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Predictive Modeling of Customer Attrition for Lennox Industries: Insights and Recommendations

This presentation outlines the predictive modeling project conducted for Lennox Industries, aiming to analyze and forecast customer attrition. With a focus on their residential and commercial heating and air-conditioning business, the project utilizes historical transaction-level data from 2008-2010 to identify attrition predictors. Key deliverables include T-Tests, automation of analyses, and transition probabilities to enhance sales force capabilities. The recommendations emphasize improving accuracy and establishing tools for attrition forecasting, ensuring strategic growth and customer retention in a competitive market.

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Predictive Modeling of Customer Attrition for Lennox Industries: Insights and Recommendations

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Presentation Transcript

  1. Lennox Industries: Attrition Probabilities Diana Batten and Maddie Kamp

  2. Presentation Overview Company Information The Opportunity Project Objective Our Approach Deliverables Test Cases Recommendations

  3. Company Information • Residential & commercial heating and air-conditioning • Seasonality of industry • 13-15% market share • Have business in 80 countries • Manufacturer & distributer • Direct relationship to customer • Customer is contractor

  4. The Opportunity • Enable sales force to predetermine attrition of customers • About 1,000 customers drive about 60% of revenue • Relations between data transactions and customer attrition • 2008-2010 transaction level data • “Proof of Concept”

  5. Project Objective • Determine a type of predictive model and analysis to predict attrition • Use the older historical data given to test and create our model • Address seasonality in sales

  6. Our Approach

  7. Deliverables • T-Tests • Excel spreadsheets – Attrition Predictors • Automation • “Plug and Chug” • Transition Probabilities • Percent Correct & Test Cases • Regional and Order Type Data

  8. Entering Data

  9. Transition Probabilities

  10. Conditionals for Decile Drops

  11. Percent Correct Example for 2010 Monthly Data

  12. Test Cases

  13. Entering Data – Order Type and Geographic Location

  14. Summary of Data

  15. Looking at Order Type - ZMPO

  16. Variability Did Not Decrease

  17. Recommendations & Functionality • Ability for expansion • Percent correctness • Use Three Period Outlook • Use red/orange to red/orange analysis • Allows for user to define attrition

  18. Questions?

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