Online tire seller Allopneus began A/B testing in 2013, with the aim of optimizing the user experience at a time when customer acquisition costs were rising. A/B testing has quickly delivered major improvements across a range of customer journeys.
Following success in UX optimization, Allopneus moved onto segmentation and personalization. Based on the idea that the more we know about our visitors, the more customer value can be maximized, Allopneus focused on a personalization strategy for those visitor segments that matter most for the brand.
Analysis of both visitor behavior and the buying context now enables Allopneus to offer personalized experiences and messages.
Our goals are to increase turnover but also our margins, so we plan our promotional operations to meet these two objectives. In this case, we wanted to go further than the traditional analysis of customer data by using the predictive capacities of Kameleoon’s machine learning algorithms.
After a test run, we started the experiment, keeping a close eye on the evolution of conversion rates. The results were conclusive – with clearly higher conversion rates than running manual experiments.