## What
Adds `stats:ai-cohort-report` — a monthly scheduled command that posts a
**weekly per-cohort** retention/conversion time series for the
onboarding AI suggestions feature to Discord.
## Design (pre/post, directional — not causal)
We measure whether shipping AI suggestions moves retention/conversion
among the users who can actually receive it. This is an **observational
pre/post** readout, deliberately chosen over a randomized A/B holdout
(low signup volume; we don't want to withhold the feature). It reports a
**correlation**, never a cause.
- **Cohort (ITT):** users who imported **≥50 transactions in their first
7 days** (a pre-treatment trait). We do *not* condition on who accepted
AI — that would reintroduce self-selection.
- **Release anchor `R`:** `MIN(ai_consents.accepted_at)`, planted by
self-accepting on deploy.
- **Metrics, all fixed-horizon from each user's own signup:**
- Retention — active ≥14d (`last_active_at ≥ signup+14d`)
- Trial — subscribed ≤14d
- Paid — `active` subscription ≤30d
- AI-acceptance — funnel-health line (descriptive, not causal)
- **Right-censoring:** too-young cohorts render as `pend`, never `0`.
- **Surge flagging:** weeks with outlier eligible volume (>2.5× median)
are flagged ⚡ so a one-time acquisition spike (e.g. the launch/YouTube
surge) isn't misread as an organic trend.
Staff/test accounts (incl. the one planting the anchor consent) are
excluded via `config('ai_suggestions.report.excluded_emails')`. Output
goes to `DISCORD_AI_COHORT_WEBHOOK_URL`, falling back to the shared
`DISCORD_WEBHOOK_URL`.
## Honest caveat (baked into every report footer)
A one-time launch+YouTube signup surge sits right before release and
there's no acquisition-source tracking, so cross-release comparisons mix
channels. Read **organic-vs-organic cohort trends over a quarter**, not
a month-one before/after diff.
## Deploy steps
1. Self-accept the AI consent in prod to plant `R`.
2. Add your email (+ staff) to `AI_SUGGESTIONS_REPORT_EXCLUDED_EMAILS`.
3. Set `DISCORD_AI_COHORT_WEBHOOK_URL` (or rely on the existing admin
webhook).
## Tests
`tests/Feature/SendAiCohortReportCommandTest.php` — 7 tests / 25
assertions covering eligibility, retention, trial/paid windows, release
anchor + pre/post split, maturity censoring, surge flagging, and Discord
delivery (incl. webhook fallback). Pint clean.
Suggests transaction categorization rules during onboarding.
After a sync or import, it groups the uncategorized transactions, asks
Gemini (via laravel/ai) to map the common merchants to categories, and
shows the results for review. The user edits or drops any and creates
the ones they want. During onboarding the accepted rules also categorize
existing transactions right away.
Off by default: it needs the `AiRuleSuggestions` Pennant flag and a
per-user AI consent. The model and thresholds are config-driven.
`ai:suggest-rules {user}` prints what a user would get.
The settings-page surface and monthly regeneration are a follow-up.
Víctor Falcón