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Marketing Mix Modelling (MMM) has long been used by consumer brands for marketing measurement. While attribution gained popularity with the rise of digital marketing, increasing signal loss – due to privacy regulations and technology policy changes – is driving renewed interest in MMM among advertisers, especially digital natives.
Unlike traditional MMM which has limitations for advertisers seeking rapid, scalable results, today’s NextGen MMM solutions simplify the entire process of modelling and reporting, providing always-on decision support and multiple advantages.
The following case study takes a closer look at how Melia Hotels International used NextGen MMM to optimize its advertising ROI and drive optimal budget allocation across all media channels.
Signal loss due to App Tracking Transparency (ATT), recent privacy regulations and technology policy changes like cookie deprecation in the advertising ecosystem present clear challenges for advertisers, particularly digital native advertisers that rely heavily on device ID-based measurement techniques like attribution. The challenges include underreporting of conversions due to blind spots caused by privacy restrictions, bias towards lower-funnel media channels due to last touch methodology, not considering advertising carry-over, and misattribution due to excluding other sales drivers like promotions and seasonality.
All this could lead to potential revenue loss or decreased ROI for advertisers due to decision making based on incomplete results interpretation.
Given these challenges, it is imperative for digital heavy advertisers to rethink their measurement strategies and pivot to privacy-first modelled techniques that are more resilient in the face of change. This case study with Melia explores what their return was from all advertising spend and how this could be optimized by using continuous MMM daily data streams.
The Analytic Edge team created MMM models for Melia with daily aggregate level data to analyze the relationship between key business drivers and sales. Daily data was used, and models refreshed weekly with the latest 7 days data points. Care was taken to ensure that the model fit as represented by R2 and MAPE was maintained at acceptable levels after each data update.
Keeping the budget constant and simulating changes by channel of up to 50% increase or decrease of the base budget, the Simulator recommended an increased spend in smaller channels such as Criteo, TripAdvisor and Trivago, and decreased spends on Display Video Channels such as Google DV360. Increased spends were also recommended for Meta Retargeting & Prospecting. The Simulator forecasted that Melia would get a revenue lift of 21.5% and an ROI lift of 21% with these recommendations.
As a follow-up to the recommendations, Analytic Edge conducted an evaluation of Melia’s marketing strategy for Jan-May 2023 to evaluate the implementation of the recommendations and actual results. The evaluation revealed the following results
With increasing privacy restrictions and technology policy changes accelerating signal loss in the digital advertising space, digital advertisers must realign their measurement strategies to privacy-first techniques. For advertisers with good data availability, the latest MMM solutions such as Analytic Edge’s Demand Drivers™ NextGen MMM platform are now able to continuously measure effectiveness on a daily or weekly basis and recommend media mix reallocations to maximize ROI.
Simulation tools also allow advertisers to define a fixed budget and determine the best media allocations that will deliver maximum lift in revenue and ROI. Melia was able to achieve a ~36% increase in ROI by leveraging continuous MMM. Other digital heavy advertisers can benefit too by adopting these latest MMM techniques.
