Senior Product Manager, User & Lifecycle Analytics

Segmentation and filtering: Make it easier for customers to define, save, compare, and reuse audiences across their analyses.

User and retention analysis: Help customers move from aggregate trends to cohorts and individual users, compare behavior over time, and identify what contributes to conversion, engagement, and retention.

Future lifecycle intelligence: Explore opportunities such as lifecycle stages, engagement scoring, automatic segment suggestions, and predictions related to conversion, retention, or churn.

Define the product strategy, roadmap, and success measures for user segmentation and lifecycle analytics.

Decide which customer problems to prioritize, balancing customer value, product quality, technical constraints, and business goals.

Make clear trade-offs about what the team will and will not build.

Communicate product choices to executives, partner teams, and customer-facing teams.

Work directly with customers to understand how product, marketing, ecommerce, and customer experience teams analyze user behavior.

Use customer research, product data, field feedback, and market evidence to guide priorities.

Follow developments in product analytics, customer data, and marketing technology, including how other products approach segmentation, cohorts, retention, and activation.

Partner with Design and Engineering to test ideas before making larger investments.

Improve how customers define, manage, compare, and reuse user segments.

Make filtering easier to understand and more consistent across relevant product workflows.

Help customers move from a chart, funnel, or retention cohort to the users behind the result without rebuilding the same audience in several places.

Develop user-level and retention analysis that helps customers identify changes in behavior and the experiences associated with stronger or weaker outcomes.

Investigate opportunities such as lifecycle classification, engagement scoring, automatic segmentation, and predictions related to conversion, retention, or churn.

Partner with Data and Applied AI teams to evaluate data readiness, feasibility, accuracy, explainability, cost, and customer trust.

Validate that customers can understand and act on predictive insights before adding them to the roadmap.

Work with Engineering and Product Design from problem definition through launch and iteration.

Partner with data, platform, identity, and other product teams when shared capabilities are needed.

Define product KPIs and track adoption, usefulness, quality, performance, and customer value.

Build strong relationships with Customer Success, Sales, Product Marketing, and other go-to-market teams.

Use product performance and customer feedback to adjust priorities after launch.

5+ years of product management experience in B2B software or another complex data product, with responsibility for strategy, roadmap, and measurable outcomes.

Experience in product analytics, customer data, audience platforms, marketing technology, lifecycle and retention products, experimentation, or a closely related area.

A practical understanding of segmentation, funnels, cohorts, conversion, engagement, and retention analysis.

Strong customer discovery skills and experience using qualitative feedback and product data together to make decisions.

Experience working with Engineering and Product Design on products that rely on shared data, APIs, identity, or analytics services.

Enough technical depth to discuss data quality, semantics, performance, and architectural trade-offs with engineers.

Strong communication and prioritization skills, including the ability to lead across distributed teams without direct authority.

Experience spanning both product analytics and marketing technology.

Work on audience builders, customer data platforms, personalization, lifecycle marketing, or marketing automation products.

Experience building retention, cohort, or user-level analytics.

Familiarity with event-based data, user identity, cross-device behavior, or high-volume analytical queries.

Experience developing predictive, recommendation, or machine-learning-assisted product experiences.