Finding which customers a message actually persuades, accepted at an IEEE conference
Most campaigns send everyone the same message and measure the average effect, which hides the people who respond strongly and the ones who are put off. Saanvi built an uplift modeling framework using X-Learner models to estimate each individual's response to different interventions and choose the best one per person. Personalized targeting produced $6.50 more incremental spending per customer than a uniform approach, and Qini curve analysis revealed a concentrated group of persuadable customers driving most of the gain.
A student at the North Carolina School of Science and Mathematics interested in how data can drive better decisions.
First author of a causal machine learning study on personalized interventions, accepted at an IEEE conference.
Pick a program.Apply in minutes.
1,000+ students from 50+ countries- A PhD mentor, 1:1 every week
- Our team edits every draft with you
- We get it published in IEEE, Springer or Elsevier
- Your whole college application, built with you
- 2 to 3 papers and the full competition season
- The founder works with your family personally