(by Deanna Danelon)
I began working for Heartbeat Ai as an intern through Cisco’s Women Entrepreneurs’ Circle program. This program connects University of Waterloo engineering students with female entrepreneurs to provide technical support and services so that women entrepreneurs can optimize their businesses. Though I haven’t been a part of the Heartbeat team for long, these past few months have provided some incredible experiences, and I have learned so much.
The greatest experience has been the opportunity to witness what incredible things women are capable of, thanks to founder Lana Novikova. Seeing Lana’s success and hard work as a women entrepreneur, despite working a tech field, has been incredibly inspiring. Working and studying in a male dominated field, such as engineering, it is easy to feel discouraged or inferior. Being a part of the heartbeat team has shown me that women are capable of absolutely anything. This team consists majorly of inspiring women who work hard to support each other, and reach success, defying all odds. This has provided me new found confidence that being a woman is not a setback, all we have to do is support one another.
One of the most important things I’ve learned since working here has been how important empathy is. Having a technology that can understand emotions is a complete game changer. We are not human without our emotions, and having a technology that we can work with to help better understand emotions is a huge step towards greater human potential.
I am so lucky to have become a part of Lana’s team and to have been able to gain the experiences I did. I work with incredible, inspiring, and hardworking people, who are working to help make the world more empathetic.
Heartbeat new team is going to IIeX Atlanta on June 12 to showcase Empathy Analytics - a new way to build deep understanding with people - customers, consumers, shoppers, employees.
“Society will get the AI it deserves.”
- Joscha Bach, What to Think About Machines That Think
Today, AI-powered machines can defeat the most skilled players in chess, a game of Go, Jeopardy, and poker. They can outperform best doctors in diagnosing most complex health conditions. Yet when it comes to understanding a wide range of human emotions, AI is not very intuitive. In his 2016 Google talk, Professor David Gelernter said: “ Understanding emotion as an encoding and simulating function is one of the most important unsolved problems - in fact, ignored problems and missed opportunities in AI today.”
Why do we need to teach thinking machines to understand human emotions? While applications for emotionally intelligent AI are too many to list here, let me mention three areas that we, at Heartbeat AI, are most fascinated about.
Deeper understanding and better prediction of irrational human behavior
We don’t need to try hard to convince anyone today that human beings are emotional first and rational second. Decisions that we make every minute - from what flavor of Starbucks to buy to who to vote for - are made deep inside our emotional minds influenced by many conscious reasons and sub-conscious “primes.”
Last Summer, we decided to run an experiment and see if we could build a predictive algorithm for national elections using unstructured text data from surveys. Traditional election polling asks questions like “If elections were held today, who would you vote for?” or “Who do you think will become the next Prime Minister of Australia?”. We picked 3 traditional questions and added two open-ended questions asking how people feel about each candidate winning the elections. The survey ran on Google Survey Platform for 6 weeks prior to the June 2nd Australian elections and November 8th US elections. Then we used a predictive model to call the results. In both cases, emotional measurements helped improve the level of predictability, and therefore made correct predictions for Australia (predicted for Bill Shorten vs. Malcolm Turnbull: 44% vs 56%, actual: 48% vs. 52%) and US (Donald Trump vs. Hillary Clinton):
- Florida: predicted 52% versus 48% actual: 51% vs. 49%;
- Iowa: predicted 58% vs. 42%, actual 55% vs. 45%;
- Ohio: predicted - 63% vs. 37%, actual - 54% vs. 46%;
- Pennsylvania: predicted - 54% vs. 46%, actual - 51% vs. 49%
What was even more interesting is that the models showed what particular emotions drove voters’ behavior, which explained the deep “why” behind people’s decisions.
Emotion text analytics is a new, possibly groundbreaking, area of research and analytics. What if we could rely on text data to be the “secret sauce” for accurate prediction of future events that are based on human decisions, such as elections, consumer behavior, public opinion and social movements.
Apps that will help people deal with stress, change, and suffering
Eight years ago, I took a break from my career in market research and went to study psychotherapy. I had a small private practice for a couple of years, helping people deal with loss, trauma, distractive anger, and debilitative fear and anxiety. Inside my own family and friends, I witnessed hard struggles my loved ones fought with alcohol and drug addictions. At the end, I did not find the stamina to be a psychotherapist, but I never stopped wondering if good therapy could be scaled, free and available when and where people need it the most. Then two years ago, an idea of an algorithm that could recognize a wide range of emotions from language - written and spoken - came alive. That’s how Heartbeat was born.
This Spring, we are starting a pilot with a small residential drug and alcohol addiction rehab in Ontario. We will give people a chance to share how they feel in the most difficult moments of their lives, recognize and name their emotions, and track their progress. We hope to build predictive algorithms that could help prevent relapse and foster resilience.
This is just the first step in a long journey. With 6 billion people with access to mobile phones by 2020, apps can help us not only find a good restaurant or a Pokemon, but peace of mind too. In 2016, the Guardian stated that “AI could leave half of world unemployed”. Imagine the stress and anxiety this would trigger! AI-powered apps could emotionally support millions of people transition from losing a job to retraining for new careers, and building new lives.
Building the foundation for benevolent AI
“Artificial minds will be faster, more accurate, more alert, more aware and comprehensive than their human counterparts. AI will replace human decision makers, administrators, inventors, engineers, scientists, military strategists, designers, advertisers, and of course AI programmers,” writes Joscha Bach of MIT Media Labs. He continues: ” The motives of our artificial minds will (at least initially) be those of the organizations, corporations, groups and individuals that make use of their intelligence. If the business model of a company is not benevolent than AI has the potential to make that company truly dangerous. Likewise, if an organization aims at improving the human condition than AI might make that organization more efficient in realizing its benevolent potential.”
When something possesses that much power, its makers ought to carry a high level of responsibility. It’s absolutely paramount to ensure that our powerful machines have benevolent “souls”, including intentional kindness, deep understanding of human condition, and cognitive empathy. What if AI could help us solve the biggest Global challenges, help humanity create Global governance, social and corporate systems, and ultimately rebuild trust?
Heartbeat is building a platform for people to share emotions and feelings about “hot” topics, burning political issues, the future, and life in general. We want to use emotion text analytics to help people understand their own emotions, see emotions of others across the world, and with that, have a window into each other’s souls. I hope that a Global “empathy meter” will grow if instead of opinions and agendas, people share personal feelings and see how fear and anger drives us away from each other.
If we are honest with ourselves, we would admit that we get the partner and the government we deserve. At the end of the day, we will get the AI we deserve.
(This article was published in RE-WORK - see here)
Speaker: Lana Novikova, Founder & CEO - Heartbeat AI
Description: “Human emotions and feeling are part of a complex system that is not yet completely understood.” Emotional connection to brands are a big part of the consumer experience today, and yet one of the most difficult aspects to understand, measure, and improve upon. Lana Novikova, founder & CEO of Heartbeat AI, is an expert in exactly that - understanding and measuring human emotions. In this session, Lana provided tips on how to better foster emotional connection in your marketing efforts.
At IIeX EU in 2016 the winner of the Insight Innovation Competition was Heartbeat AI. Now in honor of the next round of the IIeX EU Competition next week we touch base with Lana on the Heartbeat journey, where they are now and where they are going.
At IIeX EU in 2016 the winner of the Insight Innovation Competition was a unique emotionally-based text analytics solution led by our first female CEO winner, Lana Novikova of Heartbeat.ai. Now almost a year later, and in honor of the next round of the IIeX EU Competition next week, I decided it would be interesting to touch base with Lana on the Heartbeat journey, where they are now and where they are going.
First, a bit about their SaaS platform in their own words:
True deep insights can’t be found among scores and scales. Numbers have a definite role in research, yet only words and actions can point to our true hearts’ desires. Like raw diamonds, words often need skilled work to transform into a genuine sparkle. Survey open-ended comments that contain deep insights into human emotions, needs and motivations, often get discarded. Manual data coding is time consuming and labour intensive, pricey and inconsistent from study to study. HEARTBEAT brought together market research, psychology, neuroscience, data analytics, and technology to enable researchers to find and polish these “insight diamonds” – quickly, affordably, and consistently.
You’ll notice the use of almost poetic language in their description, which is absolutely “on brand” for Lana and her team; they approached the development of their platform not as an exercise in technology advancement but as an effort to combine myriad disciplines to address a need to create scalable “understanding” tools. Market research was one example of use cases, but the needs of healthcare, public policy, education and other categories drove their thinking as much as anything else.
The result is a uniquely powerful, beautiful, and elegantly simple solution that can be used by novices and experienced pros alike, with an API based model that allows their tech to be plugged in to power or augment many different complementary platforms.
Read the full article here and watch the interview:
Most companies think about customer experience and loyalty in rational terms: How do customers rate our performance on these touchpoints? How do customers assess our work on various functional criteria? How do they evaluate the quality of the experience/product/service we provide?
All of these are important questions, as they go to the core of the functional dimension of any business: delivering products and services that meet the assured quality standards at the promised price to meet the use-needs of customers. This check-list approach is eminently rational, but is simply insufficient to delivering positive customer experiences and building customer loyalty. Sure, use-needs must be met, but the market is littered with competitors providing comparably excellent products. The reality, moreover, is that most ex-customers – churners, defectors, call them what you will – actually give their ex-companies solid performance scores. But they left anyway.
One Part Brain, Multiple Parts Heart
Strong scores on performance are a must. But what’s more important is whether the company is able to translate its performance into preference for their brand relative to competitors. This is the heart part of the equation (a very rational sounding term!) in delivering great customer experiences that translate into enduring relationships. Performance without preference is like being the nerd in the class: you get great grades and recognized for being super smart, but you eat lunch alone, don’t get chosen in the pick-up games or aren’t invited to parties.
Preference grows from an emotional connection with customers. Customers prefer your brand not just because of your performance. Rather, they prefer your brand because they feel a sense of a stronger bond with your brand than they feel for the competitors. This is very much an emotional issue, a matter of the heart.
Most companies, however, do a better job at delivering against the rational expectations of customers than capturing their hearts. As a consequence, great performance doesn’t necessarily translate into preference and comparably solid loyalty levels.
A Rational Approach: Experiment in Retail Banking
Being the rational researchers that we are, we did the natural thing: test for empirical validation that the heart has a direct effect on customer loyalty. Kantar TNS partnered with Heartbeat Ai Technologies to gauge the impact of emotions on customer loyalty to their primary retail banks in the US. We surveyed 750 customers across five of the largest commercial banks in the US: JP Morgan Chase, Bank of America, Wells Fargo, US Bank and PNC.
Our approach entailed measuring the customer’s level of loyalty to their bank using the TNS TRI*M metric based on performance and preference, as well as their NPS rating. We also asked customers to tell us in a few words how they feel about their bank. This open-ended response was run through the Heartbeat Ai algorithm to determine the feelings of the respondent. Feelings were than classified as love, joy, trust, sadness, anger, fear or “void,” essentially the absence of an emotional expression. Depending on how varied a respondent’s comments are, a single respondent might express multiple emotions, so the number of emotions classified exceeds the number of respondents.
The Love Connection
The results are quite clear: how customers feel about their banks is strongly correlated with their level of loyalty (as captured in the TRI*M score). On the positive side of the emotional scale, those who expressed emotions we characterized as love for their bank registered the strongest customer relationships by far, followed by respondents whose feelings were classified as joy and then trust. All three of the positive emotion groups register above average on their relationship strength, including both the performance and preference scores.
On the negative side of the emotion scale, feelings that express fear are associated with the lowest relationship scores, with anger and sadness performing only slightly better. All three of the negative emotion groups display sharply weaker than average relationship scores and below average readings on both the performance and preference dimensions.
The absence of any emotional expression is correlated with scores that fall between the positive and negative sides of the scale, providing near-perfect symmetry. Interestingly, however, the relationship scores of the emotionally neutral group are below average, especially with regards to preference. Emotionally neutral, in other words, isn’t truly neutral in terms of its impact on the customer relationship: emotional neutrality skews towards weaker than average customer relationships.
On the positive side of the scale, both performance and preference drop sharply from love to joy to trust to neutral. In each instance, however, preference drops more precipitously than performance, confirming that emotions are more closely associated with preference than with performance – at least in terms of positive feelings. On the negative side of the emotional ledger, the performance and preference scores are more compressed and don’t fall dramatically from sadness to anger to fear. In each instance, the TRI*M, performance and preference scores of those whose descriptions of their bank are associated with negative emotions are very, very weak.
The individual bank results closely conform to the overall results for the group. PNC, for example, which registered the strongest relationship scores, also outperformed the field in terms of the positive emotional attachment expressed by customers. Chase ran second in terms of its loyalty scores and recorded the next best results in the degree of positive feelings. At the opposite end of the spectrum, Wells trailed the group in terms of both relationship strength and emotional expression. (Note: Surveys were completed in May 2016, months before the sales goals scandal and illegal account openings at Wells Fargo.)
The NPS Connection
As would be expected, NPS scores also show a correlation with emotions. The emotional connection to NPS, however, appears more muted than the link between emotions and TRI*M scores. Promoters and Passives, for example, display quite similar emotional profiles. Almost half of all Promoters express feelings that are characterized as joy. Among all respondents who projected a sense of joy with their bank, however, one-third are Passives and almost 30% are Detractors. Similarly, almost half of the Detractors express an emotional void towards their bank, but Detractors also are just as likely to project joy (15%) as anger (16%) and trust (8%) as fear (5%). In banking, at least, NPS appears to be less of an emotional expression than others have previously hypothesized.
So Should You Care How Customers Feel about Your Company?
Absolutely. While the dynamics between rational assessments and emotions, between performance and preference, are complex, the evidence is clear that emotions are strongly correlated with the strength of the customer relationship. Positive feelings about a brand translate into stronger levels of loyalty, while negative feelings undermine customer relationships. Although neutral feelings on the face of it are just that – neutral – the absence of feelings is associated with below average relationship scores.
Customer feelings towards a company emanate from the various experiences customers have with that company over time. While many interactions are fleeting and irrelevant, leaving no emotional residue on customers, other interactions are more meaningful and memorable. Experiences are memorable because they stimulate some type of emotional reactions on the part of the customer. Just as each experience can affect the overall customer relationship, the feelings customer have about those experiences can affect their overall emotions towards a company.
The mandate for banks and other firms is clear. Great performance, of course, will bolster loyalty more than weaker performance. Customer relationships that rest exclusively on excellent performance, however, are inherently less stable than relationships supported by positive emotional attachment. That sense of emotional attachment stems in large part from those experiences that leave an emotional imprint on the customer. And in the final analysis, loyalty requires some sense of positive emotional attachment.
(This article is originally published by CustomerThink in December 2016)
by Seth Grimes , Alta Plana Corporation
Interactions, facts and feelings shape our relationships. A truism: It's not what you say, but how you say it. Expressions matter, as do the sentiment behind each encounter and the emotions raised. Emotion is entwined with the literal meaning of words used.
This fact/feeling principle applies to both inter-personal and business relationships. "Emotional and factual appeals cannot be easily separated," writes Nigel Hollis of Kantar Millward Brown in an analysis of advertising approaches. "[A] distinction between emotional and rational is one that exists only in the minds of marketers, not consumers," according to Hollis.
The fact/feeling equation is central to corporate customer experience (CX) initiatives. CX practitioners map customer journeys that are defined by both the what and the how-did-it-make-you-feel? of customer-brand interactions. "Emotion drives loyalty," according to CX visionary Bruce Temkin, and loyalty drives profit.
Another truism: You can't improve what you don't measure, not systematically, on a corporate scale.
Sentiment analysis and the varieties of emotion AI
Enter sentiment analysis, software technology that quantifies mood, attitude, opinion, and emotion in digital media, in images, video, audio, and text. One subspecies infers emotion via facial-expression analysis. Providers include Affectiva, CrowdEmotion, Eyeris, Kairos, nViso, Noldus Information Technology, RealEyes, and Sightcorp. Another variety analyses emotion in speech. Check out audEERING, Beyond Verbal, EMOspeech, Good Vibrations, NICE, Verint, and Vokaturi. On the text front, natural language processing (NLP) techniques can identify and extract emotion in online, social, and enterprise sources, delivered by companies that include Clarabridge, Crimson Hexagon, Feedback Ferret, IBM (AlchemyAPI and Watson Tone Analyzer), indico, Receptiviti, and an advisee of mine, Heartbeat AI Technologies.
This article aims to get a handle on the state of emotion analytics — specifically, emotion in text — via an interview with Heartbeat founder Lana Novikova. Lana describes herself as a marketer by training and a market researcher by trade, never satisfied with numbers and observations, always pushing to understand the "deep why" behind consumer (and her own) decisions. She'll be presenting at LT-Accelerate, a conference I organise, November 21-22 in Brussels, alongside Odile Jagsch, a consultant at global market research consultancy Kantar TNS, topic "The 'Why' Behind Customer Loyalty."
Seth Grimes. Heartbeat designs "emotionally intelligent technologies." OK, what's an "emotionally intelligent technology"?
Lana Novikova. Let's start with the concept of Emotional Intelligence (EQ), popularised by a psychologist Daniel Goleman in 1990s.
Imagine a newborn human who comes with a basic wiring for recognising and expressing key emotions, and with an enormous capacity to learn. She can cry or stay calm, smell and turn her head towards her mother’s breast. Once she can see faces, her mirror neutrons start learning and mimicking facial expressions; then she develops more and more capacity to read and express emotions — from touch, to tone & voice recognition, to basic language to more complex if-then scenarios. In a perfect world, she grows into a secure and happy person who can recognize and name a wide range of her own emotions, understands what other people feel from multiple expressions, and has a capacity to express and manage her emotions.
SG. So you apply the EQ concept to and via technology.
One day, this technology might surpass humans in understanding human emotions because it will tap into data that humans can not perceive on their own.
LN. Technology today has a super high IQ — it can beat the best human chess, Jeopardy, and Go players — yet it has a very low EQ. At Heartbeat, we want to be a part of an academic and business community that changes this. Emotionally intelligent technology is never going to feel emotions or express them like our baby can, but it will eventually become very good at perceiving and understanding human emotions from data.
One day, this technology might surpass humans in understanding human emotions because it will tap into data that humans can not perceive on their own: biometrics, brain waves, subtle cues from body language and facial expressions, and more.
SG. Relating the tech to Heartbeat...
LN. We are focusing on training the (metaphorical) technology infant to recognize explicit feelings from language, from text, and to guess the range of emotions it communicates. Just as some people can intuitively differentiate between many emotions, our growing algorithm can tell what kind of Joy or Anger is expressed in language. There could be as little as 2-3% and as much as 50% affect words and phrases in any given unstructured text. We find these words and assign them to a cluster of emotions. This process mimics how our brain deciphers emotions from language.
SG. What aspects of emotion does Heartbeat detect and measure? Do you adopt a particular emotion model?
LN. There are a few models and classifications of emotions developed by brilliant psychologists like Paul Ekman and Robert Plutchik, and even by Human-Machine Interaction Network on Emotion (HUMANE). I was inspired by a more intuitive model of W. G. Parrott (2001), originally described by Shaver in 1987. It has a tree structure and includes Primary, Secondary and Tertiary emotions. I also did a lot of reading about effective neuroscience, and tried to combine Parrott's model with what I took from the work of J. LeDoux, R. Davidson, and J. Panksepp. Then my "practical life-long quant researcher” side took over and asked, “How is this segmentation going to be useful to a brand of chocolate, or a bank, or a political party?"
The art of analysing good quality text data lies in understanding (a) how to ask a good question, and (b) how to infer meaning from people’s answers.
We ended with a 2-level clustering of 99 complex emotions and feelings into nine primary emotions: Joy, Love, Trust, Anger, Fear, Disgust, Sadness, Surprise and Void (which is explicit lack of emotion like in “I don’t care"). We also added Body Sense (positive, negative and neutral) as a way to analyse words and phrases that don’t point to a particular emotion, yet are useful for understanding human perception overall, especially for marketing food and body care products.
Many words and phrases are coded into multiple emotion clusters. Would you agree that there is a large overlap between Disgust and Anger, or Love and Joy, or even Anger and Fear? For example, we put the word "terrific" into both Joy and Fear, and let context decide which emotion it is more likely to represent. This is the most challenging part of our journey — understanding how different context colors "terrific" into happy or unhappy expression. It's the domain of Machine Learning that needs lots of training data. We are just scratching the service here, but this is also the most exciting part of my job.
SG. How does the tech work?
LN. Today, our tech is very simple yet very accurate. It’s called "bag of words": 8,000+ words and phrases (including negation, metaphor, and other multigrams) professionally coded into categories and validated by skilled psychologists and psycholinguists. Our software consumes unstructured text from survey responses and social media, and produces a set of visualisations and charts in a simple elegant dashboard.
We do our best work when we analyse data from survey responses that focus on people’s feelings. Data like that produces over 30% affect words, and has the context controlled by researcher. Once the report is ready (which is almost instantly), we curate results by removing some words that do not apply to the report. This is how we deal with another industry challenge, ambiguity. Finally, to prove that we are very good at what we do - we show all words and phrases for each Primary emotion. You can click on any word and see exactly how it appeared in the original text - no “black boxes” here. Since our taxonomy reached 5,000, our match rate — the percentage of affect words that we recognise — is over 95%. Heartbeat is committed to accuracy, depth, and transparency.
SG. How is Heartbeat different or better than the competition?
LN. Heartbeat is different because it was created by a market researcher (myself) who spent hundreds of hours coding open ended survey data. My team built our award winning app for researchers and marketers who appreciate the depth of consumer insight. I love working with good quality data, and would choose quality over quantity any time.
Survey data is under-used and often abused. The art of analysing good quality text data lies in understanding (a) how to ask a good question, and (b) how to infer meaning from people’s answers. I believe Heartbeat is better for distilling emotions from open-ended survey questions than any other company on the market today including IBM/Alchemy and other powerful APIs. They can do a lot of advanced text analytics with huge amounts of data. We made it simple, transparent, and fun. Just check out our dashboards — clients love it! Another big differentiator is that we are 100% focused on emotions — not sentiment or basic emotions, but fine-grained feelings. Our reports can be useful for anyone - from a CEO and CMO to a brand manager to a CX analyst to an agency creative director.
SG. How do Heartbeat emotion text analytics findings complement or compare with insights discovered via neuroscience and biometrics, via facial image recognition and brain-activity measurement?
LN. I strongly believe in cooperation over competition. I think putting our best technologies together will create a better future for our businesses and for this planet. A graphic will communicate how I see the future of emotion AI integration. (See the image below.)
SG. Could you please sketch out 2 or 3 use cases? What's your target market — clients and applications?
LN. Let’s start with the customer, the consumer or shopper. What do all customers have in common? They're human, they feel emotions all the time, and those emotions drive many (if not most) of their decisions.
Why not measure emotions on every step of your customer journey? It's not easy to put an eye tracking and EEG device on thousands of people, but it's easy to ask one simple question, "Please share a few words that best describe how you feel about X." This natural human question fits nicely into any survey and customer feedback tool, and it’s surprisingly powerful. It not only invites a wide range of un-aided and unbiased feelings on the subject, but it can also aid at predicting what people will do in the future.
We're a start-up, but we have already published two case studies to show the predictive power of emotions measured with HEARTBEAT emotion analytics: banking and political elections. The best application of HEARTBEAT is in on-going customer experience measurement and foresight.
SG. What's on your product roadmap?
LN. We are driving in the fast lane! We launched our first prototype last December and won an international startup competition (Insight Innovation Competition by GreenBook) in March 2016 in Amsterdam. All Spring and Summer, we tested our tool with some of the best research companies in the world. Finally, we launched enterprise SaaS this fall, which will enable anyone — brands, market research suppliers, consultancies, marketing agencies — to access the engine. No more manual coding: leave it to a machine, fast and accurate.
Next, we're venturing into a long complex journey of NLP and machine learning, to crack the challenge of context and meaning.
Here's a quote that resonates with me, especially when it comes to trying to solve one of the biggest puzzles in the Universe, the puzzle of human consciousness: "The most influential thinkers in our own era live at the nexus of the cognitive sciences, evolutionary psychology, and information technology." That's New York Times columnist David Brooks.
The mission of Heartbeat Ai is to design emotionally intelligent technologies and tools to help machines understand peoples' feelings and improve our emotional wellbeing. I don’t know exactly how we'll get there in the end, but I know that we are on the right track today.
SG. Thanks, Lana!
Readers, I've written up a couple of other emotion analytics interviews — an IBM Watson blog contribution, Sentiment, Emotion, Attitude, and Personality, via Natural Language Processing, based on a conversation with IBMer Rama Akkiraju and On Facial Coding, Emotion Analytics, and Emotion Aware Applications with Affectiva principal scientist Daniel McDuff — and of course you can learn more about Lana's Heartbeat work atLT-Accelerate in November. If you can swing a trip to Brussels, see you there!
(This article was originally published at www.mycustomer.com on 12 October, 2016)
No one knows the outcome of 2016 US election, yet many are trying to predict it. Very few seem to understand what's really happening in the minds and hearts of American voters when they watch the battle between Hillary and Donald.
It reminds me of a viral phenomenon "What color is this dress?" that took over the Internet last year. Some saw the dress as white and gold, others saw blue and black. There was no middle ground.
What do Trump's followers see when they say they trust him more than Hillary Clinton?
We at Heartbeat just finished the first week of a 6-week US Polling project in five "swing" states: Florida, Pennsylvania, Ohio, Iowa, and Virginia. Here's what 500 people over 18 years old said they felt about Hillary Clinton when we asked how they felt about her as the next US President. Anger is high, Trust is low.
Here's what they felt about Donald Trump:
Donald Trump is honest, while Hillary Clinton is a liar to a large group of US voters. It just does not make any sense, right? How on earth, despite all the logical reasons, fact checking and statistics that Hillary Clinton stands by, a large number of Americans still trust Donald Trump more?
Trust has very little to do with common sense. Trust is an emotional concept for most people, and it has to be re-built using emotional approach: congruent words, genuine body language, authentic facial expressions, and more. Hillary Clinton's words are more rational than emotional, her body language does not project warms that is expected from a woman. People tend to apply different standards towards politicians and businessmen, men and women. Subconscious biases run our worldview, opinions, and decisions. It's sad but true.
Here's what pro-Trump people say about him:
He wants to better America, isn't a politician and is a very good man with strong feelings for and what America can and will become.May not always like what he has to say, but at least he'll tell you his thoughts and feelings. Doesn't tell you what he thinks you want to hear.Rough around the edges, tells the truth.
Some people "sit of the fence" but prefer Trump's authenticity:He is also a liar but speaks his mind.I'd rather have someone that speaks too much of the truth.
People who lost trust in politicians in general (and there are many of them), need more than numbers and fact checks from Hillary Clinton to open up to her.She has her own agenda and does not have this country's best interest in mind.She scares me because she know how to act as president but is she honest is what she says real or just an act?
Hillary Clinton’s campaign manager Robby Mook says voters will trust her after she’s elected president:I don’t think people will fully appreciate who she is until — knock on wood — she’s elected president because when she is president, I think she will be phenomenally successful because she’s a work horse.
He might be missing the point. Being a "work horse" does not necessarily make people trust you more. Not being a "robot" might.
According to our "swing" state polling, 22% of voters are undecided (or "can't say") today who they are going to vote for on November 8th. These people don't express much Trust or Joy about either candidates, and they are more angry at Hillary Clinton.
Some of them will not vote, others will choose "lesser of two evils", while some can still be reached with a simple yet powerful heart-to-heart message and a real smile.
It has been over a year since I started asking one simple open-ended survey question:"Please share a few words that best describe how you feel about ..." The topics were wildly different: "eating chocolate" vs. "craving chocolate", "shopping at your favourite store", "doing banking" vs. "being responsible for my family banking", "the future of the planet", Millennial's feelings about their career, and using Facebook, brand Uber, having a period, Donald Trump, Bernie Sanders, competition vs. collaboration, and many more. Here are the three things I learned.
First. I was surprised that the overwhelming majority of people choose to answer my open-ended questions. They don't skip them, don't write the typical "dk", or angry "get lost." They are very open and honest. Some responders share a couple of words, some write a sentence, a few write a whole paragraph. For a market research analyst turned psychologist, this text data is incredibly rich and multi-coloured. It also gives me exactly what I am looking for - clean text data within the context that I can control.
So what? I encourage market researchers to stop asking "Why" questions and start asking "How do you feel" questions if you really want to understand your customer in a time- and cost-effective way.
Then, I started wondering if people ACTUALLY LIKE ANSWERING my questions. Despite the common belief that panel responders are sick and tired of surveys, I felt I found one question that people actually enjoy.
But why? A 2012 study published in Psychological Science showed that putting feelings into words could help us cope with stress. Psychologists did an experiment to see if verbalizing current negative emotions might be effective in helping people with spider phobias.
"Participants who put their negative feelings into words were most effective at lowering their levels of physiological arousal. They were also slightly more willing to approach the spider. The findings suggest that talking about your feelings - even if they're negative - may help you to cope with a scary situation."
On June 24th, right after I learned about Brexit news, I launched a HEARTBEAT Pulse survey in UK - a one-question survey on Google Survey platform asking 300 "gen.pop" responders across UK how they feel about the future of their country. People shared their emotions from their heart: "concerned and saddened," "Very bleak and old fashioned. Us young people have been neglected," "When i think about the future of our country, i feel that unless we all learn how to pull together, we'll be in for a rocky ride...until we Do learn how to!"
Finally, when I started sharing our Emotion Analytics maps during presentations or on social media, people often displayed a strong reaction of empathy and curiosity. Seeing other people's emotions - especially Sadness, Anger, and Fear - quantified in a way that does not explain or simplify them into positive/negative creates an opening, a possibility to learn something new, and wonder what other people feel.
It's a 12-minute vibrant conversation about big "startup" dreams and our vision to help bring more empathy into market research, business and the world in general.