generic-kpi

name

generic-kpi

description

Generate a shareable, generic SaaS KPI dashboard as a brand-new standalone Google Sheet, filled with FICTIONAL sample data (no real company or customer data, no external links). Produces a fully-formed B2B SaaS operating workbook — MRR/ARR, MAU usage, retention (NRR), cash collection, autopay, A/R aging, and per-customer detail across a 42-month history — safe to publish publicly (e.g. on LinkedIn or in a portfolio). Use when someone says "make a generic KPI dashboard", "create a shareable SaaS KPI dashboard", "generate a sample/demo dashboard with fake data", "regenerate the generic dashboard", or "refresh the LinkedIn dashboard".

Generic SaaS KPI Dashboard (fictional data)

Builds a self-contained, shareable SaaS operating dashboard using entirely made-up data — a fictional B2B SaaS company (default: "ErasCloud AI"), fictional customer names, and randomly-generated-but-realistic revenue/usage numbers. Nothing links to any real sheet, customer, or system, so the result is safe to share publicly.

Standard SaaS terminology throughout: MAU (Monthly Active Users), Seats, Active Users, MRR/ARR, NRR, autopay, A/R aging.

What it produces

A new Google Sheet with 21 tabs, in this order:

1. 📕 How to Use — directions so anyone who opens it knows how to read/refresh it
2. 📖 Definitions & Sources — every metric defined + where each number comes from (live)
3. 📊 Combined Summary — one-screen exec snapshot (accrual + cash revenue, usage, autopay)
4. 🔍 Compare — pick any metric + two periods, see the delta (dropdown-driven)
5. 📊 Revenue Dashboard — headline KPIs, MoM deltas, movers, new/churned lists
6. 📊 MRR & ARR Summary — monthly revenue cascade (ARR → discounts → net → NRR)
7. 📊 Usage Summary — MAU, over-minimum, seats, active users
8. 📉 Churn & New Revenue — MRR bridge (start + new − churn ± expansion = end)
9. 💳 Autopay Collections — recurring auto-collected, net of discounts
10. 💵 Revenue → Cash — accrual → invoiced → cash cascade
11. ⏱ Collections & A/R — A/R aging + on-time collection
12. 💵 Invoiced Volume — monthly cash-out schedule (quarterly billers land as lumps)
13. 📈 Usage Monthly History — MAU/Seats/Active Users over time
14. Over-Min History — % of customers over their MAU minimum, over time 15–21. Data layer — Customers — MRR / ARR, Usage — MAU/Seats/Active Users per Customer, Minimum MAU, Pricing Engine

Every dashboard reads from the data-layer tabs with formulas, so the whole thing is internally consistent and recomputes if a number changes. History spans 42 months (Jan 2023 → Jun 2026), and downstream formulas resolve the latest/prior month dynamically by date — so adding a month "just works" (see Monthly update below).

Monthly update (adding a month) & month-add robustness

Every "this month / last month" figure resolves the latest/prior month by its row-1 date, not by a fixed column (AT). So when you add a new month column, all dashboards, MoM growth, cash, churn, and per-customer "current month" cells follow automatically — nothing to re-point. Build these refs with the wb.py helpers:

• wb.latest(tab,row) / wb.prior(tab,row) — cross-tab latest / prior-month cell
• wb.latest_range(tab,r1,r2) / wb.prior_range(...) — whole latest/prior column (for SUM/SUMPRODUCT/N)
• wb.nth(tab,row,k) / wb.nth_date(tab,k) / wb.nth_range(tab,r1,r2,k) — the k-th most-recent month (k=1 latest). Use these for recent-first tables (row 1 = latest), so every row auto-shifts when a month is added.
• wb.colbyname(tab,row,"Disc %") — a structural column by header name (survives column shifts)
• wb.latest_self(row) / wb.prior_self(row) — same-sheet (e.g. MoM-Growth columns, if added to the data tabs)

Leave column-aligned month mirrors alone (e.g. Customers — ARR month cells that reference their own column) — those auto-adjust on a column insert; only the off-grid pins need the helpers.

To add a month (monthly close):

1. On each data tab (Customers — MRR, Usage — MAU/Seats/Active Users): if the current latest month is fed by a formula, freeze it (Copy → Paste values-only), then insert one column right after the last month (before the "MoM Growth" column).
2. Set the new column's row-1 header to =EDATE(<prev month cell>,1).
3. Enter that month's numbers in the new column (or point 📥 Paste Data Here at it). Copy the Customers — ARR ×12 mirror and the Customers — MRR totals-block formulas into the new column.
4. Done — every dashboard, growth, cash, churn and per-customer "current month" cell updates itself (it finds the newest month by date).

Generator month-add hardening. All build scripts emit dynamic month refs: single-latest cells (Combined Summary, Usage Summary, Minimum MAU, Pricing Engine, Autopay, Revenue Dashboard movers, Revenue → Cash top) use wb.latest()/wb.prior(); recent-first tables (MRR & ARR Summary, Usage Monthly History, Over-Min History, Churn, Revenue → Cash trailing) use wb.nth()/wb.nth_date()/wb.nth_range(). Chronological tables (Compare model, Invoiced Volume) stay column-aligned (append a row per month). The same monthly-close procedure is written on the generated sheet's 📕 How to Use tab. Keep using these helpers for any new month-referencing formula — never hard-code the column letter of the current latest month.

How to run

bash ~/.claude/skills/generic-kpi/scripts/run.sh [seed] [n_customers]

• seed (optional) — integer; same seed → same data. Omit for a random fresh dataset.
• n_customers (optional, default 35) — keep it in the ~30–40 range for a clean look.

The script generates the data, builds all tabs, turns on link sharing (anyone with the link can view), and prints the shareable URL. It creates a brand-new sheet each run — it never overwrites a previous one. Requires the authenticated gws CLI.

Notes / how it works

• Engine: scripts/gen_data.py (deterministic fake-data generator, seedable) → build.py (data layer) → build2.py/build3.py (dashboards) → build4.py (How-to-Use + Definitions + tab order) → fmt_pass.py (final number-format pass, applied last so percent/pp formats stick). wb.py is the shared Sheets-API helper.
• The data generator guarantees a few quarterly billers (so the "invoice timing vs recurring MRR" story shows), ~⅓ of customers under their usage minimum, some churn, and ~half on autopay — so the dashboards look realistic.
• To change the fictional company name, metric labels, customer-name pool, or number ranges, edit scripts/gen_data.py and scripts/wb.py; do not edit the output sheet by hand (a regen would overwrite your edits — change the scripts instead).
• Everything is fictional. The How-to-Use and Combined Summary tabs both carry an explicit "sample data" disclaimer.