Community hub
Recent from talks
Contribute something to knowledge base
Content stats: 0 posts, 0 articles, 0 media, 0 notes
Members stats: 0 subscribers, 0 contributors, 0 moderators, 0 supporters
Subscribers
Supporters
Contributors
Moderators
Hub AI
Uplift modelling AI simulator
(@Uplift modelling_simulator)
Hub AI
Uplift modelling AI simulator
(@Uplift modelling_simulator)
Uplift modelling
Uplift modelling, also known as incremental modelling, true lift modelling, or net modelling, is a predictive modelling technique that directly models the incremental impact of a treatment (such as a direct marketing action) on an individual's behaviour.
Uplift modelling has applications in customer relationship management for up-sell, cross-sell and retention modelling. It has also been applied to political election and personalised medicine. Unlike the related differential prediction concept in psychology, uplift modelling assumes an active agent.
Uplift modelling uses a randomised scientific control not only to measure the effectiveness of an action but also to build a predictive model that predicts the incremental response to the action. The response could be a binary variable (for example, a website visit) or a continuous variable (for example, customer revenue). Uplift modelling is a 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.
However, many marketers define lift (rather than uplift) as the difference in response rate between treatment and control, so uplift modeling can be defined as improving (upping) lift through predictive modeling.
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).
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 using one of a number of predictive modelling techniques, such as decision trees or regression analysis.
This model uses only the treated customers to build the model.
Uplift modelling
Uplift modelling, also known as incremental modelling, true lift modelling, or net modelling, is a predictive modelling technique that directly models the incremental impact of a treatment (such as a direct marketing action) on an individual's behaviour.
Uplift modelling has applications in customer relationship management for up-sell, cross-sell and retention modelling. It has also been applied to political election and personalised medicine. Unlike the related differential prediction concept in psychology, uplift modelling assumes an active agent.
Uplift modelling uses a randomised scientific control not only to measure the effectiveness of an action but also to build a predictive model that predicts the incremental response to the action. The response could be a binary variable (for example, a website visit) or a continuous variable (for example, customer revenue). Uplift modelling is a 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.
However, many marketers define lift (rather than uplift) as the difference in response rate between treatment and control, so uplift modeling can be defined as improving (upping) lift through predictive modeling.
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).
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 using one of a number of predictive modelling techniques, such as decision trees or regression analysis.
This model uses only the treated customers to build the model.