AnalyticsFunnels, cohorts and retentionIntermediateAnalyses your data

App retention diagnostics: D1, D7 and D30 by acquisition source

Compares day-N retention across acquisition sources with maturity and sample-size checks, so weak channels are flagged only when the data supports it.

For: Performance marketers, Marketing analysts, Growth marketers · Works with: Any capable chat model (ChatGPT, Claude, Gemini, others), Google Sheets / Excel

Public beta. This prompt was drafted with AI assistance and checked by automated rules, but it has not been reviewed or tested by a person yet. Treat it as a starting point and check the output. It is hidden from search engines while in beta. Use the “Was this prompt useful?” box to tell us what works.

Your prompt

Act as a mobile growth analyst. Analyse app retention by acquisition source using ONLY the data below. Definitions: Day-N retention means user opened the app on day N after install. Install date range: 1-31 Aug 2026. Data export date: 5 Oct 2026. Data (source | installs | D1 retained | D7 retained | D30 retained | cost per install): Source | Installs | D1 | D7 | D30 | CPI Meta | 12000 | 3600 | 1100 | 380 | 2.1 Google UAC | 8000 | 2900 | 1000 | 420 | 2.8 TikTok | 3000 | 700 | 150 | 40 | 1.4 Tasks: 1. State assumptions, and check maturity: for each install cohort, say whether D7 and D30 are fully observable given the export date; exclude or flag immature data. 2. Compute D1, D7 and D30 retention rates from counts and verify the arithmetic. 3. Compare sources only where installs are large enough to judge (state the working threshold you use as an assumption), and report the uncertainty qualitatively. 4. Combine retention with cost per install to suggest a cost per retained user at D7 (formula shown), where the data supports it. 5. List hypotheses for differences (audience mix, creative promise vs onboarding, attribution quality, fraud or low-quality installs) as hypotheses with the data to check each. 6. List missing data (revenue, activation events, cohort age). Rules: do not invent retention benchmarks or numbers. If data is missing or not provided, say so. Distinguish correlation from causation. Do not rank sources on immature data.

Customize

The fields start with example values so you can see how the prompt works. Everything stays in your browser.

How retention is measured.

Installs included.

When data was pulled.

Paste your table. The default is small fictional sample data so the preview works; replace it with your own.

Example of what to expect

Illustrative only. It describes the kind of result this prompt aims for; real output varies by tool, model and run.

A maturity check, retention rates with formulas, a comparison limited to adequately sized sources, cost per retained user at D7, hypotheses with checks and missing data. (Illustrative.)

Expected format: Assumptions, maturity check, retention table, comparison, hypotheses.

How to use it

  1. Use mature cohorts only.
  2. Add revenue and activation events for fuller picture.
  3. Check attribution quality.

Limitations

  • Attribution for installs is imperfect, especially on iOS.
  • Retention alone does not equal value.

Platform notes

Any capable chat model (ChatGPT, Claude, Gemini, others)

Official docs read · checked 2026-10-11

Works in any capable chat model. Paste a small anonymised sample and recompute the key numbers yourself: chat models can misread columns or miscalculate, and they cannot run your statistical tests reliably.

General notes on Any capable chat model (ChatGPT, Claude, Gemini, others)
  • These prompts are plain text and work in any chat assistant. For analytics prompts, paste a small, anonymised export; a chat model can misread columns or miscalculate, so recompute key numbers yourself.

Google Sheets / Excel

Not verified: third-party guidance only · checked 2026-10-11

Test any formulas on a small range first; syntax depends on locale and version.

General notes on Google Sheets / Excel
  • Formulas are written for common functions in both products. Locale settings (decimal and argument separators) can change what you must type.

Source, license and attribution

Origin
Original by MarketerTools
Publisher
MarketerTools
License
Original work by MarketerTools, free to copy and use. Informed by the linked documentation; no third-party text is reproduced.
Platform assumptions checked
2026-10-11

Documentation and references behind this prompt

These informed the structure and the platform notes. A reference is not a license, and no third-party prompt text is copied here.

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