JD Case Study · Synchrony
VP, Co-Brand & Lifecycle Analytics
A VP role that lives at the intersection of customer-lifecycle analytics, test-and-control measurement, and executive storytelling for co-brand card portfolios. I built exactly this engine for a lease-to-own book — acquisition→retention funnel, LTV, experimentation, an exec scorecard. This case study points it at a co-brand card portfolio.
What they’re actually buying
Strip the title down and the mandate is simple: grow profitable purchase volume and new accounts, and prove it. That means owning the full analytical cycle — segmentation, value-prop measurement, forecasting, and portfolio diagnostics — and wiring it into a decision-management framework that lifts campaign and channel ROI. The hard part isn’t the SQL; it’s turning account-level data into a story GMs and client marketing teams will act on, with test-and-control rigor underneath every recommendation.
How I’d own it — first 90 days
Days 1–30
Map the portfolios
Audit each co-brand portfolio’s lifecycle — acquisition → spend → rewards engagement → retention. Baseline the KPIs, meet the GMs and client marketing teams, and inventory the decisioning + data stack (SQL/SAS, Tableau). Find where the money leaks.
Days 31–60
Stand up the diagnostic
Ship automated portfolio-diagnostic dashboards — KPI trajectory, segment health, rewards/loyalty cost vs. wallet spend. Refresh the segmentation, and size the top two or three ROI opportunities with a dollar figure on each.
Days 61–90
Run the first test-and-learn
Design an account-level test/control on a targeting or value-prop hypothesis, and deliver a business case — targeting, channel optimization, cost/benefit — to the client and SYF marketing. Set the experimentation governance and the analyst-pod operating model (onshore + offshore).
Signature analysis: the co-brand cardholder lifecycle
Where I’d aim first: approved→activated is the highest-leverage leak. In an illustrative test/control, a redesigned activation offer lifts that step +4.2 pts (95% CI +2.1 / +6.3) — and because activation compounds through every later stage, a few points here move lifetime spend across the whole book.
Illustrative figures to demonstrate the method — a lifecycle funnel with the highest-ROI intervention identified and sized by test/control. The real version is built per portfolio, off account-level data.
Requirement → proof
| What the JD asks for | What I’ve already shipped |
|---|---|
| Customer lifecycle analytics (acquisition → spend → retention → loyalty) | My Acima build models the full lifecycle funnel — calibrated to the filed FY2025 results. |
| Test & control / experimentation, statistical models | An A/B + incrementality readout with confidence intervals and a sample-size calculator. |
| Segmentation & customer value models (LTV, unit economics) | An interactive LTV / unit-economics model with behavioral cohorts. |
| Portfolio diagnostics & executive dashboards — insight, not just metrics | An exec growth scorecard built to surface actions, plus market-intelligence dashboards. |
| SQL / SAS, data warehouse, Tableau | Show-the-SQL blocks under each chart; years of Tableau Public work, ported native to this site. |
| Co-brand credit / payments domain | Fintech / lease-to-own background, plus four BNPL & consumer-credit competitor spotlights. |
The same engine
Every requirement above runs on one engine — an acquisition→retention funnel, an LTV model, an experimentation framework, and an executive scorecard — which I already built end-to-end for a lease-to-own portfolio. Swap Acima for a co-brand card book and the machine is the same. That’s the whole point of this portfolio: I don’t learn lifecycle analytics on your dime; I’ve shipped it.
See the engine — the Acima build →I don’t need ramp time to learn what lifecycle analytics looks like — I’ve already shipped it, in public, calibrated to real filings. Let’s talk about pointing it at your portfolios. — Paul Brown