Behavioral Segmentation

8 posts · Nov 2022 – May 2026

What this trail covers

Hex has evolved its data analysis capabilities by detailing six patterns for behavioral segmentation, focusing on how data teams write SQL and Python to group users by their actions. This includes RFM analysis, cohort retention, engagement scoring, lifecycle stage segmentation, product-qualified lead scoring, and churn risk detection. The post emphasizes building trustworthy segments at scale and making them actionable beyond the data team, covering both batch processing and the importance of up

All Hex trails

May 20266 Behavioral Segmentation Examples For Data TeamsThis post details six patterns for behavioral segmentation that data teams can implement using SQL and Python. It covers RFM analysis with percentile-based scoring and temporal tracking, cohort retention analysis using SQL and pandas for usage frequency, engagement scoring derived from correlation analysis with business outcomes, lifecycle stage segmentation based on temporal event data patterns (activation, engagement tiers, churn risk), and product-qualified lead scoring using binary thresholds or point-based systems. It also discusses the trade-offs of clustering versus hand-coded thresholds and emphasizes building trustworthy segments at scale and making them actionable beyond the data team.
Aug 2025How to perform churn analysis efficiently: step by step guideThis post details how to perform churn analysis efficiently using a step-by-step guide. It covers data collection and preprocessing, including handling nulls and data type conversion for columns like 'TotalCharges', and encoding categorical features using LabelEncoder. It also discusses customer segmentation by cohort and the importance of tracking key churn metrics such as churn rate, revenue churn rate, customer retention rate by cohort, customer engagement metrics, and Net Promoter Score (NPS). The post highlights the use of Hex notebooks for this analysis, leveraging both SQL and Python, and mentions Hex AI for assistance.
Jul 2025Cohort analysis explained: purpose, types, and how to useThis post explains cohort analysis, its purpose, and types (time-based, behavior-based, size-based, event-based). It details how to perform cohort analysis using SQL and Python libraries (Pandas, Polars) and R packages, highlighting Hex as a platform that combines these tools. A practical example of building event-based cohort tables from raw 'users' and 'events' data is provided, focusing on defining cohorts based on the first sign-up month.
Jun 2025Product analytics blueprint for data-driven growthThis post details how to use product analytics data for data-driven growth, focusing on use cases like conversion, retention, and workflow insights. It covers cohort analysis, funnels, and churn modeling, and highlights how ETL, automation, and Hex's no-code tools help teams work faster and smarter. It also emphasizes the importance of event tracking, statistical methods, and data visualization for turning user interactions into actionable insights. The post provides a blueprint for building a product analytics process that transforms raw data into actionable insights and drives continuous growth, including unifying data sources and focusing on the right product metrics.
Sep 2024How GameChanger used LTV modeling to predict and prevent churnThis post details how GameChanger used LTV modeling to predict and prevent churn. The data team utilized Hex notebooks for data preparation and exploration, and Snowflake for data storage. They redefined LTV by incorporating both 'Lifetime' (estimated via survival regression) and 'Value' (using ARPU, considering both paid and free users with advertising potential and conversion likelihood). The multimodal workflow in Hex allowed for SQL and Python integration. Insights were ported to Snowflake and then a BI tool for 'what' questions, while Hex enabled 'why' questions through model explainability. The LTV model was also used for revenue forecasting and validating business opportunities.
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Nov 2022How to do Funnel Analysis with SQL and PythonThis post details how to perform funnel analysis using SQL and Python. It explains the concept of funnel analysis, introduces the AIDA model, and provides a step-by-step guide to building and interpreting funnels using a simulated e-commerce dataset. The post includes SQL queries for event counts and Python code using Plotly to visualize funnel charts. It also discusses how to assess funnel health by comparing observed pass-through rates to business goals and provides a Python function to identify the stage with the largest customer drop-off.
Nov 2022Cohort analysisThis post introduces cohort analysis as a method for understanding customer behavior, specifically focusing on retention. It details how to define cohorts based on the month of first purchase, process e-commerce data to calculate customer retention over time, and visualize this data using relative and absolute retention matrices. The technical implementation involves using pandas for data manipulation, matplotlib and seaborn for visualization, and calculating period numbers to track customer activity relative to their initial purchase month.