Define retention cohorts before calculating a heatmap

Specify cohort membership, return behavior and observation maturity so retention percentages remain comparable.

PLPromptByte Library@promptbytePublished Sep 8

TextData & Analysis #cohort analysis #product analytics #retention

Version 1 of 1 · published Sep 8, 2026Version 1 · only version

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Version 1 · 3 variables · 3 required

Fill in the values below — the customized prompt updates as you type. marks a required value.

Retention question{{retention_question}}

The behavior and decision under study. Default: How often do newly activated accounts return to create another report?

Event data{{event_data}}

Fields, identity model, timestamp meaning and coverage. Default: events(account_id, event_name, occurred_at UTC); report_created events cover 2026-01-01 through 2026-08-31. Account merge history is unavailable.

Cohort rules{{cohort_rules}}

Entry event, interval choice, return event and exclusions. Default: Assign accounts by month of first report_created. Measure at least one report_created in each later calendar month. Exclude the signup month from return comparisons.

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Resolved user prompt10 lines

Design a retention-cohort analysis for How often do newly activated accounts return to create another report?.

Event schema and data coverage: events(account_id, event_name, occurred_at UTC); report_created events cover 2026-01-01 through 2026-08-31. Account merge history is unavailable.
Cohort and return-event definitions: Assign accounts by month of first report_created. Measure at least one report_created in each later calendar month. Exclude the signup month from return comparisons.

Specify cohort assignment, identity resolution, timezone, interval boundaries and the event that counts as a return. Define the denominator and distinguish exact-period retention from on-or-after retention; do not mix them.

Show the calculation steps or SQL outline using only known fields. Account for duplicate events, users with no return and cohorts that have not reached a full observation interval. Mark immature cells unavailable rather than zero.

Describe a cohort-size table and retention matrix, then list validation checks for membership, denominators and coverage. Do not infer why retention changed or invent heatmap values when only a schema is supplied.

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Usage notes

Resolve account identity and calendar rules before calculating percentages. A schema-only answer should remain an analysis specification.

Adapted by PromptByte from prompts/analysis/user-behavior-analysis-expert.md by AJ Geddes, under MIT. Focused User Behavior Analysis Expert's retention and segmentation workflow on cohort definitions. Removed psychology inference and expected-impact claims; added denominator and maturity controls. No model run or example result is claimed.

Original MIT notice:

MIT License

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The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

code · sql
```
**Assumptions**

- `order_items.order_id` joins to `orders.id`, and `products.category_id` joins to `categories.id`. These are inferred from the naming convention, since no foreign keys were listed.
- "Last 90 days" is measured against `orders.order_date` relative to today, inclusive of today.
- Revenue is `quantity * unit_price` summed over line items; there is no discount or tax column in the schema, so none is applied.
- Cancelled orders are excluded. If `status` uses different values than `'
```
Code preview: **Assumptions** - `order_items.order_id` joins to `orders.id`, and `products.category_id` joins to `categories.id`. These are inferred from the naming convention, since no foreign keys were listed. - "Last 90 days" is measured against `orders.order_date` relative to today, inclusive of today. - Revenue is `quantity * unit_price` summed over line items; there is no discount or tax column in the schema, so none is applied. - Cancelled orders are excluded. If `status` uses different values than `'
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Define retention cohorts before calculating a heatmap

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Example output

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Resultsfor version 1

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