The Power of Social Networks in Predicting Non-Contractual Customer Activity: Evidence from Venmo

Loupos, Pantelis; Nathan, Alexandros; Cerf, Moran

doi:10.2139/ssrn.3001978

Cited by 1 publication

(1 citation statement)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These are products that, when purchased by a customer in their first transaction, are an indicator of the customer being less likely to purchase again and, thus, provide less value to the firm. Our work also relates to Loupos, Nathan, and Cerf (2019), who use social network data for recently acquired customers to explain heterogeneity in their future value to the firm. To the best of our knowledge, our approach is the first to integrate several types of information collected at the moment of acquisition and to differentiate responsiveness to marketing actions-not only individual propensity to transact-on the basis of customers' first purchases.…”

Section: Previous Literaturementioning

confidence: 99%

Overcoming the Cold Start Problem of Customer Relationship Management Using a Probabilistic Machine Learning Approach

Padilla¹,

Ascarza²

2021

Journal of Marketing Research

View full text Add to dashboard Cite

The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to identify and leverage differences across customers — a very diffcult task when firms attempt to manage new customers, for whom only the first purchase has been observed. For those customers, the lack of repeated observations poses a structural challenge to inferring unobserved differences across them. This is what we call the “cold start” problem of CRM, whereby companies have difficulties leveraging existing data when they attempt to make inferences about customers at the beginning of their relationship. We propose a solution to the cold start problem by developing a probabilistic machine learning modeling framework that leverages the information collected at the moment of acquisition. The main aspect of the model is that it exibly captures latent dimensions that govern the behaviors observed at acquisition as well as future propensities to buy and to respond to marketing actions using deep exponential families. The model can be integrated with a variety of demand specifications and is exible enough to capture a wide range of heterogeneity structures. We validate our approach in a retail context and empirically demonstrate the model's ability at identifying high-value customers as well as those most sensitive to marketing actions, right after their first purchase.

show abstract

Section: Previous Literaturementioning

confidence: 99%