Zainab Jamal

Evaluating Promotional Activities in an Online Two-Sided Market of User-Generated Content

We measure the value of promotional activities and referrals by content creators to an online platform of user-generated content. To do so, we develop a modeling approach that explains individual-level choices of visiting the platform, creating, and purchasing content as a function of consumer characteristics and marketing activities, allowing for the possibility of interdependence of decisions within and across users. Empirically, we apply our model to Hewlett-Packards (HP) print-on-demand service of user-created magazines, named MagCloud. We use two distinct data sets to show the applicability of our approach: an aggregate-level data set from Google Analytics, which is a widely available source of data to managers, and an individual-level data set from HP. Our results compare content creator activities, which include referrals and word-of-mouth efforts, with firm-based actions, such as price promotions and public relations. We show that price promotions have strong effects but are limited to the purchase decisions, whereas content creator referrals and public relations efforts have broader effects that impact all consumer decisions at the platform. We provide recommendations as to the level of a firms investments when “free” promotional activities by content creators exist. These free marketing campaigns are likely to have a substantial presence in most online services of user-generated content.

Online Shopping and Social Media: Friends or Foes?

As social network use continues to increase, an important question for marketers is whether consumers’ online shopping activities are related to their use of social networks and, if so, what the nature of this relationship is. On the one hand, spending time on social networks could facilitate social discovery, meaning that consumers “discover” or “stumble upon” products through their connections with others. Moreover, cumulative social network use could expose consumers to new shopping-related information, possibly with greater marginal value than the incremental time spent on a shopping website. This process may therefore be associated with increased shopping activity. On the other hand, social network use could be a substitute for other online activities, including shopping. To test the relationship between social network use and online shopping, the authors leverage a unique consumer panel data set that tracks peoples browsing of shopping and social network websites and their online purchasing activities over one year. The authors find that greater cumulative usage of social networking sites is positively associated with shopping activity. However, they also find a short-term negative relationship, such that immediately after a period of increased usage of social networking sites, online shopping activity appears to be lower.

What Are Your Customers Still Doing? A Bivariate Generalization of the Latent Attrition Framework

Latent attrition models serve as the foundation for customer base analyses in transactional settings. Despite their intuitive appeal, limited research has been conducted to generalize these “buy till you die” models to multivariate contexts to examine the nature of attrition when a firm offers multiple types of transactional activities. Attrition for the different activities may be independent, simultaneous, or separate but related events. To investigate this issue, we develop a coupled hidden Markov model in which we use a Gaussian copula distribution to correlate customers’ latent lifetimes, as well as correlate the inter-activity durations. The proposed dynamic model extends the univariate “buy till you die” framework to a bivariate context and nests models that allow for latent attrition in different ways.Using the data provided by a website that allows users to purchase digital content and/or use a free ancillary service that promotes customer engagement, we find positive correlations between the two latent lifetimes, as well as between the times at which the two activities occur, suggesting that the firm can benefit from encouraging use of its ancillary service in a timely manner. We further demonstrate how our bivariate model offers a more complete picture of customers’ dynamic behavior than is afforded by examining multiple transactional activities in isolation.