Building agent-based decision support systems for managing word-of-mouth programs: a freemium application

Abstract

Marketers have to constantly make decisions on how to implement word-of-mouth (WOM) programs and a well-developed decision support system (DSS) can provide them with valuable assistance. The authors propose an agent-based framework that aggregates social network-level individual interactions to guide the construction of a successful DSS for WOM. The framework presents a set of guidelines and recommendations to: (1) involve stakeholders, (2) follow a data-driven iterative modeling approach, (3) increase validity through automated calibration, and (4) understand the DSS behavior. This framework is applied to build a DSS for a freemium app, where premium users discuss the product with their social network and promote the viral adoption. After its validation, the agent-based DSS forecasts the aggregate number of premium sales over time and the most likely users to become premium in a near future. The experiments show how the DSS can help managers by forecasting premium conversions and increasing the number of premiums via targeting and reward policies.