Lennox Industries: Attrition ProbabilitiesDiana Batten and Maddie Kamp

Presentation OverviewCompany InformationThe OpportunityProject ObjectiveOur ApproachDeliverablesTest CasesRecommendations

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

The OpportunityEnable 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”

Project ObjectiveDetermine 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

Our Approach

Data Mining T-Tests Seasonalit

yMarkov Process

Sorting and

Automating

Deliverables T-Tests

Excel spreadsheets – Attrition Predictors Automation “Plug and Chug” Transition Probabilities Percent Correct & Test Cases Regional and Order Type Data

Entering Data

Transition Probabilities

Conditionals for Decile Drops

Percent Correct

Red/Orange Attrition

Drop From and To Attrition

(Purple)Count of

Customers Labeled as Attriting:

3367 88

Count If Attrition: 2017 70

Percent Correct Assesment: 59.9050% 79.5455%

Example for 2010 Monthly Data

Test CasesTest Case #

Number of Decile Drops

Percent Correct – Number of Decile Drops

Drop From

Drop To Percent Correct – Drop from and to

#1(2 Periods)

2 66.65% 8 4 78.77%

#2 (3 Periods)

2 76.74% 8 4 88.40%

#3 (2 Periods)

3 47.55% 9 2 58.71%

#4(3 Periods)

3 59.91% 9 2 79.10%

Entering Data – Order Type and Geographic Location

Summary of Data

Looking at Order Type - ZMPO

Variability Did Not Decrease

Recommendations & FunctionalityAbility for expansionPercent correctness

Use Three Period Outlook Use red/orange to red/orange analysis

Allows for user to define attrition

Questions?