Marketing Mix Modeling vs. Agent-Based Simulation: Which One Should You Use?

By Dejan Duzevik, December 16, 2014

Often we are asked about how agent-based simulation compares to marketing mix modeling.


How do the methodologies compare?


Although parallels between the two exist in the context of planning paid investments in marketing, the question is flawed because the two methodologies serve very different purposes.

Marketing Mix and Simulation for Planning Investments in Marketing

Marketing Mix Modeling is a statistical approach that explains variation around some baseline value in a dependent variable (often times sales) due to marketing activities. A regression is constructed between time series of sales data and marketing to attempt to correlate changes in one to another. The model’s coefficients are calibrated to minimize the error between actual and modeled sales. The model is then applied to answer questions around marketing investment level and mix.


Agent-based modeling is an approach based on simulation that recreates a market and its sales from the bottom-up. Interdependent variables within the simulation recreate the dynamics of the market. Each sale in the simulation results from the action of an individual simulated agent. The initial conditions and parameters define how consumers behave, influence one another, make decisions, and interact with media.


The parameters of the simulation are adjusted to minimize error between actual and simulated sales. The calibration processes confirms that the simulation is an accurate representation of the world. The simulation is then applied to answer a broad set of questions about marketing and product development resource allocation. The simulation is then applied to answer a broad set of questions about marketing and product development resource allocation.


A number of parallels between the two methodologies often create confusion about their similarity. Each has coefficients or parameters that are adjusted to minimize error in an attempt to better reflect reality. Each approach may be used to guide decisions on paid marketing investments.


The key distinction is that marketing mix analyzes a single brand’s activity in aggregate, while agent-based simulation recreates a brand’s marketplace based on individual consumer actions. The differences are summarized further below:

Marketing mix modeling is well-suited to answer questions related to the optimization of paid marketing investments.


Agent-based simulation provides the capability to answer a broader set of strategic questions about resource allocation. That brings us to the second section.


We have seen a number of cases where a decision maker has both a marketing mix model and an agent-based simulation in their toolkit for making decisions. In this context, simulation is used to extend marketing mix to answer more business questions.


A typical process is described below.


The marketing mix model is calibrated to specify the impact of the different marketing channels a brand uses. The coefficients of the marketing mix model are then used to guide the selection of parameters for the agent-based simulation. The simulation is tuned to replicate a marketing mix model’s response curves and marketing attribution results, while also calibrating to sales and other KPIs.


At this point the simulation is calibrated to multiple objectives and also incorporates insights on consumers from research or other data. The mix model or simulation may now be used to answer questions around marketing investment and mix. The simulation may be applied to address a range of questions.



Market Simulation can extend Marketing Mix Modeling

Because a simulation incorporates multiple KPIs, segments, attributes, competitors, and probabilistic outcomes it may extend marketing mix modeling to do the following:


Interdependent KPIs: Consider trade-offs between sales, perceptions, awareness, and word-of-mouth


Consumer segments: Develop a segment targeting strategy


Attributes driving choice: Test creative messaging


Competing alternatives: Respond to competitive actions and/or optimize a portfolio


Probabilistic outcomes: Assess risk based on a distribution of likely outcomes


So in summary, marketing mix modeling is an approach for performing optimization of paid marketing investments for one brand in a stable environment. Simulation explores a broader range of strategic options that consider the competitive market and consumer behavior.


So the question on how marketing mix compares to market simulation always comes back to what questions you need to answer as a decision-maker.