Examinando por Autor "Grau-Carles, Pilar"

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Customer segmentation in e-commerce: Applications to the cashback business model
(Elsevier, 2018-07-01) Ballestar, María Teresa; Grau-Carles, Pilar; Sainz, Jorge
This paper presents a segmentation of cashback website customers. The segmentation is based on customers' commercial activity and role within the site's social network. In this social network, customers profit from the transactions they make on affiliate websites. Mixing traditional marketing strategies with word-of-mouth recommendations is crucial for the success of this business model because these recommendations boost new customer acquisitions and strengthen the loyalty of existing customers. This study shows how the customer's role within the cashback website's social network determines the customer's behavior and commercial activity on the website. The segmentation presented describes the customer journey in terms of customer profitability and seniority. The findings explain customer behavior in e-commerce and the value of applying personalized retention strategies to each cluster rather than generic strategies or customer acquisition strategies. This paper describes how customers move between clusters, enabling practitioners to increase customer loyalty and long-term profitability.
Predicting customer quality in e-commerce social networks: a machine learning approach
(Springer, 2019-12-14) Ballestar, Maria Teresa; Grau-Carles, Pilar; Sainz, Jorge
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 becoming partners in cashback websites. These websites are based on a specific type of affiliate marketing whereby customers access a wide range of merchants and obtain financial rewards based on their activities. Besides using this mix of traditional marketing strategies, cashback websites attract new target customers and increase existing customers’ loyalty through recommendations, using a word-of-mouth marketing 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 profitable 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 methods and big data, to personalize financial 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.