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Saas usage dashboard

@usagemetrics/saas-usage-dashboard

Turn a product events CSV (timestamp, account_id, user_id, event) into one self-contained HTML dashboard: DAU, WAU, MAU, stickiness, active accounts, top features, activation rate, weekly retention, and accounts trending down. No external scripts; open it in any browser or attach it to an email.

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  1. 1---
  2. 2name: saas-usage-dashboard
  3. 3description: Build a single-file HTML product usage dashboard from a raw events export (timestamp, account_id, user_id, event). Shows DAU, WAU, MAU, stickiness, active accounts, top features, activation rate, weekly retention, and accounts trending down, with inline charts and no external scripts. Use when the user asks for a usage dashboard, product analytics, "how are customers using the product", DAU or MAU, stickiness, engagement, feature adoption, or has an events export from their app, warehouse, or analytics tool.
  4. 4license: MIT
  5. 5---
  6. 6
  7. 7# SaaS usage dashboard
  8. 8
  9. 9One command, one HTML file. The file has no external scripts or fonts, so it opens anywhere, works offline, and can be attached to an email or dropped in a shared drive. The numbers come from a script, so they are the same every time.
  10. 10
  11. 11## Inputs
  12. 12
  13. 13An events CSV in `data/` with these columns (extra columns are ignored):
  14. 14
  15. 15```csv
  16. 16timestamp,account_id,user_id,event
  17. 172026-10-30T09:12:00Z,acme-co,acme-u1,created_report
  18. 18```
  19. 19
  20. 20- `timestamp`: ISO date or datetime, or unix seconds. Times are read as UTC.
  21. 21- `account_id`: the customer (company or workspace). `user_id`: the person.
  22. 22- `event`: the action name. Use the product's own names; snake_case reads best.
  23. 23
  24. 24Where to get it: a warehouse query (`select occurred_at as timestamp, account_id, user_id, event_name as event from events where occurred_at >= now() - interval '120 days'`), an analytics tool's raw export, or the app's own event log. 90 to 120 days of history gives every chart enough data.
  25. 25
  26. 26Ask the user:
  27. 27
  28. 281. **What is "activated"?** The one action that means a new user got value (for example `shared_dashboard`). If they do not know, use the default: active on 3 or more distinct days in the first 14 days.
  29. 292. **Which events are noise?** Logins and page views are ignored for features by default (`login,logout,session_start,page_view,signup,signed_up`). Add others with `--ignore`.
  30. 303. A title for the page (company or product name).
  31. 31
  32. 32## Steps
  33. 33
  34. 341. Check the file: row count, date range, distinct accounts and users, and the 15 most common events. Point out obvious issues (test accounts, internal users, bots) and offer to filter them into a new file `data/events-clean.csv` before building.
  35. 352. Run:
  36. 36
  37. 37 ```bash
  38. 38 node scripts/usage-dashboard.mjs data/events.csv --activation-event shared_dashboard --title "Acme usage" --out out/usage-dashboard.html
  39. 39 ```
  40. 40
  41. 41 Other options: `--as-of 2026-10-30` (defaults to the last day in the data; pick a weekday or a full day if the export ends mid-day), `--activation-days 14`, `--ignore login,page_view,heartbeat`.
  42. 423. Tell the user where the file is (`out/usage-dashboard.html`) and give a five-line read-out from `out/usage-dashboard.json`: MAU and its change, stickiness, activation rate, the top feature, and the accounts trending down.
  43. 434. Offer next steps that fit what you saw: the `retention-cohorts` kit for monthly or account-level retention, `activation-funnel` to see where new users drop, or `customer-health-score` for the accounts trending down.
  44. 44
  45. 45## How to read it (include when sharing)
  46. 46
  47. 47- **Stickiness** = average DAU over the last 30 days / MAU. Under 15% is light use, 20 to 30% is healthy for most B2B tools, over 40% is a daily habit.
  48. 48- **Activation** only counts users first seen at least `--activation-days` ago, so new users are not counted as failures.
  49. 49- **Weekly retention** is per first-seen week: the share of that cohort active in each later week. The current, partial week is blank.
  50. 50- **Trending down:** accounts with at least 20 events in the prior 28 days whose events or active users fell 30% or more in the last 28 days, or with no events for 14 days.
  51. 51
  52. 52## Output template (chat read-out)
  53. 53
  54. 54```markdown
  55. 55Dashboard: out/usage-dashboard.html (as of 2026-10-30)
  56. 56- MAU 42 (-2% vs prior 30 days), WAU 32, DAU 14. Stickiness 25%.
  57. 57- Activation 59% (did shared_dashboard within 14 days).
  58. 58- Top feature: created_report, used by 98% of MAU.
  59. 59- Trending down: bluebird-labs (quiet 21 days), harbor-retail (-64% events), juniper-bank (-43%).
  60. 60```
  61. 61
  62. 62## Guardrails
  63. 63
  64. 64- Events often carry user emails or names in IDs. Do not paste raw rows into chat; the dashboard shows IDs only in the accounts table.
  65. 65- Do not upload the events file or the dashboard anywhere. The user shares the HTML file.
  66. 66- If more than 1% of rows are skipped (bad timestamp, missing user), stop and show examples before trusting the numbers.
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