Motivations in the Marketing Mix

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Direct targeting of consumers based on their motivations increased message relevance

To establish relevance, Resonate enables direct targeting on values and purchase motivations in addition to traditional audience definitions like demographics and behaviors.

Advanced machine learning models, trained on hundreds of thousands of survey responses and content signatures, can be applied to target 150 million online adults.

As measured by trusted 3rd parties, targeted campaigns on motivations outperform other tactics and industry benchmarks.

WHAT HOW WHY

What’s the difference between a consumer and a customer? Motivation.

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Marketing is undergoing a fundamental change. As brands war for share of voice and share of wallet, busy consumers are looking for just one thing: Relevance. Does this product meet my needs?

What is a motivation? Think back to the last car you bought…Did you research safety ratings and fuel economy? You are motivated by safety and value. Did you seek out horsepower and surround sound? Innovation and enjoyment are important to you. Or did you scan the driveways of your friends and neighbors? Belonging and self-image drive your actions. Your income and zip code may inform how much you can afford, but your ultimate brand and model selection is a much more personal decision driven by what you value.

•  Values are attributes that are important to a person.

•  Values are motivations because they cause that person to act.

Media targeted on consumer motivations is more relevant, resulting in higher incremental response and longer engagement. In this overview of our methodology, we’ll introduce you to why values matter, how we model motivations to a population, and the performance delivered on values-targeted media.

Motivation targeting, in combination with demographics, is a more powerful predictor of purchase than demographics alone

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A New Application for Decades of Academic Research Consumer motivations and values (the “why” behind behaviors) and their application in marketing have been studied for more than 30 years. Resonate uses a customized version of the “List of Values” (LOV) methodology developed at the University of Michigan Survey Research Center in the early 1980s by researchers (Kahle, Douvan, and Kulka), building on pioneering work by Abraham Maslow among others to define systems for classification of values. LOV was validated in large-scale research in 1987 by Novak and MacEvoy (Columbia University and SRI, respectively), in which its predictive power for lifestyle activities, product purchases, and media consumption was measured.

Specifically, their study tested the predictive potential of LOV versus demographics alone, and what they found was striking: The addition of values segments improved correlations by an average of 20% across 64 tested behaviors. And the improvement for specific categories of behavior was substantially higher: 23% for home improvement, 29% for categories of TV programming, and 44% for civic involvement. Novak and MacEvoy conclude that values systems are of ”considerable practical importance for media decisions…”

Resonate Analytics 2013

Kamakura & Novak, JCR 1992

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Motivations increase the likelihood of purchase

SELF-IMAGE BELONGING

© H&M, 2013 © Urban Outfitters, 2013

Fast-forward to 2013: Resonate has validated our customized LOV methodology against self-reported and third-party purchase data and found similar results. Using our methodology, Resonate has successfully replicated 23% lifts in prediction of purchase in Apparel, 20% in home décor, and 18% in Auto while decreasing false positives associated with demographic models by 10%.

For example, the age, gender, and income distributions of H&M and Urban Outfitters customers are 98% correlated, but values like “Belonging”, “Self Image” or purchase drivers such as “Fun & Exciting” display 10x the differentiation between those customers than demographics alone. Meaning that both customer sets look exactly the same, until motivations are factored in.

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Values and motivations, for insights and targeting, can be effectively scaled from a super sample of survey and content consumption to the entire online population

Resonate takes a novel approach to solving this challenge. Inspired by techniques developed at Microsoft in 2006, Resonate built the largest primary research platform in the U.S. matching hundreds of thousands of online interviews to lengthy individual observed online content signatures. An ensemble of machine learning algorithms identify patterns of behaviors (up to 60,000 per attribute) which train our models to recognize the motivations of individuals as expressed in their content selection. This method requires no PII and thus protects the privacy of browsing behaviors while providing deep insight into the motivations of each individual.

Marrying behavioral data with massive survey sample

Until now, identifying values and purchase motivations required that consumers be surveyed directly. This may be acceptable for market research, but to use values for targeting of addressable audience and in-market insights, it’s necessary to model values segments out to Internet scale.

Motivations can be modeled with precision

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How does this work in practice? Here’s an example: About 6% of consumers are strongly motivated to buy Health and Beauty products which make them feel “Fun and Exciting”, as established by 8,000+ Resonate panelists.

Our models analyze the traffic of those panelists and compare it, daily, to traffic we observe across 150 million cookies. The result: We can detect (and target) those “Fun and Exciting” Health and Beauty buyers at 2.9x higher sensitivity. Similarly, if an electronics marketer wants to reach consumers who value “Unique” devices or “Popular” devices, we can identify those shoppers at 2.5x and 2.8x their natural rate of occurrence. The ability of our models to predict individual attributes from “super samples” has been validated by comScore, which in 2013 verified that Resonate delivered up to a 5x lift in prediction of cookie attributes.

Microsoft: 2006 Gender prediction Resonate: 2014 Motivation prediction

Media invested on values and purchase motivations returns incremental campaign performance and content engagement versus other targeting tactics

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Ad  Type Resonate  Completion

TM  Benchmark  Completion

Resonate  Improvement

Pre-­‐roll  :30 76% 65-­‐75% 9%

Pre-­‐roll  :15 90% 65-­‐75% 29%

iPre-­‐roll  :30 68% 55-­‐60% 19%

2Q2013  Campaign  Data/Results

Ad  Type Resonate  Completion

TM  Benchmark  Completion

Resonate  Improvement

Pre-­‐roll  :30 76% 65-­‐75% 9%

Pre-­‐roll  :15 90% 65-­‐75% 29%

iPre-­‐roll  :30 68% 55-­‐60% 19%

2Q2013  Campaign  Data/Results

Top Performer; 30x Control on KPI Improved complete rate by up to 29%

Leading Appliance Manufacturer TubeMogul

Building on the success of one thousand completed campaigns.

It makes intuitive sense that more relevant messages generate increased ad response, but do the numbers prove it?

One of our larger engagements, via a top holding-company media agency, is for an appliance manufacturer with annual sales exceeding $10B. They have a broad portfolio of brands and line extensions, and used Resonate to better understand the motivations of consumers who prefer each brand, and target media accordingly. With an objective of driving engaged visits, Resonate delivered a 97% incremental lift (30x that of a control group) and far exceeded the demographic, contextual, and behavioral targeting options on the plan.

Earlier this year, Resonate completed a first-in-class user-level data and targeting integration with TubeMogul.

•  For 15-second Pre-Roll, Resonate delivered video complete rates of 90%, which is 29% higher than industry average.

•  We see similar levels of performance in branding and persuasion campaigns, recently delivering a 15% (40% to 46%) and 20% (69% to 83%) lifts in behavior intent against versus 3rd-party control.

•  The ultimate indicator of Resonate’s effectiveness is the satisfaction of our clients and partners. In 2013, we received confidential campaign evaluations from 42 unique engagements, and 93% reported that Resonate equaled or exceeded the performance of competing solutions, with 88% satisfied with the performance of their campaigns, and 100% satisfied with the quality of the analysis provided.

Resonate unifies Research and Targeting

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Resonate is the only firm in the industry that targets online audiences based on consumer’s motivations. We start by filtering the audience on

demographics and behaviors. But where others stop and claim to deliver a “targeted audience,” we’re just beginning. Resonate then targets your audience based on their values, beliefs and product preferences - the

reason why they act

Display Mobile

Social Video

•  Personalize delivery and creative with motivations based targeting

•  Target your audience with effective, relevant messaging

•  IAB standard ad sizes

•  Deliver ads across mobile and tablet

•  Target your audience cross device to echo messaging

•  Retarget cross-device

•  Reach users of all social platforms

•  Native and off platform inventory

•  The only way to target personal motivations on Facebook

•  Premium Pre-roll

•  Brand Safe In-banner inventory

•  Target audiences across all video sources

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Michael Horn, VP Research Michael has been measuring digital media since 1998, and has led marketing performance strategy engagements for ten members of the Fortune 100. Prior to joining Resonate, Michael spent three years in San Francisco as SVP Managing Partner of Universal McCann’s Decision Sciences unit, directing a team of 32, including seven Partners in five North American offices. There he directed measurement and optimization across global and North American clients including Microsoft, Wells Fargo, Johnson & Johnson, Verizon, Chrysler and Charles Schwab.

Lauren Kreisberg, Research Director Lauren oversees research collection and survey methodology development for Resonate’s core survey instrument, developed the company’s ad effectiveness methodologies, and supported the creation and maintenance of much of the insights reporting. 

Aleksey Ashikhmin, Data Scientist Aleksey is a Lead Data Scientist at Resonate, working on design, optimization, and developing machine learning that reveals consumer motivations, values, and beliefs. Prior to joining Resonate, Aleksey Ashikhmin worked in Security Operations Center at Humana Inc. as Operations Analyst, concentrating on analysis of intrusion detection systems alerts.

Christopher Cox, Client Services Director Christopher has over eight years’ experience addressing such issues as brand strategy, audience measurement and social media marketing, for both brand and public affairs clients. Christopher holds a MA in Communications, Culture & Technology from Georgetown University and a BA in Political Science from the University of North Florida and resides in Arlington VA. 

ABOUT THE AUTHOR

ABOUT THE CONTRIBUTORS

About Resonate

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Resonate has pioneered a new model for using “big data” to develop a sophisticated understanding of consumer motivations, values, attitudes and beliefs. Marketers need to understand “why” the audiences they target take action. Resonate answers that question, while making it simple to put that knowledge to work creating positive results for political campaigns and marketing initiatives. For more information, visit: resonateinsights.com

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“The essential difference between emotion and reason is that emotion leads to action while reason leads to conclusions.”

Dr. Donald Caine, Neurologist BA, MA, DM Oxford University