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Net Lift Model Tutorial Part-2
SAS Logistic Modeling and Video from Dr. Larsen

Net Lift Model Tutorial part-1: Introduction to Probability Decomposition Model

Net Lift Model Part-3: the whole Process flow

• NET LIFT MODELS

There has been recent mentions of a target selection (i.e., case selection) technique referred to as previously: net lift, uplift, incremental response, differential response,
and possible other names. When posed as a return maximization problem, net lift and the usual target selection practice coincide.

Net lift applies to target selection in situations with a binary treatment; return maximization provides direction on how to handle problems in situations with more than one treatment.

Sample code for logistic regression:
/******************************************************/
proc logistic data=Consumer plots(only)=(oddsratio(range=clip)) outmodel=mg1;
class Treatment Sex education /param=ref;
model Results(event='Yes')= Treatment Sex Age income education Children
/ selection=stepwise expb clodds=wald rsq lackfit scale = none aggregate;
/* clodds option to calculate confidence interval */
/*option scale = none for Deviance and Pearson Goodness-of-Fit Stats*/
output out=dinf prob=p resdev=dr h=pii reschi=pr difchisq=difchi;
oddsratio Treatment;
oddsratio Sex;
oddsratio Age;
contrast 'Pairwise A vs P' Treatment 1 0 / estimate=exp;
contrast 'Pairwise B vs P' Treatment 0 1 / estimate=exp;
contrast 'Pairwise A vs B' Treatment 1 -1 / estimate=exp;
contrast 'Female vs Male' Sex 1 / estimate=exp;
effectplot slicefit(sliceby=Sex plotby=Treatment) / noobs;
effectplot / at(Sex=all) noobs;
run;

proc logistic inmodel=mg1;
score data = gdata1 out=gpred1; run;
/*generate the prediction value based the model estimate*/

The following tutorial is presented by Dr. Larsen in the data mining meeting 2009.
• Definition of Uplift modeling: Analytically modeling to predict the influence on a customer's buying behavior that results from choosing one marketing treatment (customer-facing action) over another. The secondary treatment is often passive - make no contact - as evaluated over a control group.

The uplift model answers the question, " How much more likely is this treatment to generate the desired outcome than the alternative treatment?" For each customer, the model's prediction drives the decision of which treatment to apply.

• Summary of probability decomposition modeling process:

1. Build stepwise logistic regression purchase propensity model and record model score for every customer in a modeled population.

2. Use past campaign results or small scale trial campaign results to create a dataset with two equal size sections of purchasers from contact group and control group. Build a stepwise regression logistic model predicting which purchasers are from the contact group. The main task of this model will be to penalize the score of model built in the step 1 when purchaser is not likely to need contact.

3. Calculate net purchasers score based on probability decomposition formula

• CONCLUSION
The probability decomposition model is just one in a group of methods known as net lift models. The net lift models help maximize ROI of marketing campaigns as they let us avoid contacting customers or prospects who are highly likely to buy a product or service anyway.

The traditional purchase propensity model may do a good job ranking customers based on their probability to make a purchase but it does not have the ability to select the true responders, the customers who will only make a purchase if contacted.

The probability decomposition model has its challenges; it is relatively difficult to interpret as it combines scores of two separate model scores. Following is a list of conditions required for net lift model:

• presence of randomized control group
• analyzed marketing contact is not the only communication leading to purchase
• purchase rate is not correlated to lift, purchase propensity model is not sufficient
• presence of similar/repetitive marketing campaigns or small scale tests
• variation in average lift across scoring ranks

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