A global car manufacturing company wanted to increase its response rate create customer 360 model view of unique customers.

Client’s data had many inconsistencies, inaccuracies and duplicate entries; unusable for data analytics

  • TransOrg developed a customer 360 model view of unique customers, from multiple data sources, viz., sales enquiry, sales, after-sales, post-service feedback, value added services and old vehicle buy-back
  • Develop data driven customer analytics use cases


  • Analyzed customer data and fixed inconsistencies viz., incorrect, missing values, wrong customer tagging, duplicate entries etc.
  • Assigned a unique identifier code to all records of the same customer after consolidating customer data from various data sources:
    • Data exploration: Filled missing values and explored data distribution
    • Deduplication rules on data: Identified ‘KEY’ customer identifiers common across data records  
    • EDA: Identified all available data fields and their fill rates
    • Customer view development: Finalized KPIs for customer 360
  • Developed data driven use cases from customer 360 database 
    • Customer loyalty segments based on historical transactions
    • Churn prediction, customer retention and targeted marketing


  • Increased response rate on targeted marketing campaigns for multiple products by up to 48%
  • Identified drivers behind customer churn from client’s value-chain, for example:
    • Vehicle model
    • Lifetime service attributes
    • Post-service feedback
Want to learn more about TransOrg’s value proposition, solution methodology and implementation approach?