Propensity modelling helped our client use customer segmentation in cross selling

Authored by Ameex Technologies on 08 Jun 2020

Our Client

Client is a US-based leading publisher with print and digital media properties.


  • Customer level segmentation and analytics performed on ad hoc basis with smaller segments of customer data for critical campaigns 
  • Client wants to understand which section of customers are likely to move up/down in the conversion funnel and how to improve customer loyalty using promo codes and targeted campaigns 


  • Lacking intelligence to identify customer segments to roll out promotions 
  • Lacked a robust data management platform for streaming data, harmonization and enabling consumption of insights 
  • Narrow focus on data-driven decisions along with balkanized data strategies stand as obstacles for analytical maturity 

Solution Approach

  • Multi-channel customer touchpoints were analyzed using website traffic data streamed into Snowflake and enabled integrated with secondary datasets at SKU level 
  • Creating Customer 360 view consisting of website behavior, past purchase data, and current subscriptions and/or newsletter sign-ups 
  • Snowflake was also used for hosting dev/test environments for sampling 
  • Using python, data connection was established with Snowflake to access the customer journey data 
  • XGBoost is used to train models to generate propensity scores for customers 
  • Post model training, Snowflake is used to create features for the entire dataset and to score models with a JavaScript UDF on each user row
  • Segments are created based on scores for customers 

Business Impact

  • Client able to generate insights on user behavior for upsell/cross-sell opportunities using easily accessible data platform 
  • Deep dive analysis based on integrated data helped them optimize the marketing budget and reduce cost per conversion 
  • Campaign performance analysis helped in getting insights on the effectiveness of campaigns based on propensity and conversions 
  • Success of new platform influenced leadership to migrate legacy databases to snowflake thereby breaking data silos and drive decision-based data strategy and analytics maturity

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