Senior Product Manager, User & Lifecycle Analytics

Contentsquare is the all-in-one experience intelligence platform designed to be easily used by anyone who cares about digital journeys. With our flexible and scalable platform, organizations quickly get a deep understanding of their customers’ whole online journey.

We are a global leader in the experience analytics space, with a growing presence across 15 offices worldwide. We’re here to stay—and we’re looking for team members who are excited to drive impact and help us scale even further.

Our aim is to create an inclusive workplace where everyone learns and succeeds. Contentsquare has built a community of individuals who are daring, understanding, and deliberate. We invite you to join us in making the complex simpler—for our customers, their customers, and each other.

Important note: Be careful of scammers pretending to be from Contentsquare. We will never ask for money or contact you through random texts. Any communication from our in house Talent Acquisition team will only ever come from our contentsquare.com or @contentsquare-ext.com domain. For more information, visit our careers blog.

The Role

We're looking for a Senior Product Manager to help customers understand how different groups of users behave over time: who converts, who comes back, who becomes more engaged, and who may be starting to drop away.

You will lead the product strategy for user segmentation and lifecycle analytics. The work sits between product analytics and marketing technology. You will help customers define meaningful audiences, compare their behavior, understand what drives conversion and retention, and use those insights within Contentsquare and third party tools and platforms.

You will work closely with Engineering and Product Design, along with data, identity, AI, and other product teams. Some of the experiences you lead will depend on shared data and analytics services, so the role requires both clear product judgment and the ability to work collaboratively across different domains.

The immediate priorities are:

• 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.

What You'll Do

Set Product Direction

• 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.

Understand Customers and the Market

• 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.

Build Better Segmentation and Lifecycle Analysis

• 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.

Explore Predictive Intelligence

• 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.

Lead Across Teams

• 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.

What You'll Need to Succeed

• 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.

What Makes You Stand Out

• 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.