ytpartners transformation story.
Data system and KPI engine
The scale plan was built on truth. We turned raw transaction ledgers into decision-grade outputs: a core KPI dashboard (including LTV), cohort reporting that shaped product defaults and offer simplification, and an automated churn risk model that classifies customers by stage using order-gap signals.
Executive summary
Before changing strategy, distribution, or digital systems, the business needed clear answers: which customers repeat, when churn happens, what drives reorder behavior, and how those realities translate into product defaults and marketing motions. We built an analytics engine that produced consistent outputs leadership could use weekly.
How the system works
A live engine: ledger in, decision outputs out, run weekly.
Starting point and diagnosis
The constraint was visibility: retention and churn dynamics were discussed, not staged and operationalized.
- Transaction history existed, but reporting was not systematized into weekly outputs
- No stable cohort views to guide offer design and defaults
- Churn was not staged by behavioral signals
- No repeatable insight extraction method tied to owned actions
KPI dashboard sample
Illustrative weekly view. Percentages shown, no customer counts.
| Area | KPI | This week | WoW | Target band | Owner |
|---|---|---|---|---|---|
| Revenue | Total revenue (index) | 100 | +6% | 95–115 | GM |
| Customers | Returning customers | 68% | +2 pts | 65–75% | Growth |
| Retention | Repeat rate | 44% | +3 pts | 40–50% | Lifecycle |
| Unit economics | LTV (index) | 112 | +4% | 100–120 | Finance |
| Quality | Support tickets per 100 orders | 9 | -2 | 6–10 | Ops |
Values are illustrative to show structure and readability.
Primary plan cohorts
We focused the strategy on the cohorts that actually repeat. Names are anonymized.
| Cohort | Description | % of customers | Repeat signal | Default implication |
|---|---|---|---|---|
| Plan A: Custom | High personalization, stable repeat | 28% | Strong | Default for high-intent buyers |
| Plan B: Pro | Performance baseline plan | 22% | Strong | Default recommendation for new buyers |
| Plan C: Lean | Lightweight, price-sensitive | 18% | Mixed | Offer as secondary option |
| Plan D: Signature | Chef’s selection, variety-led | 20% | Mixed | Use as upgrade path |
| Program E: Reset | Short-duration, high drop-off risk | 12% | Weak | Gate behind education + onboarding |
Churn risk and mitigation
Elastic, per-customer churn staging driven by order-gap signals.
- Per-customer monitoring of order gaps relative to historical cadence
- Stage classification driven by observable signals: healthy, drifting, at-risk, churned
- Mitigation motions mapped to stage with owners and success criteria
What changed
- Retention and churn became measurable and staged
- Product defaults were set by cohorts that actually repeat
- Lifecycle motions became trigger-based instead of generic outreach
- Weekly reporting converted into owned actions
Assets delivered
- KPI dashboard pack and KPI definitions
- Cohort framework and repeat behavior summaries
- Churn risk bands and mitigation prompts by stage
- Automated refresh workflow from raw ledger exports
Outcomes
- A single source of truth for weekly performance
- Faster diagnosis of churn and repeat behavior
- Clearer underwritable story for strategy and capital readiness
- Decision system the team can run consistently
Applied AI in execution systems
- Automated KPI pack generation from ledger exports
- Automated churn monitoring report and mitigation prompts
- Automated cohort refresh and switching summaries
- Structured insight extraction into weekly action items
Testimonial
“The data work changed how we talked about the business. Cohorts and churn stopped being opinions and became a weekly operating tool that guided priorities.”
Founder (anonymous)