NxNE presented a week of virtual sessions that was originally planned to be presented at SXSW 2020. Experts from brands, creative agencies and academia shared insights to help influence behavior, build brands and navigate the new normal.
Habits and How They Get Formed – June 15, 2020
Finding and understanding the autopilot control in viewers’ behaviors interests the two researchers in the first session of the ARF’s North x Northeast Virtual Event. The hour-long “Habits and How They Get Formed” featured Marsha Lindsay, CEO of Lindsay Foresight & Stratagem, and Julie DeTraglia, VP and Head of Research & Insights at Hulu, in two complementary presentations delving into habit-forming data, ways that brands can better leverage it and how COVID-19 has effectively disrupted viewing routines. Where streaming has become a verified behavior in this time of disruption, both researchers agree that change will not necessarily guarantee previous habits will adjust long-term. Doubling down on current strategies that train consumers’ behaviors is where advertisers and marketers will find the most success.
Marsha Lindsay, CEO, Lindsay Foresight & Stratagem: With the thousands of subconscious decisions that people make every day, the physical and mental shortcuts that humans take to optimize time become the habits that occur in every context. “System 1” thinking translates as the auto pilot behavior that comes from habits—and is the sweet spot that advertisers and marketers seek. There are six best practices that can maximize return:
- Implement two objectives: get current customers on autopilot; become the “habit” of more people.
- Identify habits and auto-pilots with potential to generate greater returns
- Design and strategize with habits in mind
- Cast marketing plan as training strategy
- Identify and leverage aspirational points: “who we are, who we want to be”
- Apply all best practices to yourself and your teams.
Lindsay concluded with the recommendation that advertisers and marketers must consider themselves “behavioral strategists” and excel at creating habits as they develop their plans.
Julie DeTraglia, VP and Head of Research & Insights, Hulu: TV is part of the habit loop which consists of the cue, which triggers habit, the routine, driven by repeated physical and mental actions, and the reward, which merits returning to an action if it meets or exceeds expectation. Hulu’s key tenets of the habit loop form around the connection, the social cues, the emotional escape, and the allotted time that viewers attribute to the network. Although Covid-19’s complete upending of viewer habits benefited Hulu (53% of Hulu subscribers say they are more likely these days to watch TV as an escape vs. before stay-at-home orders), there is evidence that habits are very hard to change, regardless of the event. Hulu expects the near future to continue to be impacted in the following ways:
- As the stay-at-home restrictions ease and summer outside activities resume somewhat, viewing numbers will decrease.
- Live sports return will come back BIG with all of the different leagues overlapping.
- Production delays will impact Hulu Originals
- Observing how cord-cutting either continues to accelerate or not
- Streaming TV will continue to grow more than linear TV: 71% of Hulu subscribers are watching more video content, and 64% are actively seeking binge-able shows.
Hulu will continue to double down on giving its subscribers great content and a “great experience that cements in their brain.”
Scott McDonald, ARF CEO, led the Q&A discussion with the following comments:
- Marsha Lindsay urged several kinds of research to figure out habituation of behavior: A/B testing, self-reporting, surveys, etc.
- Julie DeTraglia admitted that although loyalty was largely based on a specific program or series, Hulu has a strong and distinct brand that benefits from its original product as much as the habit loop that keeps them coming back. Knowing their audience and feeding their habits will continue to be their strategy.
- Where streaming TV has become System 1 or an auto-pilot choice, the next step (System 2) will not be easy; Hulu released 532 new shows in 2019 and viewers shared that it typically takes 20 minutes to decide what to watch. Too many choices are the enemy of habit.
Why Emotions in Advertising Matter – Tuesday, June 16, 2020
Balancing the capabilities of targeted data with emotional connections in brand building and advertising may not be as contradictory as alleged. As part of the ARF’s North x Northeast Virtual Event, “Why Emotions in Advertising Matter”, Abby Hollister, President of Ameritest, introduced a session with Orlando Wood, Chief Innovation Officer at System1 Research, and Chuck Hemann, Head of Digital Analytics at W2O, who presented their respective findings on neuroscience and data’s influence on advertising and how branding might work post-Covid-19. The many ways the human brain responds to commercial styles and the abundant data collected that comprise human behavior can both be mined to effectively connect with the right consumer, at the right time, and the right place.
Orlando Wood, Chief Innovation Officer, System1 Research: In utilizing neuroscience to understand the decline in advertising effectiveness, commercial creative is tested against the reactions of the left and right sides of the brain. Where the left brain is goal-oriented and prefers the familiar, the right brain has a broad perspective and is open to novelty; these individual characteristics correlate with how ad creative has become flat and abstract as opposed to an in-depth connection to the world. Over the past 30 years, advertising has shifted from right brain (the more emotional side) to left brain (the more literal side) orientation. This left-brain drift reflects that the most effective features for advertising—the right brain attributes—are disappearing.
However, the coronavirus pandemic changed how people were looking at the world and effectually initiated a right brain reset. Ads not connecting with the public were left brain in nature—direct hard sells, aggressive, superficial—and right brain ads performed better with a more escapist approach.
Moving forward in the post-Covid world, Wood recommends maintaining distinctiveness in all advertising and to be aware that brand strength pre-Covid will not carry through in post-Covid advertising without some right brain thinking.
Chuck Hemann, Head of Digital Analytics at W2O: Leveraging data often has creatives believing that researchers are telling them what to do, but Hemann believes the researcher’s role is consultative rather than prescriptive. Distilling data into actionable insights provides the right audience, the right content, the right language, the right channels, and the right time to target.
Understanding audience is possible with the representational data already collected; social data for instance provides unique macro audience info just for being on the platform. Though seemingly antithetical, the more specific information generates better click-throughs. Push beyond segmentation.
Understanding channels, whether it be social, tv, etc., requires going beyond broad platforms. Untapped data sources lie in search media. Hemann cited that 80% of patients use search before booking a doctor’s appointment, supporting search’s standing as the most relevant go-to for consumers. Paying attention to which results are text-based vs. video provides insights that help define key words and context. Because search plays a central role in the customer journey, it provides a mechanism to help inform PR, identify trusted authorities, monitors the negative and positive topical segments. Data drives channel strategy and understands audiences at a deep level.
Abby Hollister, President, Ameritest, led the Q&A discussion:
- Orlando Wood confirms that if brands can invest in brand building from a media spend perspective in post Covid environment, they should come out stronger as SOV is greater with fewer brands engaging. Right-brain advertising will connect the best, across all categories.
- Chuck Hemann encourages brands to be actively engaged during post Covid as consumers expect more from them. There is a distinct advantage to being first to market, especially with supportive messaging.
- Both researchers agree that it is critical to show support and actively do more during Black Lives Matter and the current racial unrest. Hemann stresses that people are looking at brands closely; brands need to solidify their relevance to their audience, like Nike. Right brain thinking prioritizes care and fairness and is needed more than ever in this atmosphere, according to Wood.
- Emotions and data do not have to conflict: data inform emotions and can be a creative solution as long as there is no overreliance on its capabilities. Too much data can lead to risk aversion, but with the right amount and right kind of data, it can help make the right decisions.
Painting by Numbers – Wednesday, June 17, 2020
Can AI generate good advertising? Can machine learning supplant human creativity? What are the best practices for harnessing the power of sophisticated computer algorithms in the creative process? These questions formed the core of the discussion at this session featuring professionals from Kantar and Google.
Kerry Benson (Kantar) and Michael Joffe (Google) agreed on most points:
- Advanced algorithms can help identify themes, patterns and differences in data that can assist humans in developing ad creative, but they cannot autonomously come up with great creative ideas.
- The algorithms provide a wider view and facilitate quicker iterative feedback loops that enable human teams to make better decisions.
- AI and ML mostly serve as tools for humans rather than as machine substitutions for human creative inspiration – much as art directors have come to use Photoshop as a tool.
- It is still early days in the application of AI and ML to analysis of ad creative. Many of the most advanced applications draw on the established use of algorithms in programmatic buying – matching ad creative elements (messages, offers, images, language) to targeted market segments.
- Uses of AI and ML still need to be driven by human-stated hypotheses and problem formulations. This usually is managed by supervised learning algorithms, but even unsupervised learning is subject to the disciplines of hypothesis testing.
- There is a high risk of human biases seeping into algorithmic solutions. Biases enter from the data when historical creative treatments are taken as inevitable. Biases also enter when humans make unconscious or conscious choices about how to frame the problems or enable the analytics.
- Norms are still elusive in that they require very large numbers of cases with comparable measures of dependent and independent variables.
- Clients continue to be interested in AI-based methods of developing, testing and iterating ad creative. Clients pursuing data-driven creative span the spectrum of categories. They tend to be large companies, but also include some smaller companies – especially in DTC – that seek competitive advantage from data-driven approaches to ad creative development.
- Though AIPAC is farther ahead in ways of integrating ecommerce with video, it is wrong to claim that any part of the globe has a huge lead in the application of AI to advertising. And most creative trends are still originating in North America and Europe.
Approaches for Impulse and Considered Purchases, Thursday, June 18, 2020
The consumer’s quest for meaning in the massive amounts of media they consume and the advertiser’s pursuit of their overloaded attention were examined in the ARF’s North x Northeast Virtual Event, “Approaches for Impulse and Considered Purchases,” by Karen Nelson-Field, Founder and CEO of Amplified Intelligence; and Emmanuel Probst, SVP of Brand Health Tracking at Ipsos. With a focus on richer meaning for consumers and clearer identity of the brand, it is the happy familiar and the unpredictability of emotional elements that appeal most to the eyes, ears, and hearts of viewers.
Emmanuel Probst– SVP, Brand Health Tracking, Ipso: In this chaotic world where we consume 11 hours of media a day, check our phones every 12 minutes and post over 49,000 pictures on Instagram, most consumers do not care about brands—they are looking for meaning, which carries deeper impact than fads or trends. Where advertisers have relied on the limitations of demographics, the rise of psychographics has proven just as important to show the differences in the kinds of meaning that people seek—be it personal, social, or cultural.
Three essential meanings for consumers are happiness, nostalgia and the sacred & secular. Finding joy and comfort, remembering simpler times, and shifting one’s belief from organized religions to secular “higher powers”, like searching on Google or visiting the “cathedrals” of Apple Stores, interests consumers more profoundly. Including these meanings in advertising will deepen the relationship with the brand.
Karen Nelson-Field – Founder and CEO, Amplified Intelligence: During the attention economy’s rise in the last three years, attention has had to differentiate itself from engagement in the various metrics and methodologies used. In the findings that matter most to marketers, however, it is clear that unpredictability and visibility are critical factors for catching viewers eyes at the crucial moments of a brand’s ad.
Keep in mind that attention is not the focused version marketers imagine—there are too many distractions. Viewers switch in and out focus, sometimes five times during one ad. Unexpected stimuli or guidance triggers can jumpstart attention, if only momentarily. Emotion, sound, and creative unexpectedness prime the brain to take in new information. Seventy percent volume is a sweet spot on social platforms.
Remember ad visibility is vital. Because ad pixels, screen coverage, time playing (and sound factors) differ significantly, attention varies just as much.
Quality branding is the most underrated variable for success. Do not be shy about including the brand—its mere presence increases attention and prevents the audience from filling in the blank space.
There is value in low attention. Low attention processing punches above its weight in terms of impact. When a viewer moves from a pre-attentive state to low or passive attention, the greatest uplift in sales impact occurs. Distinctive brand assets become more important.
Nelson-Field concluded with a reminder that not all reach is equal but making brands in ads easy to identify will aid in clearing the clutter around it.
Jane Clarke, CEO and Managing Director at CIMM, led the Q&A discussion:
- Emmanuel Probst endorsed brands building new relationships with consumers during the pandemic since meanings have even more relevance now than they did pre-Covid. Keep learning and foster a deeper connection to what matters to your audience. What was superfluous before may be essential now.
- Karen Nelson-Field advised that attention cannot be a currency of measurement on its own; it is a validation. Use the data as a supplementary layer to the human interaction with the individual platforms
- Responding to the importance of purpose in today’s heightened environment, both researchers spoke to the challenges for brands. Nelson-Field agreed that brands will need less attention to punch through and Probst warned that brands will have to live up to and demonstrate their pre-Covid purpose or risk backlash.
- Final takeaways included the acknowledgement of human complexity and the need to be aware of the meanings audience seek, as well as over-engineered results that do not represent realistic attention.
Understanding Your Customers: Big Data vs. Small Data – June 19, 2020
Both panelists run highly quantitative “Big Data” operations at their companies – Kendra Clarke at agency Sparks & Honey, Robie Opie at Oracle Data Cloud. Both provide examples of widely divergent use cases for analytics rooted in Big Data. Yet both argued that relying solely on Big Data will ultimately handicap companies since Big Data tends to tell you “who”, “what” and sometimes “where”, but rarely can tell you “why”.
Robin Opie began by sketching out the history of Big Data starting with analytic techniques from the 1980s developed in direct marketing and direct mail catalogs, moving to examples from 1995 the then-new online bookstore, Amazon.com. Its introduction of collaborative filtering improved book shopping. Google’s introduction in the late 1990s of natural language processing algorithms allowed a quantum improvement in internet search. Since advertising targeting was an early and successful application of Big Data, Opie gave a detailed account of how targeting is done – starting with a “seed” definition of a buyer group, finding others who would buy the same stuff by analyzing URL visitations of the seed group, and building propensity models based on that. The resulting “Performance Frontier” assesses the incremental improvement in targeting resulting from the application of equations based on this sequence of analytical steps.
However ODC believes that marketers also need to understand motivations. While Big Data can be great at generating hypotheses about motives and emotions, analysts need “Small Data” techniques to probe these questions. ODC often uses panels available through companies like Nielsen, Kantar, T-vision and others to supplement their Big Data sources. ODC also uses surveys to assess the compositional “purity” of their market segments. Opie noted that the use of these small data tools also helps to keep Big Data targeting on the right side of privacy regulations like GDPR and CCPA since the data segments are not linked back to PII.
Kendra Clarke described a very different application of Big Data at ad firm Sparks & Honey. In her role there, she monitors broad cultural trends by parsing such data sources as news media, Twitter, Reddit, YouTube, search trends, product reviews, financial market data, academic papers, patent filings, case law and other forms of unstructured data. Big Data analytical techniques are then used to normalize and structure the data inputs, while machine learning algorithms connect them to 150 trends in culture and human behavior that Sparks & Honey is monitoring. As such, the firm uses Big Data to cultivate a living taxonomy of trends to help their clients navigate cultural shifts. However as with ODC, Sparks & Honey views the Big Data output as a springboard to deeper research that can require approaches rooted in anthropology, history, or experimental psychology. It’s Big Data-based visual intelligence program “Q” can help marketers find the right visual language to speak to cultural trends, but this doesn’t mean that marketers can avoid asking direct questions to small-data samples of consumers. Clarke described Sparks & Honey’s use of qualitative and ethnographic techniques such as in-home interviews and observations of micro-interactions – in part to fill in gaps left by big data analytics, but also to check for potential biases.
Both Clarke and Opie agreed that machine learning is not currently good at generating creative ideas on its own. Big Data can help in the creative process by providing insights as a point of departure, a starting point for creative ideation. Both big and small-data approaches can also help to test ad creative once it is created.
Habits and How They Get Formed – Monday, June 15, 2020
Why Emotions in Advertising Matter – Tuesday, June 16, 2020
Painting by Number – Wednesday, June 17, 2020
Approaches for Impulse and Considered Purchases – Thursday, June 18, 2020
Understanding Your Customers: Big Data vs. Small Data – June 19, 2020