Converting Customer Emotions into Actionable Insights (Peter Dorrington, TTEC)

1. Converting Customer Emotions Into Actionable Insights Peter Dorrington Director, Analytics at TTEC 20 June 2019

3. But first… ©2019 TTEC. Confidential and Proprietary3 I started my career as an applied scientist (engineer) Spent the last 20+ years thinking about and applying quant-based predictive analytics (data mining) MBTI® Type: INTJ (‘the Scientist’ – bring me your data, not your emotions) …not the kind of person you would expect to be talking to you today about the world of feelings, attitudes and beliefs MBTI® personality type is a registered trademark of the Myers & Briggs foundation

4. TTEC: We help companies connect with their customers We were founded on one guiding principle: customer experiences that are simple, inspired, and more human deliver lasting value for everyone. ©2019 TTEC. Confidential and Proprietary4 6 50 47.8k 3.5M +56 $4B 116 continents languages employees globally customer interactions daily client NPS incremented sales annually patents leader in the Gartner Magic Quadrant for Customer Management Contact Centre BPO, Worldwide

5. Why connecting emotionally matters ©2019 TTEC. Confidential and Proprietary5 Customer experience management is the art and science of coaxing lifetime loyalty from daily transactions.” Steve Curtin“

6. Emotions impact business results ©2019 TTEC. Confidential and Proprietary6 52%95% 60 - 80% Customers who are emotionally connected with a brand are 52% more valuable than customers who are just highly satisfied - Harvard Business Review …of our purchase decisions take place unconsciously - Prof. Gerald Zaltman … of customer defectors score themselves as "satisfied" or "very satisfied" on surveys – Bain & Company

7. Customer Experience (CX) is… ©2019 TTEC. Confidential and Proprietary7 …the sum of all observations, encounters and feelings that a customer has before, during and after their interaction with an organisation.

10. Robert Plutchik’s ‘wheel of emotions’ ©2019 TTEC. Confidential and Proprietary10 In 1980, Robert Plutchik constructed a wheel-like diagram of emotions visualising eight basic emotions: joy, trust, fear, surprise, sadness, disgust, anger and anticipation. The wheel combines the ideas of levels of emotional intensity as circles representing arousal (closer to the centre = greater strength of feeling) and valence (positive / negative emotions and opposite emotions). Plutchik also recognised that combinations of emotions are important (which he called dyads (adjacent emotions) and compound emotions) – for example: Joy + anticipation = optimism For our purposes, this is part of a scoring model that categorises core emotions (which are intrinsic to the human condition), rates them on a continuous scale and accommodates complexity.

11. Arousal and valence Emotional experiences can be described on 2 dimensions: Valence Positive Neutral ≠ not present Negative Arousal High (exciting, agitating) Neutral = ‘comfortable’ Low (calming, soothing) ©2019 TTEC. Confidential and Proprietary11 neutral high low positive negative Arousal Valence calmness sadness happiness angry agitation fear shame liking disliking sureness uncertainty surprise

12. Observable behaviour is a combination of… ©2019 TTEC. Confidential and Proprietary12 Practical needs Price, availability, fit-to-need, convenience… Emotional wants Increase joy, reduce anger, alleviate anxiety… Internal biases Beliefs, attitudes, ‘personality’, cognitive biases… External influences Societal norms, peer reviews, reputation… The context What I am trying to do, where, when, how… Prior experiences Habit, familiarity, recency, repetition, complexity… Internal biases Practical needs External influences Emotional wants Context BEHAVIOUR Prior experience Cognitive Affective

13. Emotions, customer journeys and ‘moments that matter’ Emotions are: Individual - how we react to an event or situation is dependent upon what we have experienced before and no two people (including twins) have had exactly the same experiences. Situational - if and how strongly we feel emotions is also dependant upon the context - for example, what we are trying to do and how we are already feeling. Fleeting - even strong emotions don’t last for long, they can come and go in moments. However, that doesn’t mean that the effects don’t accumulate over time. Unpredictable - try though we might and because of the above, its impossible to accurately predict how a specific person will feel as a result of an particular event. What we experience is not what we remember: According to the ‘Peak-End’ rule1, what we remember from an experience is the most intense emotion felt and what we were feeling at the end of the experience. So… § What we ‘experience’ is in the moment § What we remember will not be what we experienced § It’s what we recall that influences our next decision ©2019 TTEC. Confidential and Proprietary13 1 - "When More Pain Is Preferred to Less: Adding a Better End" by Kahneman, Fredrickson, Charles Schreiber, and Donald Redelmeier Emotional Arousal End Start Finish Peak ?

15. Identify the ‘moments that matter’ Score the whole customer database Monitor events and update the scores Using customer narratives as a research source: § Categorise the ‘topics’ § Identify what emotions are generated, and to what degree § Analyse how these impact behaviour For every customer: § Use the research and customer records to identify an initial starting position for emotional state-of-mind Customer Experience Vector (CXV) § Append the initial CXV to the customer data record. Every day, and for every customer: § Use the event stream to identify which ‘moments that matter’ have been encountered by the customer § Update their emotional score (CXV) by an appropriate amount Why we conducted the research - the first step on delivering the Customer Experience Vector ©2019 TTEC. Confidential and Proprietary15 It is now possible to anticipate the emotional state of every customer, whether you are in an active conversation with them or not.

16. Research methodology § We used existing customer survey and transactional data, covering a 2 year period § Analysed behaviour for the 12 months immediately after each survey § Found than most customers take no significant action within a year of the survey § Those that do, do so within the following quarter1 § Verbatims analysed using ML-based Natural Language Processing (NLP) for both emotions and sentiment ©2019 TTEC. Confidential and Proprietary16 Customer records Narrative Quantitative data Emotion analytics Sentiment analytics Topic modelling Predictive analytics Correlation models Grouping models Explanatory models Predictive models Attrition (churn) Cross-sell Optimisation …1 – This is likely to reflect some bias in the data because in-bound contacts were over-represented in the data

17. We can detect emotions within customer comments Sentiment: Positive (90% : 10%) Negative Emotions detected: § Liking: 24% § Anger: 17% § Happiness: 9.4% § Calmness: 7% ©2019 TTEC. Confidential and Proprietary17 Calmness Happiness Liking Surprise Uncertainty Disliking Agitation Anger Sadness Fear Sureness Shame “…John Doe was super and helpful. But, when I am there for my day-to-day business that is handled by a member of staff, the wait is long and frustrating…”

18. We can detect emotions without asking consumers directly about how they feel ©2019 TTEC. Confidential and Proprietary18 “In physics, the observer effect is the theory that simply observing a situation or phenomenon necessarily changes that phenomenon.” - Wikipedia When you directly ask someone about their feelings – either to name their emotions, and / or how strongly they are feeling them, you rarely get a reliable answer, because of: subconscious à conscious translation societal norms / self-censorship lack of self-awareness Instead of directly asking a customer how they feel, ask them to describe an event or situation

19. Emotions do play a role in customer decision-making ©2019 TTEC. Confidential and Proprietary19 Of those that changed their product portfolio, there was a clear correlation between emotions and behaviour: § Positive emotions are associated with a greater increase in number of products held (cross-sell: added ≥1 product to their portfolio), § Negative emotions correlated with a decrease in the number of products (churn: removed ≥1 product from their portfolio) Disliking Sadness Fear Anger Shame Surprise Happiness Liking Increase in number of products Decrease in number of products Average increase Negative emotions are bad for business: customers are more likely to churn, less likely to buy.

20. Alone, or in combination, emotions have a quantifiable impact Revenue growth comparisons As with the cross-sell study: § Customers exhibiting positive emotions are correlated with higher than average revenue growth. § Customers who display negative emotions are associated with slower than average revenue growth § That said, we typically found more positive- feeling customers than negative ©2019 TTEC. Confidential and Proprietary20 Happiness Sadness Liking Disliking Average revenue growth Relative width of histogram represents population size 15% 23% - 40% - 26%

21. Not all negative emotions are equal Fear is second only to sadness as a negative emotion that effects behaviour. Surprise in this study came out as slightly negative; that said, it typically is fairly neutral BUT does act as a multiplier for other emotions (positive or negative). Although not many people exhibited ‘shame’ – for those that did, the effect was very negative. Impact of emotions on revenue growth ©2019 TTEC. Confidential and Proprietary21 Fear Anger Shame Surprise Average revenue growth Relative width of histogram represents population size - 30% - 26% - 22% - 6% Sadness - 40%

22. Defection rates - ‘grudge purchase’ Sentiment analytics and emotions analytics are different Both emotion and sentiment are useful in explaining some aspects of customer loyalty. They use different techniques and therefore find different groups within the analysed population. In this case, emotion analytics shows more of a ‘hockey stick’ effect than sentiment Our studies also showed that the two metrics can be poorly correlated ©2019 TTEC. Confidential and Proprietary22 Sentiment Emotion (liking) 50% PositiveNegative Neutral DefectionRateHighest 12% There came a point where ‘liking’ appears to stop influencing loyalty

23. Adding emotions to predictive models improves their performance Model factors that explain cross-sell As we expected, quant-based (e.g. frequency, recency, value, …) predictive models are the more useful initial modelling technique. Adding emotion scores to the models improved their performance …More so than sentiment scores or topic modelling. However, the degree of effect varies from case-to- case but emotion scores consistently improved the accuracy of predictive models. ©2019 TTEC. Confidential and Proprietary23 Random selection + basic account details + emotion scores + sentiment scores + topic model 10.6% 3.9% 3.3% 0.1% 1.5% Adding emotion scores to models improves their performance

24. Emotion-driven outcomes are not correlated to segmentation / persona ©2019 TTEC. Confidential and Proprietary24 We applied existing segmentation membership to the emotion results We found that there is no significant correlation between how a customer is likely to react to an emotion and their membership of a segment or customer persona Caveat: We have not yet studied whether you are any more / less likely to feel a specific emotion as a result of an experience. People are people – irrespective of age, income, occupation, etc.

25. Emotions and optimisation modelling In our optimisation model, we considered: § How many customers were in each group § The baseline differences in revenue § The difference in revenue between groups § How much we could spend on each group to lift them up a tier (e.g. sad to neutral) § What percentage of each group would respond positively § The result was a very positive RoI Which is better? (for revenue) – Make sad, not surprised people neutral, then – Neutral surprised people happy ©2019 TTEC. Confidential and Proprietary25 sad neutral happy not surprised surprised Optimisation (goal / constraint) modelling that considers emotions can help identify where to focus CX investment to secure the best return revenue

27. Summary & conclusions § Customer experience is about more than what happens 'in the moment’ § Human decision making is a combination of logic and emotions § A range of emotions can be detected from within customer narratives § We can do this without directly asking customers how they feel § Emotions do play a quantifiable role in customer behaviour § Negative emotions are typically bad for business, positive is good for business § Not all negative (or positive) emotions are equal (or ‘bad’) § Sentiment analysis and emotion analysis are different, and not well correlated § Adding emotion scores to predictive models makes them perform better § People are people; how they react to what they feel is not linked to their segment / persona § Optimisation models can use emotion scores to identity where to invest in CX ©2019 TTEC. Confidential and Proprietary27

28. Next steps ©2019 TTEC. Confidential and Proprietary28 Ask yourself: § How do you measure your Customer Experiences? § How do you document your customers journeys – linear paths or a summation of episodes? § Do you understand the emotional implications of each event on customers § If you knew how your customers were feeling, could you act differently? § Can you quantify the return of your CX investments on the business bottom line? § What if you could do all of that, for every customer, every day?

29. Next steps ©2019 TTEC. Confidential and Proprietary29 Email me at: peter.dorrington@ttec.com Read more at: www.ttec.com/resources Subscribe to: The CX Pod: www.ttec.com/resources/cxpod Customer Strategist Journal: www.ttec.com/customer-strategist Follow me: Twitter: @pdorrington LinkedIn: www.linkedin.com/in/peterdorrington/

Converting Customer Emotions into Actionable Insights (Peter Dorrington, TTEC)

Converting Customer Emotions into Actionable Insights (Peter Dorrington, TTEC)

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