Marketing Mix Modelling: Challenges and best practices

marketing mix model


The optimal allocation of funds across different channels of marketing is crucial for all organizations since investment decisions need to be made depending on the contribution each channel makes to the overall sales. Marketing Mix Modelling (MMM) helps quantify the contribution of various factors to sales and recommends fund allocation across multiple channels in order to achieve better ROI, efficiency and effectiveness. MMM is an analytical approach which is widely adopted across industries today to measure and optimize marketing budgets. While MMM has proved to be an effective technique to allocate funds more analytically, its implementation is key to achieve optimum results.

Key Challenges:

  • When it comes to an organization, there could be multiple campaigns running simultaneously. The data (clicks, impressions, leads, etc.) collected from multiple touchpoints needs to be understood thoroughly and should be adjusted keeping in mind any effects from overlapping campaigns
  • Validation of coefficients with borderline significance is important (i.e checking if the variable is consistently significant for different time periods). This is required to maintain stability and consistency of new data before implementation
  • Irregular market segments with thin and discrete history poses a serious challenge when it comes to modelling and prediction. Such markets are dealt with by ‘Proxy Modelling’using higher levels of data and predictions, which are levelled by their proportional representation in the portfolio
  • During implementation, the prediction and optimized allocation of funds is made for all market segments by default without considering their real-time demands. If needed, depending on the marketing plans and priorities, budgeting and allocation should be revised based on prevailing business or forecasting scenarios

Best Practices

  • For superior insights, the objectives of MMM and what it plans to achieve should be clearly set by:
    • Identifying drivers of revenue and quantifying impact
    • Optimizing spend across different marketing channels for maximum return
    • Time-series forecasting for future plan of action
  • Every touchpoint in the customer journey should be defined, tracked and measured for proper accounting of cost and revenue components by marketing levels such as geography, channel etc.
  • Revenue regressed on cost or raw variables (clicks, impressions) by channel should be accounted and available at the same granular level (either through derivation or already set up by the company). It’s important to check key variables for both statistical and business significance
  • Building an ‘S curve’ (sigmoid shape curve) to plot the growth rate of revenue as a function of cost will help determine the ‘Spend Limits.’ Appropriate ‘S Curve’ (logistic, gompertz, etc.) should be chosen based on the underlying distribution of the data to get accurate relationship between cost and revenue. ‘Optimal Point’ should be discovered where revenue growth rate is maximized for a given cost
  • Test and control markets should be compared and then the feedback can be used to refine the model performance

Factors to keep in mind:

  • To prevent incorrect results, disproportionate values and volatile distribution of data should be checked, trimmed and transformed accordingly. Do not choose incorrect transformation for data in order just to ensure the linearity and stability of the variables.
  • Before modelling, missing data should be dealt with, else it could lead to inefficient results
  • To avoid wrong attribution to marketing promotions, time-series data should be converted into a cross-sectional form before building the models by accounting and adjusting for seasonality and auto correlations in the data. If needed, models should be built on de-seasonalized and stationary data
  • Data must be aggregated and summarized at requisite time intervals to correct data imbalance, if any
  • Spend limits are acceptable up to the saturation point in an ‘S curve.’ Promotional costs should be planned in a range between the discovered minimum and saturation points to avoid losses. Similarly, a minimum spend threshold should be maintained for stable markets

Since the stakes are high for brand building, following the best practices while implementing the model and taking care of the challenges that come along the way can provide high ROI and improve marketing decisions extensively. An MMM model can provide a consistent and more accurate set of metrics, which will help marketers influence the overall consumer journey.

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