Overview of uplift modeling in SAS

I found several good presentation on how to approach uplift modeling in SAS, and here is their overview.

What is uplift modeling? Uplift modeling is a technique that allows us to determine which targets are more likely produce incremental response when exposed to marketing material.

What do you need to create an uplift model? To create uplift model, we need to conduct an experiment. An experiment is a setup where we have targets from various groups, some of which have been exposed to our driver (aka marketing material). Sometimes a natural experiment will work, but in a classic case, we designate test and control targets intentionally, often randomizing our selections.

What does the uplift model do? It models what kind of lift the presence of driver can make on the behavior of the targets. It compares the response of similar targets with and without presence of the driver, and uplift or incremental lift simply means difference in that response.

Is uplift modeling the same as direct response modeling? No. There is a very important difference: uplift model adjusts for direct response activity that can be traced to direct response, but would happen anyway. It also adjusts for any activity driven my marketing but not captured in direct response. In other words, while direct response is a surrogate, uplift is the real deal.

Do we need to use advanced methods to build an uplift model? No. In many cases, we can create well thought out experiments that deliver stable and replicatable results by using simple methods. For example, a very simple method affectionately called “slice and dice”, i.e. measuring your uplift by customer segments, can be very powerful tool in shaping your targeting strategy.

Overview of modeling by SAS Institute
https://support.sas.com/resources/papers/proceedings13/096-2013.pdf

This presentation is about using SAS to conduct actual analysis.

Presentation on Improving Marketing ROI by Ryan Zhao
http://www.sas.com/content/dam/SAS/en_ca/User%20Group%20Presentations/Toronto-Data-Mining-Forum/RyanZhao-MarketingCampaignROI.pdf

Quote: Propensity/Response model is NOT necessary to drive neither campaign lift nor ROI.

 

Overview of analytical approaches to uplift modeling by Kim Larsen
http://www.sas.com/events/aconf/2010/pres/larsen.pdf

Quotes:

“Most methods to date do not work when the overall net purchase rate is small”

“Although standard propensity models often generate lifts, they are not designed to optimize the incremental impact of a marketing campaign”