Manager, Customer Retention Strategy & Analytics

Grubhub•Chicago, NY

7d•Hybrid

About The Position

You’ll lead the analytical agenda for customer retention for Grubhub’s Growth organization, focused on the levers that drive lifetime value. This includes diagnosing what makes customers loyal, identifying where retention is strongest or weakest across segments and behaviors, and shaping how the organization invests in marketing or experiences to deepen the relationships that matter most. You’ll work closely with the analysts embedded in our loyalty and partnerships ecosystem, bringing a cross-program view that connects their work to broader retention strategy. This is a business analytics role, not a reporting role. You’ll diagnose what drives lifetime value, identify opportunities the organization hasn’t yet recognized, and translate that work into the narratives that shape where retention investment is directed. Executives will turn to you for a point of view on customer behavior, and that perspective will influence investment decisions. You will also own the measurement frameworks the Growth organization relies on: customer lifetime value, retention curves, cohort economics, and the goal-setting that connects initiative-level optimization to organization-level KPIs. Reporting in this role is treated as a data product, designed for clarity, adoption, and decision impact rather than as a recurring deliverable. You’ll partner closely with CRM, Portfolio Analytics, Product, and Finance. The problems are ambiguous, the pace is fast, and the work has visibility from team leadership through the executive level. The path forward for the right person is broader strategic scope and increasing executive engagement.

Requirements

4-6+ years in an analytics role with a track record of influencing meaningful business decisions through data
Demonstrated ability to independently structure ambiguous business questions, conduct sophisticated analysis, and land the resulting narrative with senior stakeholders
Deep SQL fluency, with the ability to independently extract and analyze complex data to answer high-stakes business questions
Strong instincts for experimentation and causal measurement, including the judgment to match methodology to the question at hand
Strong experience with data visualization tools (Tableau, Looker, or similar), with a clear point of view on what makes reporting useful and adopted, and an automation mindset that reduces manual effort over time
Cross-functional credibility across Marketing, Finance, Product, CRM, and executive stakeholders
Fluency with adopting AI tools (LLMs, MCPs, and emerging technologies) in your analytical workflow, with a demonstrated portfolio of applied work
A track record of building reusable analytical infrastructure–systematized datasets, modular reporting layers, or self-service tools–not just delivering one-off analyses

Nice To Haves

Experience in subscription, marketplace, or multi-sided platform businesses
Hands-on experience with loyalty, CRM, or lifecycle marketing analytics
Experience designing and evolving CLV or retention measurement frameworks
Experience partnering with cross-functional stakeholders to align on metric definitions and build shared reporting foundations
Python or similar for advanced analysis and automation
Consulting background or other exposure to multiple business models

Responsibilities

The retention insight agenda. Diagnosing what drives lifetime value across the customer base, where retention is strengthening or eroding, and what the organization should do about it. Setting analytical priorities proactively rather than responding reactively to requests.
Customer lifetime value and retention measurement. The frameworks that quantify customer value, how it evolves over time, and how marketing, pricing, product, and care investments compound across the customer lifecycle.
Cross-program retention strategy. Connecting insight across GH+, partnerships, and the broader customer base to inform where retention investment should be directed. Partnering with program-level analysts to translate their findings into organization-level decisions.
Experimentation across the lifecycle. Test design, results synthesis, and the translation of findings into decisions, including the causal inference judgment around when a methodology fits the question and when results generalize.
The reporting layer for retention. Dashboards, analysis-ready datasets, and self-service tools, designed for reusability and adoption, not one-time use. Reinventing what isn't working and automating what shouldn't require manual effort.
Goal-setting and performance analysis. Partnering with Growth leadership to develop the goals the organization is measured against, then running the analysis that informs whether targets are being met and what to adjust.
The narrative. Translating analysis into executive-ready stories that move strategy and unlock investment.