Uplift or Persuasion ModelingA combination of treatment comparisons (e.g. send a sales solicitation to one group, send nothing to another group) and predictive modeling to determine which cases or subjects respond (e.g. purchase or not) to which treatments. Here are the steps, in conceptual terms, for a typical uplift model: 1. Conduct A-B test, where B is control 2. Combine all the data from both groups 3. Divide the data into a number of segments, each having roughly similar numbers of subjects who got treatment A and control. Tree-based methods are typically used for this. 4. The segments should be drawn such that, within each segment, the response to treatment A is substantially different from the response to control. 5. Considering each segment as the modeling unit, build a model that predicts whether a subject will respond positively to treatment A. The challenge (and the novelty) is to recognize that the model cannot operate on individual cases, since subjects get either treatment A, OR control, but not both, so the “uplift” from treatment Z compared to control cannot be observed at the individual level, but only at the group level. Hence the need for the segments described in steps 3 and 4. Note: Traditional A-B testing would stop at step 1, and apply the more successful treatment to all subjects.
Explore Data & Analytics Statistics
- Businesses that use big data saw a 10 percent reduction in overall cost.
- 59 percent of executives say big data at their company would be improved through artificial intelligence (AI).
- Insights-driven businesses are growing at an average of more than 30% each year, and by 2021, they are predicted to take $1.8 trillion annually from their less-informed peers.
- Customer/social analysis is considered the second most important big data analytics use case, followed by predictive maintenance.
- 29 percent of investment professionals use search trends to derive data.
- 90 percent of IT professionals plan to increase spending on BI tools.
- 55 percent of North American businesses have adopted big data analytics.
- 21 percent of investment professionals use web traffic to derive data.
- 90 percent of the world’s data was created between 2015 and 2016 alone.
- 73 percent of businesses consider Spark SQL critical to their analytics strategies as a big data access method.
Check Out Data & Analytics Tools
Recent Blogs on Data & Analytics
- Exploring DEV / TEST / PROD Environments in Power BI
- Implementing a Proof of Concept (POC) Approach
- What is a Data Strategy & Why is it Important?
- Surprise! Payers’ Data Struggles with the No Surprises Act
- How to get started with Data Governance
- Mothers in Tech: Design your Own Work-Life Balance
- Gaining executive buy-in through Data Governance
- Data Governance: Lessons learned for best practices
- Accelerating Data Initiatives for the Banking & Finance Industry
- The Benefits of Data Warehousing in Finance