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Predicting Life stage of a customer using the retail data

It is desirable for a retailer to have information of the life cycle stage of  a customer. This helps greatly to a retailer in offer optimizations. Targeting right customer with rights offers is greatest challenge for a retailer. They run hundreds of promotions and optimizing it saves huge cost and helps in retaining customers.  In the era of super markets, large retailers issue loyalty cards and maintain customer purchase data. But most of the demographic data is not available with them as customers normally do not fill these fields and leave them blank. Although customer demographic data are not available using purchase data certain life stages of customer can be predicted using rule base techniques or predictive modeling. Life stages bring changes in purchasing pattern. 1.  A single male will mostly buy his needs and daily eatables.  Eating habits gets changed if he is married etc.  A man can occasionally buy woman's product but it will be regular if ...

Enterprise Miner for the performance evaluation of the propensity models using actual campaign results

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Product propensity models are developed to identify   the  customers or observations those  have high likelihood of responding to any cross-sell campaign or any event of interest.  Model d evelopment which includes has multiple steps requires extensive efforts. 1. Identification of business objective or business problem. 2. Identifying analytical objective  3. Identifying data scope and time window.  4. Data preparation and exploration 5. Data treatment and variable reduction 6. Model training and validation 7. Model selection and interpretation  8. Business approval  Steps to automate the model scoring for its periodical usage. 9. Model deployment  10. Periodical Model Scoring  Once the models are scored and used in the campaign for targeting customers to cross sell the product of interest, effectiveness of model must be measured. I am going to discuss the different ways to ev...