Case study · Mixed — 2 utility apps, 2 casual games, 1 lifestyle subscription
How a 5-App Portfolio Recovered $8,400 in 30 Days With ASAPilot AutoPilot
A multi-app indie portfolio used ASAPilot AutoPilot 7-stage automated review across 5 apps over 30 days, recovering $8,400 in waste through structured daily optimization recommendations.
- Customer
- Indie developer with multi-app portfolio
- Portfolio
- 5 published apps, $45K/month aggregate ASA spend
- Outcome
- $8,400 in waste recovered through AutoPilot 7-stage daily review recommendations
- Timeframe
- Continuous 30-day measurement; recommendations reviewed and approved daily
Note: This case study describes a 30-day AutoPilot rollout pattern observed across multiple ASAPilot Growth-plan customers with multi-app portfolios. Developer identity, app names, and exact figures have been anonymized at the customer’s request.
The developer
A solo indie developer based in Australia, 4 years into a portfolio-strategy product business. Background: ex-mobile engineer at a Big Tech company; now full-time on a 5-app portfolio with combined monthly net revenue of ~$60K.
The portfolio:
- 2 utility apps (file management + a converter tool): $12K/month combined ASA spend
- 2 casual games (puzzle + endless runner): $20K/month combined ASA spend
- 1 lifestyle subscription app (habit tracker): $13K/month ASA spend
Total ASA spend: $45K/month. The developer worked solo, no support staff, no contractors.
The problem
The developer’s pattern before AutoPilot:
- Monday: Deep audit on App 1, surface-level review on Apps 2-5
- Tuesday: Deep audit on App 2, surface-level review on Apps 1, 3-5
- Wednesday: Deep audit on App 3…
- Each app got a deep review once every 5 working days
This was 30-40 minutes per app deep review (3 hours of dashboard time per day) plus 5-10 minutes surface review per other app (another 20-30 minutes). Total daily: ~3.5-4 hours.
Even at that cadence, things fell through:
- Zero-conversion keywords accumulated between deep reviews on each app
- CPI drift on App 4 wasn’t noticed for 6 days
- Search Match expansion on App 2 had been running for ~10 days unnoticed when caught
- Daily Cap on App 5’s Discovery had been hitting consistently (under-budgeted) without the developer noticing
The developer’s own estimate: 15-20% waste across the portfolio from missed daily opportunities — roughly $6,750-$9,000/month leaking.
The implementation
The developer signed up for ASAPilot Growth plan ($99/month) on March 15, 2026. Setup:
- Connected all 5 apps via OAuth (10 minutes total, ~2 min per app).
- Ran initial audit across the portfolio. The audit took 38 seconds and surfaced 23 prioritized findings — 9 on Apps 1-3 (the utility + games), 14 on Apps 4-5 (the subscription).
- Cleared the initial backlog over 4 days — actioned 18 of 23 findings, deferred 3 as not worth the effort, declined 2 (judged inappropriate for the apps’ specific contexts).
- Enabled AutoPilot in daily mode starting March 20, 2026.
AutoPilot’s 7-stage daily review
Each day, AutoPilot runs 7 stages across the portfolio:
- Waste control — surface candidates: zero-conversion keywords, low-CR Search Match terms
- Bid tuning — keywords with CPT >> ad-group average and CR below threshold
- Keyword expansion — Discovery search terms ready for promotion to Exact
- Budget rebalancing — campaigns under-pacing vs over-pacing
- Anomaly detection — CPI deviations, sudden volume shifts
- Performance summary — yesterday’s pacing, top movers, weekly trend
- Audit-log capture — what changed (decisions approved by the operator)
The developer reviewed AutoPilot’s output each morning over coffee — typically 12-15 minutes of review across all 5 apps.
The outcome (30-day measurement)
Cumulative recommendations across 30 days:
| Stage | Recommendations | Approved | Deferred | Declined |
|---|---|---|---|---|
| 1. Waste control (negatives) | 312 keywords | 247 | 41 | 24 |
| 2. Bid tuning | 89 bid changes | 71 | 12 | 6 |
| 3. Keyword expansion | 47 promotions | 38 | 6 | 3 |
| 4. Budget rebalancing | 18 reallocations | 15 | 2 | 1 |
| 5. Anomaly detection | 11 alerts | 9 acted | 2 dismissed | — |
| Total | 477 | 380 (80%) | 63 | 34 |
Quantified waste recovery
ASAPilot tracked the estimated impact of each approved recommendation:
| Source | Estimated monthly waste prevented |
|---|---|
| Negative keyword additions (247) | $4,200/month |
| Bid reductions on overpriced keywords (71) | $1,800/month |
| Search Match exposure cleanup on Brand campaigns | $1,400/month |
| Budget rebalancing toward higher-CR ad groups | $700/month |
| Anomaly responses (9 paused before significant burn) | $300/month |
| Total monthly waste prevented | ~$8,400/month |
This was sustained — at the end of the 30-day window, the run-rate of waste prevention was approximately the same as the cumulative recovery. The leaks had not been accumulating before; they were freshly emerging and being caught daily.
Time saved
Daily operator time:
| Activity | Pre-AutoPilot | With AutoPilot |
|---|---|---|
| Cross-app review | 3-4 hours | 12-15 minutes |
| Action execution | 30-60 minutes | 30-45 minutes |
| Weekly deep-dives | 2-3 hours | 30 minutes (consolidated) |
| Daily total | 3.5-4.5 hours | 45-60 minutes |
The developer recovered roughly 2.5-3 hours per day for product development, customer support, and new feature work.
The 30-day economics
| Component | Value |
|---|---|
| ASAPilot Growth plan (1 month) | $99 |
| Operator time on AutoPilot reviews | ~45 hours over 30 days |
| Waste recovered | $8,400 |
| Time freed for other work | ~75 hours |
| Net return (waste recovered − tool cost) | $8,301 |
The 30-day ROI was 83× the subscription cost.
What worked
Daily cadence prevented accumulation. Pre-AutoPilot, zero-conversion keywords accumulated for 5-10 days between deep reviews per app. Daily review caught them within 24-48 hours of emerging.
Prioritization made review fast. Each daily AutoPilot output was a ranked list with estimated impact per item. The developer reviewed top-impact items first; lower-priority items took proportionally less attention.
Read-only model preserved control. The developer initially worried about losing manual control. After 30 days, they had approved 380 of 477 recommendations (80% approval rate) — high enough that AutoPilot was clearly providing value, low enough that the operator was meaningfully filtering.
Multi-app overview eliminated context switching. Pre-AutoPilot, the developer switched between 5 ASA dashboards constantly. AutoPilot consolidated all 5 apps into a single morning review.
What was harder
- First week of approvals was slower than later. The developer reviewed every recommendation in detail to calibrate trust. By week 3, they had developed a faster scan-and-approve workflow for high-confidence recommendations.
- Some recommendations needed contextual override. AutoPilot suggested pausing a keyword on App 3 that the developer knew was about to be valuable for an upcoming feature launch. Manual override added context AutoPilot did not have.
- Anomaly alert tuning per app. Default sigma threshold was too sensitive for App 5 (low daily volume). The developer raised it to 2.5σ for App 5; default 2σ stayed on the higher-volume apps.
Recommended for
This pattern works best for:
- Multi-app indie developers (3-10 apps) managing solo
- Portfolios with $20K-$200K monthly ASA spend where daily optimization compounds
- Operators who already have an ASA understanding and benefit from automation rather than from learning the basics
- Apps with moderate complexity — not single-keyword brand campaigns, not 10,000-keyword enterprise accounts
The Growth plan ($99/month) is the standard tier for this workflow because of its 50-automation limit (AutoPilot consumes 1 automation per app per cadence). Agency plan ($299/month) accommodates 50+ apps with the same workflow.
See pricing for plan tiers, or read the audit guide for the initial audit structure used before AutoPilot enablement.