Implementation of Mix Marketing Modelling for a global corporation
Leverage advanced Marketing Mix Modelling and identify further efficiency optimization within digital
Context & Objectives

Objective

  • Measuring the full and unbiased impact of marketing actions on sales (without relying on cookies)
  • Simulating the future sales and comparing different marketing scenarios
  • Optimizing performance marketing (media and promo), branding marketing and the balance between the two
  • Internalizing & industrializing the MMM capabilities

Outcome
  • Improvement of overall marketing ROI by +35%, thanks to:some text
       
    • Rebalancing of performance vs. branding marketing budget
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    • Optimization of both performance & branding marketing strategies
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    • Optimization of media campaigns (channel mix, content, format, media-buy strategy…)
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  • Confirmation of the success of attention-based media steering
  • Client team ramp up for further autonomous operation of MMM

Our approach

Step 1: Initial modelling

After a business & IT framing, data are collected & analyzed, feeding the first set of models, but also providing valuable learnings to later streamline and automate their collection as much as possible.

Similarly, the initial modelling provides both

Reliable measurements of the marketing performance & associated learnings (accounting for complex but important effects such as long-term impact, halo, cannibalization, saturation, evolution in dynamics, anticipation & hangover of promotion...)

Learnings for MMM in other business areas & industrialization (zones of confidence, useful data, feature engineering, extrapolation rules, deep-dive feasibility...)

Step 2: Footprint expansion

Implementing MMM on new markets, leveraging the learnings & technical assets from the initial modelling, providing similar measurement & learnings to optimize a larger proportion of the marketing investments, while assessing the possible convergence & economies of scale across the MMM community

Step 3: AI for scalability

Exploring & developing AI-powered modules lowering MMM-related effort and cost, across its lifecycle (initial modelling, updates, retraining) and all steps of its value chain (from data collection to modelling to output analysis & consumption)

Step 4: Industrialization

Building an MMM tool tailored to our client's business & IT specifics, owned and operated by our client, allowing for autonomous operations (automated and/or streamlined data collection, high-frequency models update, 24-7 results consumption, simulation & optimization features)

Step 5: Deployment

Deploying the tool across geographies & business units, embedded in a transformative program with a significant change management component, maximizing business adoption & marketing optimizations

Our experts
Ugo Philippart
Associate Partner
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