DYMATRIX has devoted considerable effort to developing uplift modeling techniques for the KNIME community. Being one of the hottest topics in Predictive Modeling these powerful methodologies are now available to predict the direct marketing influence of each decision on customer response behavior. Marketing campaigns can easily be optimized, contact costs and the return per unit spend can significantly be improved.
The uplift (or incremental response) 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. Professional and profitable marketing teams will only take credit for the uplift effect of their marketing campaigns.
Uplift modeling uses both the target group and control group to train a predictive model that focuses on the incremental response. The provided uplift tree algorithms provide configurations for both target group and control group.
The DYMATRIX Uplift Modeling solution nodes come with the following KNIME nodes:
- Uplift Tree Learner
- Uplift Tree Predictor
- Uplift Tree Evaluator
This node is contained in the DYMATRIX Customer Intelligence Extensions provided by DYMATRIX CONSULTING GROUP GmbH, Germany. More commercial Customer Intelligence Extensions are available like the Interactive Scorecard Builder, PMML2SQL Converter and Interactive Binning Nodes. For more information please refer to www.dymatrix.de or write an Email to info@dymatrix.de.
Source Code
The source code can be accessed at https://anonymous:knime@community.knime.org/svn/nodes4knime/trunk/org.dymatrix.
License
The DYMATRIX Uplift Modeling Nodes are released under GPLv3.
