Measurement of what really counts emotions - paul roberts

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BrainJuicer® Ltd, 1 Cavendish Place, London, W1G 0QF

Fresher Insights, Better Marketing.

MEASUREMENT OF WHAT REALLY COUNTS:

EMOTION! How customers’ emotions can reveal actionable insights for business

by Paul Roberts, BrainJuicer® & Orlando Wood, BrainJuicer®

BrainJuicer Ltd, 1 Cavendish Place, London, W1G 0QF

Abstract

Traditional customer experience research rarely uncovers the fresh customer insights that can unlock

business growth. This paper reveals an exciting new emotional approach to real-time customer experience

measurement that can reveal actionable insights for business using mobile platforms. It will outline the

shortcomings of current customer experience research approaches and, with references to case studies

undertaken with HSBC and a global telecommunications company, explain how a simple, intuitive and

emotional approach can lead to insights that can unlock business growth, and indeed some other unexpected

benefits.



Introduction Ask yourself this question: if your customer satisfaction measurement programme stopped working

tomorrow, would it slow down your company’s growth? The answer we hear from many companies we have

spoken to is a resounding ‘no’. The primary focus of most existing customer experience research programmes

is the on-going measurement of performance against internal benchmarks. This means that they rarely

uncover the sort of fresh customer insights that can unlock new business growth or lead to efficiencies. But

how would you go about developing a whole new approach that could drive growth?

Advances in psychology and neuroscience are beginning to tell us of the important role that emotions play in

guiding our choices and our subsequent behaviour. At the same time, new technology gives us unprecedented

opportunities for real-time data monitoring.

This paper reveals a new approach to customer experience monitoring that uses a proven and intuitive device

for measuring emotion to provide highly actionable, real-time customer feedback across numerous customer

channels, overcoming the many problems of traditional customer experience research

Why Good Customer Experience is Important

Few would contest that delivering excellent customer experience is important. It is well-documented that high

levels of customer experience are beneficial to companies for several reasons:

1) They can lead to improved sales. If customers are delighted with their service they are likely to spend

more on that service, reducing the need for customer acquisition costs and transaction costs, and

increasing cash flow.

2) They can contribute to higher customer retention levels. If customers are happy with their brand

experiences, they are less likely to look elsewhere. This can be achieved through perceived

improvements to customer service during the product lifecycle or by providing great customer

experience at key service moments.

3) They can lead to higher levels of acquisition via word of mouth. If service levels are truly excellent,

there is a greater likelihood of referral, reducing costs associated with new customer acquisition

drives and accelerating cash flows. Conversely, if service levels are poor, then negative word of mouth

can be costly.

4) They can increase the opportunity for cross-selling. High levels of service satisfaction as well as

product delight are likely to lead to an openness towards other products and services. This is likely to

improve cash flows.

5) They help to reduce price sensitivity and therefore to resist downward pressure on prices. A loyal

customer will be less susceptible to competitor offers and may even tolerate price increases relative

to the competition.

6) They give companies greater bargaining power to brands with their suppliers and distributors,

enabling companies to extract greater value for their services.

It is not surprising therefore that customer experience has been shown to have a direct impact on shareholder

value. Anderson, Fornell and Mazvancheryl (2004) have shown how an increase of 1% in customer satisfaction

results (as measured by ASCI – the American Customer Satisfaction Index) is associated with an expected

1.027% change in shareholder value (as measured by Tobin’s q).i Tobin’s q is defined as the ration of the firm’s

market value to the current replacement cost of its assets. Figure 1 illustrates the relationship between

customer satisfaction and shareholder value, which is measured using Tobin’s q. In nearly every industry, the

correlation is positive and tends to be strong.ii



Figure 1

Measuring Customer Experience: Context

Before discussing how customer experience is typically measured, it is worth taking stock for a moment of

how marketing’s view of the customer has changed over the years, because slow and gradual shifts in

marketing philosophy do not seem to have been reflected in the way the research industry approaches

customer satisfaction research today. As a general observation, where formerly marketing practice had a

tendency to be transaction-focused, it is now more relationship-driveniii. Whereas brands were once seen as

static and lifeless artefacts to be constructed and then maintained in stasis, they are now seen as living,

breathing entities that evolve and develop in the mind of the customer. Our view of the customer has

changed too; whereas the customer was once seen merely to respond to brand stimulus and messages,

he/she is now regarded as someone it is vital to emotionally connect and interact with for brand success. So

where we had used to talk about customer satisfaction with product or service quality we now talk about

customer experience journeys.

Advances in communication technology have also made it possible for the first time in recent years to monitor

customer experience in real time across different locations and in different channels, in fact, whenever a

customer transaction takes place. We now live in a world of integrated marketing communications, where in

theory feedback can be obtained and the customer offer adapted almost immediately. There is also an

increasing awareness among service providers that a customer’s emotional response to their transaction is

vitally important and could have a bearing on their future behaviour.

A research outsider might say that customer satisfaction research has not really kept pace with these

developments. Any review of research papers written in recent years on customer experience measurement

will reveal the industry’s preoccupation with complex statistical models, with analyses designed to overcome

the effects of multicollinearity or with debates around merits of different linear scale lengths, rather than a

desire to understand how people really respond to experiences. These statistical debates are, naturally,

important for researchers to have, but what is striking is that they seem to miss the bigger picture; that as an

industry we need to find new, sensitive, intuitive and emotional methods of measuring customer experience

that take account of what customers actually feel. We need to be able as an industry to surface problems to



management immediately, so that brands can capitalise on good experiences and remedial action can be

taken before it is too late when bad ones have occurred.

The Problem with Traditional Approaches to Customer Experience Measurement

The direct impact of customer satisfaction on shareholder value stresses the importance of excellent

customer service and underlines just how important it is that business should be able to measure it. Any

customer experience research needs to provide a measure of satisfaction and throw light on what is making

customers feel the way they do and what it is that would make them feel happier about their experience. It

needs to measure and inform. It needs to find ways to improve a company’s reputation, and in the most basic

of terms, it needs to help management identify problem areas and find opportunities to either make or save

money.

Yet the market research customer satisfaction approaches that are typically used are flawed in many respects.

The main problem with traditional approaches to measuring satisfaction is that they don’t reveal what

customers are really feeling, because customers are never given the opportunity to express what they are

really feeling, but are asked instead to evaluate their experience. Figure 2 depicts a satisfaction score

obtained in a research experiment conducted by Westbrook & Oliver (1991). Westbrook & Oliver asked both

traditional satisfaction questions and established emotional response in their research. Figure 2 shows the

overall satisfaction score (on a 7 point scale) alongside the satisfaction scores measured among those feeling

different emotions. Those who felt positive emotions such as ‘contented’ gave higher-than-average

satisfaction scores, but those who felt neutral or negative emotions gave lower-than-average satisfaction

scores. The analysis shows that if a company were relying purely on the overall satisfaction score, it would be

blind to what people were really feeling. It reveals that traditional measures (i.e. a linear 7 point scale) are

something of a blunt instrument, lacking the resolution of a multi-dimensional emotional approach and are

therefore somewhat limited in their scope. The problem is that linear scales are one-dimensional and don’t

even attempt to capture customers feelings at the moment of transaction. Respondents struggle to translate

their experience meaningfully into a linear scale (i.e. is this a 3 or a 4 out of 7?). At the other end, it is

impossible for the researcher to divine from any resulting mean score what customers were really feeling at

the time (what does a 3 or 4 out of 7 actually mean?). An emotional question and response tells us so much

more about a customer’s experience than a linear scale, because we immediately get a glimpse into the

customer’s state of mind at the transaction moment – a sense of the emotional outcome. Scales are linear, a

customer experience is not.

Figure 2



This is the first and most important of a number of common problems with traditional customer experience

research, which I summarise below.

1. As outlined above, traditional satisfaction scales are not a faithful or revealing reflection of how

people actually feel about their experiences and do not have the resolution required to provide a full

picture of the customer transaction.

2. The resulting one number mean satisfaction score for management can hide a multitude of problems

below the surface, which run the risk of never being properly voiced at senior levels. An average score

of 7.5, for instance, might give management an impression of whether satisfaction has improved or

deteriorated, assuming multiple waves are conducted, but it does not reveal to management what

the current problems are or the strength of feeling associated with those problems, meaning that

problems go undetected along with their underlying causes.

3. The agenda for surveys is rarely consumer-driven. Components of service tend to be broken down

into many sub-categories (e.g. price, staff, store layout) which are asked about in the form of

attributes. The relationship between these attributes and an overall satisfaction variable are then

typically examined in regression analysis to establish the bearing that each might have on overall

satisfaction levels. This has numerous shortcomings:

a. It means that customers aren’t ever really given the opportunity to simply express how they

feel overall about their experience (other than via a linear evaluative scale), but are asked

instead to artificially break down and evaluate components of the service they receive. No list

of components will ever be exhaustive, meaning that the real reason for dissatisfaction can be

missed and even that the wrong problems are prioritized for improvement. As Cohen & Neira

(2005) point out ‘The level of detail required by managers may not be as important to

customers, who tend to make more global evaluations’.

b. The long list of questions required means that this approach cannot realistically be adopted at

the moment of the transaction itself, but only sometime after the event, losing the emotional

immediacy of the experience. If surveys are not tied to a specific occasion or place, they can

only be viewed as general indicators of the overall experience some time afterwards. They are

what we call ‘marinade’ measures – general and non-specific responses that companies

surround themselves with but don’t know what to do with.

c. It is difficult to overcome the multicollinearity seen in this type of test, making it difficult to

establish the real relationships between individual attributes and overall satisfaction in

regression analysis. If the overall experience is thought to be good then scores on all

individual attributes will be high, if the overall experience is poor, then scores on all individual

attributes will be low. It is also only really possible to look at the correlation between

individual attributes and overall satisfaction; it is not strictly speaking possible to say for

certain whether any of the individual attributes is causing dissatisfaction.

d. This approach results in long lists of tiring and tedious questions for the customer, which can

impact on data quality.

e. Long and unwieldy surveys reflect poorly on the client’s business and result in low

participation rates.

4. If surveys are tied to a specific transaction, results often arrive well after the moment itself, weeks or

even months later, and are less actionable than they could be because they do not provide for direct

managerial intervention.

5. Traditional continuous customer experience monitoring will typically be limited in terms of the

amount of data available for specific time periods or by location (e.g. Week 42 in a specific retail

store) and analysis will therefore be restricted by low base sizes. This kind of approach does not lend

itself to the sensitive measurement of new initiatives.

6. Consistency of approach when using evaluative questions is difficult to achieve across different

consumer-facing channels, because each channel is likely to work differently and require a different

set of evaluative questions.



A New Emotional Approach

For these reasons, BrainJuicer has developed a completely new approach to satisfaction monitoring, using our

multi-dimensional emotional measure, FaceTrace®. Based on the work of psychologist Paul Ekman, FaceTrace

uses a set of human faces to measure the 7 basic human emotions that Ekman has identified as being

universally understood and expressed in the face quickly and intuitively in the transaction moment (see Figure

3)iv. It captures the intensity of their emotion using three further faces at different degrees of intensity and

pinpoints why customers feel that way in an open-ended follow-up question following their transaction.v The

question is asked following the customer’s transaction because it is important to understand the emotional

impression that the experience has left upon themvi. Incorporating faces in the scale enables us to access how

people are feeling much more sensitively than if we were to use just words alone. The use of faces means that

it is, in a sense, a pre-cognitive measure, accessing how people feel extremely quickly and intuitively without

them having to think or evaluate their experience. Asking a follow-up open-ended question to understand the

trigger for the emotion selected tells us immediately what it was that led them to feel that way. It can be

administered quickly and in the transaction moment rather than days or weeks afterwards. The approach is

also is universally understood regardless of race, background or culture,vii

and might even work better than

traditional approaches when customer literacy levels are low.

Figure 3

Whereas traditional customer experience approaches require customers to translate their feelings into a one-

dimensional linear scale, only for the researcher to then attempt later to de-code what that really means

using other variables and regression analysis, this approach pinpoints the problem immediately without the

need for any complex statistical modelling. It is not a measure of claimed importance because we are not

asking people to tell us what is important, we are simply asking people to tell us why they feel the way they

do – it is a given that their emotional frame of mind is important.

From the customer’s perspective, it is very quick and easy to complete, fun even, and gives them the

opportunity to voice their feelings immediately and tell us why. Because the measure records the customer’s

emotional response rather than asking them to evaluate the service, it can be used in many different

contexts, across numerous transaction types. For instance, it can be administered via touch-screens in a store,



using a customer’s mobile phone or even digitally after an online transaction. Figure 4 shows how different

customer channels might be measured with the FaceTrace emotional metric: an in-store customer service

kiosk, a laptop for measurement of online experiences, and a mobile phone for on the go customers.

Figure 4

Customer feelings captured in this way can be fed back to management in real-time. Results can be fed

immediately via an internet portal to customer-facing and managerial staff, revealing problems and

opportunities to all levels of management as they occur. The results provide a window into customers’

emotions as they happen and put problems into sharp relief for managers. Problems are prioritised intuitively

by the nature of the emotion. For instance, if a manager can see that his or her customers are angry or

disgusted, and can see in the customer’s own words the reason for that anger or disgust, then their natural

instinct will be to do everything possible to put the situation right as soon as possible. Framing customers’

problems emotionally makes the problems less abstract, less theoretical and more human for management.

Managers do not have to be researchers to understand the results – they are easy and intuitive to interpret,

require little training and can be understood at all levels of the business. Customer-facing staff are more likely

to be motivated because they know that the emotional outcome of every transaction they undertake with a

customer could be monitored. This means that staff do all they can to vouchsafe that every customer leaves

feeling happy.

Most importantly of all, perhaps, the approach surfaces the problems that are a priority for customers, rather

than pursuing the company’s internal and inward-looking agenda via a long list of pre-determined attributes

— issues that might be assumed to be important to customers by management, but which might in fact be of

little interest or importance to them. The overall result is customer feedback that is much more revealing and

actionable for management than the causal relationships derived from a regression analysis conducted

several weeks or months after the event.

As a universal measure of emotion, the approach can be used anywhere in the world. One single emotional

measure can be used to compare experiences across multiple consumer touchpoints. Managers can review

customers’ emotional response by different types of transaction, product or service, by geography or time of

day. With the right data protection safeguards in place, managers can even build up a picture of any given

customer’s emotional experiences with their company over time and across different customer-facing

channels. This might help management to anticipate future customer behaviour, prevent customer exit and

implement pro-active customer and contact management strategies. If it is known, for instance, that anger is

typically a pre-cursor to exit, then a customer who registers anger at their treatment can be contacted quickly

and their exit pre-empted.



Case Study 1: How an Emotional Customer Experience Model Has Helped HSBC

Introductory Overview

Over the course of one month in 2009 across six HSBC branches in the UK, BrainJuicer used touchscreens

connected via the mobile phone network (as shown in Figure 4) to measure customers’ emotional response

immediately after their transaction in-branch. The pilot took place from 30th

September 2009 to 31st

October

2009 and in that period 2618 responses were collected.

Customers were approached as they left the branch by independent customer experience representatives

recruited by BrainJuicer® and invited over to the touchscreen to tell HSBC how they felt. The touchscreen

terminal was connected via 3G to the internet. This enabled us to feed the survey results back to BrainJuicer

and subsequently to HSBC in real-time via an online portal.

With only one touchscreen in each branch only one customer could provide feedback at any one time, but

recruitment continued throughout the day at all times when the touchscreen was free for use. A record of

those declining to participate was kept and the average strike rate was established to be around 50% (i.e.

proportion of those stopped who agreed to participate). This compares favorably with traditional off-line

recruitment strike rates.

In addition to FaceTrace (emotion felt, intensity of emotion, plus reason/trigger for emotion), a short series of

other questions was asked including general demographics and customer type, reason for visit and

recommendation questions. The survey was taken anonymously (the data was not attributable to specific

customers) and took on average between 2-3 minutes for each customer to complete.

The research provided understanding and direction that stand to unlock considerable opportunities for the

bank, from a better understanding of how the bank is performing against customer expectations. The

following four sections of the paper will examine the results of the research in detail.

i) Understanding the Role of Each Emotion in Customer Experience

The first important finding of this pilot was confirmation that it is happiness that service providers should aim

to instil in their customers. Regression analysis suggests that it is only with gains in happiness that we might

also see gains in recommendation scores; each of the remaining 6 basic emotions (including surprise) and

neutrality are associated with a drop in recommendation scores.viii

See figure 5 for details of this analysis.



Figure 5

The implication is clear: maximise happiness, because increases in other emotions (and neutrality, or lack of

emotion) are likely to lead to lower levels of recommendation from customers.

Cross-analysis of the emotions experienced with the recommendation scale helps us to ascertain the point at

which different emotions come into play. Figure 6 shows the emotional response of customers giving different

scores on the recommendation scale. As we would expect from the analysis above, happiness is the

predominant emotion associated with strong positive recommendation, and as positive recommendation

decreases, so does happiness. If customers are only slightly inclined to recommend, it could well be in part

because they felt no emotion during their experience (neutrality becomes more apparent for customers

registering points 5-7 on the recommendation scale). Towards the bottom of the recommendation scale we

see anger and contempt emerging, suggesting that these might be an early precursor to negative word of

mouth and perhaps even the first warning signals of intention to take their custom elsewhere (exit). Surprise

also seems to make an appearance. At the very bottom of the recommendation scale we see the emergence

of disgust and sadness – customers’ disgust and sadness (disappointment) at the way that they have been

treated (it should be noted that these types of emotion were only seen at relatively low levels).



Figure 6

This analysis begins to give us a sense of the role of each emotion in customer experience research, their likely

impact on subsequent behaviour and the consequences of leaving emotions unchecked.

Let’s turn now to look in general terms at what led people to feel each of the different emotions, starting with

the most helpful emotion – happiness.

Happiness was triggered by a warm welcome, quick service and an overall positive experience

that surpassed expectations. Personal, human interaction with helpful staff was a common driver of intense

happiness (we expand on this further below).

Surprise was sometimes triggered when the experience was better than expected, but usually

because expectations had leading up to that point been low. There was also some (negative) surprise at the

revelation that certain mortgages were only for existing customers. New branch surroundings following a re-

fit also triggered surprise.

Fear was felt in relation to personal financial circumstances: customers’ anxiety over their

overdraft and whether defaults on mortgages might lead to the re-possession of their home. There was also

some mention of being scared of the staff.



Neutrality was the result of normal service, where little challenged or exceeded expectations.

It was evident from this that normal or expected levels of service are not sufficient in themselves to trigger

happiness.

Contempt was triggered by irritations at the branch resulting from a lack of care or attention

to detail: when problems were encountered with the machines, staff were deemed to adopt an unhelpful

tone of voice or to be unclear, or when waiting times were deemed to be too long (and staff seemed in no

particular hurry). The background music in branches also evoked contempt when customers were trying to

concentrate.

Sadness was also triggered by disappointment on a number of levels – inability to perform a

task or to resolve a problem, being turned down for an application of some kind. Sadness was also triggered

to a lesser extent by a tinge of sadness at personal financial circumstances (taking out money that customers

thought they didn’t really have) and resignation at long waiting times.

Anger was usually triggered by perceived incompetence. For instance, when customers were

made to wait and it was clear that the problem could be rectified with more staff, when ATM machines were

not working and there were no signs up saying so, when accounts had been closed without consultation or

when multiple customer trips had to be made to the branch to perform the same task.

Disgust was triggered by when customers felt their custom was being taken for granted and

that they were not being looked after. In this most unhelpful of emotional states, the smallest things seem to

irritate: poor interest rates, poor service (being made to wait), machines and pens not working, blurry

receipts, staff exiting the branch on breaks when there were evidently long queues and customers waiting.

These emotional triggers reveal how the smallest of things can lead to strong levels of both positive and

negative emotion, and how small changes in the branch environment can promote happiness and defuse the

most unhelpful emotions of anger and disgust.

Closer analysis of what made people very happy reveals the importance of a personal touch. When we look at

the reasons given for happiness in customers’ own words against the intensity of happiness felt, it becomes

clear that customers who mentioned specific staff members were far happier than those who talk in more

general terms about the service they received (see Figure 7).



Figure 7

Comments from these customers reveal just how important the staff are in making the experience happy and

memorable: ‘The lady named Jill helped me but I come here everyday and am always happy’, and in another

example: ‘Very happy. The lady that helped me was lovely – definitely like coming here’.

Whilst it might seem self-evident that the good personal encounters between staff and customers are

important, it was not so evident that all branches were exhibiting this practice universally. In fact, there were

very few mentions of this type of ‘first name’ reference relationship, suggesting this was a very important

direction for improvement and one which might help the degree to which Happiness is felt.

The data captured in this research was anonymous and not attributable to customers. However, if such data

were captured on an attributable basis (i.e. the data were not anonymous but the bank knew who had

provided the feedback) then the bank could tailor the way they talk to the customer based on the emotion

they felt at their previous transaction. If a service provider knew the emotional state of a customer at their

last transaction, then they might be better able to service him/her appropriately next time. If it is known, for

instance, that a customer was fearful the last time they had contact with the bank because of their financial

circumstances, then staff might adopt a friendlier and more approachable tone of voice next time. Similarly if

customers are known to have been angry or disgusted in the past, staff can be prepared to make sure that the

next transaction goes as smoothly as possible and that steps are taken to improve the relationship.

ii) How the Approach Can Help on the Ground at Branch or Store Level

The research helped us to understand how operational factors affected customers’ emotions at a branch

level. Levels of happiness were seen to vary enormously from week-to-week for the same branch as a result

of environmental changes in the branch. This can be seen in Figures 8 and 9. Figure 8 shows the recruiter’s

observations of activity in Branch A across the project period. Weeks 1, 2, and 3 were considered ‘normal’

from the recruiter’s perspective, but in Week 4 it was observed that the branch was short-staffed at the

helpdesk, which led to long queues. Week 5 was a half-term holiday which also led to long queues.



Figure 8

Figure 9

Figure 9 shows that the conditions creating long queues at Branch A in Weeks 4 and 5 led to lower levels of

customer happiness (dropping from well over 60% to only 48% and 52% in Weeks 4 & 5). One Week 4

customer who did not report feeling happy said, ‘nobody offered help, just carried on without saying “be with

you in a moment”’. In another branch, drops in happiness were seen when the bank manager went on holiday

and also during a branch re-fit. This type of information can empower branch managers and staff to take

ownership of local problems and put solutions in place to overcome them. Armed with information that



shows the implications of these everyday problems for customer happiness, the branch manager can

proactively make arrangements for what are known to be potential problems in the weeks ahead, such as

school holidays or student registration weeks, when branches are typically busy. It might even be possible to

forewarn regular customers that the branch will be busy in these periods and to help customers plan their

visits accordingly. In other words, it might well be possible to go the extra mile to delight customers, knowing

how external events have affected customers in the past. The focus on customer happiness also gives other

staff the permission and focus they need to do whatever they can to ensure that customers leave the branch

happy, if tactical or short-term problems in the smooth functioning of the branch are ever evident.

iii) How an Emotional Response Can Help to Prioritize Transaction Type

Analysis of individual transaction types revealed how certain transactions were more likely to lead to negative

emotions than others, and therefore which areas of the business needed attention. Simple transactions

involving face-to-face personal contact (transactions H-L in Figure 10) resulted in the highest levels of

happiness. Transactions that were at least partly automated (transactions A-D) resulted in lower levels of

happiness. Complex or lengthy transactions that had to be dealt on an ad hoc basis or that involved a number

of visits to the branch (transactions E-G) resulted in the highest levels of negative emotion.

This granular level of understanding helps senior management to understand the difficulties associated with

all kinds of transactions on the ground. Some of these transactions are themselves sales opportunities and to

alert management to less than satisfactory emotional outcomes is to help it identify problem areas that need

attention. If one branch achieves strong emotional scores for a particular transaction type and another branch

achieves poor scores for that same transaction type, the approach can also point management to best

practice and instigate cross-branch learning. The approach puts the spotlight on procedural and personnel

issues, can help to address and minimize time-consuming costly complaints, and generate future sales

through improved customer experiences.

Figure 10



iv) How Emotional Patterns Over Time Can Lead to Strategic Insight

The project also provided a new level of detail about how customers’ emotion varied over various time

periods. Analysis confirmed that customer happiness varies significantly according to time of day for example.

It seems that there is a significant drop in happiness in branches after lunch that never recovers during the

course of the afternoon (see Figure 11). This insight has important implications for customer experience

strategy. In addition to pointing to an opportunity to improve customer experience in the afternoons, it

suggests that the bank might consider scheduling customer and sales meetings in the morning. Branches also

now know that they need to improve levels of customer happiness in the afternoon – ensuring that frontline

staff are not diverted away from customers to take care of backroom duties and that the branch remains

fresh and tidy in the afternoon, for instance.

Figure 11

A further insight that was uncovered by the research was that customer happiness varies by day of the

month, as depicted in Figure 12. In the UK, pay day is traditionally near the end of the month. The orange

bars, which represent the emotional intensity any emotion felt, indicate that people seem to be more

intensely emotional toward the end of the month and pay day. The blue line, which refers to percentage of

customers who feel happy, also seems to spike near the end of the month, before reverting to the lower

levels seen at the start of the month. It would be worth investigating this finding further with an extended

fieldwork period, but if the pattern were confirmed, it would have strategic implications for the timing of

customer meetings, sales initiatives, branch communication activities and even national advertising

campaigns.



Figure 12

HSBC Case Study: Summary of Key Research Findings

HSBC currently has a customer satisfaction survey in place, but as we have seen, this new emotional approach

yielded a number of new tactical and strategic insights for the bank:

1. It confirmed that customer experience managers should aim for customer happiness.

2. It helped the bank to understand how to maximise happiness, by revealing for instance that personal

interaction with staff at the branch resulted in the greatest intensity of customer happiness; it also

revealed which interactions did not lead to happiness and prioritised improvement areas for HSBC

3. It provided tactical insight by revealing how emotion varied by week in the same branch in response

to staffing levels and other branch-specific conditions. It demonstrated how it can empower branch

managers and staff to seize the initiative and take action to solve problems particular to their branch.

4. It also provided tactical direction by demonstrating that emotions vary widely according to reason for

visit and customer type.

5. It revealed patterns in happiness according to time of day, week and month, which have strategic

implications for the scheduling of sales appointments, communications and initiatives.

It is not difficult to see how the insights revealed by the research could open the door to new opportunities

and real business improvements.

How the Research Was Received by HSBC

Front-line branch managers and central management were delighted with the information they received from

the research. Branch managers found the research much more helpful than other survey methods currently in

place, as one commented:

“Our existing process doesn’t give us negative comments and actions. The emotion collection process could

and should form part of each customer engagement.”

HSBC was quickly able to alter the way that it dealt with certain customer situations based on the data

delivered by the research, whereas traditionally it might have taken many months and numerous rounds of

research to decide on whether a course of action was required and what this might be.



There were also some unforeseen benefits to the research reported by front-line staff, who were delighted

with the physical presence of the touchscreen machines, and who were sorry to see them removed at the end

of the research. It seems that branch managers could identify greater customer focus in their staff once the

touchscreens were in place. Some even went as far as to suggest that the presence of the touchscreen

resulted in an increase in sales during the period of the research. One manager was pleased to report:

“Sales were up in October, not sure why but am convinced it was to do with the machine. Feel we are more

engaging, it gives customers an opportunity to feedback. Shows we care.”

Another branch manager declared:

“Our mystery shop was 100%, I’m not sure, but this increase could have been related to the running of the

pilot.”

There was even evidence to suggest that the touchscreens had helped to improve customer engagement with

other automated devices, as another branch manager reported:

“I saw an old couple try the machine and complete survey, after which they tried the ATM machines. It even

cured some technophobes!”

The pilot was deemed a success by HSBC and the approach was thought to be particularly helpful for the

diagnosis of problems at specific service centres and for the monitoring of new pilot initiatives before wider

roll-out.

HSBC plan to use the approach next in branches in other countries and across other channels, and have

commissioned BrainJuicer to provide four sets of FaceTrace faces specifically for HSBC that are culturally

attuned to their various key markets around the world.

Emotional Response Measurement Using FaceTrace versus Net Promoter Scores

A measure that is growing in popularity in customer experience research is the net promoter score (NPS),

pioneered by Bain consultant Fred Reichheldix. Whilst the measure can be revealing, like all one-dimensional

linear scales, we assert that the NPS measure has its limitations. As we saw in Figure 6, a given point on any

linear scale, such as the NPS measure, might conceal a number of different emotional responses, each of

which needs to be known and understood because each needs to be handled differently by the organisation.

Transactions are emotional experiences and are multi-dimensional; a linear scale is not sufficient to capture

this when used in isolation.

Whilst the NPS measure is simple to administer and interpret, the way that the score is calculated means that

a large proportion of customers are discounted from the analysis, resulting in small base sizes and volatile

scores that do not lend themselves to stable longitudinal measurement. It is also prone to cross-cultural

differences in the way that people respond to scales. It is known that certain countries are more inclined to

use the top of research scales than others, and that other countries are particularly prone to giving lower

scores. Given that the NPS score is calculated by subtracting the proportion of people giving low scores (0-6)

from those giving higher scores (9 or 10), we assert that NPS scores in one country are not comparable with

scores in other countries and can therefore mislead.x

Discussion

In the HSBC study, we chose to use touchscreens as the data capture medium, but the approach could be

applied to almost any environment – mobile phones, iPads, PCs, touchscreens, internet-enabled Wii boxes,

set-top boxes, websites, even the new generation of Smart TVs.



Case Study 2: How an emotion-model on mobile phones helped an international telecommunications

company

Introductory Overview

In a 2010 we conducted a project for an international telecommunications company. Customers who have

recently purchased a mobile phone were sent a text message which had within it a link to WAP based survey

asking them how they felt about their recent transaction using the FaceTrace method.

Paper surveys or surveys sent by email were felt to be inconsistent with the telecommunications brand, so a

mobile survey was developed to reach customers immediately after they had bought a mobile phone, while

their retail experience was still fresh. Response rates were much higher than the telecommunications

company had expected. Typically they expect to see response rates of around 2-5% for this type of study, but

the visually arresting FaceTrace question asked as close as possible to the purchase moment resulted in

completion rates that were four times that at 16%.

Completion times for the survey were less than 60 seconds on account of the approach, which dramatically

simplifies the respondent’s task compared with traditional satisfaction measurement approaches.

The telecommunications company was able to derive actionable insights specific to the exact store where the

phone was purchased, and was alerted to fundamental cultural differences amongst shoppers, as the survey

identified also the profile of the respondents: 52% were German speakers, 32% were French speakers, 7%

were Italian speakers and 8% were English speakers.

German Speakers are Happier than English, French, or Italian

As Figure 13 demonstrates, German speakers overwhelmingly felt ‘happiness’ when asked about their general

retail experience, followed closely by Italian speakers, English speakers. The measurement also looked at the

reasons behind the disparities between languages by recording verbatim responses from consumers about

their transaction experience. From this we know that nearly half of French speakers reported feeling

emotions other than ‘happiness’, often resulting from long wait times and unresponsive staff. This insight

allowed the telecommunications company to look at what can be done to improve the stores where French

speaking customers’ shopped frequently.

From further work on the word analysis we were are able to see that happiness for German speakers was

evoked more by friendliness, and for the French speakers, more by efficiency and staff knowledge.



Figure 13

What Matters Most?

We also asked the respondents how they felt about general in-store behaviour; including friendliness, staff

competence and waiting time. The results indicate that friendly support in the telecommunications

company’s branches was the largest driver of customers feeling happy and is a greater contributor to

customer happiness than perceived staff competence. Long waits understandably prompt negative responses,

specifically anger. While the telecommunications company may have recognized previously that shorter wait

times are always appealing to their customers, the fact that friendly staff is a larger proponent for happy

emotions than staff competence allowed them to resolve immediately to emphasise in training programs the

importance of a friendly manner. These results can be seen in Figure 14.



Figure 14

When are Shoppers Happiest

The immediacy of a mobile platform helped us to understand how days of the week influenced people’s

emotions, as demonstrated in Figure 15. Saturday and Sundays generate the most negative emotions,

resulting from long queues and short-tempered staff. The approach led the telecommunications company to

acknowledge this sudden drop in happiness from 78% of respondents feeling positive emotions on Fridays to

only 42% reporting any positive emotion on Saturdays and institute immediate corrective measures by

allocating more staff to address the long wait times on busy weekends and prompt them to be more

understanding of busy consumers.



Figure 15

Case Study With the Telecommunications Company Results

The results clearly indicated which aspects of customers’ in-store experience were most likely to produce

positive feelings and make them more likely to recommend the brand, and which aspects were less desirable

and might impact on recommendation. The results prompted the telecommunications company’s

management to implement immediate solutions to solve what was not working and to also support what was.

The immediacy offered by mobile research only aided management to ensure that these organizational

changes occurred quickly and could be implemented as they occurred.

Conclusions

Customer satisfaction is a critical component of business success that requires constant, real-time and

effective monitoring. A customer’s experience is inextricably linked to their emotional response, and so it

seems odd that we continue to track customer experience by asking respondents to re-interpret their feelings

through traditional linear and evaluative scales, and often well after the event.

This emotional measurement approach is not just limited to external customer satisfaction, either. It can be

used for internal employee satisfaction monitoring. We surveyed staff of a large technology company using

this approach and it brought staff feelings to the fore where traditional approaches were less revelatory. As

with customer experience research, the company’s traditional approach relied upon a long list of key

questions that were thought to be important by management. While very few of these pre-determined

indicators suggested that there might be any cause for concern among the workforce, the emotional

approach suggested otherwise. The output it provided quickly led HR to make changes to their internal

electronic staff assessment tool.

An emotional model of customer experience monitoring using an intuitive data capture vehicle such as

FaceTrace can provide managers with real-time emotional customer feedback. In this model, customer

experience managers are alerted to problems instantly, and can intervene immediately. The approach



presents companies with an opportunity for a new form of customer relationship, a more human

conversation between customers and business, placing customer feelings at the heart of all marketing activity.

Measuring customers’ feelings surfaces insights that would otherwise remain hidden, and can lead to

strategic and tactical customer experience improvements that will drive growth.

This intuitive emotional approach is superior to traditional approaches because it:

1. is a faithful representation of how customers feel rather than a flat one-dimensional evaluative

measure of satisfaction.

2. reveals problems and positive experiences that are important enough to the customer to have

resulted in an emotional response, rather than asking customers to evaluate an incomplete menu of

factors that might have little bearing on their feelings or experience. We do not need to derive what is

important using statistical models, it is explicit; causal relationships are identified, problems are

intuitively prioritised. It is a completely consumer-driven approach; it is bottom-up rather than top-

down research.

3. both measures customer experience performance and diagnoses problems in one piece of research,

without the need for further rounds of research, such as customer focus groups, to explain shifts in

scores from one wave of research to the next.

4. can be administered at the moment of the transaction itself, when customers are most inclined to

provide their feedback, improving quality and quantity of feedback.

5. is intuitive, fun even, for customers and reflects well on the client business.

6. surfaces customer problems and experiences to front-line staff and senior management within an

analytical framework that is human and intuitive to understand.

7. can provide real-time data that allows managers and front-line staff to intervene immediately when

problems arise.

8. is simple and quick enough for customers to complete in large numbers, providing sufficient

resolution in the data for analysis by time and location at a later date.

9. can provide an on-going emotional dialogue between individual customers and customer relationship

managers.

10. allows for direct and consistent comparisons between channels, geographies, transaction and

customer types, and can be incorporated easily into clients’ operational frameworks.

BrainJuicer’s work with its customers has uncovered numerous actionable insights for the organizations. It

engaged and empowered staff in both companies it operated in and demonstrated to customers that the

organizations are serious about knowing how they feel.

This new approach to customer satisfaction works well because it applies an emotional and non-evaluative

framework to customer experience and does so at the closest point to the transaction experience as possible.

It does not impose a long and restrictive set of questions on customers but gives them permission to voice

their feelings at the moment when they are most willing to air them. It is a research approach that provides

actionable insight, but more than that, it is also an effective PR tool; it demonstrates that the company

actually wants to know how its customers feel and that it cares for them. It marks a shift in research from the

complex to the intuitive and from the reflective to the reflexive. It is at the same time both research and

marketing — a new customer experience approach for an age of integrated marketing communications.



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Authors

Paul Roberts, Managing Director, Customer & Employee Experience

BrainJuicer® PLC

13-14 Margaret Street

London W1W 8RN, UK

Email: [email protected]

Phone: +44 (0) 7590 367472

As BrainJuicer’s Managing Director of Customer and Employee Experience, Paul led the launch of

SatisTraction™, BrainJuicer's revolutionary approach to measuring customer satisfaction. Paul brings nearly 30

years of experience in technology, operations, and sales, and most recently worked in business development,

management, and consulting at various start-ups and mid-sized companies. Paul is passionate about truly

understanding how customers feel in order to drive growth, give customers what they want, and build better

companies.

Orlando Wood, Managing Director, BrainJuicer Labs

BrainJuicer® PLC

13-14 Margaret Street

London W1W 8RN, UK

Email: [email protected]

Phone: +44 7980 671768

Orlando Wood is Managing Director of BrainJuicer® Labs. His work on emotional response to communication

has won an ESOMAR award (Best Methodological Paper 2007) and the ISBA Advertising Effectiveness Award

(2007). Orlando is a frequent speaker and has spoken at ESOMAR (Berlin, 2007), MRS (London 2007, 2009),

AMSRS (Sydney, 2009) and EphMRA (Paris, 2009) research conferences. He has also been published in AdMap

(Jan 2010) as well as in Marketing Week (March 2010).



i See Anderson, Fornell and Mazvancheryl, ‘Customer Satisfaction and Shareholder Value’, Journal of Marketing, Vol. 68,

pp. 172-185, 2004 ii Anderson et al observe that the number of competitors in any given market has a bearing on the relationship between

customer satisfaction and shareholder value. ‘When an industry is fragmented and concentration is low, the degree of

rivalry in the industry is likely to be more intense. Even satisfied customers are likely to be more difficult to retain and

more price sensitive and to find other supply sources. At the same time, the firm’s ties to its customers will be weaker

and the relative bargaining power of the firm reduced’. In other words, the greater the number of competitors in a

sector, the less positive the impact of customer satisfaction on shareholder value; the fewer competitors in a sector, the

more positive the impact of satisfaction on shareholder value. This might help to explain why negative relationships are

seen for hotels, discount stores and athletic shoe stores in Figure 1. iii Please see Stuart-Menteth, Wilson & Baker, ‘Escaping the Channel Silo – Researching the New Consumer’, Vol. 48,

No.4, International Journal of Market Research, 2006 for a full account of the shifts that have occurred. iv In parallel tests, we have found our emotional FaceTrace® approach to be much more sensitive than a measure that

uses just the words alone. This, we assert, is because it is a visual and pre-cognitive measure that requires very little

cognitive processing on the part of the customer. It is for this reason that we believe it to be an intuitive measure. v For a full description of the technique and its development, please refer to Wood, ‘Using Faces;

Measuring Emotional Engagement for Early Stage Creative’, ESOMAR, 2007 vi We favour asking emotional response once and only after the transaction, rather seeking to establish a change in

emotional condition pre- vs post-experience for two good reasons. First, it has been shown by Daniel Kahneman (2002)

that people assess their experiences by how they feel at the end of the episode (rather than ‘averaging’ their feelings

during the course of an experience). Second, we would not want to sensitise customers to their emotional state prior to

the experience as this could influence the experience itself. vii

Validation work we have conducted across multiple continents has shown that the facial expressions shown in the

question are universally recognised and understood regardless of the market – whether this is India, China, Brazil or the

UK. viii

The approach we used in the regression analysis was very simple. For each emotion we classified each respondent as

having that emotion or not (1 vs 0) and then regressed that against the net recommendation scale (simplified to

promoter +1, neutral 0,and detractor -1). As this was not a series of individual regressions co-linearity did not affect this

analysis. ix

For a good description of the approach, read Marsden, Samson & Upton (2005), ‘Advocacy Drives Growth; Customer

Advocacy Drives UK Business Growth’, who reference Reichfeld’s article ‘The one number you need to grow’, Harvard

Business Review, December, 2003, pp.1-11. x For a fuller discussion of the merits and drawbacks of the NPS measure, the reader might read Joe Mullich’s article,

entitled ‘What’s your score?’, The Advertiser, April, 2007

This paper was presented at Merlien Institute’s International conference on Market

Research in the Mobile World, 2-3 December 2010 in Berlin

For more information about this event, please visit: http://www.merlien.org