The next frontier for AI is to be able to program AIs to be sensitive to human feelings and emotions. Motivations are one layer further in: feelings and emotions are what get triggered when a stimulus touches a motivation. This is why it’s important for brands to fully understand and activate against the things that motivate their users.
Brands for the most part are already doing a phenomenal job at it. Using modestly budgeted custom or DIY surveys and other traditional research tools, and tapping into high quality massive surveys through companies like MRI/Simmons in the USA and Vividata in Canada, then cooking those ingredients in the intuition of the CMO and other brand runners and their agencies, dishing up ads messaged by segments, with media selected against a combination of data sources.
This is a century ahead of where the industry was 30 years ago, when MRI/Simmons and Vividata (then called PMB) were each in their own country “the Nielsen for magazines”. The same services emerged from that narrower use case and now provide survey-based guidance into the psyches of brand users.
RMT is an add-on to that brand use case, as well as being an add-on to the entire field of AI, to help AIs communicate with users in a friendlier, more human way. RMT has trained the Semasio AI to be able to read Motivations from content consumption signals. Gerald Zaltman established that 95% of consumer decision making is subconscious, meaning untappable by asking questions of the conscious mind. RMT found a way: passively and with complete privacy protection, measure the memes within the content people consume, for this will tell you the memes that motivate this person.
In the media field, what RMT adds is a proven quantitative set of methods which translate a person’s content consumption patterns into a rich nuanced array of 15 Motivations and their granular subsets. Proven by independent studies that show sales and branding effects both increase when RMT method is applied to survey data and/or to big data (to IDs and to media contexts). Now for a given respondent or ID or media context, you have 366 additional datapoints offering deep understanding of that human being or media vehicle.
RMT describes the individual better than most of the benefit segmentation data collected in the large and small surveys. RMT of course does not attempt to duplicate the ~600 attitude questions in the largest surveys. Those questions are mostly product specific, things you care about in a car or a toothpaste. RMT is about the person without the lens of product category. These RMT data then bring in a lot of new information and signals, incremental to the fine work being done by surveys.
Adding both together as Vividata is now pioneering in Canada, is of course the ideal way of using RMT data with survey data.
The third piece of the equation are your ads and brand content, which is also coded by the RMT method, so that brands can solve the eternal three questions:
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- Whom should I target?
- What should I say?
- How should I reach them?
What has been proven is that sales and branding lifts will occur when (either or both):
A. The ad resonates with the individual
B. The ad resonates with the media context
We now know that motivations are important and can be measured by the RMT method, because of the size of the sales effect lifts: +36% for ad-context resonance and +95% for ad-person resonance.
The proof studies are:
On November 16, 2022, the Advertising Research Foundation (ARF) gave a webinar in conjunction with the Market Research Council in which they revealed that very large percentages of “advanced audiences” are invalid. Here are two slides as examples. A score of “0.33” means that only a third of the targets were valid. If basic demographics are that distorted, what hope is there for modeled targets built on them?
