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
- 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
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
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
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
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
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
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)
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