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

Authored by William Rand, Manuel Chica

Date Published: 2017

DOI: 10.1509/jmr.15.0443

Sponsors: Spanish Ministry of Science and Innovation (MICINN) European Regional Development Fund (ERDF)

Platforms: Java

Model Documentation: Other Narrative

Model Code URLs: https://www.comses.net/codebases/5191/releases/1.0.0/

Abstract

Marketers must constantly decide how to implement word-of-mouth (WOM) programs, and a well-developed decision support system (DSS) can provide them valuable assistance in doing so. 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 in which premium users discuss the product with their social network and promote its 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 the near future. The experiments show how the DSS can help managers by forecasting premium conversions and increasing the number of premiums through targeting and implementing reward policies.

Tags

Social networks Agent-based modeling Communication behavior Adoption Innovation Diffusion Word of mouth influentials Strategies Marketing decision support systems Targeting and referrals Freemium business model Reward programs Modeling seasonality Product diffusion