As social media, mobile marketing, and word of mouth become a bigger part of the marketing mix, traditional measurement approaches struggle to keep up. Traditional mix modeling approaches can’t effectively measure new touchpoints due to insufficient historical spend/sales data. And in-market testing is tricky for social media and word-of-mouth, since you can’t effectively control for exposure to messages that spread virally. Marketers have added new metrics for emerging media, but these don’t look at the mix holistically or take touchpoint interactions into account.
Enter Agent-Based Modeling (ABM) – a technique best known for predicting disease outbreaks by simulating interactions between individuals and organizations. This approach tests different marcomm mixes using computer simulations. The “Agent” is the simulated consumer – programmed to act like the brand’s target population in terms of media consumption and purchase habits. By simulating the likely impact of various touchpoints on consumer purchase behavior (rather than relying purely on historical spend/sales data), ABM can predict the influence of emerging touchpoints. And since ABM takes all touchpoints into account, it can shed light on cross-touchpoint synergies. Further, by modeling the behavior of individual consumers, ABM can account for differences between segments.
This technique won’t (and shouldn’t) replace mix modeling or in-market testing, but can be used in conjunction to gain deeper understanding of how the mix influences consumer purchase behavior.
How Agent-Based Modeling Works
Step 1: Recreate the target population’s media consumption and purchase habits (ABM vendors, such as ThinkVine or DemandROMI have expertise here).
Step 2: Simulate the impact of proposed marcomm plans on consumers.
Step 3: Validate the model against actual sales data and refine over time.
What You Can Model:
- Different touchpoint mixes, including emerging media and word of mouth
- Different messages/creative
- Price changes
- Changes in distribution
- Competitors’ actions
- Impact on multiple segments
Benefits
- Behavioral Insights: ABM explains how the mix works (e.g., what actions each touchpoint drives, how different segments react), not just that it works.
- Holistic Understanding: ABM takes touchpoint interactions into account and also isolates the impact of both emerging and traditional media.
Drawbacks
- Cost: An annual subscription with ThinkVine will set you back $150,000, versus about $85,000 for a one-off advanced mix model.
- Culture Change: Shifting from “last click” attribution of sales to a more accurate, multi-touchpoint attribution can be threatening to direct marketers who may feel the impact of their work is diminished.
Agent-Based Modeling in Practice
ThinkVine – an agency specializing in Agent-Based Modeling – shared a few case studies with us.
- One company wanted to optimize digital media investments in the mix and planned to eliminate Print in order to boost digital media spend. ThinkVine’s agent-based model revealed that print drove a significant portion of digital traffic (after seeing print ads, consumers would search online). As such, the company decided to reduce Print spend only slightly and increase creative alignment between Print and digital media to capitalize on touchpoint synergies.
- Another company used ThinkVine to understand the impact of Hispanic-focused marketing campaigns on non-Hispanics. While traditional regressions only look at one segment at a time, agent-based models can test knock-on effects more effectively.
MLC members, to learn more about agent-based modeling please attend our 2011 meeting series on Simplifying Consumer Purchase Decisions. For a comparison of marketing measurement methods (Mix Modeling, In-Market Testing, Market Contact Audit and ROMI), please click here.
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