Agent-Based Modeling: Assessing the Different Possible Outcomes of Marketing Decisions

Posted by: Robert "Mick" McWilliams
  • September 6, 2012
  • 3

Though we often don’t explicitly recognize it, the fact is that product designers and marketers try to predict the future on an ongoing basis. A product planner decides what features a product will have and thereby predicts that the product will be more attractive to consumers than competitive products in the future. A brand manager decides what aspects of his brand’s promise and image to emphasize and so predicts that the resulting “brand story” will win against competitors’ brand stories in the future.  Designing and marketing successful products is all about deciding which of various competing decision alternatives will most likely influence the future in your  favor.

The venerable Jedi master, Yoda, once wisely noted, “Always in flux, the future…” And he was right. Anyone who tells you they can confidently and accurately predict the exact future outcome of a given product design or marketing decision is lying; either to themselves, to you, or both. The key challenge is that, in a very real sense, there is no single, certain future out there. The future is more realistically thought of as a range of different outcomes. Marketing planners would do well to recognize that any specific product design or marketing decision – or set of decisions – actually has many possible outcomes, some of which are more likely to happen than others. The product planning and marketing challenge would be best approached through comparing different sets of decision alternatives by comparing the many possible outcomes.

Agent-based modeling is an improvement upon other simulation modeling techniques in that does not implicitly ignore the reality of this situation. A well designed agent-based modeling simulator does not report a single “best guess” as to the future outcome of a given set of product design and marketing decisions. Rather, it reports the probability of each outcome in a set of possible outcomes.

 

An excellent example of determining the probability of different possible outcomes is forecasting the paths of hurricanes.  The six curving dotted lines extending from the hurricane’s present location combine to create a curving “cone of possible outcomes” containing several paths which the hurricane might take, depending on how multiple semi-predictable influences actually evolve over the days to come.

The forecast illustrated above is powerfully useful, in large part, because it does not create a false illusion of certainty. The forecast is actually a combination of six models which, together, project that the greatest likelihood is that the storm will mainly impact the coast of North Carolina. However, the forecast nonetheless warns disaster planners in Florida, Georgia and South Carolina to be ready for the possibility that any one of their states could suffer a direct hit.

Most methods of simulation modeling currently used in marketing research are like a single-model hurricane forecast that produces just one curving dotted line. And, importantly, success in the marketplace will suffer to the extent that that single-outcome forecast turns out to be wrong. Agent-based modeling combines multiple simulation models to produce something like a marketing decision “cone of possible outcomes.” ABM thereby enables the marketing planner to consider the wider range of possible results from one set of decision alternatives versus another. In our next agent-based modeling post, we’ll delve more deeply into the powerful techniques through which agent-based modeling identifies a range of possible decision outcomes and gauges their relative probabilities.

– Mick

Editor’s Note: This post by Mick McWilliams, LRW’s SVP of Research Methods, is part of a series of 4-posts on the topic of agent-based modeling. You can read the first post in the series which is an introduction to agent-based modeling.

 

Categories: Brand, Modeling
3 Comments
  • LRW Blog - Agent-Based Modeling: Improving Decision Support
    September 9, 2013
    [...] 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 [...]
  • LRW Blog - Agent-Based Modeling: Modeling the Complexity of the Marketplace
    September 28, 2012
    [...] fail in a “complex adaptive system,” in this case the modern marketplace. As noted in our last agent-based modeling blog post, the job of marketers is to try to predict the differing probable outcomes of alternative product [...]
  • Matt
    September 12, 2012
    Mick, love the easy-to-understand and practical example! Thanks.

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