ARF Cognition Council Analysis Finds RMT Creative Codes Explain 48% of Sales

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The Cognition Council of the Advertising Research Foundation (ARF) has performed its own analysis of RMT content codes placed on specific ads, in relation to the IRI sales trends for the brands using these ads. Eleven RMT Motivations were found to together explain 48% of sales. The historic significance of the finding is that it opens up new vistas for the improvement of marketing mix modeling and all other forms of ROAS attribution.

Since its introduction in 1958 by Herb Krugman of Interpublic's Marplan Division (Herb called the method "Grand Scale Analysis"), MMM has always used media data as its input, not creative data. On average, MMM attributes only 7% of sales to advertising.

The groundbreaking ARF analysis, performed by ARF executive staff members Jay Mattlin and Deepak Garg, suggesting to me that combining media and creative inputs in future MMM work would tend to show advertising having a higher degree of impact on sales than has been believed for decades.

The ARF Cognition Council study used multiple regression analysis, the same statistical platform used by MMM.

This will turn into a new use case for RMT. To date, RMT has been used to increase sales and branding effects of advertising by weighting CPMs in media selection by the degree of creative code resonance between the campaign creative and each alternative environment, and programmatically targeting people whose anonymous ID content consumption reveal their psychological resonance with campaign creative. Previous third-party independent validations of RMT for these use cases have been done by Nielsen NCS, Simmons, 605 and Neustar, all showing double digit sales and brand equity improvements 23%-95%. RMT considers that the derivation of creative codes empirically explains why the RMT creative codes are so strongly predictive of behavior.

The ARF analysis used 49 ads in three product categories (deodorants, salty snacks, pasta sauces) across 19 brands, with IRI the source of sales data. RMT provided 86 Need States (rollups of the DriverTags) and 15 Motivational Types (rollups of the Need States). Six additional microclusters of DriverTags were also provided: Trust, Kindness, Helpful, Responsibility, Friend and Caring.

The analysis showed that 11 of the 15 RMT Motivations explained 48% of year-over-year changes in sales across these three categories. The most powerful RMT Motivations were found to be Wealth and Status (correlation with sales 0.51-0.52), unsurprising given that these are the doominant (hopefully not dominant) drivers in the world culture. In ads, these are communicated subconsciously based on characters and settings, giving each brand its image over a lifetime of campaigns.

Right behind these "ego" motivations came RMT Motivation Service To Humanity (correlation 0.47), which RMT also refers to as Altruism and as Self-Transcendence. RMT Need States Support, Positive/Ethical, Good Role Model were close behind (correlation 0.43-0.47) and RMT Motivation Aspiration, and Prosocial Value Helpful were also in that latter correlation range. The combination of the six Prosocial Value microclusters earned a correlation of only 0.26.

These findings were presented in the ARF Cognition Council webinar "New Lenses on Brand Identity" on August 10, 2022. The takeaways to this presentation were:

  1. Brands should use Service to Humanity in advertising. RMT provided ARF Members with exemplary television commercials from which creative teams could draw inspiration as to how to achieve this without backfiring cliches.
  2. Brands should make sure to actually provide such service to humanity or such ads would backfire.
  3. RMT should be used to increase the resonance between ads and (a) contexts (b) audiences.
  4. Empirically derived creative codes such as RMT should be used in marketing mix modeling and in other audience and outcomes measurement to ensure that the creative element is present in all equations where it is important to reduce unexplained variance.

In the end, it's not surprising that leaving out the creative element has reduced the accuracy of MMM. TRA, Apollo and Mars Catalyst found that 65% of sales is explained by creative, the rest by media/promotion. Nielsen NCS latest analyses shows a slightly lower number for creative, still over 50%. Meg Blair's RSC working with IRI sales data, then again when Comscore owned RSC, and Chuck Young's work at Ameritest (now part of Dynata) for the QSR industry -- all these pioneers have used creative as inputs to MMM. In the same timeframe when TRA and others were coming back with the 65% number, Comscore working with RSC creative parameters plus the usual media variables explained 80% of the sales. This 80% number is possibly the right target for the new pioneers who will first use MMM with creative plus media predictive variables. RMT's empirically derived creative codes will help maximize explained variance and minimize unexplained variance in sales.

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