Agent-Based Modeling: A New Approach to Simulating the Marketplace
- August 30, 2012
At LRW, we are continually asking ourselves “so what?” Usually, we’re actually asking, “So, based on the results of the research, what should our client do?” How often have you asked yourself these questions when trying to increase trial, market share, or revenue of a brand, product, or portfolio of products?
- How many products should you have in your portfolio?
- How should the products in your portfolio differ in their features and how should they be priced?
- Which of the brand’s strengths should be emphasized?
Every marketer asks themselves these questions and of course there are many methods that can be employed to help answer these questions. One of the most powerful is simulation modeling. A well designed simulation modeling tool helps compare potential outcomes from various product design and marketing options.
For example, a product designer can input different possible product design options into a modeling tool and get an idea of how that specific product with those specific features will compete in the current market place. Experimenting with different product design inputs enables the product planner to identify the most competitively promising product designs.
A new tool in a marketer’s toolbox is called “Agent-based simulation modeling” – ABM for short – and it offers the potential to significantly extend the capabilities of simulation modeling. ABM has multiple advantages as compared to more typical simulation modeling approaches.
How is agent-based modeling better than current simulation modeling techniques?
ABM can deliver more realistically forward-looking decision support: Typical simulation modeling approaches provide what is basically a single “best guess” of modeled outcomes. Agent-based modeling provides more realistically forward looking planning support tools because it can predict the relative probabilities of multiple potential planning scenario outcomes.
ABM can model the “complex interactivity” in the marketplace: For example, ABM can model the effects of real-world consumer interactions, such as word-of-mouth and social media. And it can concurrently model the effects of consumers’ interactions with brands (e.g. through exposure to different brands’ advertising). Through this ability to model the “complex interactivity” that characterizes the real-world marketplace, ABM is capable of indentifying potential “emergent” outcomes that cannot be foreseen using current popular simulation modeling approaches, such as conjoint or choice modeling.
ABM can integrate disparate simulation models into a synergistic “uber-model”: The ABM approach is flexible enough to lend itself to integrating other simulation modeling perspectives, such as conjoint modeling for product optimization or messaging, that have typically only been studied in isolation. For example, you might previously have used two separate simulation models to support decisions regarding product design optimization vs. product advertising. However, ABM offers the potential to incorporate both product planning and brand communications planning into an integrated decision support tool. Such an integrated simulator would take both inputs and more realistically predict consumers’ probable brand choice decision outcomes.
In upcoming blog entries, we’ll go into further detail about how ABM works and how it can deliver next generation simulation modeling improvements.
Editor’s Note: This post is part of a 4-part series by Mick McWilliams, LRW’s SVP of Research Methods, on the topic of agent-based modeling. This is the first post in the series.