Marketing Mix Modeling

Updated by Ron Jacobson

Rockerbox Marketing Mix Modeling (MMM) helps brands explore the relationship between marketing investments and other factors and their impact on revenue based on historical data. This statistical method allows brands to make confident budget decisions for the best possible revenue outcome.

What is Marketing Mix Modeling?

Marketing Mix Modeling (MMM) uses a "top-down" statistical approach that uses aggregate marketing and revenue data to determine the influence of marketing on sales. Advertisers use MMM to budget across channels and forecast predicted revenue.

Rockerbox's MMM will help answer questions like:

  • What is the overall impact of each channel and tactic on sales?
  • What is the optimal budget allocation across channels to pursue a specific ROAS target?
  • At what spend should I expected to see diminishing returns?
  • What is my forecasted ROAS and revenue based on different budgets?

How does MMM differ from MTA

Marketing Mix Modeling is complementary to multi-touch attribution, as both measurement methodologies are well suited to solve different use cases.

Multi-Touch Attribution: a "bottoms-up" approach to measurement, unlocking

  • In-channel optimizations and shifts in budget across similar channels
  • Overlap of marketing channels on individual user paths
  • User level paths to conversion for granular user behavior insights

Marketing Mix Modeling: a "tops down" approach to measurement, unlocking

  • Long-term, high-level trends that impact revenue
  • Channel and tactic-level budgeting and forecasting
  • No limitations on measurement due to privacy (ex GDPR cookie blocking) or tracking methodology
    • Visibility into "hard to track" channels like out of home, radio
    • Visibility into impact on retail revenue (owned retail, 3rd party retail, Amazon)

What is included in Rockerbox's MMM?

Rockerbox's MMM product offers a robust UI to easily identify insights, create tactical plans, and view recommendations. This includes

  • A scenario planner to identify projected ROAS/Revenue based on spend levels.
    • I plan to spend X on TikTok, what's my expected revenue or ROAS?
    • I'm comfortable with X ROAS on TikTok. How much should I spend?
  • Recommended tactical adjustment per channel and tactic to create further efficiencies in your marketing mix
    • Based on the optimal spend of each channel and subchannel, where should I gradually increase or decrease spend?
  • A media planner to set budgets across channels (based on current, recommended, or custom spend) and see the expected impact to overall and per channel revenue or ROAS
    • I plan to spend a certain amount of each channel daily. What is my projected revenue or ROAS based on this spend?
  • Training data visibility + data validation views
    • How closely did the model predict actual revenue?
    • What data is an input for the existing model?

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