Vol.:(0123456789) Review of Managerial Science https://doi.org/10.1007/s11846-018-0316-x 1 3 ORIGINAL PAPER Predicting customer quality in e‑commerce social networks: a machine learning approach María Teresa Ballestar 1  · Pilar Grau‑Carles 2  · Jorge Sainz 2,3 Received: 1 March 2018 / Accepted: 27 November 2018 © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract The digital transformation of companies is having a major impact on all business areas, especially marketing, where audiences are most volatile and loyalty is at its scarcest. Many large retail brands try to keep their client base interested by becom- ing partners in cashback websites. These websites are based on a specifc type of afliate marketing whereby customers access a wide range of merchants and obtain fnancial rewards based on their activities. Besides using this mix of traditional mar- keting strategies, cashback websites attract new target customers and increase exist- ing customers’ loyalty through recommendations, using a word-of-mouth market- ing strategy built on economic incentives for users who refer others to these sites. The literature shows that this strategy is one of the major areas of success of this business model because customers who join following recommendation are more active and are therefore more proftable and loyal to the brand. Nevertheless, the new users who are referred to these sites vary considerably in terms of the number of transactions they make on the site. This study advances research on the design of recommendation-based digital marketing strategies by providing companies with a predictive model. This model uses data science, including machine learning meth- ods and big data, to personalize fnancial incentives for users based on the quality of the new customers they refer to the cashback website. Companies can thus optimize and maximize the return on their marketing investment. Keywords Cashback · Social network · E-commerce · Machine learning · Artifcial neural network · Predictive model Mathematics Subject Classifcation 62 · 68 · 91 * María Teresa Ballestar mariateresa.ballestar@esic.edu Extended author information available on the last page of the article