Case Studies

Media Mix Optimization


Background & Challenges

  • Quantify Media Drivers of Short Term Sales Performance
  • Compute Marginal return on investment figures for each Media Channel
  • Optimise Spend on each Media Driver


Our Approach

  • Unobserved Component Modelling: Decompose Total Sales to generate time varying stochastic base
  • Panel Data Modelling : for Incremental Sales for each Media Channels Salest = β0Baset+ β1 x1t+ β2 x2t+… + εt
  • Transformations: The Marketing Channels are transformed to reflect their behaviour in marketing. This involves creating Ad stock, Power & Lag transformations.
Media Channels are modeled as Fixed Effect, Random Effect & Fixed Random Effects

PHASE II: AROI vs. MROI
We compare the RoI based on diminishing law of returns. As an implication, we need to look for the threshold beyond which, even though ARoI increases, MRoI begins to provide lower returns.

PHASE III: MEDIA CHANNEL OPTIMIZATION
  • Treated as a non-linear optimization problem
  • Provide reallocation spend recommendations, maximizing the returns
  • Constraints: individual & overall spend constraints


Deliverables

Optimum reallocation spend recommendations of each media driver, maximising the returns for the budget quarter.