Integration of Product and Personal Motivation Yields KPI Lifts

Concept integration Target People Who Have the Motivations Your Ad Promises Your Product Will Deliver

By Bill Harvey and Bill McKenna, RMT,
Kasper Skou, Semasio


Ads convey promises on two levels: conscious product-specific motivations, and subconscious personal motivations1. A case study is presented in which combining both types of motivations yields the best results.


Ernst Dichter introduced the term “motivational research” to the marketing industry along with his own method of applying the concept2. This concept flowered into a widely used procedure of using qualitative research to detect the benefits sought by consumers in a specific product category, and then using quantitative research to measure the size of each “benefit segment” in a product category. Different ads could then be created for different benefit segments, or as many benefits as possible could be combined into the same ad. For example, a brand of toilet paper found that combining “softness” and “strength” in the same ads raised sales compared to earlier when it had separated the two benefits into separate ads3.

During the latter part of the 20th Century, as this benefit segmentation methodology spread across the industry, MRI and Simmons questionnaires for example expanded to include many hundreds of benefit statements specific to individual product categories. However, something had been lost relative to Dichter’s original idea. Dichter was aware that some of the most powerful motivations do not relate to any specific product category, they are not motivations of a focused shopper, they are what motivates individuals in life itself, such as finding love, finding oneself, finding a spiritual compass, achieving something worthwhile, and so on. These can be called personal motivations to distinguish them from product motivations.

Recently RMT has resurrected the notion of including personal motivations in marketing research4 To tap into the subconscious the method does not use questionnaires but uses passive observation of behavior, specifically content consumption on television and digital media. A household or person (anonymized in compliance with all privacy best practices and laws) is characterized based on the characteristics assigned to the content by the RMT coding methodology. RMT has been third-party validated by Nielsen NC Solutions, 605, and Simmons in three independent studies, suggesting that the RMT coding system is measuring something real5 6 7.

Dr. Gerald Zaltman has reported that 95% of decision making is subconscious, therefore questionnaire-based methods are leaving out 95% of what really counts8.


This awareness of two types of motivations raises the question of which ones to use in advertising development and media selection. This paper examines a case study designed to begin answering this question.


The product selected for convenience is a new science fiction novel. RMT applied its 265 content codes called DriverTags to the book9.

When DriverTags are rolled up into their highest clustering in the RMT system, they enable the ranking of 15 personal motivations by the degree to which each one applies to any content (programs, ads, books, etc.). This showed that the four most important motivations conveyed by the book are:


Ads were then created to convey these motivations. These ads were put through the RMT process, which confirmed that these same four personal motivations came through loud and clear from each ad.

RMT works with partners such as Semasio, who tracks the digital content consumption of billions of people worldwide in a privacy-compliant manner; that is, the identity of each ID is not known, but these IDs are persistent over time and can through connected activation platforms be sent ads based on their individual content consumption profiles. Working with RMT, 276 million Americans have been profiled based on the 15 RMT Motivations, so that it is possible to send ads to people motivated, for example, mostly by self-transcendence (altruism, nobility, spirituality) or by whatever mix of motivations is relevant to a specific product and ad.

The book’s author provided 30 keywords representing salient qualities of the novel. Semasio used these to analyze the content consumption of 276 million Americans and found 5 clusters of people whose interests can be summarized by these words:


A sixth cluster related to the book itself and was a combination of the other five clusters. These clusters, based on the book’s qualities, were called product motivations as distinct from the four key personal motivations reported above. It then became possible to target people based on the product motivations alone, by their personal motivations alone, and by both combined.

The relevant Personal and Product Motivations for substantially all of the US internet population were sent from Semasio to the one digital programmatic system that is fully integrated with RMT, giving the combination the name Neuroprogrammatic. The targets were of the following three types:

Three advertising strategies were set up based on these Motivations:


The same level of advertising, the same ads, through the same contexts, were used across the three groups.


A benchmark was established of a 0.10% clickthrough rate for a book10.

The data showed that each of the two types of motivation when used for targeting more than doubled this benchmark. The combination of the two types of motivations more than quintupled the benchmark:
Sophisticated practitioners will at this point be asking themselves the question, “Why did they use click-through as their KPI?” Click-throughs started out as the darling of advertisers in the early days of online marketing but soon lost their luster as result of discoveries that, for offline sales of packaged goods brands, there is no correlation between click-throughs and sales lifts.

However, this is not true in selling books through digital media. The best practice in advertising and selling books digitally is to drive traffic to the book’s Amazon Kindle page where there are 3000 words of selling text, much more than can ever be packed into a digital ad: the Kindle page is the real ad. Then, if successful, a free sample of the beginning of the book is offered; hopefully the ad and landing page have done their job, and the user clicks to download the beginning of the book. Some time later, having become caught up in the book, they find they can’t get to the next page and a text box offers them to be able to buy with one click and so to be able to continue reading the rest of the book immediately.

In this sort of regime, the clickthrough is a necessary but not sufficient gate through which to pass.

The reason for reporting the click-through results alone is that the ad campaign ran a week before the ARF presentation was recorded, leaving only a few days for those who downloaded the free sample to get to the point where they hit the pay wall. Our plan is to update this paper when the complete results are available, including the analysis of conversions, individual ads, frequency, etc.

Conclusions & Recommendations

This first case study supports the hypothesis that using both types of motivations together, aimed at engaging with both the conscious and the subconscious mind, is a practice from which all marketers could benefit. We recommend that marketers test the method for themselves – your current targets with and without adding non-questionnaire product and personal motivations. We anticipate that the integrated sum of good targeting variables will always win, especially when your individual ad is considered in the equation. Media plans have been drawn up without regard for the specific creative for too long.

Don’t leave out what is truly human, our deepest motivations, and don’t ignore your own creative.

Don’t throw out questionnaire data, but don’t stop there when there is also passive data available. In the post-COVID era it’s time to make the big changes that have been stalling for years. The current economic challenges justify taking bold actions now.

And who knows how long we will have the passive data to learn from?


1 Freud, Sigmund, The Unconscious, 1915.
2 Dichter, Ernst, The Strategy of Desire, 1960.
3 Personal experience of Bill Harvey while analyzing the few exception cases in the following study:
4 connection-the-harvey-hierarchy-of-needs/
5 Harvey, Bill and Shimmel, Howard, “Quantifying the ROI Impact of DriverTag Context Resonance”, Proceedings of the Advertising Research Foundation, 2018.
6 Harvey, Bill and Karam, Fadi, “Accelerating Brand Growth Using Psychological Resonance”, Proceedings of the Advertising Research Foundation, 2019.
7 Pellegrini, Pat and Hutton, Graeme, “Empowering ROI by Connecting Psychographics & Programmatic”, Proceedings of the Advertising Research Foundation, 2017.
8 Zaltman, Gerald, How Customers Think, 2013.
9 Harvey, Bill, “The Next Frontier: Content Analytics”, Proceedings of the Advertising Research Foundation, 2015.
10 McMullen, Chris, Book Marketing by the Numbers, 2017.