant exposure. You’ll usually find one friction spike. Validate with session replays and a quick copy-only test. If price is blamed, run a price holdout and change only the value framing. If trust is blamed, add known logos and a one-line refund policy near the CTA, not in a modal.

7

Two fixes that moved the needle:

  • Removed email gate before trial start. Asked for email after payment. Paywall-to-intent went up 11%.

  • Showed refund policy next to CTA. Reduced payment errors and lifted success rate by ~4%.

Both were quick web pushes.

8

Exit poll on paywall helps. Keep it short.