When Data Science Lacks Data – “Cold Start” Approaches for E-Commerce
November 19, 2019
Estrelsaal C1 & C2
E-commerce today as a sub-sector of the national economy is represented by almost every major retailer, and therefore requires from entrepreneurs additional efforts to overcome competition and increase consumer loyalty. The use of modeling and customer analysis – with predictive analytics at its core – has long been a major driver of innovation in this field. One of the reasons behind this success is the availability of large amounts of historic data collected by major online retailers. On the other hand, companies launching new products and services or entering a new market are often confronted by the chicken-or-egg problem: How to apply predictive analytics if historical data is lacking? This use case will therefore showcase special approaches developed by ELEKS in multiple projects on so-called “cold start models”. This class of methods uses for example historical data from related, external sources to allow the prediction and analysis customer behavior for new products right from the start. This presentation will cover modules such as customer analysis, prediction of future consumer behavior and recommender systems and will highlight possible bottlenecks and benefits. It will also focus on models for consumer segmentation, churn risk, price sensitivity, and recommender systems to predict and steer consumer behavior are today state of the art.