As multi-device usage and channel complexity increase, tools like Media Mix Models help reveal influence when hard data on per-user behavior is missing or incomplete. They provide an additional layer of knowledge and understanding that can be used in concert with other forms of quantitative management. The result? Better, more efficient results even when there are data gaps and complex behavior.
What Benefits Do I Get From Media Mix Modeling?
- Mix Models help bridge the gaps in directly tracked data
- Mix Models can explain influence from traditional media on web-based activity
- Mix Models help reveal interaction effects between channels
- Mix Models are critical for taking advantage of Mobile or other cases where hard data gaps exist
What Are Media Mix Models?
Media Mix Models attempt to predict and explain the influence on sales from advertising and marketing activity when there is no user-level data to connect the dots from ads to revenue.
Media Mix Models typically use linear regression or time-series econometric modeling to explain individual channel influence on sales when those sales occur either in a different channel, or in the “unknown” bucket comprised of direct (“No Referrer”) or brand traffic. Because Media Mix Models use aggregated data (typically, impressions and sales), channel influence cannot be ascribed to individual sales. However, overall patterns revealed by Media Mix Modeling can be used to powerful effect in making decisions about Profit Driven Marketing.
Working Planet uses Mix Models as a layer above tightly data-driven Predictive Modeling and Attribution Modeling.