Uplift modelling, also known as incremental modelling, true lift modelling, or net modelling is a new predictive modelling technique that directly models the incremental impact of a treatment (such as a direct marketing action) on an individual's behavior.
Uplift modelling has applications in customer relationship management for up-sell, cross-sell and retention modelling. It has also been applied to personalized medicine.
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Uplift modelling uses a randomized scientific control to not only measure the effectiveness of a marketing action but also to build a predictive model that predicts the incremental response to the marketing action. It is a new data mining technique that has been applied predominantly in the financial services, telecommunications and retail direct marketing industries to up-sell, cross-sell, churn and retention activities.
The uplift of a marketing campaign is usually defined as the difference in response rate between a treated group and a randomized control group. This allows a marketing team to isolate the effect of a marketing action and measure the effectiveness or otherwise of that individual marketing action. Honest marketing teams will only take credit for the incremental effect of their campaign.
The table below shows the details of a campaign showing the number of responses and calculated response rate for a hypothetical marketing campaign. This campaign would be defined as having a response rate uplift of 5%. It has created 50,000 incremental responses (100,000 - 50,000).
| Group | Number of Customers | Responses | Response Rate |
|---|---|---|---|
| Treated | 1,000,000 | 100,000 | 10% |
| Control | 1,000,000 | 50,000 | 5% |
Traditional response modelling typically takes a group of treated customers and attempts to build a predictive model that separates the likely responders from the non-responders through the use of one of a number of predictive modelling techniques. Typically this would use decision trees or regression analysis.
This model would only use the treated customers to build the model.
In contrast uplift modelling uses both the treated and control customers to build a predictive model that focuses on the incremental response. To understand this type of model it is proposed that there is a fundamental segmentation that separates customers into the following groups:
The only segment that provides true incremental responses is the Persuadables.
Uplift modelling provides a scoring technique that can separate customers into the groups described above.
Traditional response modelling often targets the Sure Things being unable to distinguish them from the Persuadables.
Because uplift modelling focuses on incremental responses only it provides very strong return on investment cases when applied to traditional demand generation and retention activities. For example by only targeting the persuadable customers in an outbound marketing campaign the contact costs and hence the return per unit spend can be dramatically improved.
One of the most effective uses of uplift modelling is in the removal of negative effects from retention campaigns. Both in the telecommunications and financial services industries often retention campaigns can trigger customers to cancel a contract or policy. Uplift modelling allows these customers, the Do Not Disturbs, to be removed from the campaign.
It is rarely the case that there is a single treatment and a control group. Often the "treatment" can be a variety of simple variations of a message or a multi-stage contact strategy that is classed as a single treatment. In the case of A/B or multivariate testing Uplift Modelling can help in understanding whether the variations in tests provide any significant uplift compared to other targeting criteria such as behavioural or demographic indicators.
The first appearance of true response modelling appears to be due to Radcliffe and Surry[1].
Victor Lo also published on this topic [2] and more recently Radcliffe again [3]
Radcliffe also provides a very useful frequently asked questions section on his web site [4]
Similar approaches have been explored in Personalized Medicine [5]