Customer Insight Autumn 2020

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AI & THE CUSTOMER INSIDE… ContactEngine on conversational AI Pegasystems roundtable on AI & bias Natterbox on contact in the current era The Index of Consumer Sentiment

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EDITORIAL

Foresight In this edition we’ve focused on a topic that

strengths and weaknesses of machine learning

always seems to be just about to create seismic

tools, in a way that cuts through the hype,

change: Artificial Intelligence. Is it time to start

therefore explaining how damaging they can be if

believing the hype, or to move on from it? By the

we don’t use them in the right way.

time you finish this issue, you’ll probably conclude that the answer is a bit of both.

Editor

We start with an in-depth piece from Professor

launched in the Spring) tells us about the impact

Mark Smith of ContactEngine (page 6), who

of the pandemic on how customers are feeling, and

believes that his organisation has found the

what that might mean for their behaviour (page

niche where conversational AI can both improve

12), which sits really well alongside some new

customer experience and make organisations more

research that we’ve conducted into how customers’

efficient. Some of the principles we discussed

spending habits are changing (page 33).

could be taken as general rules for AI deployment,

We also have a new Brand Health product from

I think.

TLF panel (page 26), and a guest feature from

On page 19 is a report from an interesting

Natterbox on the challenges for contact agents in

roundtable event hosted by Pegasystems, looking

working from home (page 23).

in particular at the issue of algorithmic bias, and

Enjoy the articles, and please drop us a line

how it relates to human biases. Do we expect more

if you’ve got an interesting story to share for a

from machines than we do from people, and are

future issue.

we right to?

Our book review this time, on page 31, is the excellent Artificial Unintelligence. Meredith Broussard is able to delineate very precisely the

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DESIGN & PRODUCTION Creative Director Rob Ward

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Designers Becka Crozier Jordan Gillespie Rob Egan

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EDITORIAL Editor Stephen Hampshire

CONTACTS

Stephen Hampshire

Elsewhere, we look at what the Index of Consumer Sentiment (which we somewhat rashly

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NB: Customer Insight does not accept responsibility for omissions or errors. The points of view expressed in the articles by contributing writers and/or in advertisements included in this magazine do not necessarily represent those of the publisher. Whilst every effort is made to ensure the accuracy of the information contained within this magazine, no legal responsibility will be accepted by the publishers for loss arising from use of information published.

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ISSN 1749-088X

www.tlfresearch.com | Autumn 2020 Customer Insight 3

C O N T E N T S

-

A U T U M N

2 0 2 0

18

Whose Bias Is It Anyway?

A report from a recent Pegasystems roundtable looking at AI and human biases.

CONTRIBUTORS

06

ContactEngine AI implementations have often been underwhelming, but Mark Smith from ContactEngine believes he has the answer.

12

How Do You Think Consumers Are Feeling Now? UK Consumer Sentiment has been on a rollercoaster ride this year, what does it tell us about future behaviour?

Nigel Hill

Tom Kiralfy

Stephen Hampshire

Wine-lover, Munroist and customer satisfaction guru

Panel wrangler, banana lover and chinchilla owner

Conference speaker, book-lover and occasional climber

4 Customer Insight Autumn 2020 | www.tlfresearch.com

CONTENTS

GUEST FEATURE Contact Engine 06

23

How Can Contact Agents Respond To Current Challenges?

Ian Moyse from Natterbox reflects on customer service in the "work from home era".

26

How Healthy Is Your Brand?

A new product from TLF Panel, that might help you understand your brand health.

33

Your Customers' Spending Habits Are Changing Are you ready for the long-term impacts of the pandemic on your customers' spending habits?

RESEARCH Sentiment Index: How Do You Think Consumers Are Feeling Now? 12

LATEST THINKING Pega Roundtable: Whose Bias Is It Anyway?

18

GUEST FEATURE Natterbox: How Can Contact Agents Respond To Current Challenges? 23

31

Book Review: Artificial Unintelligence

RESEARCH How Healthy Is Your Brand?

26

BOOK REVIEW Artificial Unintelligence

31

LATEST THINKING Your Customers' Spending Habits Are Changing

33

DESIGNERS

Published by

Becka Crozier

Jordan Gillespie

Rob Egan

Right brain mastermind, music enthusiast and have I told you I’m vegan?

Creative magus, genuine tyke and 20ft wave rider

Beer drinker, pixel pusher and dour Yorkshireman

www.tlfresearch.com | Autumn 2020 Customer Insight 5

G U E S T F E AT U R E

In recent editions we’ve featured a series of articles from the Natural Language Understanding experts at ContactEngine. The more we found out about the company, the more interesting we found their slightly leftfield take on the role of Machine Learning and AI in the customer experience, so we sat down with their charismatic CEO Prof. Mark K. Smith to find out more about ContactEngine, proactive conversational AI, and his view of the future of customer experience. Along the way we’ll pick up some crucial insight as to where AI does and doesn’t fit in your customer journeys.

“You just can't differentiate between a robot and the very best of humans”

The problem with Chatbots When you talk about AI and the customer experience, many people think immediately of Chatbots. I’m sure we’ve all

- Isaac Asimov, “I, Robot” 6 Customer Insight Autumn 2020 | www.tlfresearch.com

encountered them, and I’m equally sure we’ve learned to be

G U E S T F E AT U R E

suspicious of vendors who claim that theirs

be almost anything, worded in a massive

we take

are indistinguishable from humans. Those

variety of ways. ContactEngine’s approach is

away

vendors, it seems to me, must know a lot of

different.

unengaging

very stupid, boring, people.

“Because we asked the question, we know the

tasks that

context of the reply. We might ask the question

humans would rather

purpose. When you’re honest about what they

about a loan application, or an insurance

not be doing, and we give

are, which is essentially a user-friendly skin

product, or a washing machine, but we know

those tasks to machines that not

built on top of a FAQ, that’s fine. The mistake

what was first said.”

only don’t mind that they’re boring, but

Used in the right way, Chatbots serve a

is to see them as an alternative to a proper

they actually perform them better and more

conversation. As Mark comments,

reliably (not to mention more cheaply).

“Use humans to do what humans are best at, and then machines.”

“Why do you have chatbots? Why do they exist? It's containment for overspill of people going to websites, stopping them reaching call centres. They even call it ‘containment’, as if customers have a virus. It's just wrong.”

You may be wondering why someone who runs a company specialising in conversational

when someone calls to make a claim on a life insurance policy. As Mark points out, that logic may apply only to the initial call, and automation may well have a role later on in the journey: because those calls are long, and dealing with

Chatbots. The answer is that Mark believes he

Focused

grief. The first call is counselling, this person is in bits, so that's where it has to be humans.

be used to enhance the customer experience, rather than to save cost at the risk of making

wouldn’t want to automate, for instance

“A machine won’t be the best to do that,

AI is so dubious about the benefits of has identified a unique niche in which AI can

We can easily think of situations which we

ContactEngine sells itself on using

After that, the machine is fine, but initially you

the customer experience worse. It’s a

communication to improve the small

need a human being because machines can't do

niche where customer experience, business

moments of inefficiency that bedevil so many

empathy. Use humans to do what humans are

efficiency, and the strengths of Machine

businesses: the missed appointments, the

best at, and then machines.”

Learning line up to allow automation to help

unhappy customers who need an opportunity

everything flow more smoothly.

to be heard, the information updates that

Conversation

prevent inbound calls.

AI and the Customer Experience

“We start a conversation with somebody that

Of course a lot of this kind of

says something like, ‘we’re coming to your place

communication is already automated, but

in three days’ time, is that still on?’ And when

what is relatively rare is for an organisation

are 5 crucial elements that make this kind

somebody says ‘yes’, we'll say, ‘we're coming to

to automate conversation in this context, so

of automated communication work, which

number one, the high street, is that the correct

that the customer can get an SMS or email

address?’ and then we carry on the conversation.”

and interact intelligently with a computer at

Based on our conversation, I think there

we can take as generalisable rules for where automation makes sense in the customer experience. I believe AI makes sense when it is proactive, focused, conversational, learning, and context-aware. Let’s look at each of those in turn.

The point is that, because you know so much about the context for the customer’s

technical challenges of conversation are vast, of

conversation, you have naturally constrained

course, but if you are connected into what the

the possibilities for what they are going to

company wants and the service the customer

want.

wants, then you could make massive cost

with Chatbots is that they are reactive; they respond to a request or enquiry from a customer,

savings.”

reduced by quite a lot, particularly if it's a single question. You can maybe say 15 intents cover 98%

One of the problems

“We’re dialogue, not monologue. The

response, and because you started the

“The intents, when you ask a question, are

Proactive

the other end of it.

Learning

of the objectives, something like that. Machine learning algorithms fly when they are fed training data like that.”

AI is a frustratingly vague term. Even if we restrict our definition to Machine Learning

What about the 2%, then?

(ML), the plethora of algorithms, approaches,

“There will always be the need for human beings to deal with exceptions, but machines

and that request

are better at a lot of that sort of work.”

or enquiry could

This is a crucial point. When we use AI well

www.tlfresearch.com | Autumn 2020 Customer Insight 7

G U E S T F E AT U R E

that it’s common sense not to try to upsell a customer while they’re unhappy, and that they’d rather you didn’t!

The ethics of AI “It's a really fine line. You have to travel very carefully through that, and you have to make sure your GDPR compliance and all those things are right

There are times when AI can slide from creepiness to impacts that are downright unethical. Some of the key issues, all of which are related, are interpretability, bias, and the impact on society.

and implementations often makes it very

there, but there are things that you can do. Take

difficult to know what vendors are talking

telco as an example: for some processes someone

about. I suspect, though I can’t prove,

has to have something before the next thing can

that the much-hyped AI solutions of some

happen, like receiving something in the post

vendors are often very simple algorithms

before the connection can be made. If you choose

rests, directly or indirectly, on people tapping

applied with varying degrees of cleverness to

not to connect those two events, there will be

into big “AI as a service” providers, especially

very simple problems.

10-15% people where it will not have happened,

a few key players such as Amazon, Google,

One of the trademarks, it seems to me,

in which case the second communication makes

Apple, Microsoft and IBM. Mark is glad that

of true ML, and one that is rare because it’s

no sense. So what you need to do is confirm that

ContactEngine decided early on to develop

relatively difficult to do, is ongoing learning.

they’ve got it before the second communication

their own algorithms in-house:

As Mark says,

happens. That's a very logical sequence and it's

“It's got to be learning, it's got to get better with time, and that's really rare. By labelling

not creepy, it's just sensible.”

Judging that line between personalisation

Interpretability Perhaps the majority of AI at the moment

“What they do is not open, and it's a GDPR nightmare. We recognised that some years ago and decided to build our own, which really went

the data you arrive at a point where you can

and creepiness can seem difficult, but a good

against the flow. We were lucky we made that

outperform a human agent very rapidly. The

starting point is to ask who benefits from

decision, because there's now a big kickback

learning bit comes from when you take the

the use of the data that we’ve got. If, like the

against the black box AI solutions that people use.”

exceptions, deal with them, and then that’s added

telco example, it’s 100% in the customer’s

to the algorithm. So it gets better, and better, and

interest, then it falls on the right side of the

because it’s often the case that we can train

better.”

line. We can even make a good argument, as

the machine to get the right answer, but we

Mark does, that judging the timing of a sales

don’t know how. If we can’t explain how,

message is ultimately showing respect for the

then there is always the possibility that the

customer’s feelings:

machine will make unexpected mistakes*,

Context Making outbound contact to a specific

“In the world of financial services, where

Interpretability is a big challenge in ML,

or bake in bias. Developing explainable AI is

customer about a particular event means

someone has a successful mortgage application,

that the context for the conversation is well

and then is surveyed on NPS - if they give a 10 out

understood. That has benefits in terms of

of 10, then it's perfectly reasonable to offer them

arriving at singularity or sentience, but you are

language understanding, as we’ve already

an additional product, maybe home insurance. If

absolutely performing like a human and getting

seen, by narrowing the scope of likely

the answer was zero, then don't do that right now.

better with time. Therefore, by doing this, you

responses. It also opens up the ability to

That's rapport as well, because you're looking

can not only out-perform the agent, but you can

personalise the conversation.

at patterns in the data to make an offer at an

explain it as well. You can visualize it. You can

appropriate time, which isn't irritating.”

actually say, ‘we made this decision because of

That opportunity can be a risk—there’s a very fine line between intelligent personalisation and creepiness—but there are

That’s obviously in the organisation’s interest as well, but I think it’s fair to argue

cases in which it clearly makes sense.

important to Mark: “There is an argument you're not ever

that’. So we're not trying to make a life or death decision, we are living in a simpler world than that, and that is proper AI; applied, and white box, and explainable.”

*There’s a great apocryphal story about an early neural network that the US Army trained to spot camouflaged tanks, but which was really detecting photos taken on a cloudy day. Sadly it’s not really true: https://www.gwern.net/Tanks 8 Customer Insight Autumn 2020 | www.tlfresearch.com

G U E S T F E AT U R E

are simply not possible with traditional approaches

“The way I see it is that computers take away jobs that humans simply don't want to do, and they make them happen better.”

(although, frankly, we were never making the most of our data anyway!). Before we dive into it, we need to stop and think about what we should and shouldn’t be doing with the data with which customers have trusted us. Mark gives an example: “You could imagine a situation where you were trying to do inferred importance of value to a client based on the quality of the language that's coming back to you. We don't do that, but there is quite a lot of work that suggests you can work out people's educational background based on the way they write. So you could make that inference.

Humans do it all the time.”

That last point is really interesting, isn’t it? Here we are wringing our hands about algorithmic judgements, but what about the

Bias

judgements that our human staff are making every day? It’s true that algorithmic biases

Most ML applications work by working

can scale in a way that an individual

with a set of training data, and learning to

human’s wouldn’t, but again there

replicate the label a human would apply by

seems to be a wider point here about

looking at patterns of association between

the ways in which we make decisions

features of the data and the label applied.

about how to deal with individual

If there are systematic biases in the way

customers. People are nervous

that humans apply those labels, then the

about self-driving cars, but what

algorithm will learn those too, which has the

about the human drivers who

potential to introduce biases. Importantly, the

are killing 2,000 people a year

machine doesn’t do this on purpose,

on British roads? As Mark

“I dislike intensely the notion that the AI itself possesses human traits of bias. Algorithms are not

comments, “The autonomous vehicle is

racist, or sexist, or homophobic, or antisemitic.

held to a higher standard than

The data reflects society. It is not the computer's

the human.”

fault.”

In fact, there’s an interesting parallel

What about the impact of AI on jobs? When should

between the ideas of algorithmic bias in

we expect to be replaced?

machines and unconscious bias in humans—

With a few very specific

both reflect structural problems in society

exceptions, we should

that probably need to be addressed at a

probably take the more

societal level. It’s not really fair to expect

extreme predictions with a

AI developers to address these issues, but

pinch of salt:

I think it is fair for them to be expected to

“I think there's a tremendous

engage with the issue, and at least not make

arrogance from the tech

the situation worse. Explainable AI means

community to imagine that

that the biases and the model are there to be

computers will cross into sentience.

checked and talked about and discussed. If

It's just ridiculous. I also think that

it's a black box, you can’t.

every 10 years there will be cataclysmic predictions about the end of humanity

Robots in society

because of AI.” As far as Mark is concerned, the

AI opens up the potential to use the data that we hold about customers in ways that

most effective use of AI is in very specific, limited, domains. Jobs that a machine can

G U E S T F E AT U R E

“The call centre person would normally be a long-serving staff member, because they have a better job dealing No one, I think, can really object to machines replacing humans in a do better than a human, and that humans find unengaging.

will normally be better paid as a consequence of that loyalty, and they will stay longer because

job that sees that kind of churn, and this is

we've got rid of all the crap that otherwise would

exactly the kind of interaction that a machine

have made them leave after six months.”

can handle better than a human. Not only that but, by handling it effectively, the

“The way I see it is that

machine is able to create a better emotional

computers take away jobs that humans

experience for the customer. This is a really

simply don't want to do, and they make

with actual problems that humans deal with. That

Coders & linguists Effective Natural Language Understanding

crucial point—don’t imagine that customer

(the work to teach computers to understand

emotions can only be influenced by human-

human language as it is really used) happens

who have 12,000 people in a call centre dedicated

to-human contact. Proactive automated

at the intersection between linguistics and

to taking a call when broadband goes down.

communication, like this example or even

machine learning. ContactEngine employs a

The call centre churn is a hundred percent,

Amazon’s simple delivery status notifications,

variety of specialists from different disciplines

every eight months. No one wants this job. You

can do a lot of work to reduce customer

to work together at this point of intersection

need automated proactive communications in

anxiety.

and, with one of their offices at Bletchley Park,

them happen better. I know one large telco

that situation. We know your broadband has a

And if the automated interaction can’t

Mark sees a parallel with the code-breaking

problem, or an imminent problem, so I will give

handle a particular customer’s needs, or if

you all the information about what's happening

they just want to speak to a human being,

when it's happening, and keep you informed

then there is always the option to escalate

employed at the time: there were men and women

across all available channels until such time as

those cases to the call centre. Those cases

that were the equivalent of dev ops, they were

the situation is resolved. And that reduces the

which, almost by definition, will be more

programmers, there were people putting the tapes

anxiety of the customer and lets them know

unusual, and more interesting for a human

in the machines, so the equivalent of software

what's going on.”

to handle.

engineers, there were mathematicians looking

teams assembled during WW2: “There were four types of people that were

at the statistical patterns of data, and there were linguists. They are exactly the disciplines we Mark K. Smith CEO ContactEnginge

employ now. What we do is a little less important than stopping a world war…. but it's intriguing that 75 years later, it's the same group of people, addressing very similar challenges.”

Getting machines to understand humans

Mark is a serial entrepreneur who IPOd his first business on the London Stock Exchange in his early

speaking or writing naturally is extremely

30s. He is credited with inventing online conferencing in the 1990s, built the first Content Manage-

difficult, and it’s not something that

ment System for blind people in the 2000s, built ‘Parasport’ to help talent spot disabled athletes in

you can expect mathematicians or

the run-up to the London 2012 games, and invented a live streaming audio product that allowed

programmers to solve on their

commentary from anywhere in the world via phone. Mark is now CEO of ContactEngine, a conversational AI technology used by large corporates to automate customer communications. The company employs linguists, behavioural scientists, mathematicians and software engineers to design machine-learning algorithms that automate human-like conversations. The company began as an idea in Mark’s head 10 years ago and is now a multi-£million company. Throughout his career, Mark has relentlessly applied science over instinct and believes technologies like AI can be a force for good.

10 Customer Insight Autumn 2020 | www.tlfresearch.com

own. These are problems that need to be solved with real world knowledge, and by testing the impact of approaches with real customers.

opportunity that exists in a huge number of customer

What we do is a little less important than stopping a world war…. but it's intriguing that 75 years later, it's the same group of people, addressing very similar challenges.

journeys across most consumer sectors and not a few business to business ones. Despite all the hype around AI and the potential for machine learning to improve the efficiency of many business processes, nowhere near enough attention has been paid to the potential that it offers to not just save costs, but also to improve customer journeys.

By focusing on proactive, outbound, communications (backed by smart conversational AI), rather than reactive enquiry handling, ContactEngine has built a very successful business which is demonstrably saving its clients money.

More importantly, I think this is a great example of the way in which AI should be approached, not as an alternative to humans which is cheaper and “nearly as good”, but as an enhancement. In ContactEngine’s

“The language that you use in

case, they’re adding conversation at a

communication can massively affect response

point in the journey which currently

rates, and you can personalise that as well,

has either one-way communication

based on additional information. The next

or nothing at all.

generation of what we're doing we call human-

Should you build AI into your

computer rapport, which is a phrase we had

journeys? This, for me, is the

to invent. You can market to individuals as

acid test: will it make the

individuals based on the patterns of what they

customer experience

do and using a concept of rapport means that

better?

you learn ways of communicating better over time, by building up an understanding of their communication needs.”

The future of customer-facing AI The niche that ContactEngine has found is extremely revealing of an

RESEARCH

You may remember that, back in the Spring issue, we launched a new measure of consumer attitudes to their own financial situation and the wider economy – the Index of Consumer Sentiment. In some ways it wasn’t ideally timed, to say the least, but because we had been tracking the measure for some time before we launched it, it does give us a very good picture of how consumer feelings have evolved over this very strange summer that we’ve all lived through.

12 Customer Insight Autumn 2020 | www.tlfresearch.com

RESEARCH

It’s hardly surprising that consumers are, rightly, worried about the economic impact

THE MEASURE - A REMINDER

of the Coronavirus and the measures taken

The Index of Consumer Sentiment measures three things

to combat it. What’s much more interesting

(using a total of 5 questions):

is that their attitudes to their personal

• How people feel about their own financial situation

finances and the wider economy, over the

• How people feel about the general economy in the short term

short and long term, have been affected very

• How people feel about the general economy in the longer term

differently.

Understanding consumer attitudes, and therefore being better able to predict their

As well as the overall index, there are two sub-indices – the Index of

behaviour, makes the Index of Consumer

Current Economic Conditions, and the Index of Consumer Expectations.

Sentiment an important tool for businesses

Comparing these gives a good sense of how customers feel right now

to understand and predict the economy,

versus their view of the future prospects for the economy.

particularly in the wake of seismic events like a pandemic.

The headline You were probably expecting this. UK consumer sentiment plummeted between January and April this year (we run the survey quarterly).

That’s neither surprising nor very interesting, but the picture over the 6 months since then is more complicated, and more informative.

85

80

76.9 75

70

69.5

65

64.7

60

55

Index of Current Economic Conditions

Index of Consumer Sentiment

Oct-20

Sep-20

Aug-20

Jul-20

Jun-20

May-20

Apr-20

Mar-20

Feb-20

Jan-20

Dec-19

Nov-19

Oct-19

Sep-19

Aug-19

Jul-19

Jun-19

May-19

Apr-19

Mar-19

Feb-19

Jan-19

Dec-18

Nov-18

Oct-18

50

Index of Consumer Expectations

Looking at the sub-indices, it seems at first glance that consumers’ confidence in current financial conditions bounced back surprisingly strongly over the summer, whilst their expectations for the future stayed depressed.

www.tlfresearch.com | Autumn 2020 Customer Insight 13

RESEARCH

Comparison to the USA

The Index of Consumer Sentiment 110

allows us to compare consumer sentiment in the UK with the University of Michigan’s Index of Consumer Sentiment1. This had been running at a considerably higher level than in the UK, but plunged even more steeply during 2020 so that the scores for consumers in the UK and the USA were at their closest point in July. Since then the gap has again begun to widen, although it

Index Value (1966=100)

We have chosen a methodology that

100 90 80 70 60 50

2010

2011

remains much smaller than before.

2012

2013

2014

2015

Monthly data

2016

2017

2018

2019

2020

3 Month moving average

Beneath the index expressed as an index based on positive

means that there were more negative

we need to turn to the individual questions

versus negative answers. In other words, a

answers. Let’s have a look at what’s

that make up the indices. Each of the

score of 100 means that the same number

happened to each of the five questions over

three headline index numbers is built on

of people gave a positive answer as gave

time…

a combination of questions, which are

a negative answer, and a score below 100

To understand what’s really going on,

Change in questions Better or worse off than last year?

100

Oct-18

104

98

99

Jan-19

Apr-19

Jul-19

103

Oct-19

102

Jan-20

95

Apr-20

99

Jul-20

99

Oct-20

Is now a good time to buy big things?

104

104

109

104

107

110 69

Oct-18

Jan-19

Apr-19

Jul-19

Oct-19

Jan-20

Apr-20

96

Jul-20

99

Oct-20

Long term business conditions

106

Oct-18

107

Jan-19

http://www.sca.isr.umich.edu https://www.home.barclaycard/media-centre/press-releases.html

1

2

14 Customer Insight Autumn 2020 | www.tlfresearch.com

107

Apr-19

109

Jul-19

111

118 100

Oct-19

Jan-20

Apr-20

98

Jul-20

93 Oct-20

RESEARCH

Next year better or worse off?

99

Oct-18

105

101

99

Jan-19

Apr-19

107

102

Jul-19

95

Oct-19

Jan-20

Apr-20

99

101

Jul-20

Oct-20

Short term business conditions

86

Oct-18

80

Jan-19

87

81

100

87

Apr-19

Jul-19

Oct-19

Jan-20

64

63

55

Apr-20

Jul-20

Oct-20

modest growth in August and September2.

You can see that, although the other

that, do you think now is a good or a bad

questions have experienced what would

time to buy major items?” plummeted in

normally be seen as significant shifts, the

April, but has since recovered (driving the

negative responses per quarter, you can see

really seismic change to consumer sentiment

Index of Current Economic Conditions). This

that as many consumers are positive about

is restricted to two questions. The first of

tallies with data from Barclaycard showing

this as they were before the pandemic,

these, “Thinking about the big things people

year-on-year drops in consumer spending of

although more are negative…

have to spend money on such as their car,

36.5% in April and 26.7% in May, followed

a new television, furniture and things like

by a slow recovery from June onwards to

In fact if we look at the positive and

Is now a good time to buy big things?

50%

40%

30%

20%

10%

0%

-10%

-20%

-30%

-40%

-50% Oct-18

Jan-19

Apr-19

Jul-19

Oct-19

A good time

Jan-20

Apr-20

Jul-20

Oct-20

A bad time

www.tlfresearch.com | Autumn 2020 Customer Insight 15

RESEARCH

bad times?” plummeted and has stayed low,

no-deal Brexit, a fairly large majority of

business conditions in the country as a

driving the Index of Consumer Expectations.

consumers expect business conditions to be

whole, do you think that during the next 12

No doubt reflecting fears over both the

bad over the next year.

months we’ll have good times financially, or

impact of Coronavirus and a potential

The other big mover, “Now turning to

Short term business conditions 100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

Definitely bad times

Probably bad times

Not sure

Probably good times

Definitely good times

So what have we learned?

Let’s pull together some key conclusions: • Consumers are very concerned about the short-term future of the economy.

• Consumers are relatively positive about

• Confidence in the long-term future of the economy is declining steadily, and continuing to fall (where other questions

Stephen Hampshire

have bounced back). This suggests a

Client Manager

their own financial position. In fact more

quieter, but more profound, unease about

expect to be better off next year than expect

the UK’s economic future. It’s impossible to

to be worse off.

know which combination of Coronavirus,

• The early days of lockdown, when there

Brexit, or other factors are causing this

was much uncertainty about jobs, were

lack of confidence; but it will inevitably

not seen as a good time to make a big

have repercussions in terms of consumer

purchase, but many have returned to their

spending if it is not reversed soon.

former confidence.

• Data from our Index of Consumer

Get in touch if you have any questions

Sentiment tracks well with Barclaycard’s

about the index, or if you’d like more details

spending data, showing the link between

about the data and methodology, and keep

consumer attitudes and behaviours.

your eyes open for future results.

16 Customer Insight Autumn 2020 | www.tlfresearch.com

TLF Research stephenhampshire@leadershipfactor.com

Oct-20

Sep-20

Aug-20

Jul-20

Jun-20

May-20

Apr-20

Mar-20

Feb-20

Jan-20

Dec-19

Nov-19

Oct-19

Sep-19

Aug-19

Jul-19

Jun-19

May-19

Apr-19

Mar-19

Feb-19

Jan-19

Dec-18

Nov-18

Oct-18

0%

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We can develop a bespoke 30 or 60 minute webinar for up to 500 of your staff.

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L AT E S T T H I N K I N G

18 Customer Insight Autumn 2020 | www.tlfresearch.com

L AT E S T T H I N K I N G

WHOSE BIAS IS IT ANYWAY? In September Pegasystems and UCL ran

“People view companies as if it is a single

a virtual roundtable on the topic “Do AI

organism or as a ‘person’. This requires not

biases and human biases overlap more than

just intelligence but also empathy. Sense

we think?”, presented by Peter van der

my emotions in the moment, learn from

Putten (an assistant professor of AI at Leiden

interactions to understand my needs, but more

University and director at Pegasystems) and

fundamentally, put yourself as the company in

Dr Lasana Harris (Senior Lecturer in Social

the shoes of the consumers: say if we want to put

Cognition at UCL).

some message or nudge in front of a customer,

It’s interesting to hear from the experts about both the potential and the limitations that AI tools bring. We’ve seen that AI tools

promote what’s right for the customer not just what’s right for the company.”

Customers are, for the most part, sceptical

can perpetuate biases that exist in society,

about organisations’ willingness to do the

but is that any less true of humans? Do we,

right thing for customers, beyond what

and should we, expect more from computers

they’re legally required to, and that creates

than we do from people?

opportunities for companies who can show customers that they do.

AI and customers

Pega shared the example of CommBank, who introduced a Customer Engagement

AI, in the sense of machine learning

Engine, using AI to proactively select the

approaches to automation of particular tasks,

“Next Best Conversation” that is best suited

is increasingly a necessity. The pandemic is

to the needs of each customer at that time

a good example of a crisis which demands

and through that channel. As Peter explained,

systems, like track and trace, which are able

“…the library contains a wide variety of messages, in line with the mission. Not

Stephen Hampshire

just selling the product of the month and

Client Manager

often fail because they are not able to feel

personalised sales recommendations; also

TLF Research

and express empathy in the way that humans

warning about credit card points that expire,

are. As Peter van der Putten commented,

how customers could avoid upcoming fees and

to handle vast amounts of data efficiently.

When dealing with customers, Ai systems

stephenhampshire@leadershipfactor.com

www.tlfresearch.com | Autumn 2020 Customer Insight 19

L AT E S T T H I N K I N G

charges. But also beyond their products and

out when it is used in ways that sort of threaten

something that we can easily take steps to

services: the Benefits Finder identifies specific

things that people have, like their privacy, for

prevent? Peter highlights three fundamental,

government benefits specific customers qualify

instance. So it's really about the goal of the

related, problems:

for; or emergency assistance when customers

company.”

live in an area affected by bushfires. Since COVID-19 hit, it communicated 250m COVID-

in society which then reinforces our own

What should the future of AI be?

related messages to customers, from payment holidays to home loan redraws.”

If customers are increasingly sceptical wider society begins to worry about the impact of algorithmic bias, now is a good

mundane problems such as data issues.

• That’s not an excuse to blame it on the data – the more systemic issue is not having an eye open for the bias that could occur or not

for customers, and society, as well as their

having the tools to detect and fix it.”

bottom line.

As Peter commented, actions are more important than words here: “Just defining AI principles is not enough.

I think there are two things which are really important. One, you need to translate these principles into something tangible. When you say you need to be transparent around automated decisions, you need to offer some form of automated explanations on how this decision was reached. If you say we’re against bias in models, but also in automated decision in systems, you need to have an ability to measure how much bias there is in those decisions in the first place.”

places. Used well these approaches have the potential to deliver much quicker, more responsive, more personalised customer

as if they are people? Because they spend

experiences at scale. Customers will make

millions of pounds on advertising to position

up their minds based on the results they see.

themselves in that way. One of the biggest

The aim, as Lasana says, should be to…

causes of customer dissatisfaction is the

driving automated decisions, through more

to deploy AI solutions in a way that is good

It’s also about making sure that AI tools

Why do customers think of organisations

models, through bias in decision logic

time for organisations to consider how best

are used in the right way, and in the right

Brands as people

biases, it is the same for AI. • Through the data that we use to train

of the benefit to them of AI tools, and as

"The most important thing is that when companies use AI, they must balance their self-interests with those of the consumer."

• “In the same way that humans see bias

“Improve the life of your customer somehow,

disconnect between the friendly, personal,

and the AI can facilitate that…given the power

brand they’re promised in the adverts and

and the influence of AI, AI can make decisions

the impersonal treatment they often receive

across thousands of customers very quickly.”

"I don't think that perception of AI in general is that it's evil. I think that comes out when it is used in ways that sort of threaten things that people have, like their privacy, for instance."

in practice. Dr Harris commented, “The most important thing is that when

Where does the bias come from?

This is really important, and links into

companies use AI, they must balance their self-interests with those of the consumer. When

Algorithmic bias is not inevitable, but

the points made in Artificial Unintelligence

deciding how they want to use AI they need to

something which comes about because of

(our book review on page 31). To see

consider whether it will impact their brand and

the way we build and train AI models. Those

algorithmic bias as merely a problem that

their reputation.”

biases reflect tendencies in the data, in other

reflects the training data, and therefore as

words they may recapitulate systematic

society’s problem rather than AI’s problem,

information with companies and AI presumably

biases in society, but they may also be

is missing the point. Peter continues, with

can help smooth some of that transition if used

exacerbated by who works in tech and the

some examples:

appropriately. I don't think that perception of

way they think.

“People typically aren't very trustful of their

AI in general is that it's evil. I think that comes

So what does cause these biases, and is it

20 Customer Insight Autumn 2020 | www.tlfresearch.com

“The more systemic underlying issue is that ultimately it's humans that build AI systems. So

L AT E S T T H I N K I N G

the systemic problems are added that people are

program. Bias was caused by how the data set

maybe not aware enough of, or bias problems

was defined for modelling.”

happen, or the systemic problem could be that people don't care enough.”

What can we do to prevent bias?

“One example is the 2020 A-Level results (in this case, not AI, but algorithmic bias).

To build algorithms that are unbiased

Boris Johnson blamed a “mutant algorithm”

requires active work, and making sure you

for the A-level and GCSE grades. You can’t just

understand the nature of the data that you’re

blame it on the algorithm. Algorithms are not

using. Knowing how the data was collected,

silver bullets, nor are they inherently evil. And

and the nature of the society in which it

algorithms are certainly not objective, nor ‘back

was collected, is as important as being able

boxes’ we can shift any blame to.”

to build an efficient algorithm. As Lasana

“Another is a study in Science in 2019 which reported on a predictive model used across the

comments, “I think when discussing bias, it's really

"Humans are sometimes eager to push responsibility to an AI algorithm, which is not correct."

US to identify patients for preventive care and

important to understand that the bias exists

care management programs, clearly an example

all around us…If there's no bias detection

where AI was used with the best intentions.

mechanism and there's no person who's aware

The problem is that the model predicts future

of these biases intentionally looking to see that

question of scale. A biased human makes

healthcare costs, and in the historical data

they are not present in the AI, then the AI is

far fewer decisions than a biased machine.

used to build the model, considerably less

going to appear to be biased.”

Nonetheless, it’s important to remember

money is spent on black patients that have the

“In reality, the way to combat social bias

that, however flawed a computer’s decisions,

same health conditions as white patients. By

is to be aware of your own biases – the same

the fault remains with humans, as Lasana

correcting for the bias in the healthcare data set,

thing is true for AI. Therefore, those who are

comments:

more than two and a half as many black patients

creating AI need to be aware of their own

would be eligible for a care management

prejudices.”

The conclusion is clear: if you want to

"If you say we’re against bias in models, but also in automated decision in systems, you need to have an ability to measure how much bias there is in those decisions in the first place."

“Humans are sometimes eager to push responsibility to an AI algorithm, which is not correct. AI algorithms are built and trained by

build AI algorithms which are free from

humans, based on a range of choices made by

bias, then you’re going to need to build

humans.”

transparency and bias detection into your

Perhaps the most important thing of all

systems. This can’t be done passively, but

for a customer, whether the decision was

needs to be consciously approached with an

made by a human or a machine, is that it

understanding of the potential biases that

seems fair and is explained. As Peter says,

training data may reflect.

“For a customer being declined for a loan

You also need to evaluate the decisions or

it doesn’t matter that much who made the

predictions that your algorithms are making,

decision, the human or the AI. She or he wants

and make sure that they are fair.

the loan and didn’t get it, so wants to get an explanation and wants that decision to be fair.”

Are we being unfair on the machines?

“Make the customers feel that for every single customer and every single interaction you're really trying to do the right thing for

People within tech often feel that all this is a bit unfair, after all machines are, by

them.”

Ultimately, like everything else in the

definition, free from bias themselves. If a

customer experience, what really matters

computer learns to replicate decisions which

is that customers believe that you are on

are biased, based on a stack of data about

their side, and have their interests at heart.

how humans have made decisions in the

If AI is serving that end, then it has the

past, then that’s hardly the computer’s fault,

potential to contribute to excellent new

is it? And yet we seem to be suggesting that

customer experiences, but it can’t do that

computer decisions should be more heavily

until we take a clear-eyed look at the biases

scrutinised than their human colleagues. Is

that we’re building into decisions and

that right?

predictions. To do that, we’re first going to

To some extent that’s our pro-human

have to face up to our own biases.

bias speaking, but there is also the

www.tlfresearch.com | Autumn 2020 Customer Insight 21

Consumer Insight The data for the Index of Consumer Sentiment article came from TLF’s panel.

The TLF Panel offers you an easy way to access the views and opinions of UK consumers. It’s a flexible research solution with a range of uses, including: Insight into consumer behaviour, attitudes and usage Facts and figures for compelling content and PR stories Brand awareness and competitor surveys Testing advertising and product concepts Recruitment for focus groups and interviews

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G U E S T F E AT U R E

For those in the world of customer service,

relationships. More than ever, consumers Hangouts

taking work home has been a big challenge to

turned to the phone when contacting

overcome. Contact agents, synonymous with

businesses to make complaints and pose

provide

working in call centres, have been badly hit,

queries. One borough council customer

appropriate

with concerns being voiced over the social

service centre in Wales, recorded an increase2

alternatives.

distancing measures available1 in offices.

of 12,000 calls under lockdown compared to

However,

the same time the previous year. How can call

such platforms

recognise the new challenges that call agents

agents and staff keep up with such spikes in

do not cater for

are forced to deal with and offer them

volume?

contact agents’ needs;

It is therefore vital that organisations

the tools and support needed to succeed remotely.

In response, some businesses have had to boost their number of call agents with truly rapid deployment, with Scottish firm

How businesses have responded to the work from home era

Ascensos, having increased its number of

their challenges are far greater than these solutions can deliver on.

Call routing, case deflection, call queues, wallboards, listen-in coaching, data syncing

call agents by sixty times3. Equally, some companies like Virgin Media4 went so far

Since March, many businesses have experienced ‘customer distancing’ in a struggle to nurture their customer

as to ask customers not to call at all while customer service lines were under such

Ian Moyse EMEA Director

pressure.

Natterbox

One highly personal communication channel that has become increasingly popular during the crisis has been video-calling apps. For individuals whose roles include little external phone interaction, the likes of

Ian has led the EMEA sales team at Natterbox for over three and a half years. Before Natterbox he worked as a sales director at industry leading companies such as Rackspace and programmer at IBM. He sits on the techUK Cloud Leadership Committee.

Zoom, Microsoft Teams, and

www.natterbox.com

Google

www.tlfresearch.com | Autumn 2020 Customer Insight 23

G U E S T F E AT U R E

of customers through click-to-dial, and

behind the change. It affects their phone

screen pops are all vital tools that businesses

system capabilities and configuration, not

with high customer demand need to be able

just for redirecting calls to mobiles, but

to provide an effective customer service.

for handling call queues and hunt groups

With the pressure mounting, it begs the question: how have call agents adapted to operate under the new working circumstances and what lessons must they take forward?

effectively. So, what might seem at first like a simple transition, is not.

The concept of making or receiving a call, for example, is easy. It is a one-to-one direct connection between a customer and

Handling the complexities of home working

an agent. In this sense, moving agents to the home does not affect the fundamentals of customer service.

Working remotely as a call agent is certainly not as simple as just ‘logging on’ from home. There is a mass of hidden complexities

However, with 93 percent of European and North American businesses still using desk phones5, many customer service agents don’t have access to their usual calling devices from home. This presents

Working remotely as a call agent is certainly not as simple as just ‘logging on’ from home.

the unexpected challenge of which device they use to speak with customers and subsequently, which number is presented to the customer when they make an outbound call. Does the agent use their mobile or does the business shell out and provide them with a work mobile? What happens when the agent’s mobile is out of reach? And what happens when an agent needs help from a colleague and must redirect the call?

These questions lead to further complexities. For one, agents shouldn’t use a personal device for security reasons, and at the very least shouldn’t use a personal mobile number for professionalism. Equally, if an agent uses their personal device, that device and associated number becomes the customer's direct contact point. So when that agent is ill or on holiday, the call has nowhere to be routed to and the customer is limited to one point of contact that isn’t available. A company needs to be present for around-the-clock support to make their customers feel valued.

24 Customer Insight Autumn 2020 | www.tlfresearch.com

G U E S T F E AT U R E

Cloud communications provides flexibility and control One of the tools that is helping businesses tackle these problems and ford the widening divide between customers and call agents is intelligent telecommunications technology. cost-effective,

This brand of customer service tech opens up

and productive,

a range of possibilities for a workforce that is

some even working

more inclined towards flexibility. In fact, it is

from entirely virtual

shifting the concept of flexibility entirely.

offices.

Cloud-supported interfaces, for example,

So, with the world

are already available to give agents the

emerging

agility to work from anywhere, on whichever

technologies

device they want. These interfaces can

will create a wider

give them complete control over who can

acceptance that a home worker

contact them and on whichever device,

in any role can be productive with the right

the status quo and explore areas previously

all through a centralised company phone

tools.

ignored. Doing this, they may well find that

number. If one agent is unavailable, the call

re-aligning itself during a crisis, it’s vital that companies reassess their business tools. They should question

As a result, organisations can now

their legacy technologies are not so well

will be automatically routed through to an

widen their hiring pool, no longer limited

equipped for an environment that requires

alternative agent.

by the location of a call centre, and with

greater agility.With change comes greater

the ability to offer more flexible working

opportunity.

In effect, this technology means there’s

This may well

minimal difference between the way a

hours and working from home offerings.

call agent operates in a call centre and at

This will ultimately lead to a new workforce

be a challenge

home. All data is then easily shared back

market made of a wider range of people

that improves

into their business’ CRM system, improving

who previously might have been eliminated

customer service

the efficiency and personalisation of future

from consideration, but who can now be

for good.

customer communications. Employee admin

utilised in this new dynamic way of working

time is also drastically reduced.

in a way that wasn’t possible before. For example, working mums and home carers,

With change comes opportunity

who benefit from roles that offer flexible hours and the ability to

In the long term, this combination of remote working during the pandemic and

work from home. Businesses will also benefit in becoming more efficient,

https://www.insider.co.uk/news/one-two-call-centre-staff-21861045 https://newsfromwales.co.uk/news/customer-services-staff-take-over-12000-telephone-calls-more-during-lockdown-compared-to-same-period-last-year/ 3 https://www.economist.com/britain/2020/04/04/britains-call-centres-are-overwhelmed-and-overhauling-how-they-work 4 ?

5 https://community.spiceworks.com/blog/3103-data-snapshot-the-lifespan-of-computers-and-other-tech-in-the-workplace 1

2

www.tlfresearch.com | Autumn 2020 Customer Insight 25

RESEARCH

• TLF NEW PRODUCT FEATURE •

How healthy is your brand?

New for 2020

moment of the day, and brands are accepted or dismissed with just the swipe of a finger

At TLF, we’ve been running surveys for

or click of a mouse, it has never been more

longer than most of us care to remember.

relevant to track your brand’s performance,

We’re experts in customer satisfaction, and

and, perhaps more importantly, take action

tackle a multitude of different survey topics

from the findings.

day in, day out.

The world we immerse ourselves in, that

Sometimes it can be overwhelming to know where to start with tracking your

of market research, is ever-changing. With

brand, so we have done the hard work for you

new technology comes new ways in which we

and whittled it down to the key questions you

can interact with people, and that changes

really need to know:

peoples’ expectations and perceptions of what constitutes meaningful research and actionable insight.

With this in mind, we went out to our

• Usage & Awareness – how aware are consumers of your brand, and how often do they use/see/purchase it?

• Customers vs. Non-customers – how do

clients and asked them – what do we not

results differ between these demographics,

currently offer that you would find useful?

and how can you turn the latter into the

Looking at the responses, one thing emerged as a very clear need: an easy way to monitor brand health for brands who might not be able to afford a full-blown brand tracker.

What do we mean by brand health, and why might you want to track it?

former?

• Brand reputation – measure the reputation of your brand across a myriad of factors • Consumer opinion – what do consumers really think of your brand, and how do you stack up against the competition?

• Customer expectations – what expectations

Why track your brand’s health?

come with your brand, and how do these change across sectors/products?

Never has brand loyalty moved at such

• Likelihood to purchase – how likely are they

a fast pace. In this digital age, where

to buy your product/service? And what will

consumers are bombarded with brands every

affect this?

RESEARCH

• TLF NEW PRODUCT FEATURE •

What does it look like?

you need.

essential for the results, but useful to see the

Then we get into the meat of the report,

demographics, especially if you want to track To show you what a brand tracker can do for you, here are some of the outputs from our new Brand Health Package.

starting with the biggie – brand awareness.

how your brand health changes over time

How aware are consumers of your brand?

across different groups.

We ask them both unprompted and prompted

You’ll want easy-to-read figures on, among other things; gender, age brackets,

awareness to gather the fairest findings.

was surveyed, how long the survey was open

socio-economic groups and regional

These results are then analysed and compared

for and what the incidence rate was. Not

breakdowns, along with any other data splits

to your brand’s competition:

We’d always start with a summary of who

Brand Awareness (Fig 1) 86%

Company A

87%

Company B

82%

Company C

84%

Company D

68%

Company E

Company F

53%

This general awareness gives a good starting point to understanding your brand’s

Brand Awareness by Age (Fig 2) 100%

health. Depending on your requirements, you will then get this broken down by

80%

particular demographics. In the example to the right (Fig 2), by age.

After establishing your brand’s awareness levels, we’ll turn to brand usage. We measure this using two categories:

60% 40% 20%

• Brand Usage – have they heard of your brand but never used them, heard of your brand and used them in the past, or heard

0% Company A

of your brand and currently use them?

18-24

• Brand Consideration – Is your brand something they would never consider,

consider?

Company C 25-34

35-44

Company D 45-54

Company E 55-64

Company F

65+

Brand Usage - total consumer base (Fig 3)

might consider, one of two or three brands they’d consider, or the ONLY one they’d

Company B

Company A Company B

21%

64%

16%

25%

46%

29%

At this point you’ll have seen just how aware consumers are of your brand; did your brand spring to their minds unaided, did they require some prompting, or had they never heard of it at all? With the sample

Company C Company D Company E

who were aware of your brand, you now also know how many of them use your brand, and how likely they are to consider it in the future (Fig 3).

20%

70% 23%

55%

21%

51%

Company F

79% Heard of before but never used

Heard of before and have used in the past

10% 22% 27% 14%

7%

Heard of this and I am currently a customer

www.tlfresearch.com | Autumn 2020 Customer Insight 27

RESEARCH

• TLF NEW PRODUCT FEATURE •

Brand Awareness and Consideration (Fig 4)

We can then look at awareness alongside consideration, a really useful visual tool

100%

to see how you stack up compared to your 80%

brand’s main competition (Fig 4).

Now we look at satisfaction – how

60%

satisfied are your brand’s customers, and also, equally as important, how satisfied are

40%

your competitors’ customers?

20%

A simple, but reliable, measure to gauge brand health - satisfied customers will talk

0%

and act positively about your brand, and

Company A

vice-versa (Fig 5).

Company B Would definitely not consider it

Not aware

Another popular indicator of how your

Company C

Company D

I might consider it

Company E

One of 2 or 3 I’d consider

Company F

The only one I’d consider

brand is perceived, and one with strong links to customer loyalty, is NPS, or Net Promoter

Customer Satisfaction (Fig 5)

Score. Using a scale from 0 to 10 (0 being 0%

‘not likely to recommend’ and 10 being ‘very likely to recommend’), how likely are they to

100%

Mean

Company A

8.1

Company B

7.7

Company C

7.9

Company D

7.9

Company E

7.2

Company F

8.4

recommend your brand to friends and family?

A high NPS is often associated with brand loyalty and revenue growth.

The NPS section gives you a detailed breakdown of your brand’s overall NPS score – namely, how many ‘promoters’ your brand has (how many consumers scored 9 or 10), how many ‘passives’ it has (those who scored 7 or 8), and how many ‘detractors’ it has (how many scored 0 to 6). These are the figures that are then converted into your

1 (Not at all satisfied)

brand’s final NPS score (Fig 6).

2

3

4

Net Promoter Score (Fig 6)

5

6

7

8

9

10 (Completely satisfied)

Now there’s some solid data and understanding behind your brand’s

Net promoter score = % Promoters minus % Detractors

health. We’ve measured awareness, usage, consideration, satisfaction and NPS. All valuable pieces of insight in their own right, but usefully collated together in one report,

1

2

3

4

5

6

Detractors

7

8

9

Passives

prepared in detailed, easy to understand

10

charts (that often paint a stronger picture than numbers alone), that can be run again,

Promoters

50%

and again (if required) to really track your brand’s health over time, for example;

Passives

Mean: 7.4 NPS: 14.5%

42.9%

40%

Promoters

35.7%

Detractors

30%

21.4%

20.2%

19.6% 16.1%

9.5% 10% 1.8%

0.0%

0.6%

1.2%

1.8%

1(0)

2(1)

3(2)

4(3)

campaign.

But the report doesn’t end there.

22.6%

20%

before and after a major advertising

Now we delve deeper into consumers’ emotional connections to your brand, after all, everything derives from emotions.

6.5%

Emotions create attitudes, which in turn drive behaviours, which ultimately lead to

0% 0 (3)

Extremely unlikely

5(16)

6(11)

7(34)

Recommend score

28 Customer Insight Autumn 2020 | www.tlfresearch.com

8(38)

9(27)

10(33)

Extremely likely

which brands people choose to use, trust and promote. The Brand Image Statements

RESEARCH

• TLF NEW PRODUCT FEATURE •

section of the report covers all of these emotional connections in detail, and compares your brand against your competition on each metric.

First, we start with brand association - what words and phrases are associated with your brand? Examples include: • [Your brand] has a good reputation • [Your brand] is known for good customer service • [Your brand] values their customers • [Your brand] keeps their promises • [Your brand] does the right thing ethically

Brand image words and phrases (Fig 7) 50%

40%

30%

20%

10%

0% Have a good reputation

Are known for good customer service

Company A

Are trusted providers

Company B

Company C

Value their customers

Company D

Are known for their quality

Company E

Are easy to deal with

Company F

Then we probe what words consumers associate with your brand, for example: • Modern • Technical • Experts • Outdated • Slow • Innovative • Customer focused

Brand image statements (Fig 8) 50%

40%

30%

20%

10%

0% Modern

Traditional

Experts

Company A

Outdated Company B

Relevant Company C

Slow Company D

Innovative Company E

Responsive

Customer focused

None of the above

Company F

www.tlfresearch.com | Autumn 2020 Customer Insight 29

RESEARCH

• TLF NEW PRODUCT FEATURE •

Brand image statements - Brand differentiation (Fig 9)

Again, this is also compared against your brand’s competition.

The final portion of the Brand Health report asks consumers to rate your brand

Company F

on a whole host of different factors. A really

Company D

useful measure to see what the public think of your brand at an expressive level, across multiple emotional drivers, and how you compare to your competition.

For example: • Brand affinity – how do consumers rate Company A

your brand on a scale from ‘love the brand’ to ‘hate the brand’?

Company E

• Brand differentiation – how is your brand rated from ‘same as other brands’ to ‘different to the competition’?

• Brand uniqueness – on the scale, how is

Company C

your brand rated from ‘follow others’ up to

Company B

‘unique and sets trends’?

• Brand empathy – where your brand is rated on a couple of scales: how

5.0

much does it meet customers’ needs,

5.2

5.4

5.6

5.8

6.0

6.2

6.4

6.6

6.8

Same as other brands

and how much does it care about its

7.0

Different to other brands

customers.

Finally, we finish with brand relevance, which is strongly linked to brand loyalty, brand

Getting the data

influence, and to a lesser degree brand cost – more relevant brands can command higher prices. This is measured on a scale from ‘out of date’ to ‘progressive’, and is also compared to your competition:

All brand managers need to know this kind of information, but many are not in a position to get it. It can be hard to justify the

Brand image statements - Out of date or progressive (Fig 10)

investment needed for such a task, which is why it’s essential to find a cost-effective solution.

We’ve developed a Brand Health Package

Company D

on our consumer panel to act like an MOT for your brand, generating all the outputs you’ve Company F

seen in this article. If you want to know more, why not drop us an e-mail or give us a call? We’re here to help, and look forward to speaking to you!

Company A Company E

Company C

Tom Kiralfy

Company B

Panel Manager TLF Panel 5.0

5.2

5.4

5.6

5.8

6.0

6.2

6.4

Out of date

30 Customer Insight Autumn 2020 | www.tlfresearch.com

6.6

6.8

7.0

Progressive

tom@tlfpanel.com

BOOK REVIEW

BOOK REVIEW:

ARTIFICIAL UNINTELLIGENCE

By Meredith Broussard

It’s almost impossible to reconcile the state of AI

about their potential; but she is also very aware of their

as depicted in the media and the reality of AI that you

limitations, and of the cultural issues within the world

encounter in the real world, isn’t it? On the one hand we

of technology which exacerbate the social impact of

seem to be just a few years from AI general intelligence

those limitations.

that will outperform humans in every way. On the other, I can’t find a machine transcription service that copes

Technochauvinism

with an even moderately-challenging accent.

Francois Chollet, the deep learning expert who created Keras, put it neatly in a Tweet:

The core problem, she argues, is what she calls “technochauvinism”—the belief that technology is

“I'm so old I remember when fully autonomous cars

always the solution to every problem. This manifests

were going to be ready for mass deployment by late 2017”

in the regular spectacle of silicon valley entrepreneurs

Autonomous vehicles are one of the best examples

“inventing” products that have existed for years, such

of the tendency for technology to over-promise and

as “reusable tissues”.

under-deliver, and I’d put customer service chatbots

That’s quite funny, and not doing anyone any harm,

in a similar category. The tools that we call AI, for

but the same outlook applied to machine learning

now and the foreseeable future, can be extremely

approaches to problems that have a real impact in

good at performing very specific tasks, but they don’t

society can be much more damaging. If you believe,

think. There is still nothing close to the AI “general

for instance, that AI is a better way to diagnose disease,

intelligence” you might see in science fiction (the closest

or make decisions about early release of criminals, or

thing I know of is GPT-3), nor even a consensus on how

to sift job applications.

(or if) building one might be possible.

“When you believe that a decision generated by a

What causes this gulf is partly the enthusiasm of

computer is better or fairer than a decision generated by

people within technology excited about the potential

a human, you stop questioning the validity of the inputs

of their tools, and partly the hyperbole of marketing

to the system.”

departments and journalists who feed us a sci-fi vision

As Mark Smith pointed out in the interview featured

of AI. What’s needed is a clear-headed view of the

earlier in this issue, the algorithms are not to blame

strengths and weaknesses of AI solutions, and the

when things go wrong. But technochauvinism can make

current state of the art, from someone who understands

us blind to the quality of the data we’re putting in, and

it but has enough distance to see it clearly. In Artificial

to the decisions and biases that are baked into it.

Unintelligence Meredith Broussard gives us exactly that. “…general AI is what we want…Narrow AI is what we

Data

have. It’s the difference between dreams and reality.”

This is not, to be clear, an anti-AI book. Broussard herself uses and develops AI tools, and she is enthusiastic

All AI tools require data, usually mountains of data.

Where does it come from?

BOOK REVIEW

“…data always comes down to people counting things… data is socially constructed.”

This is often overlooked, but enormously important.

First of all, it means that technochauvinists tend to

humans and computers.

Very much the same principle is likely to apply to the world of autonomous vehicles, which (as Francois Chollet alluded to) seem in many ways as far away as ever.

prioritise things which are relatively easy to measure.

“The machine-learning approach is great for routine tasks

It’s very difficult to measure quality, for instance, but

inside a formal universe of symbols. It’s not great for operating

very easy to measure popularity. To most of us it’s fairly

a two-ton killing machine on streets that are teeming with

obvious that there’s a distinction between those two

gloriously unpredictable masses of people.”

things, although perhaps we’d be hard put to define exactly what we mean by “quality”.

In customer service terms, this tends to come to the forefront in allowing robots (or self-serve) to deal with

In practice it’s very common for AI applications to treat

the bulk of relatively simple enquiries, but allowing

popular as a synonym for good, such as the app which

humans to handle the complex stuff. If we assume the

promised to rate your photos for quality, but ended up

computer can handle everything, the consequences will

rating them based on the extent to which they resembled

be ugly.

an attractive 20-something white woman.

AI algorithms, fed on data hoovered up without sufficient care, regularly make decisions which are racist, sexist, or simply socially inept. Why? “Computer systems are proxies for the people who made them.”

Not that the technochauvinists themselves are racist, sexist, or stupid; but there’s no question that the kinds of people who are penalised by these problems are not adequately represented.

“The edge cases require hand curation. You need to build in human effort for the edge cases, or they won’t get done.”

The phrase “edge cases” can itself be quite damaging, I think. I love this tweet from the designer Mike Monteiro: “When someone starts flapping their gums about edge cases they are telling you who they’re willing to hurt to make money. In 20+ years in this business I've never seen an edge case that contained cis white boys like me.”

No one ever thinks of themselves as an edge case, do they?

“In order to create a more just technological world, we need more diverse voices at the table when we create technology.”

Machines without humans

Conclusion The

Hollywood

vision

of

AI

coupled

with

technochauvinism has led to the rushed deployment of The other point about all that data gathered up by humans, is the amount of work that goes into it.

machine learning approaches, launched with hyperbolic claims, that are simply not delivering.

Where the data exists, great, why not make use of it.

“…we are so enthusiastic about using technology for

But don’t pretend that machine learning can operate

everything…that we stopped demanding that our new

in a vacuum without all the human-generated data.

As Broussard comments on the headline-grabbing AlphaGo algorithm:

technology is good.”

That’s not to say that AI doesn’t have potential, it does, but it makes a lot more sense to see it as a tool that

“Millions of hours of human labor went into creating

humans can use, rather than as an autonomous agent

the training data – yet most versions of the AphaGo story

that can step in to replace human decision-making in

focus on the magic of the algorithms, not the humans who

most cases.

invisibly and over the course of years worked (without compensation) to create the training data.”

“We should really focus on making human-assistance systems instead of human-replacement systems.”

We’re in such a hurry to heap praise onto the

I think we’re far better off thinking of autonomous

robots that we sometimes forget to give ourselves

vehicles as cruise control+, rather than as self-driving

enough credit. Broussard gives the example of a tool as

cars, and of chatbots as FAQ+, rather than as a replacement

everyday as Google, which to a large extent works as

for your human contact-centre agents. As Broussard says,

well as it does because we have learned how to use it

“…computers are very good at some things and very bad

well. Googling effectively is a skill, and that’s a really

at others, and social problems arise from situations in which

good example of how the best technology solutions come

people misjudge how suitable a computer is for performing

about from fusing together the complementary skills of

the task.”

32 Customer Insight Autumn 2020 | www.tlfresearch.com

L AT E S T T H I N K I N G

Your Customers’ Spending Habits are Changing TLF’s 3rd Lockdown survey was conducted over the weekend of 10-11th October. The results are based on a nationally representative sample of 2006 UK adults.

What’s happening to jobs?

after paying for all the essentials by selecting

What are people spending less on?

up to 3 categories that they were devoting In short, they’re starting to disappear. In

more money to. This really highlights the

Remembering that this survey took place

May 3/4 of respondents had a job but now

growing propensity to save and shows the

before any Tier 3 lockdowns or hospitality

it’s only 2/3. Those still in a job are more

top 6 categories receiving more of customers’

closures, here are the main categories receiving

likely to be travelling to their normal place

disposable income:

less of customers’ disposable income:

of work, up from 22% to 38%. This isn’t due to fewer people working from home, which has only fallen marginally from 42% to 38%, but down to a big drop in those on

37%

42%

Saving

Eating out

full time furlough, down from 20% to 5% (although a further 3% are partly working partly on furlough). The fall in furlough and rise in working on site has been driven mainly by the revival of many industries that shut down completely in the lockdown such

31%

24%

Drinking in pubs and bars

Home / garden improvements

29%

as retail, hospitality, leisure, building and construction plus many services normally provided in people’s homes. The new survey shows that most people who can work remotely are still working from home.

What’s happening to spending?

It’s changing a lot. Overall people are spending less and saving more but that’s far from uniform with those most affected by the economic consequences of Covid spending a lot less and saving nothing. Whilst we’re spending less overall, the mix of what we’re doing with our money has shifted considerably.

What are people spending more on?

Since saving isn’t spending we asked people how they’re allocating their money

18% More food to eat at home

16% Better quality food to eat at home

Holidays abroad

£

27% Clothes

19% Day trips

12% Home entertainment

16% Holidays UK

11%

10%

Beer / wine / spirits for home

Public transport

www.tlfresearch.com | Autumn 2020 Customer Insight 33

L AT E S T T H I N K I N G

Note that spending less far outweighs spending more. For allocating more

Several months on in October, the diagram shows that the proportions in each segment are still almost identical.

disposable income, the top category was saving and whilst 13% said they hadn’t been

October

spending less on anything, 28% haven’t been spending more on anything.

50%

52%

Are the changes temporary or more permanent?

Customers’ behaviours are driven by their attitudes and beliefs. During the national lockdown we identified 3 attitudinal

28%

segments:

22%

Appreciate life “I will be more appreciative of the little things in life like nature, seeing family, going for walks.”

Protect Life “I will avoid crowded places, I will be much more careful about health and hygiene.”

Appreciate life

Protect life

Whilst 24% can’t wait to Live Life to

24%

24%

Live life

showed a divide between consumers who

the full again, this is now matched by the

are positive and those who are negative

extremely cautious Protect Lifers and the

about their own financial prospects. This

Appreciate Life segment has cemented its

gulf is confirmed by this October TLF Panel

predominant position. So what does this

survey where 58% of respondents have no

mean for future spending? With over 3/4

worries about their finances compared with

of the population still much less inclined

42% who do. Of the 42%, the majority are

to have a hedonistic lifestyle it suggests

worried about having enough money for

that greater propensity to save and more

basics – rent/mortgage, utility bills and

spending on home life – investment in the

food. Others are worried about not affording

home and garden, food and drink for home

non-essentials such as holidays, home

consumption and home entertainment

improvements or their ability to save, but

– are set to continue. Companies in the

they are still expecting to have less money in

hospitality, foreign travel and clothing

the future than they’ve had in the past which

sectors will need to think very carefully

will have a negative impact on many sectors

about their segmentation and targeting

of the economy.

strategies.

What about the future?

Live Life “I will be making up for lost time doing the things I haven’t been able to do during lockdown.”

The latest update of TLF’s UK Consumer Sentiment Index (coming soon in the next issue of Customer Insight Magazine) shows that consumers’ confidence in current financial conditions bounced back strongly

Nigel Hill

over the summer, whilst their expectations

Chairman

for the future have stayed depressed into

TLF Research

October. However, the Sentiment Index

34 Customer Insight Autumn 2020 | www.tlfresearch.com

Customer Insight Magazine is created and published in house by TLF Research.

The magazine is our way of sharing features and latest thinking on creating an outstanding customer experience. We hope you enjoy reading the magazine as much as we enjoy creating it.

If you’ve got an interesting customer experience story to tell and would like to feature in the magazine, we’d love to hear from you. Please contact our editor Stephen Hampshire for more information.

Email Stephen at stephenhampshire@leadershipfactor.com or give him a call on 01484 467014

ABOUT TLF RESEARCH We are a full service customer research agency. Specialists in customer insight, we help our clients understand and improve their customer experience.

Get in touch to find out more about what we do.

Visit us online at tlfresearch.com or call 01484 517575

FREE WEBINARS - WATCH ON DEMAND Our range of free 30 minute webinars is designed to give you an introduction to key customer research subjects.

From how to guides & what to focus on, through to best practice & the analysis of your results, our webinars will give you lots of hints & tips to help you get the most out of research.

USER STORIES & CUSTOMER JOURNEY MAPPING This is one of our most popular training subjects and helps you understand how things look from your customers’ point of view. Mapping all the touchpoints of a specific customer journey is a must for designing positive experiences.

We can’t give you an in-depth guide to customer journey mapping in 30 minutes, but we can give you an outline of best practice, what to focus on and common mistakes.

FINDING & TELLING YOUR CUSTOMER INSIGHT STORY

NPS BEST PRACTICE

Do you struggle to find the key pieces of customer insight from your research?

We’ve all been there with really detailed presentations that provide a wealth of useful information, but the key takeaways can be lost.

If you’re using Net Promoter Score (NPS) as your headline measure, this webinar is a must. NPS should be the starting point for customer insight, not the ultimate goal.

In this webinar we talk through techniques for finding the insight that really matters and how to share this information effectively to make a positive impact.

CUSTOMER SATISFACTION INDEX: HOW & WHY TO USE IT

We’ll be discussing a range of best practice and latest thinking around the metric, from how to ensure a robust measure and common mistakes, to gaining in-depth insight and practical hints and tips to help drive change.

GUIDE TO EXPLORATORY RESEARCH: HOW TO SEE THROUGH THE 'LENS OF THE CUSTOMER'

A Customer Satisfaction Index (CSI) can take your Customer Satisfaction (CSAT) scores to another level. Combining and weighting CSAT scores for individual interactions, product or services, will give you a much more accurate view of how satisfied your customers are with your business overall.

Exploratory research is the foundation of a good customer research programme. It will help you understand how things look from your customers' point of view and see through the 'lens of the customer'.

This webinar will give you an overview of how to calculate CSI, examples of how to measure it and how it can be used to add an extra layer of detail to your CSAT scores.

In this webinar we outline the different types of exploratory research, the range of insight available and how they should form an essential part of your customer research.

TURNING INSIGHT IN TO ACTION: THE IMPORTANCE OF ACTION PLANNING

USING ONLINE COMMUNITIES FOR QUALITATIVE RESEARCH

There is no point doing customer research unless you’re planning to do something with the results. Action planning is the best way to ensure you are using the insight gained from your customer research to drive positive change to the customer experience.

Greg will guide you through best practice when creating an action plan and show you some practical examples of how they can work.

Online customer research offers you a flexible approach to connect with your customers and online communities offer an engaging platform to undertake a range of qualitative research. Online communities can sometimes be more cost effective than focus groups and allow for a much deeper understanding, with participants given time to consider their responses and supply rich media to back up their responses. In this webinar we’ll discuss the uses of online communities, such as online focus groups, in-depth interviews or bulletin boards, and how these can help you dig deeper, have longer conversations, and visualise your customers.

Sign up today at tlfresearch.com/webinars