Loyalty & Retention Analytics Lead

Overview

Fetch has created a new Loyalty & Retention organization, accountable for member retention and satisfaction. This role is the analytics function behind it. You’ll own the measurement, modeling, experimentation, and forecasting that determine what Fetch believes about why members leave and what keeps them. Your work drives decisions on lifecycle communications, channel quality, payment recovery, and how the save desk spends its time.

You own the conclusions, not just the data — and you’ll stand behind them when they cut against what people expected. Because much of what moves retention sits with teams you influence rather than direct, the case has to be won on evidence, and you’ll often be the one presenting it.

What You’ll Own

• Definitions. One agreed definition of cancellation reasons, retention, persistency, and save rate, reconciled with BI, the Chief Analytics Officer, and the lead actuary. You work inside the company data model, not a parallel one.
• Voluntary churn. Retainable vs. non-retainable cancellation, with survival curves by acquisition source and tenure — not a blended rate.
• Involuntary churn. The payment-failure view (decline code, card brand, funding type, frequency) and recovered premium, worked with Retention Operations into interventions.
• Channel quality. Attribution by partner — and where data allows, by partner rep — ranking campaigns by retained value, not acquisition cost.
• Satisfaction. Link NPS/CSAT movement to retention at the segment level, and separate real sentiment shifts from survey-mix effects.
• Experimentation. The lifecycle test portfolio, hypothesis to readout to decision, plus a durable archive so settled questions stay settled.
• Forecasting. Separate voluntary and involuntary churn models, reconciled with the CAO and actuary and back-tested against actuals.
• Save desk analytics. Save rate with defensible denominators, cost per save, offer effectiveness, win-back, and agent-level performance across 27 agents.

What Success Looks Like

• 90 days. You own recurring retention reporting and the involuntary-churn cuts, have a working agreement with BI on definitions and access, and have spent real time on the save desk floor and on live cancellation calls.
• 6 months. Granular acquisition-source attribution is in production, you’ve taken over the test portfolio, and first-version voluntary/involuntary forecast models exist.
• 1 year. You produce and defend the retention forecast to the executive team, with a documented record of what our initiatives actually moved.

What We’re Looking For

Required:

• 6+ years in analytics, with meaningful time in subscription, insurance, or other renewal-based businesses
• Advanced SQL — you work independently in a modern warehouse
• Retention and survival analysis: cohorts, survival curves, hazard modeling, and the judgment for when each applies
• Experimentation depth: tests that changed real decisions, and the rigor to know when a result you like is still noise
• Forecasting with accountability for accuracy, not just production
• The ability to bring an unwelcome finding to executives and hold your position
• Clear writing — much of your influence happens in documents

Preferred:

• Payments/billing analytics (decline codes, retry and dunning)
• Python or R
• Pet or P&C insurance
• Actuarial partnering
• Contact-center analytics
• Analytics-engineering practice