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Role of Modeling in Database Marketing

Role of Modeling in Database Marketing. Role of Modeling in Database Marketing. Forecasts (aggregate level) vs Predictions (individual level) vs Segmentation (no dep var) Forecasts obtained thru Time Series etc Applying Scoring Models to forecasts

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Role of Modeling in Database Marketing

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  1. Role of Modeling in Database Marketing

  2. Role of Modeling in Database Marketing • Forecasts (aggregate level) vs Predictions (individual level) vs Segmentation (no dep var) • Forecasts obtained thru Time Series etc • Applying Scoring Models to forecasts - Obtain average response in current period - Score current customers to get individual response rates - Obtain average forecast for next period - Proportionately adjust to get individual response rates in next period • Applying Scoring Models at individual level

  3. Example: Applying Scoring Models to forecasts • Database mktr. has 2 million names on file • Using RFM it decides that 1 million are worth mailing • Most recent summer mailing to the 1 million selected people pulled 2% • Next, logistic regression is used (0/1 response var) to score the 1 million persons mailed to • Avg response is 2% and response by deciles is obtained from model

  4. Example: Applying Scoring Models to forecasts • Analyst estimates (using forecasting techniques) that Fall mailing will pull 2.5% on average • Now, the 1 million individuals can be individually (& decile-wise) scored for a fall mailing by proportionately adjusting the average • Finally, what about the other 1 million people in dbase? • Again, analyst needs to estimate their overall response to a fall mailing, and adjust it to get individual response scores

  5. Role of Modeling in Database Marketing • RFM versus regression - RFM is arbitrary in nature - Regression can do more • CHAID versus RFM and regression - Can handle interactions - Can guide analyst about which interactions to include in a regression - Provides benchmark against regression results - A set of univariate CHAIDs can act as a quality control tool

  6. Role of Modeling in Database Marketing • Using Principal Components to model buying patterns - Factor Analysis to reduce data • What’s the right number of variables to use - Examine statistics for significance of var - Use t-stat/chi-square stat in reg/logistic reg • Typical model results - How response rates vary among deciles

  7. Role of Modeling in Database Marketing • Zip Code models - Work with outside mailing lists of zip code-based census data - Each zip code is associated with string of demographic variables - Zip code models not as good as models based on internal performance data

  8. Role of Modeling in Database Marketing • Zip code models (contd) • Some issues - Impact of demographic var in a zip code are assumed to work across all list categories and all lists in a category - Selection of independent var - Adjusting for zip code size (weighted least squares regression)

  9. Role of Modeling in Database Marketing • Lead conversion models - Similar to response models if we consider Conversion Rates to be like response rates - Divide all leads into deciles and assign a probability of conversion to each decile - Uses Falloff Rates between efforts to estimate conversion rates of subsequent efforts

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