Ipsos Encyclopedia - Marketing Mix Modelling

​Marketing mix modelling (abbreviated as 'MMM') is an analytical approach that uses historical data, such as retail audit data, syndicated point-of-sale data and companies' internal data, to quantify the sales impact of various marketing activities.

Definition

Marketing mix modelling (abbreviated as 'MMM') is an analytical approach that uses historical data, such as retail audit data, syndicated point-of-sale data and companies' internal data, to quantify the sales impact of various marketing activities. Mathematically, this is done by establishing a simultaneous relation of various marketing activities with the sales, in the form of a linear or a non-linear equation, through the econometric/statistical techniques.

MMM defines the effectiveness of each of the marketing variables (both consumer demand and logistics) in terms of its contribution to sales-volume, effectiveness (volume generated by each unit of effort), efficiency (sales volume generated divided by cost) and sometimes return on investment (this depends on the client's willingness to share internal P/L information). 

These learnings are then adopted to adjust marketing activities, reactions to competitor activities and future company strategies, optimise the marketing plan and also to forecast sales while simulating various scenarios.

In addition, MMM also helps to:

  • Identify which marketing channels are working best, in case of the existence of different trade channels
  • Understand and differentiate the degree of effectiveness of marketing activities across different brands within the category concerned
  • Depending on the product category, how seasonal, weather and operational factors impact on sales

In the recent times MMM has found acceptance as a trustworthy marketing tool among the major consumer marketing companies. Often in the digital media context, MMM is referred to as attribution modelling.

Marketing mix models provide much useful information. However there are two key areas in which these models have limitations that should be taken into account when using these models for decision-making purposes. These limitations, discussed more fully below, include:

  • Focusing on short-term sales can significantly under-value the importance of longer-term equity building activities
  • Biases in favour of time-specific media (such as TV commercials) versus less time-specific media (such as ads appearing in monthly magazines).Biases can also occur when comparing broad-based media versus regionally or demographically targeted media

Ipsos Point Of View:

​Portfolio optimisation models can be incorporated into sales forecasting exercises (of new initiatives) to understand resource allocation across different brands of the company.

In addition, MMM models can also be utilised to validate the results of the sales forecasting of Simulated Test Marketing (STM) models and to adjust marketing support in the early stages .

Recent R&D work has allowed us to include digital media in STM models during the forecasting stages, enabling us to simulate the realistic impact in-market conditions.  During the calibration process, past experience in similar cases can be incorporated to better reflect market realities.

A combination of 'short term' and 'long term' strategies can be analysed in an informative manner to better optimise the total marketing mix with high probabilities.

Recommended readings:

  1. ​BRANDAID: A Marketing-Mix Model, Part 1: Structure" and "Part 2: Implementation, Calibration, and Case Study by John D.C. Little (1975, Operations Research)
  2. Sprinter Mod. III: A Model for the Analysis of New Frequently Purchased Consumer Products by G.L. Urban (1970, Operations Research vol 18.)
  3. Marketing Model by Garry L. Lilien, Philip Kotler and K.Srildhar Moorthy (1992)
  4. Market Response Models Econometric Time-Series Analysis (2nd ed.) by  Dominique M Hanssens, Leonard J Parsons and Randall L Schultz (2001)
  5. Current State of Marketing Mix Models - A Report for the Council for Research Excellence; June 2013
  6. Making data-driven marketing decisions - by Dennis Spillecke and Andris Umblijs
  7. Measuring the Return on Marketing Investment by E. Craig Stacey for The Center for Measurable Marketing at NYU Stern

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