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Lennox Industries: Attrition Probabilities Diana Batten and Maddie Kamp
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Presentation Overview Company Information The Opportunity Project Objective Our Approach Deliverables Test Cases Recommendations
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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
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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”
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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
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Our Approach Data MiningT-TestsSeasonality Markov Process Sorting and Automating
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Deliverables T-Tests Excel spreadsheets – Attrition Predictors Automation “Plug and Chug” Transition Probabilities Percent Correct & Test Cases Regional and Order Type Data
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Entering Data
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Transition Probabilities
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Conditionals for Decile Drops
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Percent Correct Red/Orange Attrition Drop From and To Attrition (Purple) Count of Customers Labeled as Attriting: 336788 Count If Attrition:201770 Percent Correct Assesment: 59.9050%79.5455% Example for 2010 Monthly Data
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Test Cases Test Case # Number of Decile Drops Percent Correct – Number of Decile Drops Drop FromDrop ToPercent Correct – Drop from and to #1 (2 Periods) 266.65%8478.77% #2 (3 Periods) 276.74%8488.40% #3 (2 Periods) 347.55%9258.71% #4 (3 Periods) 359.91%9279.10%
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Entering Data – Order Type and Geographic Location
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Summary of Data
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Looking at Order Type - ZMPO
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Variability Did Not Decrease
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Recommendations & Functionality Ability for expansion Percent correctness Use Three Period Outlook Use red/orange to red/orange analysis Allows for user to define attrition
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Questions?
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