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 people's 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.

User profile is a summary of a consumer’s interests and preferences revealed through the consumer’s online activity. It is a fundamental component of numerous applications in digital marketing. McKinsey & Company view online user profiling as one of the promising opportunities companies should take advantage of to unlock “big data’s” potential. This paper proposes a modeling approach that uncovers individual user profiles from online surfing data and allows online businesses to make profile predictions when limited information is available. The approach is easily parallelized and scales well for processing massive records of user online activity. We demonstrate application of our approach to customer-base analysis and display advertising. Our empirical analysis uncovers easy-to-interpret behavior profiles and describes the distribution of such profiles. Furthermore, it reveals that even for information-rich online firms profile inference that is based solely on their internal data may produce biased results. We find that although search engines cover smaller portions of consumer Web visits than major advertising networks, their data is of higher quality. Thus, even with the smaller information set, search engines can effectively recover consumer behavioral profiles. We also show that temporal limitations imposed on individual-level tracking abilities are likely to have a differential impact across major online businesses, and that our approach is particularly effective for temporally limited data. Using economic simulation we demonstrate potential gains the proposed model may offer a firm if used in individual-level targeting of display ads. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2015.0956 .

We develop a modeling approach to explain demand variation for an online platform of usergenerated content, and use it to measure the impact of marketing activities on decisions to visit the platform, and on decisions to create and buy content. The model explains individual-level decisions as a function of consumer characteristics, marketing activities, and behavior of other online users, allowing for the possibility of network eects and interdependence of decisions. Empirically, we apply our model to the Hewlett-Packard's print-on-demand service of usercreated magazines, named MagCloud. We use widely available aggregate-level data from Google Analytics and summary statistics from company reports, thus making our approach generally applicable. Our results show that content price and content creator marketing actions have strong eects on the number of purchases, while advertising by the rm mainly inuences visits and creation of content. We provide recommendations to the level of marketing investments and quantify the benets of free promotional activities from content creators. According to our ndings, 8% of MagCloud's prots are directly related to these actions. This type of free marketing is likely to have a substantial presence in most online services of user-generated content and must be taken into account when allocating marketing resources.

C ustomer base analysis is a key element in customer valuation and can provide guidance for decisions such as resource allocation. Yet extant models often focus on a single activity, such as purchases from a retailer or donations to a nonprofit organization. These models do not consider other ways that an individual may engage with an organization, such as purchasing in multiple brands or contributing user-generated content. In this research, we propose a framework to generalize extant models for customer base analysis to multiple activities.Using the data from a website that allows users to purchase digital content and/or post digital content at no charge, we develop a flexible "buy 'til you die" model to empirically examine how the two activities are related. Compared with benchmarks, our model more accurately forecasts the future behavior for both types of activities. In addition to finding evidence of coincidence between the activities while customers are "alive," we find that the latent attrition processes are related. This suggests that conducting one type of activity is informative of whether customers are still alive to conduct another type of activity and, consequently, affects inferences of customer value.

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