Building the CLG Dashboard and the Dark-Funnel Case for Budget
Objective: Build a one-view CLG metrics dashboard from the lesson's seven metrics using a real activity export, then calculate a dark-funnel attribution offset ratio to make the investment case to finance.
You're the growth marketer at Sula Vineyards, India's largest listed wine company (NSE: SULA), building a formal member community around SulaFest and its wine club. Finance wants to see the community's real pipeline contribution before renewing next year's budget.
Turn a raw activity export into the lesson's seven-metric dashboard, then use a self-reported attribution survey gap to calculate and defend an undercount ratio for community-sourced pipeline.
Before you start
What you'll need
Free path (everything below is enough to finish)
Free, handles both the metrics table and the ratio math without extra setup
Free tier covers a single survey doc and a linked dashboard summary
Paid upgrades (optional, faster/deeper)
The free Google Sheets path works fine at this scale; upgrade to Mixpanel only once manual monthly exports start eating more than an hour a week.
Useful once the community crosses a few thousand members and manual exports become a weekly time sink
The process
2 steps
Step 01 of 02
The lesson's dashboard tracks MAU, activation rate, NPS by tier, community-sourced pipeline %, champion retention, CQL-to-opportunity conversion, and UGC SEO impact in one view so leading and lagging indicators show causality together.
Given raw monthly activity numbers (2,400 total members, 540 posted or reacted, 310 new members, 74 took a meaningful action in 30 days), what does the dashboard say about flywheel health before you even look at pipeline?
Procedure
- Calculate MAU% (540/2,400 = 22.5%) against the 20% floor
- Calculate activation rate (74/310 = 23.9%) against the 20-30% target band
- Add placeholder rows for NPS-by-tier, champion retention, and CQL-to-opportunity, sourced from CRM exports
- Color-code each metric red/yellow/green against its benchmark
- Write one sentence per red metric explaining the likely cause
Sula Wine Club Community Dashboard, Month 4 MAU%: 22.5% (target 20%+) -- GREEN Activation rate: 23.9% (target 20-30%) -- GREEN Community-sourced pipeline %: 9% (target 15-25%) -- RED, likely undercounted, see attribution step Champion retention (6mo): 71% (target 60%+) -- GREEN UGC SEO sessions: 1,140/mo, up from 620 last quarter -- trending up
Healthy
Five of seven metrics sit at or above benchmark, and the one red metric has a written hypothesis, not just a red cell.
Unhealthy
A dashboard with all seven metrics reported in separate decks so no one can see that low pipeline % and high MAU% are the same story.
What this means
A single red metric next to six green ones is a specific, fixable problem (attribution), not evidence the whole program is failing.
So what do I do about it?
| Symptom | Action | Effort |
|---|---|---|
| Community-sourced pipeline % sits below benchmark despite healthy MAU and activation | Run the dark-funnel attribution survey in step 2 before concluding the community isn't driving pipeline | 30 min |
Step 02 of 02
The lesson's fix for undercounted community influence is a self-reported attribution survey compared against CRM source data, with the resulting gap applied as a multiplier to the CRM pipeline number.
Your CRM attributes ₹42L in wine-club pipeline to 'community.' Your signup survey shows 27% of new members cite the community as how they first heard of Sula's wine club, against total quarterly pipeline of ₹4.2Cr. What's your undercount ratio, and what do you present to finance?
Procedure
- Calculate survey-implied community pipeline: 27% x ₹4.2Cr = ₹1.134Cr
- Calculate the undercount ratio: ₹1.134Cr / ₹42L = 2.7x
- Present both the conservative CRM figure and the survey-adjusted figure side by side
- Recommend the CRM number for forecasting, the adjusted number for the budget case
Sula Wine Club, Q3 Community Pipeline CRM-attributed: Rs 42,00,000 Survey-implied: Rs 1,13,40,000 (27% of Rs 4.2Cr total pipeline) Undercount ratio: 2.7x Recommendation: forecast on Rs 42L, justify next year's community budget on the Rs 1.13Cr adjusted figure.
Healthy
Both figures presented together, with the CRM number explicitly kept for forecasting.
Unhealthy
Presenting only the survey-adjusted number to finance as if it were the CRM-verified figure.
What this means
The gap is a measurement problem to disclose, not a number to inflate confidence with.
So what do I do about it?
| Symptom | Action | Effort |
|---|---|---|
| Finance is skeptical of the community budget renewal | Bring both the conservative and adjusted pipeline figures, with the survey methodology shown | 30 min |
Final deliverable
A seven-metric CLG dashboard with color-coded benchmarks, plus a dark-funnel attribution offset calculation ready for a finance conversation.
See a reference example
Figma Community Dashboard, Month 6 (excerpt) MAU%: 26% (target 20%+) -- GREEN Community-sourced pipeline %: 11% CRM-attributed vs. 24% survey-implied -- undercount ratio 2.2x Recommendation: forecast on the 11% CRM figure, justify headcount on the 24% adjusted figure.
Success criteria
You're done when you can:
- All seven dashboard metrics calculated correctly against their benchmark bands
- Attribution offset ratio calculated correctly and presented alongside, not instead of, the CRM figure