Fictional example · This is the real report format.
The app and findings are fictional, but the layout and report sections match what you receive after completing your own audit or study.
The App Auditor · Your free app audit
CalorieBuddy
People are finding and trying CalorieBuddy in healthy numbers — most of them stop within the first week.
How we built this audit
ConfirmedThis report is based on real numbers pulled from your app. Founder guesses were only used to fill gaps.
App Health
63/100
Early traction, with gaps
Primary Concern
Retention
Confidence high
Signals Found
4
1 positive · 3 watch
Open Questions
3
Need customer evidence
How this score was calculated
Based on 3 of 5 components (65% of the rubric)
Score is calculated by a fixed formula from the numbers you pasted; the AI writes the narrative but does not set the score.
Points below are out of each component’s original weight. Excluded components are removed and the remaining weights are re-normalized to 100.
Activation · weight 25%
15.83 / 25 points
Value: 38.0%
Inputs used: ONBOARDING_COMPLETED_ALL_TIME, TOTAL_SIGNUPS
ONBOARDING_COMPLETED_ALL_TIME / TOTAL_SIGNUPS; full points at 60%.
Retention · weight 25%
Not enough data — excluded
Value: Not available
Inputs checked for missing data or a zero denominator: DAY_30_RETENTION, DAY_7_RETENTION, NEVER_RETURNED_ALL_TIME, TOTAL_SIGNUPS
DAY_30_RETENTION and DAY_7_RETENTION are missing; NEVER_RETURNED_ALL_TIME / TOTAL_SIGNUPS is also unavailable or has a zero denominator.
Engagement · weight 20%
14.36 / 20 points
Value: 21.5% active
Inputs used: ACTIVE_30D, TOTAL_SIGNUPS
Average of available sub-scores. ACTIVE_30D / TOTAL_SIGNUPS; full points at 30%. ACTIVE_7D / ACTIVE_30D unavailable (missing input or zero denominator); excluded.
Monetization · weight 20%
10.59 / 20 points
Value: 2.6%
Inputs used: PAYING_USERS, TOTAL_SIGNUPS
PAYING_USERS / TOTAL_SIGNUPS; full points at 5%.
Momentum · weight 10%
Not enough data — excluded
Value: Not available
Inputs checked for missing data or a zero denominator: SIGNUPS_LAST_30D, SIGNUPS_BY_MONTH
Need SIGNUPS_LAST_30D and at least two valid YYYY-MM: n monthly counts, with a nonzero historical average after excluding the latest month.
Scoring formula v1
Primary concern · Retention
Of 7,847 lifetime downloads, only 1,123 users were active in the last 30 days. The biggest drop happens before day 7, so the problem is not awareness — it's what happens after install.
Secondary concern · Free-to-paid conversion
138 paying subscribers out of 1,123 active users shows some willingness to pay, but only 22% of all sign-ups were active in the last 30 days. We don't yet know why the rest aren't active.
Heads up — your data changed the picture
- —
Biggest problem
You said: I need better marketing. · Data shows: Acquisition is already working. Retention is the leak. · Why it matters: More traffic will pour into the same bucket if retention isn't fixed first.
We went with the data.
- —
User demand
You said: People probably want more nutrition detail. · Data shows: Only 38% finish logging their first meal, and no request for deeper nutrition data appears in support replies. · Why it matters: Building advanced nutrition tools could deepen the leak instead of fixing it.
We went with the data.
Where people go
as reportedLifetime · pulled from Lovable dashboard
Pasted from auth table
62% never complete first meal · pulled from events
MAU · pasted from analytics
Active subscriptions · pulled from Stripe
Audit findings
4 findings
Needs attention now
1 finding
Early usage drop-off
The largest single loss happens between first meal logged and day 30.
Worth a look
2 findings
Meal logging effort
Food search shows high abandon rate in event data, but no customer evidence yet.
Why people cancel
No cancellation reasons have ever been collected.
Working well
1 finding
Acquisition is working
TikTok is driving steady downloads without paid spend.
What appears to be working
- —Organic TikTok discovery works without ad spend
- —A core group of long-term daily users exists
- —Store rating sits at 4.4 with unprompted praise for the daily calorie ring
What you told us
- —Built on Lovable; data pulled from the Lovable dashboard and pasted back
- —Most downloads come from TikTok
- —No onboarding walkthrough exists
What you're assuming
- —"I need better marketing."
- —"People probably want more nutrition detail."
What we think may be happening
- —Daily logging costs more effort than the value returned in week one
- —People who forget to open the app never build the habit
What we can't know yet
- —Why people stop logging after the first few days
- —Whether the paywall timing feels fair
- —What the long-term users do differently
What we need to learn from your users
- —Which screen or step in the first meal flow loses the most users
- —Whether people quit because logging takes too long or because they simply forget
- —What would have brought them back
Do this today
Open your app's builder analytics or events table and find the exact step where the most users drop off during their first meal log.
You already have data showing 62% never finish logging. Finding the specific screen or field tells you whether the fix is fewer steps, better search, or clearer instructions before you spend anything.
The one thing your numbers can't answer — optional customer study · $99 one time
Everything above comes from your app's own numbers. This last question can only be settled by talking to real users. An AI interviewer talks with your users through a private link you share with them. The conversations become your findings. Completely optional; your audit and monthly check-ins stand on their own without it.
7-Day Retention Audit
Who the AI interviewer talks to: 20 users who logged at least one meal and then stopped within 7 days
Why it's worth doing: Your numbers show WHERE people disappear. If you want to verify WHY before you ship a fix, a focused customer study is the fastest way — optional, not required.
One thing we don't do: App Store Optimization (keywords, screenshots, store rankings). If store visibility is your bottleneck, dedicated tools like AppTweak, App Radar, or Astro are the right fit for that — this report focuses on what's happening inside your app and with your customers.
Audited by The App Auditor
theappauditor.com