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Building Agent-Based Decision Support Systems for Word-of-Mouth Programs. A Freemium Application

Marketers have to 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 involve stakeholders, follow a data-driven iterative modeling approach, increase validity through automated calibration, and 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 rewarding policies.