How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation

What algorithm to choose for customer segmentation? Should you use one algorithm or many? How many customer segments should you create? How to evaluate the results? In this research, we conduct a systematic literature review to address such central questions in customer segmentation research and practice. The results from extracting information from 172 relevant articles…… Continue reading How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation

Will They Take This Offer? A Machine Learning Price Elasticity Model for Predicting Upselling Acceptance of Premium Airline Seating

Employing customer information from one of the world’s largest airline companies, we develop a price elasticity model (PREM) using machine learning to identify customers likely to purchase an upgrade offer from economy to premium class and predict a customer’s acceptable price range. A simulation of 64.3 million flight bookings and 14.1 million email offers over three years…… Continue reading Will They Take This Offer? A Machine Learning Price Elasticity Model for Predicting Upselling Acceptance of Premium Airline Seating

SiloSolver: Algorithm for Aggregating Siloed Customer Segments in Facebook Ads Campaigns

Data silo problem refers to related datasets located in different databases, systems, or files. In the case of online advertising, performance metrics are fragmented in different campaigns and ad sets, making it difficult to compare customer segments. In this research, we present SiloSolver, an algorithm that: (a) retrieves performance metrics for different customer segments across…… Continue reading SiloSolver: Algorithm for Aggregating Siloed Customer Segments in Facebook Ads Campaigns

Next Likely Behavior: Predicting Individual Actions from Aggregate User Behaviors

We report results using n-grams to model user actions with only aggregated data and knowing little about the user. Employing a data set of 33,860 flight bookings from 4,221 passengers, we evaluate the n-gram model for the precision of predicting next likely actions. Results show that our approach can achieve a precision of 21% overall…… Continue reading Next Likely Behavior: Predicting Individual Actions from Aggregate User Behaviors

A Balanced View for Customer Segmentation in CRM

Introduction and the Research Problem Jongwook Yoon, Seok H. Hwang, Dan J. Kim, and Jongsoo Yoon proposed a study titled “A Balanced View for Customer Segmentation in CRM” published in the Ninth Americas Conference on Information Systems in 2003. The authors emphasize that customer segmentation is essential for enterprise customer relationship management (CRM) implementation. However,…… Continue reading A Balanced View for Customer Segmentation in CRM