Personal and Social Usage: The Origins of Active Customers and Ways to Keep Them Engaged

We study how digital service firms can develop an active customer base, focusing on two questions. First, how does the way that customers use the service postadoption to meet their own needs (personal usage) and to interact with one another (social usage) vary across customer acquisition methods? Second, how do firm-to-customer and customer-to-customer communications promote different types of usage? We study these questions using two data sets and by developing a multivariate hierarchical Poisson hidden Markov model (HMM), which fits the data significantly better than univariate and latent class approaches. We indeed find that postadoption behavior varies depending on customer acquisition method and dynamic states. At the total usage level, in one context (an annotation and note-taking service), customers who heard about the service through search and mass-invite exhibited significantly higher usage compared to those who joined through word of mouth (WOM), whereas in another context (a cloud-based file storage service), customers who joined through WOM referrals tended to exhibit higher usage. Yet, examining how routes of adoption relate to specific types of behavior, personal versus social usages, reveals a more nuanced picture. Furthermore, in both contexts, communications postadoption influenced engagement, albeit in different ways. Firm-to-customer communications, through company posts to Twitter and blog entries, had varying effects on customer behavior and in some cases led to lower personal and/or social usage; however, customer-to-customer communications tended to increase personal-use engagement across latent states and in both data sets. The findings suggest that firms offering digital services should pay attention to how the mode of customer acquisition is related to subsequent usage intensity, accounting for both personal and social activity, and encourage customers to interact with each other postadoption.