Building Agent-Based Decision Support Systems for Word-of-Mouth Programs

Manuel Chica and William Rand
Article Snapshot
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Key Takeaways
What? Companies can use their own social network data to identify influential customers.
So What? Agent-based modeling provides unique insights into the role of word-of-mouth in conversions.

Now What? Agent-based modeling provides unique insights into the role of word-of-mouth in conversions.

Article Snapshot​s: Executive Summaries from the Journal of Marketing Research

We describe how to build a social analytic tool that examines a network of customers to identify the customers that are most appropriate to target with incentives to encourage conversion.


Research Question
We were approached by the firm to help answer the question how can they incentivize additional conversions?  We quickly determined that word-of-mouth seemed to have a large impact on conversions, but the firm did not know how to use that knowledge, so we hypothesized that it would be possible to create an agent-based model that used social network data to assist them in targeting users who would have a large impact on overall conversions.

Methods
We first constructed an agent-based model that attempted to predict the rate of conversions in a massive-online game, called Animal Jam. The main components of this model were a social network and a model of social influence propagation. Once we had a model that we had shown was accurate at predicting out-of-sample conversions, we then constructed a tool that enabled the examination of the effects of different marketing policies on the social network. Using this model we were able to explore the role of targeting different users with different incentives.

Findings

We created a set of guidelines that can be generally used to construct a decision support system for understanding word-of-mouth marketing in a wide variety of domains. In the Animal Jam case, we determined that targeting users who already had a large number of premium friends but who had not converted themselves was likely to have the largest effect on conversion rates, both in terms of the targeted users, but also in terms of spillovers to network peers.

Implications

These findings illustrate how we can build analytic tools to help create more data driven decisions in a wide variety of contexts. Managers should shift away from the more intuition -based design of campaigns and instead refocus on data-driven, simulation-supported decision support tools. In particular, our applicatoin has implications for most freemium apps that are interested in encouraging a larger conversion rate. 

Questions for the Classroom

  • When developing a DSS for a WOM campaign, what is the first and most important aspect of the design?
  • How do you determine if the model that you have created for a DSS is valid? 
  • What is the relationship between marketers / stakeholders and analysts / data scientists in the development of a DSS?

Article Citation: Manuel Chica and William Rand (2017) Building Agent-Based Decision Support Systems for Word-of-Mouth Programs: A Freemium Application. Journal of Marketing Research: October 2017, Vol. 54, No. 5, pp. 752-767

doi: http://dx.doi.org/10.1509/jmr.15.0443 


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Manuel Chica and William Rand
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