Agent-Based Modeling: Improving Decision Support
- October 11, 2012
- A choice modeling exercise to support product design and pricing optimization
- Brand attribute ratings, for your brand and the main competitors, to support marketing communications
So, now you have two simulation tools. One, that is based on your choice modeling data that predicts “share of preference” for different product designs in competitive scenarios. The other, derived from your brand attribute data, models the influences of consumers’ brand perceptions on their brand choices. Both are very useful tools.
However, even more useful would be a single, integrated simulation tool that simultaneously modeled for both product design optimization and marketing communications. Ideally, that integrated simulation tool should realistically model the interaction between consumers’ product design preferences and their brand perceptions. As you’ve no doubt already guessed, agent-based modeling can provide this type of integrated modeling tool.
ABM’s bottom up approach and computational flexibility allows for the integration of separate simulation modeling perspectives. Continuing with the above example, a choice modeling simulator models consumers’ perceptions of product characteristics as they affect product choices. A brand communications simulator models the effects of consumers’ brand perceptions on their brand choices. However, in the real world, consumers’ preferences regarding product characteristics are intertwined with their brand perceptions and inform the purchase decision that all marketers seek to influence.
Agent-based modeling can effectively link two such separate simulation models mainly because it starts with modeling each individual agent’s behaviors and interactions and then builds up to the “emergent” aggregate level outcome. In such an integrated ABM simulator, consumers’ decision rules regarding product characteristics could be modeled interactively with their choice-affecting brand perceptions (also including the effects of perceptions-affecting processes like word of mouth and brand advertising). The result is a simulation modeling tool that can predict the probable results of product design decisions and brand communications decisions from a more realistic and holistic integrated perspective.
To review briefly, over the course of these blog posts we’ve described four particular strengths of agent-based modeling relative to other simulation modeling methods:
- ABM can deliver more actionably forward-looking decision support by more realistically predicting the relative probabilities of multiple potential planning decisions, rather than producing just one best guess
- ABM can model the complex interactivity that characterizes the modern marketplace, like word-of-mouth and social media, and consumers’ interactions with brands through marketing communications
- ABM can identify possible emergent outcomes which cannot be predicted by methods that rely solely on mathematical techniques.
- Agent-based modeling offers a unique ability to integrate separate simulation modeling techniques to create a multifaceted and holistically integrated decision support and planning tool.
Since the 1990s, agent-based modeling has been used increasingly in the life sciences, ecological sciences, and social sciences. These disciplines are leveraging ABM’s multiple strengths and advantages to significantly improve their predictive modeling capabilities. It’s time for the marketing research field to do the same.
We would love to hear your perspective on agent-based modeling. What are the business decisions you make that could be improved by using agent-based modeling?
Editor’s Note: This is the fourth and final post in a series on the topic of agent-based modeling by Mick McWilliams, LRW’s SVP of Research Methods. You can read the first post in the series which is an introduction to agent-based modeling, the second post in the series on the probabilities of different outcomes, and the third post in the series on modeling the complexity of the marketplace.