PAW Business Track 2 - TECH - Machine learning methods & advanced topics
Uplift modeling, marketing analytics
Lessons from: Fidelity
Randomized experiments allow us to determine the overall treatment effect of a program (e.g. marketing, medical, social, education, political, economic). Uplift modeling takes a further step to identify individuals who are truly positively influenced by a treatment or intervention through machine learning and predictive modeling by uncovering heterogeneous treatment effects in available data. This technique enables us to identify the “persuadables” and thus optimize target selection in order to maximize treatment impact. This important subfield of data science or business analytics has gained tremendous attention in recent years in application areas such as personalized marketing, personalized medicine, political election, and healthcare programs with plenty of publications and presentations from both industry practitioners and academics across the world.
However, business and medical applications often involve more than one treatment. Additionally, there are often budget and quantity constraints involved. This talk will review current uplift modeling methodologies, extend predictive modeling to multiple treatment situations, bridge the gap between predictive analytics and prescriptive analytics by introducing the mathematical problem for treatment optimization, and propose various solutions to both deterministic and stochastic optimization problems. Examples from the retail industry will be used as an illustration. While the talk is geared towards marketing type applications (“personalized marketing”), the same methodologies can be readily applied in other fields such as insurance, medicine, education, political, and social programs.
AI and Data Science Center of Excellence Leader, Workplace Investing