Research within this pillar has the general aim to investigate the role networks play for consumers and to reveal the basic mechanisms behind the formation of attitudes, preferences, values and opinions. Thereby, we focus on 3 areas: (1) the impact of social influence on consumer behavior, (2) the extent to which this influence can be quantified, and (3) the methodological tools used to disentangle peer effects.
Analyzing the impact of social influence on consumer behavior, we go beyond widely discussed scenarios like product adoption and customer churn and focus on the customer development stage. For example, our research addresses the open question whether social effects only affect customer behavior directly, or whether they additionally induce customers to ignore their private information in case of a conflict. That is, do customers follow the behavior of their peers, even when their private information suggests doing otherwise? Answering these questions reveals when it is more efficient to shape customers’ private information directly, e.g. mitigating negative experiences, to leverage social effects, e.g. increasing exposure to peer behavior, or to rely on both strategies.
Another topic that is key to the success of any firm is valuing customers. Valuing customers enables marketers to identify the most important customers. A central metric for valuing customers is customer lifetime value. Although customer lifetime value has gained in significance over the last decade, surprising the customer is still regarded as an isolated individual in most cases. This contrasts strongly with the extensive empirical evidence that has been gathered in support of the relevance of word-of-mouth advertising and the role of customer recommendations as a critical success factor for products and services. To close this research gap, we examine how social ties might impact an individual customer’s lifetime value.
Further, it is an important part of this pillar to provide the research and data science community with tools to replicate and extend our studies. To this end, we release algorithms which we wrote to conduct our analyze as open-source software. In addition, we provide extensive documentation and accompanying publications on our specific implementations to facilitate an easy adoption of these tools by interested parties.