Q2 2026 Signups: Activity, Retention & Engagement

How the Q2 2026 Rating Captain Local signup cohort behaves in-app, why they leave, and which communications move them, with the levers to engage them more.

1,030
Q2 signups
54%
connected a profile
15%
took any in-app action
6.4%
added a card
3.3%
still paying today
The engagement lever: 3+ in-app actions → 42.9% conversion (vs 1.0%)

The single strongest predictor of conversion in this cohort is whether a user performs at least three in-app actions (Quick Scan, AI reply, AI post, keyword refresh). Engaged users convert at 42.9%; users who take no real action convert at 1.0%. Only 13% of signups reach the engaged state today.


Lifecycle funnel — where the cohort drains

Stages are overlapping definitions, not strict subsets. Counts are distinct users.


What active users actually do

Distinct cohort users per feature (server-side action tables). Activity is concentrated in ~150 users.

Return activity by week

Distinct users taking a tracked in-app action each week after signup. Measures deep engagement, not logins. X: weeks since signup. Y: active users.

Engagement collapses after week 0: 147 users act in week 0, only 22 in week 1 and single digits after. The trial is the entire window.


Stickers vs. leavers — behaviour profile

Converters and even cancellers are highly active (~89% took an action); the 964 never-converted are mostly inert (9.9%). Conversion is an activity problem, not a pricing-first problem.

Conversion rate by behaviour & segment

Share of each group that added a card / subscription. Behaviour dwarfs every demographic split.


Why they cancel (offboarding survey)

26 unique survey responses from Q2 signups (deduped). Most churn is silent (the 964 who never converted), so read this as the voice of those who paid then left.

Time from signup to cancellation

17
within 7 days
6
8–14 days
3
15+ days

Representative comments

“I only have 2 paying local clients and the cost is not justifiable.”

“Co-worker permissions are limited.”

“Proszę o kontakt mam problem.”

“zamknięcie działalności (business closed).”


Communications — what drives conversion

Lifecycle automations (triggered by app state) vs. broadcast newsletters. Conversion is the Stripe card event.

Lifecycle automations (the conversion engine)

The PL trial flow is the standout: 48.5% open / 30.6% click-to-open. The Trial-Expired flows are opened but barely clicked (~0% CTO) — the winback copy isn't converting attention into action.

Email engagement vs. conversion

Conversion by email behaviour

Email engagement is only weakly predictive (clickers 9.8% vs never-openers 5.4%). A useful nudge, not a substitute for in-app activation. Correlational, not causal.

Q2 broadcast newsletters (13 sends)opens good, clicks ~1%

Sales pipeline (Pipedrive)

807 of 1,030 signups matched to a CRM deal. Most never move past "Contact"; 700 deals are lost.

36
won deals
71
open deals
700
lost deals

Engagement levers — ranked by impact

Method & limitations

Cohort: 1,030 RC Local owner accounts created 1 Apr–30 Jun 2026 (coworkers and deleted accounts excluded). In-app activity is from server-side action tables (user_usages, profile audits, posts, media) — it undercounts pure dashboard viewing, which lives only in client analytics (GTM / Microsoft Clarity) and the sessions table (swept too fast to use). Conversion = a Stripe subscription / card-on-file row. Email data is per-subscriber from the MailerLite API (694 of 1,030 cohort emails matched a subscriber). Pipedrive deals matched via users.pipedrive_id. Internal master-password logins do not fire the normal login path, so they don't inflate these tables. Conversion-by-email figures are correlational, not causal.