Where Customers Come From
A visual read of the funnel, so the drop-offs are obvious without reading model metrics.
Restricted to shane@thematchartist.com and nick@thematchartist.com. Use the Google account that matches your TMA email.
Web traffic (Plausible), HubSpot funnel events, and Stripe payments — monthly, 2023-02 → present. The big story: traffic peaked in 2024 and the visitor → lead conversion has compressed.
This is the business story in one row: how many people become leads, how many book, how many show, and how many actually become customers.
A visual read of the funnel, so the drop-offs are obvious without reading model metrics.
How the booked-lead universe currently breaks into follow-up types.
The strongest positive and negative trigger families after stripping out leakage and noisy sales-process fields.
The buyer types that close best after they show up.
Question themes from the questionnaire. This is useful for understanding intent, not just scoring.
High-level traits that keep showing up among stronger buyers.
The most important friction patterns coming out of the queue logic, text analysis, and false-positive audits.
When leads tend to book better. This is safe, top-of-funnel timing, not downstream sales-process noise.
These are the clearest operational buckets right now. Each one comes with real names behind it in the explorer below.
The acquisition and behavior combinations that keep appearing around stronger leads.
Monthly cohorts help keep the story honest when recent leads have not had time to mature yet.
| Cohort | Leads | Booked | Q / Booked | Showed / Booked | Close / Showed |
|---|
Predicting net spend per paying customer at first-payment time. Useful for ranking — TMA's tier distribution is narrow, so this is package-tier prediction (basic vs premium), not whale-finding.
Customers ranked by predicted LTV, bucketed into 10 deciles. Each bar is the average actual net spend in that decile. The wider the spread, the more useful the model is for prioritization.
From all 2,121 paying customers. The top decile predicts $2,726 mean actual spend vs $1,856 in the bottom decile (1.47×).
| Name | Predicted | Actual | Decile | Buyer |
|---|
Joins the new Stripe customer data to the HubSpot funnel by email. Where does our $4.79M actually come from — and which segments quietly leak refunds?
Reset buyers (post-divorce / long-relationship) lead at $2,527 ARPC. Ready buyers, surprisingly, have the lowest ARPC AND highest refund rate.
The pre-call question they asked. Process / profile-help themes monetize highest; price-question theme still pays full tier ($2,260 ARPC).
Refund-rate spread across card brands is small but real. Missing-brand rows are likely ACH or alt-payment paths.
Predicting who will refund using only what's knowable at first payment.
LightGBM gain. The model still ranks customers — just don't trust the absolute probability when AUC is near random.
Customers who haven't refunded yet, ranked by model score. Useful as a triage queue, not as a calibrated probability.
| Name | Risk | Spend | Stage | Buyer |
|---|
Use this to move from the big picture into actual people. The default modes are intentionally plain-English.
The core model metrics and top feature summaries are still here, just moved out of the main operating view.
Full generated text reports behind the dashboard.