Automatic Device Segmentation for Conversion Optimization : A Forecasting Approach to Device Clustering Based on Multivariate Time Series Data from the Food and Beverage Industry

Abstract: This thesis investigates a forecasting approach to clustering device behavior based on multivariate time series data. Identifying an equitable selection to use in conversion optimization testing is a difficult task. As devices are able to collect larger amounts of data about their behavior it becomes increasingly difficult to utilize manual selection of segments in traditional conversion optimization systems. Forecasting the segments can be done automatically to reduce the time spent on testing while increasing the test accuracy and relevance. The thesis evaluates the results of utilizing multiple forecasting models, clustering models and data pre-processing techniques. With optimal conditions, the proposed model achieves an average accuracy of 97,7%.