Principal Analyst, Retention Analytics

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Principal Analyst, Retention Analytics

Team: Marketing Analytics

Reports To: Senior Manager, Marketing Analytics

Role Summary

GEICO’s Marketing department is seeking a senior, hands-on analytics expert to join the Marketing Analytics team as a Principal Analyst, Retention Analytics. This role is embedded with the Retention Marketing team and will be an analytics thought partner to Retention Marketing leaders. The work centers on why policyholders stay, why they leave, and where marketing investment can measurably improve retention. At the Principal level, the analyst is trusted to lead independently from idea through delivery. They will work alongside another analyst to set the retention measurement and insights agenda. They will need to be comfortable taking complex and ambiguous questions from problem definition through actionable recommendation, and deliver executive-ready insights that shape strategy.

This is a strategic, hands-on role. The analyst spends most of their time in the data: designing and reading holdouts and tests, building the retention scorecard and forecasts, and turning the results into recommendations. They also advise senior leaders and set the standard for retention measurement. The role emphasizes:

Reframing ambiguous asks into decision-ready questions.

Setting analytical and quality standards for the team.

Influencing senior stakeholders.

Key Responsibilities

Serve as an analytics thought partner to Retention Marketing leadership and the primary point of contact for senior stakeholders. Shape the retention measurement and insights agenda.

Own the strategy for measuring retention marketing across policyholder touchpoints, including email, direct mail, digital and in-app messaging, and renewal communications. Set the standard for how incremental impact on retention, cancellations, and policies in force is measured.

Seek out undefined problem spaces and reframe loosely defined asks into decision-ready analytical questions. Anticipate what decision makers need and bring insight before being asked.

Lead the most complex causal questions in retention, such as whether and how communications change cancellation behavior. Choose the right approach, whether holdout design, test-and-control, or quasi-experimental methods for observational data, and be clear about each method’s limitations. Define measurement approaches for communications that have no direct response action, such as education messaging.

Lead forecasting and business case analysis. Use it to size retention opportunities and impacts of initiatives against business targets, and inform investment and prioritization decisions.

Build and own the retention scorecard mapped to the business scorecard. Run holdout and test-and-control reads on lifecycle journeys and deliver results on a regular cadence.

Connect retention to customer lifetime value and combined ratio, including whether CLV reflects renewal beyond the first term, and quantify the retention lift from bundling and multi-line relationships.

Automate recurring retention reporting with Business Intelligence so time goes to analysis and the story, not number production.

Develop and standardize retention analytics frameworks that the broader team can scale, including KPI definitions, scorecards, and test-read protocols. Partner with Business Intelligence team on durable, automated reporting, and influence data governance and quality standards for retention metrics.

Choose and combine data based on what the decision needs, not just on what data is available. Anticipate data limitations, guide responsible use across the team, and explore new data sources and how they could be applied to retention.

Craft executive-ready narratives that influence strategic decisions. Present to senior marketing leadership and defend analytical approaches. Surface the stories stakeholders need to hear, including unwelcome ones, and handle those conversations with empathy and diplomacy.

Set quality standards for retention analytics and develop QA frameworks and checklists for common analyses. Be accountable for decision risk in analytical outputs.

Balance 2–3 major strategic initiatives while also supporting ad-hoc asks. Scope work accurately and proactively communicate risks, dependencies, tradeoffs, and factors outside the team’s control. Stay composed under executive requests and conflicting stakeholder priorities.

Influence tool selection and best practices for the team. Evaluate and recommend new technologies, including automation and AI tools, to improve efficiency.

Partner with Data Science, BI, and Data Operations on churn modeling, metric alignment, and shared data assets.

Required Qualifications

Bachelor’s degree in a quantitative field (Statistics, Mathematics, Economics, Data Analytics, Data Science, Computer Science, or similar).

7+ years of experience in marketing analytics, customer analytics, or a closely related role, including leading measurement and insight work for retention, lifecycle, or CRM programs.

Experience working inside a renewal or subscription-based service business (insurance, financial services, banking, telecom, SaaS) where retention is central to the business model.

Expert SQL skills with deep query optimization knowledge, plus working proficiency in Python or R for analysis and workflow automation.

Expert in BI tools (Power BI strongly preferred) and the ability to adapt quickly to new tools and platforms.

Recent, hands-on delivery of analysis (not oversight only), including designing and reading test-and-control or holdout measurement.

Deep expertise in advanced segmentation, scenario modeling, business case development, and experimental and quasi-experimental measurement design.

Proven track record of presenting to senior leadership and influencing strategic decisions with analytical insight.

Preferred Qualifications

Experience coaching and developing other analysts

Experience in Property & Casualty (P&C) insurance.

Advanced degree (MS, MBA, or similar) in a quantitative or analytical field.

Hands-on experience with churn or retention modeling (e.g., propensity models, survival analysis) and causal inference methods (e.g., matched comparisons, difference-in-differences).

Experience with cloud-based data platforms (BigQuery and/or Snowflake preferred).

Experience shaping data governance, metric definitions, or tool selection for an analytics team.

Experience using AI tools to improve workflow productivity and analytical quality (e.g., accelerating research, summarization, analysis, documentation, and automation).

Experience coaching and developing other analysts.

Annual Salary

$146,575.00 - $229,600.00 The above annual salary range is a general guideline. Multiple factors are taken into consideration to arrive at the final hourly rate/ annual salary to be offered to the selected candidate. Factors include, but are not limited to, the scope and responsibilities of the role, the selected candidate’s work experience, education and training, the work location as well as market and business considerations.

At this time, GEICO will not sponsor a new applicant for employment authorization for this posi