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www.tlfresearch.com | Autumn 2020

AI & THECUSTOMERINSIDE… ContactEngine on conversational AIPegasystems roundtable on AI & biasNatterbox on contact in the current eraThe Index of Consumer Sentiment

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MONTHLTLF RESE

Our monCustomeDesign h

Stephen Hampshire

Editor

ForesightC

ON

TAC

TS

EDITORIALEditorStephen Hampshire

ADVERTISINGMarketing ManagerRichard Crowther

DESIGN & PRODUCTIONCreative DirectorRob Ward

DesignersBecka CrozierJordan GillespieRob Egan

PRINTERAB Print Group Ltd

Customer Insight is the magazine for people who want to deliver results to employees, customers and any other stakeholders as part of a coherent strategy to create value for shareholders. We publish serious articles designed to inform, stimulate debate and sometimes to provoke. We aim to be thought leaders in the field of managing relationships with all stakeholder groups.

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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. All rights reserved. No part of this publication may be reproduced or stored in a retrievable system or transmitted in any form

or by any means without prior written consent of the publisher. © CUSTOMER INSIGHT 2020

ISSN 1749-088X

In this edition we’ve focused on a topic that

always seems to be just about to create seismic

change: Artificial Intelligence. Is it time to start

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

time you finish this issue, you’ll probably conclude

that the answer is a bit of both.

We start with an in-depth piece from Professor

Mark Smith of ContactEngine (page 6), who

believes that his organisation has found the

niche where conversational AI can both improve

customer experience and make organisations more

efficient. Some of the principles we discussed

could be taken as general rules for AI deployment,

I think.

On page 19 is a report from an interesting

roundtable event hosted by Pegasystems, looking

in particular at the issue of algorithmic bias, and

how it relates to human biases. Do we expect more

from machines than we do from people, and are

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

strengths and weaknesses of machine learning

tools, in a way that cuts through the hype,

therefore explaining how damaging they can be if

we don’t use them in the right way.

Elsewhere, we look at what the Index of

Consumer Sentiment (which we somewhat rashly

launched in the Spring) tells us about the impact

of the pandemic on how customers are feeling, and

what that might mean for their behaviour (page

12), which sits really well alongside some new

research that we’ve conducted into how customers’

spending habits are changing (page 33).

We also have a new Brand Health product from

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

Natterbox on the challenges for contact agents in

working from home (page 23).

Enjoy the articles, and please drop us a line

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

future issue.

E D I T O R I A L

www.tlfresearch.com | Autumn 2020 Customer Insight 3

Conference speaker, book-lover and occasional climber

Stephen Hampshire

Panel wrangler, banana lover and chinchilla owner

Tom Kiralfy

C O N T E N T S - A U T U M N 2 0 2 0

06 12

CO

NT

RIB

UT

OR

S

Wine-lover, Munroist and customer satisfaction guru

Nigel Hill

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

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?

18 Whose Bias Is It Anyway?A report from a recent Pegasystems roundtable looking at AI and human biases.

4 Customer Insight Autumn 2020 | www.tlfresearch.com

Rob Egan

Beer drinker, pixel pusher and dour Yorkshireman

DE

SIG

NE

RS

Creative magus, genuine tyke and 20ft wave rider

Jordan GillespieBecka Crozier

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

26

33

23

How Healthy Is Your Brand?A new product from TLF Panel, that might help you understand your brand health.

Published by

GUEST FEATUREContact Engine 06

RESEARCHSentiment Index: How DoYou Think Consumers AreFeeling Now? 12

LATEST THINKINGPega Roundtable:Whose Bias Is It Anyway? 18

GUEST FEATURENatterbox: How Can ContactAgents Respond To CurrentChallenges? 23

RESEARCHHow Healthy Is Your Brand? 26

BOOK REVIEWArtificial Unintelligence 31

LATEST THINKINGYour Customers' SpendingHabits Are Changing 33

How Can Contact Agents Respond To Current Challenges?Ian Moyse from Natterbox reflects on customer service in the "work from home era".

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

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?

31 Book Review:Artificial Unintelligence


C O N T E N T S


G U E S T F E A T U R E

“Youjust can't differentiate between a robot andthe very best of humans” - Isaac Asimov, “I, Robot”

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.

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

encountered them, and

I’m equally sure

we’ve learned

to be


“Use humans to

do what humans

are best at, and

then machines.”


suspicious of vendors who claim that theirs

are indistinguishable from humans. Those

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

very stupid, boring, people.

Used in the right way, Chatbots serve a

purpose. When you’re honest about what they

are, which is essentially a user-friendly skin

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

is to see them as an alternative to a proper

conversation. As Mark comments,

“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

AI is so dubious about the benefits of

Chatbots. The answer is that Mark believes he

has identified a unique niche in which AI can

be used to enhance the customer experience,

rather than to save cost at the risk of making

the customer experience worse. It’s a

niche where customer experience, business

efficiency, and the strengths of Machine

Learning line up to allow automation to help

everything flow more smoothly.

AI and the Customer Experience

Based on our conversation, I think there

are 5 crucial elements that make this kind

of automated communication work, which

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.

Proactive

One of the problems

with Chatbots is that

they are reactive;

they respond to a

request or enquiry

from a customer,

and that request

or enquiry could

be almost anything, worded in a massive

variety of ways. ContactEngine’s approach is

different.

“Because we asked the question, we know the

context of the reply. We might ask the question

about a loan application, or an insurance

product, or a washing machine, but we know

what was first said.”

we take

away

unengaging

tasks that

humans would rather

not be doing, and we give

those tasks to machines that not

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

they actually perform them better and more

reliably (not to mention more cheaply).

We can easily think of situations which we

wouldn’t want to automate, for instance

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:

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

because those calls are long, and dealing with

grief. The first call is counselling, this person

is in bits, so that's where it has to be humans.

After that, the machine is fine, but initially you

need a human being because machines can't do

empathy. Use humans to do what humans are

best at, and then machines.”

Conversation

Of course a lot of this kind of

communication is already automated, but

what is relatively rare is for an organisation

to automate conversation in this context, so

that the customer can get an SMS or email

and interact intelligently with a computer at

the other end of it.

“We’re dialogue, not monologue. The

technical challenges of conversation are vast, of

course, but if you are connected into what the

company wants and the service the customer

wants, then you could make massive cost

savings.”

Learning

AI is a frustratingly vague term. Even if we

restrict our definition to Machine Learning

(ML), the plethora of algorithms, approaches,

Focused

ContactEngine sells itself on using

communication to improve the small

moments of inefficiency that bedevil so many

businesses: the missed appointments, the

unhappy customers who need an opportunity

to be heard, the information updates that

prevent inbound calls.

“We start a conversation with somebody that

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

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

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

number one, the high street, is that the correct

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

The point is that, because you know so

much about the context for the customer’s

response, and because you started the

conversation, you have naturally constrained

the possibilities for what they are going to

want.

“The intents, when you ask a question, are

reduced by quite a lot, particularly if it's a single

question. You can maybe say 15 intents cover 98%

of the objectives, something like that. Machine

learning algorithms fly when they are fed

training data like that.”

What about the 2%, then?

“There will always be the need for human

beings to deal with exceptions, but machines

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

This is a crucial point. When we use AI well



and implementations often makes it very

difficult to know what vendors are talking

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

that the much-hyped AI solutions of some

vendors are often very simple algorithms

applied with varying degrees of cleverness to

very simple problems.

One of the trademarks, it seems to me,

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

relatively difficult to do, is ongoing learning.

As Mark says,

“It's got to be learning, it's got to get better

with time, and that's really rare. By labelling

the data you arrive at a point where you can

outperform a human agent very rapidly. The

learning bit comes from when you take the

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

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

better.”

Context

Making outbound contact to a specific

customer about a particular event means

that the context for the conversation is well

understood. That has benefits in terms of

language understanding, as we’ve already

seen, by narrowing the scope of likely

responses. It also opens up the ability to

personalise the conversation.

That opportunity can be a risk—there’s

a very fine line between intelligent

personalisation and creepiness—but there are

cases in which it clearly makes sense.

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

telco as an example: for some processes someone

has to have something before the next thing can

happen, like receiving something in the post

before the connection can be made. If you choose

not to connect those two events, there will be

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

in which case the second communication makes

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

they’ve got it before the second communication

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

not creepy, it's just sensible.”

Judging that line between personalisation

and creepiness can seem difficult, but a good

starting point is to ask who benefits from

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

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

interest, then it falls on the right side of the

line. We can even make a good argument, as

Mark does, that judging the timing of a sales

message is ultimately showing respect for the

customer’s feelings:

“In the world of financial services, where

someone has a successful mortgage application,

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

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

an additional product, maybe home insurance. If

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

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

at patterns in the data to make an offer at an

appropriate time, which isn't irritating.”

That’s obviously in the organisation’s

interest as well, but I think it’s fair to argue

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

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.

Interpretability

Perhaps the majority of AI at the moment

rests, directly or indirectly, on people tapping

into big “AI as a service” providers, especially

a few key players such as Amazon, Google,

Apple, Microsoft and IBM. Mark is glad that

ContactEngine decided early on to develop

their own algorithms in-house:

“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

against the flow. We were lucky we made that

decision, because there's now a big kickback

against the black box AI solutions that people use.”

Interpretability is a big challenge in ML,

because it’s often the case that we can train

the machine to get the right answer, but we

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

then there is always the possibility that the

machine will make unexpected mistakes*,

or bake in bias. Developing explainable AI is

important to Mark:

“There is an argument you're not ever

arriving at singularity or sentience, but you are

absolutely performing like a human and getting

better with time. Therefore, by doing this, you

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

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

actually say, ‘we made this decision because of

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


Bias

Most ML applications work by working

with a set of training data, and learning to

replicate the label a human would apply by

looking at patterns of association between

features of the data and the label applied.

If there are systematic biases in the way

that humans apply those labels, then the

algorithm will learn those too, which has the

potential to introduce biases. Importantly, the

machine doesn’t do this on purpose,

“I dislike intensely the notion that the AI itself

possesses human traits of bias. Algorithms are not

racist, or sexist, or homophobic, or antisemitic.

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

fault.”

In fact, there’s an interesting parallel

between the ideas of algorithmic bias in

machines and unconscious bias in humans—

both reflect structural problems in society

that probably need to be addressed at a

societal level. It’s not really fair to expect

AI developers to address these issues, but

I think it is fair for them to be expected to

engage with the issue, and at least not make

the situation worse. Explainable AI means

that the biases and the model are there to be

checked and talked about and discussed. If

it's a black box, you can’t.

Robots in society

AI opens up the potential to use the data

that we hold about customers in ways that

are simply not possible with traditional approaches

(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

judgements that our human staff are making

every day? It’s true that algorithmic biases

can scale in a way that an individual

human’s wouldn’t, but again there

seems to be a wider point here about

the ways in which we make decisions

about how to deal with individual

customers. People are nervous

about self-driving cars, but what

about the human drivers who

are killing 2,000 people a year

on British roads? As Mark

comments,

“The autonomous vehicle is

held to a higher standard than

the human.”

What about the impact

of AI on jobs? When should

we expect to be replaced?

With a few very specific

exceptions, we should

probably take the more

extreme predictions with a

pinch of salt:

“I think there's a tremendous

arrogance from the tech

community to imagine that

computers will cross into sentience.

It's just ridiculous. I also think that

every 10 years there will be cataclysmic

predictions about the end of humanity

because of AI.”

As far as Mark is concerned, the

most effective use of AI is in very specific,

limited, domains. Jobs that a machine can

“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.”

Mark K. Smith

CEO

ContactEnginge

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

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

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

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

commentary from anywhere in the world via phone. Mark is now CEO of ContactEngine, a con-

versational 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.


do better than

a human, and that

humans find unengaging.

“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. I know one large telco

who have 12,000 people in a call centre dedicated

to taking a call when broadband goes down.

The call centre churn is a hundred percent,

every eight months. No one wants this job. You

need automated proactive communications in

that situation. We know your broadband has a

problem, or an imminent problem, so I will give

you all the information about what's happening

when it's happening, and keep you informed

across all available channels until such time as

the situation is resolved. And that reduces the

anxiety of the customer and lets them know

what's going on.”

No one, I

think, can really object

to machines replacing humans in a

job that sees that kind of churn, and this is

exactly the kind of interaction that a machine

can handle better than a human. Not only

that but, by handling it effectively, the

machine is able to create a better emotional

experience for the customer. This is a really

crucial point—don’t imagine that customer

emotions can only be influenced by human-

to-human contact. Proactive automated

communication, like this example or even

Amazon’s simple delivery status notifications,

can do a lot of work to reduce customer

anxiety.

And if the automated interaction can’t

handle a particular customer’s needs, or if

they just want to speak to a human being,

then there is always the option to escalate

those cases to the call centre. Those cases

which, almost by definition, will be more

unusual, and more interesting for a human

to handle.

“The call

centre person would

normally be a long-serving staff

member, because they have a better job dealing

with actual problems that humans deal with. That

will normally be better paid as a consequence of

that loyalty, and they will stay longer because

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

have made them leave after six months.”

Coders & linguists

Effective Natural Language Understanding

(the work to teach computers to understand

human language as it is really used) happens

at the intersection between linguistics and

machine learning. ContactEngine employs a

variety of specialists from different disciplines

to work together at this point of intersection

and, with one of their offices at Bletchley Park,

Mark sees a parallel with the code-breaking

teams assembled during WW2:

“There were four types of people that were

employed at the time: there were men and women

that were the equivalent of dev ops, they were

programmers, there were people putting the tapes

in the machines, so the equivalent of software

engineers, there were mathematicians looking

at the statistical patterns of data, and there were

linguists. They are exactly the disciplines we

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

speaking or writing naturally is extremely

difficult, and it’s not something that

you can expect mathematicians or

programmers to solve on their

own. These are problems that

need to be solved with real

world knowledge, and

by testing the impact

of approaches with

real customers.


“The language that you use in

communication can massively affect response

rates, and you can personalise that as well,

based on additional information. The next

generation of what we're doing we call human-

computer rapport, which is a phrase we had

to invent. You can market to individuals as

individuals based on the patterns of what they

do and using a concept of rapport means that

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

opportunity that exists in a huge number of customer

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

case, they’re adding conversation at a

point in the journey which currently

has either one-way communication

or nothing at all.

Should you build AI into your

journeys? This, for me, is the

acid test: will it make the

customer experience

better?

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.

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.


R E S E A R C H

85

80

75

70

65

60

55

50

Oct-

18

No

v-18

Dec-1

8

Jan

-19

Feb

-19

Mar

-19

Ap

r-19

May

-19

Jun

-19

Jul-

19

Au

g-1

9

Sep

-19

Oct-

19

No

v-19

Dec-1

9

Feb

-20

Mar

-20

Jan

-20

May

-20

Ap

r-2

0

Jun

-20

Jul-

20

Au

g-2

0

Sep

-20

Oct-

20

Index of Current Economic Conditions Index of Consumer Sentiment Index of Consumer Expectations

76.9

69.5

64.7

R E S E A R C H

It’s hardly surprising that consumers are,

rightly, worried about the economic impact

of the Coronavirus and the measures taken

to combat it. What’s much more interesting

is that their attitudes to their personal

finances and the wider economy, over the

short and long term, have been affected very

differently.

Understanding consumer attitudes, and

therefore being better able to predict their

behaviour, makes the Index of Consumer

Sentiment an important tool for businesses

to understand and predict the economy,

particularly in the wake of seismic events

like a pandemic.

THE MEASURE - A REMINDER The Index of Consumer Sentiment measures three things

(using a total of 5 questions):

• How people feel about their own financial situation

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

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

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

Current Economic Conditions, and the Index of Consumer Expectations.

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

versus their view of the future prospects for the economy.

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.

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.


Comparison to the USA

We have chosen a methodology that

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

remains much smaller than before.

Beneath the index

To understand what’s really going on,

we need to turn to the individual questions

that make up the indices. Each of the

three headline index numbers is built on

a combination of questions, which are

expressed as an index based on positive

versus negative answers. In other words, a

score of 100 means that the same number

of people gave a positive answer as gave

a negative answer, and a score below 100

means that there were more negative

answers. Let’s have a look at what’s

happened to each of the five questions over

time…

Monthly data

Ind

ex

Val

ue

(19

66

=10

0)

The Index of Consumer Sentiment

110

100

90

80

70

60

502010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

3 Month moving average

R E S E A R C H

Change in questions

100 98 99 104 103 10295

99 99

Oct-18 Jan-19 Apr-19 Jul-19 Oct-19 Jan-20 Apr-20 Jul-20 Oct-20

Better or worse o� than last year?

104 104 104 109 107 110

69 9699

Is now a good time to buy big things?


106 107 107 109 111 118

100 98 93

Long term business conditions


1http://www.sca.isr.umich.edu2https://www.home.barclaycard/media-centre/press-releases.html


You can see that, although the other

questions have experienced what would

normally be seen as significant shifts, the

really seismic change to consumer sentiment

is restricted to two questions. The first of

these, “Thinking about the big things people

have to spend money on such as their car,

a new television, furniture and things like

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

time to buy major items?” plummeted in

April, but has since recovered (driving the

Index of Current Economic Conditions). This

tallies with data from Barclaycard showing

year-on-year drops in consumer spending of

36.5% in April and 26.7% in May, followed

by a slow recovery from June onwards to

modest growth in August and September2.

In fact if we look at the positive and

negative responses per quarter, you can see

that as many consumers are positive about

this as they were before the pandemic,

although more are negative…

Is now a good time to buy big things?


50%

40%

30%

20%

10%

0%

-10%

-20%

-30%

-40%

-50%

A bad timeA good time

R E S E A R C H

99 101 99105 102

107

95 99 101

Next year better or worse o�?



8680 81

87 87100

5563 64

Short term business conditions


Are you looking to bring your team up to speed, build skills, or start a conversation about the customer experience?

Prices start from just £500

£200

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

Or, if you prefer, commission one of our existing webinars exclusively for your staff at a date and time convenient to you,

complete with Q&A.

Find out more about our existing webinars at tlfresearch.com/webinarsor contact [email protected] to discuss your requirement

[email protected]

Stephen Hampshire

Client Manager

TLF Research

The other big mover, “Now turning to

business conditions in the country as a

whole, do you think that during the next 12

months we’ll have good times financially, or

Short term business conditions

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

Probably bad timesDefinitely bad times Probably good times Definitely good timesNot sure

Oct-

18

No

v-18

Dec-1

8

Jan

-19

Feb

-19

Mar

-19

Ap

r-19

May

-19

Jun

-19

Jul-

19

Au

g-1

9

Sep

-19

Oct-

19

No

v-19

Dec-1

9

Feb

-20

Mar

-20

Jan

-20

May

-20

Ap

r-2

0

Jun

-20

Jul-

20

Au

g-2

0

Sep

-20

Oct-

20

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

their own financial position. In fact more

expect to be better off next year than expect

to be worse off.

• The early days of lockdown, when there

was much uncertainty about jobs, were

not seen as a good time to make a big

purchase, but many have returned to their

former confidence.

• Data from our Index of Consumer

Sentiment tracks well with Barclaycard’s

spending data, showing the link between

consumer attitudes and behaviours.

• Confidence in the long-term future of

the economy is declining steadily, and

continuing to fall (where other questions

have bounced back). This suggests a

quieter, but more profound, unease about

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

know which combination of Coronavirus,

Brexit, or other factors are causing this

lack of confidence; but it will inevitably

have repercussions in terms of consumer

spending if it is not reversed soon.

Get in touch if you have any questions

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

about the data and methodology, and keep

your eyes open for future results.

bad times?” plummeted and has stayed low,

driving the Index of Consumer Expectations.

No doubt reflecting fears over both the

impact of Coronavirus and a potential

no-deal Brexit, a fairly large majority of

consumers expect business conditions to be

bad over the next year.

R E S E A R C H


Are you looking to bring your team up to speed, build skills, or start a conversation about the customer experience?

Prices start from just £500

£200

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

Or, if you prefer, commission one of our existing webinars exclusively for your staff at a date and time convenient to you,

complete with Q&A.

Find out more about our existing webinars at tlfresearch.com/webinarsor contact [email protected] to discuss your requirement

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


In September Pegasystems and UCL ran

a virtual roundtable on the topic “Do AI

biases and human biases overlap more than

we think?”, presented by Peter van der

Putten (an assistant professor of AI at Leiden

University and director at Pegasystems) and

Dr Lasana Harris (Senior Lecturer in Social

Cognition at UCL).

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

can perpetuate biases that exist in society,

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

and should we, expect more from computers

than we do from people?

AI and customers

AI, in the sense of machine learning

approaches to automation of particular tasks,

is increasingly a necessity. The pandemic is

a good example of a crisis which demands

systems, like track and trace, which are able

to handle vast amounts of data efficiently.

When dealing with customers, Ai systems

often fail because they are not able to feel

and express empathy in the way that humans

are. As Peter van der Putten commented,


“People view companies as if it is a single

organism or as a ‘person’. This requires not

just intelligence but also empathy. Sense

my emotions in the moment, learn from

interactions to understand my needs, but more

fundamentally, put yourself as the company in

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

some message or nudge in front of a customer,

promote what’s right for the customer not just

what’s right for the company.”

Customers are, for the most part, sceptical

about organisations’ willingness to do the

right thing for customers, beyond what

they’re legally required to, and that creates

opportunities for companies who can show

customers that they do.

Pega shared the example of CommBank,

who introduced a Customer Engagement

Engine, using AI to proactively select the

“Next Best Conversation” that is best suited

to the needs of each customer at that time

and through that channel. As Peter explained,

“…the library contains a wide variety

of messages, in line with the mission. Not

just selling the product of the month and

personalised sales recommendations; also

warning about credit card points that expire,

how customers could avoid upcoming fees and

WHOSEBIAS IS IT ANYWAY?

[email protected]

Stephen Hampshire

Client Manager

TLF Research


"The most

important thing

is that when

companies use

AI, they must

balance their

self-interests

with those of

the consumer."


charges. But also beyond their products and

services: the Benefits Finder identifies specific

government benefits specific customers qualify

for; or emergency assistance when customers

live in an area affected by bushfires. Since

COVID-19 hit, it communicated 250m COVID-

related messages to customers, from payment

holidays to home loan redraws.”


something that we can easily take steps to

prevent? Peter highlights three fundamental,

related, problems:

• “In the same way that humans see bias

in society which then reinforces our own

biases, it is the same for AI.

• Through the data that we use to train

models, through bias in decision logic

driving automated decisions, through more

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

having the tools to detect and fix it.”

"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."

out when it is used in ways that sort of threaten

things that people have, like their privacy, for

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

company.”

What should the future of AI be?

If customers are increasingly sceptical

of the benefit to them of AI tools, and as

wider society begins to worry about the

impact of algorithmic bias, now is a good

time for organisations to consider how best

to deploy AI solutions in a way that is good

for customers, and society, as well as their

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.”

It’s also about making sure that AI tools

are used in the right way, and in the right

places. Used well these approaches have

the potential to deliver much quicker, more

responsive, more personalised customer

experiences at scale. Customers will make

up their minds based on the results they see.

The aim, as Lasana says, should be to…

“Improve the life of your customer somehow,

and the AI can facilitate that…given the power

and the influence of AI, AI can make decisions

across thousands of customers very quickly.”

Where does the bias come from?

Algorithmic bias is not inevitable, but

something which comes about because of

the way we build and train AI models. Those

biases reflect tendencies in the data, in other

words they may recapitulate systematic

biases in society, but they may also be

exacerbated by who works in tech and the

way they think.

So what does cause these biases, and is it

Brands as people

Why do customers think of organisations

as if they are people? Because they spend

millions of pounds on advertising to position

themselves in that way. One of the biggest

causes of customer dissatisfaction is the

disconnect between the friendly, personal,

brand they’re promised in the adverts and

the impersonal treatment they often receive

in practice. Dr Harris commented,

“The most important thing is that when

companies use AI, they must balance their

self-interests with those of the consumer. When

deciding how they want to use AI they need to

consider whether it will impact their brand and

their reputation.”

“People typically aren't very trustful of their

information with companies and AI presumably

can help smooth some of that transition if used

appropriately. I don't think that perception of

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

This is really important, and links into

the points made in Artificial Unintelligence

(our book review on page 31). To see

algorithmic bias as merely a problem that

reflects the training data, and therefore as

society’s problem rather than AI’s problem,

is missing the point. Peter continues, with

some examples:

“The more systemic underlying issue is that

ultimately it's humans that build AI systems. So



the systemic problems are added that people are

maybe not aware enough of, or bias problems

happen, or the systemic problem could be that

people don't care enough.”

“One example is the 2020 A-Level results

(in this case, not AI, but algorithmic bias).

Boris Johnson blamed a “mutant algorithm”

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

blame it on the algorithm. Algorithms are not

silver bullets, nor are they inherently evil. And

algorithms are certainly not objective, nor ‘back

boxes’ we can shift any blame to.”

“Another is a study in Science in 2019 which

reported on a predictive model used across the

US to identify patients for preventive care and

care management programs, clearly an example

where AI was used with the best intentions.

The problem is that the model predicts future

healthcare costs, and in the historical data

used to build the model, considerably less

money is spent on black patients that have the

same health conditions as white patients. By

correcting for the bias in the healthcare data set,

more than two and a half as many black patients

would be eligible for a care management

program. Bias was caused by how the data set

was defined for modelling.”

What can we do to prevent bias?

To build algorithms that are unbiased

requires active work, and making sure you

understand the nature of the data that you’re

using. Knowing how the data was collected,

and the nature of the society in which it

was collected, is as important as being able

to build an efficient algorithm. As Lasana

comments,

“I think when discussing bias, it's really

important to understand that the bias exists

all around us…If there's no bias detection

mechanism and there's no person who's aware

of these biases intentionally looking to see that

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

going to appear to be biased.”

“In reality, the way to combat social bias

is to be aware of your own biases – the same

thing is true for AI. Therefore, those who are

creating AI need to be aware of their own

prejudices.”

The conclusion is clear: if you want to

build AI algorithms which are free from

bias, then you’re going to need to build

transparency and bias detection into your

systems. This can’t be done passively, but

needs to be consciously approached with an

understanding of the potential biases that

training data may reflect.

You also need to evaluate the decisions or

predictions that your algorithms are making,

and make sure that they are fair.

Are we being unfair on the machines?

People within tech often feel that all this

is a bit unfair, after all machines are, by

definition, free from bias themselves. If a

computer learns to replicate decisions which

are biased, based on a stack of data about

how humans have made decisions in the

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

is it? And yet we seem to be suggesting that

computer decisions should be more heavily

scrutinised than their human colleagues. Is

that right?

To some extent that’s our pro-human

bias speaking, but there is also the

question of scale. A biased human makes

far fewer decisions than a biased machine.

Nonetheless, it’s important to remember

that, however flawed a computer’s decisions,

the fault remains with humans, as Lasana

comments:

“Humans are sometimes eager to push

responsibility to an AI algorithm, which is not

correct. AI algorithms are built and trained by

humans, based on a range of choices made by

humans.”

Perhaps the most important thing of all

for a customer, whether the decision was

made by a human or a machine, is that it

seems fair and is explained. As Peter says,

“For a customer being declined for a loan

it doesn’t matter that much who made the

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

the loan and didn’t get it, so wants to get an

explanation and wants that decision to be fair.”

“Make the customers feel that for every

single customer and every single interaction

you're really trying to do the right thing for

them.”

Ultimately, like everything else in the

customer experience, what really matters

is that customers believe that you are on

their side, and have their interests at heart.

If AI is serving that end, then it has the

potential to contribute to excellent new

customer experiences, but it can’t do that

until we take a clear-eyed look at the biases

that we’re building into decisions and

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

have to face up to our own biases.

"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."

Consumer Insight

Visit tlfpanel.com

60,000 UK consumers

Fast turnaround2,000 responses

within 48hrs

Targeted surveysWe can find the

people you need

Range ofquestion typesIncluding open

comment and media

In depth reportingand analysis

Demographic splitsas standard

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

The TLF Panel o�ers 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 usageFacts and figures for compelling content and PR storiesBrand awareness and competitor surveysTesting advertising and product conceptsRecruitment for focus groups and interviews

Want to try us out?We’ll give you 2 free questions (worth £375) – email [email protected] for details


Consumer Insight

Visit tlfpanel.com

60,000 UK consumers

Fast turnaround2,000 responses

within 48hrs

Targeted surveysWe can find the

people you need

Range ofquestion typesIncluding open

comment and media

In depth reportingand analysis

Demographic splitsas standard

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

The TLF Panel o�ers 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 usageFacts and figures for compelling content and PR storiesBrand awareness and competitor surveysTesting advertising and product conceptsRecruitment for focus groups and interviews

Want to try us out?We’ll give you 2 free questions (worth £375) – email [email protected] for details

For those in the world of customer service,

taking work home has been a big challenge to

overcome. Contact agents, synonymous with

working in call centres, have been badly hit,

with concerns being voiced over the social

distancing measures available1 in offices.

It is therefore vital that organisations

recognise the new challenges that call agents

are forced to deal with and offer them

the tools and support needed to succeed

remotely.

How businesses have responded to the work from home era

Since March, many businesses have

experienced ‘customer

distancing’

in a struggle to

nurture their

customer

relationships. More than ever, consumers

turned to the phone when contacting

businesses to make complaints and pose

queries. One borough council customer

service centre in Wales, recorded an increase2

of 12,000 calls under lockdown compared to

the same time the previous year. How can call

agents and staff keep up with such spikes in

volume?

In response, some businesses have had

to boost their number of call agents with

truly rapid deployment, with Scottish firm

Ascensos, having increased its number of

call agents by sixty times3. Equally, some

companies like Virgin Media4 went so far

as to ask customers not to call at all while

customer service lines were under such

pressure.

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

Zoom, Microsoft

Teams, and

Google

Hangouts

provide

appropriate

alternatives.

However,

such platforms

do not cater for

contact agents’ needs;

their challenges are far greater than

these solutions can deliver on.

Call routing, case deflection, call queues,

wallboards, listen-in coaching, data syncing


Ian Moyse

EMEA Director

Natterbox

Ian has led the EMEA sales team at Natterbox

for over three and a half years. Before Natter-

box 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.

www.natterbox.com



of customers through click-to-dial, and

screen pops are all vital tools that businesses

with high customer demand need to be able

to provide an effective customer service.

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?

Handling the complexities of home working

Working remotely as a call agent is

certainly not as simple as just ‘logging on’

from home. There is a mass

of hidden complexities

behind the change. It affects their phone

system capabilities and configuration, not

just for redirecting calls to mobiles, but

for handling call queues and hunt groups

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

an agent. In this sense, moving agents to the

home does not affect the fundamentals of

customer service.

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

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.

Working

remotely as a

call agent is

certainly not as

simple as just

‘logging on’

from home.



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.

This brand of customer service tech opens up

a range of possibilities for a workforce that is

more inclined towards flexibility. In fact, it is

shifting the concept of flexibility entirely.

Cloud-supported interfaces, for example,

are already available to give agents the

agility to work from anywhere, on whichever

device they want. These interfaces can

give them complete control over who can

contact them and on whichever device,

all through a centralised company phone

number. If one agent is unavailable, the call

will be automatically routed through to an

alternative agent.

In effect, this technology means there’s

minimal difference between the way a

call agent operates in a call centre and at

home. All data is then easily shared back

into their business’ CRM system, improving

the efficiency and personalisation of future

customer communications. Employee admin

time is also drastically reduced.

With change comes opportunity

In the long term, this combination of

remote working during the pandemic and

emerging

technologies

will create a wider

acceptance that a home worker

in any role can be productive with the right

tools.

As a result, organisations can now

widen their hiring pool, no longer limited

by the location of a call centre, and with

the ability to offer more flexible working

hours and working from home offerings.

This will ultimately lead to a new workforce

market made of a wider range of people

who previously might have been eliminated

from consideration, but who can now be

utilised in this new dynamic way of working

in a way that wasn’t possible before. For

example, working mums and home carers,

who benefit from roles that offer

flexible hours and the ability to

work from home. Businesses will also

benefit in becoming more efficient,

cost-effective,

and productive,

some even working

from entirely virtual

offices.

So, with the world

re-aligning itself during a crisis,

it’s vital that companies reassess

their business tools. They should question

the status quo and explore areas previously

ignored. Doing this, they may well find that

their legacy technologies are not so well

equipped for an environment that requires

greater agility.With change comes greater

opportunity.

This may well

be a challenge

that improves

customer service

for good.

1https://www.insider.co.uk/news/one-two-call-centre-staff-218610452 https://newsfromwales.co.uk/news/customer-services-staff-take-over-12000-telephone-calls-more-during-lockdown-compared-to-same-period-last-year/ 3https://www.economist.com/britain/2020/04/04/britains-call-centres-are-overwhelmed-and-overhauling-how-they-work4?5 https://community.spiceworks.com/blog/3103-data-snapshot-the-lifespan-of-computers-and-other-tech-in-the-workplace

Howhealthy

is yourbrand?

New for 2020

At TLF, we’ve been running surveys for

longer than most of us care to remember.

We’re experts in customer satisfaction, and

tackle a multitude of different survey topics

day in, day out.

The world we immerse ourselves in, that

of market research, is ever-changing. With

new technology comes new ways in which we

can interact with people, and that changes

peoples’ expectations and perceptions of

what constitutes meaningful research and

actionable insight.

With this in mind, we went out to our

clients and asked them – what do we not

currently offer that you would find useful?

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?

Why track your brand’s health?

Never has brand loyalty moved at such

a fast pace. In this digital age, where

consumers are bombarded with brands every

moment of the day, and brands are accepted

or dismissed with just the swipe of a finger

or click of a mouse, it has never been more

relevant to track your brand’s performance,

and, perhaps more importantly, take action

from the findings.

Sometimes it can be overwhelming to

know where to start with tracking your

brand, so we have done the hard work for you

and whittled it down to the key questions you

really need to know:

• Usage & Awareness – how aware are

consumers of your brand, and how often do

they use/see/purchase it?

• Customers vs. Non-customers – how do

results differ between these demographics,

and how can you turn the latter into the

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

come with your brand, and how do these

change across sectors/products?

• Likelihood to purchase – how likely are they

to buy your product/service? And what will

affect this?

• TLF NEW PRODUCT FEATURE •

R E S E A R C H


What does it look like?

To show you what a brand tracker can do

for you, here are some of the outputs from our

new Brand Health Package.

We’d always start with a summary of who

was surveyed, how long the survey was open

for and what the incidence rate was. Not

essential for the results, but useful to see the

demographics, especially if you want to track

how your brand health changes over time

across different groups.

You’ll want easy-to-read figures on,

among other things; gender, age brackets,

socio-economic groups and regional

breakdowns, along with any other data splits

you need.

Then we get into the meat of the report,

starting with the biggie – brand awareness.

How aware are consumers of your brand?

We ask them both unprompted and prompted

awareness to gather the fairest findings.

These results are then analysed and compared

to your brand’s competition:

This general awareness gives a good

starting point to understanding your brand’s

health. Depending on your requirements,

you will then get this broken down by

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:

• Brand Usage – have they heard of your

brand but never used them, heard of your

brand and used them in the past, or heard

of your brand and currently use them?

• Brand Consideration – Is your brand

something they would never consider,

might consider, one of two or three brands

they’d consider, or the ONLY one they’d

consider?

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

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).


Company A

Company B

Company C

Company D

Company E

Company F

Brand Awareness (Fig 1)

86%

87%

82%

84%

68%

53%

Brand Awareness by Age (Fig 2)100%

80%

60%

40%

20%

0%

Company A Company B Company C Company D Company E Company F

18-24 25-34 35-44 45-54 55-64 65+

Brand Usage - total consumer base (Fig 3)

Heard of beforebut never used

Heard of before andhave used in the past

Heard of this andI am currently a customer

Company A

Company B

Company C

Company D

Company E

Company F 7%

27%

22%

10%

29%

16%

79%

51%

55%

70%

46%

64%

14%

21%

23%

20%

25%

21%

R E S E A R C H

R E S E A R C H


We can then look at awareness alongside

consideration, a really useful visual tool

to see how you stack up compared to your

brand’s main competition (Fig 4).

Now we look at satisfaction – how

satisfied are your brand’s customers, and

also, equally as important, how satisfied are

your competitors’ customers?

A simple, but reliable, measure to gauge

brand health - satisfied customers will talk

and act positively about your brand, and

vice-versa (Fig 5).

Another popular indicator of how your

brand is perceived, and one with strong links

to customer loyalty, is NPS, or Net Promoter

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

‘not likely to recommend’ and 10 being ‘very

likely to recommend’), how likely are they to

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

brand’s final NPS score (Fig 6).

Now there’s some solid data and

understanding behind your brand’s

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,

prepared in detailed, easy to understand

charts (that often paint a stronger picture

than numbers alone), that can be run again,

and again (if required) to really track your

brand’s health over time, for example;

before and after a major advertising

campaign.

But the report doesn’t end there.

Now we delve deeper into consumers’

emotional connections to your brand, after

all, everything derives from emotions.

Emotions create attitudes, which in turn

drive behaviours, which ultimately lead to

which brands people choose to use, trust

and promote. The Brand Image Statements


Brand Awareness and Consideration (Fig 4)

100%

80%

60%

40%

20%

0%

Company A Company B Company C Company D Company E Company F

Not aware

Would definitely not consider it

I might consider it

One of 2 or 3 I’d consider

The only one I’d consider

Company A

Company B

Company C

Company D

Company E

Company F

Mean

8.1

7.7

7.9

7.9

7.2

8.4

1 (Not at all satisfied) 2 3 4 5 6 7 8 9 10 (Completely satisfied)

0% 100%

Customer Satisfaction (Fig 5)

0 (3) 1(0) 2(1) 3(2) 4(3) 5(16) 6(11) 7(34) 8(38) 9(27) 10(33)

1.8%0.0% 0.6% 1.2% 1.8%

9.5%6.5%

20.2%22.6%

16.1%19.6%

50%

40%

30%

20%

10%

0%

Mean: 7.4 NPS: 14.5%

Detractors

21.4%

Passives

42.9% Promoters

35.7%

Mean: 7.4 NPS: 14.5%

Detractors Passives Promoters

Net promoter score =% Promoters minus % Detractors

1 2 3 4 5 6 7 8 9 10

Net Promoter Score (Fig 6)

Recommend scoreExtremely unlikely Extremely likely

R E S E A R C H


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

Then we probe what words consumers associate with your brand, for example:

• Modern

• Technical

• Experts

• Outdated

• Slow

• Innovative

• Customer focused


Are easy todeal with

Are known fortheir quality

Value theircustomers

Are trustedproviders

Are known for good customer service

Have a goodreputation

50%

40%

30%

20%

10%

0%

Brand image words and phrases (Fig 7)

Company FCompany ECompany DCompany CCompany BCompany A

SlowRelevantOutdatedExpertsTraditional None of the above

Customer focused

ResponsiveInnovativeModern

50%

40%

30%

20%

10%

0%

Brand image statements (Fig 8)

Company FCompany ECompany DCompany CCompany BCompany A

R E S E A R C H

[email protected]

Tom Kiralfy

Panel Manager

TLF Panel


Again, this is also compared against

your brand’s competition.

The final portion of the Brand Health

report asks consumers to rate your brand

on a whole host of different factors. A really

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

your brand on a scale from ‘love the brand’

to ‘hate the brand’?

• Brand differentiation – how is your brand

rated from ‘same as other brands’ to

‘different to the competition’?

• Brand uniqueness – on the scale, how is

your brand rated from ‘follow others’ up to

‘unique and sets trends’?

• Brand empathy – where your brand

is rated on a couple of scales: how

much does it meet customers’ needs,

and how much does it care about its

customers.

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

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:

Getting the data

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

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

on our consumer panel to act like an MOT for

your brand, generating all the outputs you’ve

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!


Brand image statements - Brand di�erentiation (Fig 9)

5.0 5.2 5.4 5.6 5.8 6.0 6.2 6.4 6.6 6.8 7.0

Same asother brands

Di�erent toother brands

Company B

Company E

Company A

Company C

Company F

Company D

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

Company E

Company A

Company C

Company B

Company D

Company F

5.0 5.2 5.4 5.6 5.8 6.0 6.2 6.4 6.6 6.8 7.0

Out of date Progressive

BOOK REVIEW:ARTIFICIAL UNINTELLIGENCE

By Meredith Broussard

B O O K R E V I E W

It’s almost impossible to reconcile the state of AI

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

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

seem to be just a few years from AI general intelligence

that will outperform humans in every way. On the other,

I can’t find a machine transcription service that copes

with an even moderately-challenging accent.

Francois Chollet, the deep learning expert who

created Keras, put it neatly in a Tweet:

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

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

Autonomous vehicles are one of the best examples

of the tendency for technology to over-promise and

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

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

now and the foreseeable future, can be extremely

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

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

intelligence” you might see in science fiction (the closest

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

(or if) building one might be possible.

What causes this gulf is partly the enthusiasm of

people within technology excited about the potential

of their tools, and partly the hyperbole of marketing

departments and journalists who feed us a sci-fi vision

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

strengths and weaknesses of AI solutions, and the

current state of the art, from someone who understands

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

Unintelligence Meredith Broussard gives us exactly that.

“…general AI is what we want…Narrow AI is what we

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

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

limitations, and of the cultural issues within the world

of technology which exacerbate the social impact of

those limitations.

Technochauvinism

The core problem, she argues, is what she calls

“technochauvinism”—the belief that technology is

always the solution to every problem. This manifests

in the regular spectacle of silicon valley entrepreneurs

“inventing” products that have existed for years, such

as “reusable tissues”.

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

but the same outlook applied to machine learning

approaches to problems that have a real impact in

society can be much more damaging. If you believe,

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

or make decisions about early release of criminals, or

to sift job applications.

“When you believe that a decision generated by a

computer is better or fairer than a decision generated by

a human, you stop questioning the validity of the inputs

to the system.”

As Mark Smith pointed out in the interview featured

earlier in this issue, the algorithms are not to blame

when things go wrong. But technochauvinism can make

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

to the decisions and biases that are baked into it.

Data

All AI tools require data, usually mountains of data.

Where does it come from?

B O O K R E V I E W


“…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

prioritise things which are relatively easy to measure.

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

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

obvious that there’s a distinction between those two

things, although perhaps we’d be hard put to define

exactly what we mean by “quality”.

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

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

promised to rate your photos for quality, but ended up

rating them based on the extent to which they resembled

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.

“In order to create a more just technological world, we need

more diverse voices at the table when we create technology.”

Machines without humans

The other point about all that data gathered up

by humans, is the amount of work that goes into it.

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

But don’t pretend that machine learning can operate

in a vacuum without all the human-generated data.

As Broussard comments on the headline-grabbing

AlphaGo algorithm:

“Millions of hours of human labor went into creating

the training data – yet most versions of the AphaGo story

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

invisibly and over the course of years worked (without

compensation) to create the training data.”

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

robots that we sometimes forget to give ourselves

enough credit. Broussard gives the example of a tool as

everyday as Google, which to a large extent works as

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

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

good example of how the best technology solutions come

about from fusing together the complementary skills of

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.

“The machine-learning approach is great for routine tasks

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

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

gloriously unpredictable masses of people.”

In customer service terms, this tends to come to the

forefront in allowing robots (or self-serve) to deal with

the bulk of relatively simple enquiries, but allowing

humans to handle the complex stuff. If we assume the

computer can handle everything, the consequences will

be ugly.

“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?

Conclusion

The Hollywood vision of AI coupled with

technochauvinism has led to the rushed deployment of

machine learning approaches, launched with hyperbolic

claims, that are simply not delivering.

“…we are so enthusiastic about using technology for

everything…that we stopped demanding that our new

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

humans can use, rather than as an autonomous agent

that can step in to replace human decision-making in

most cases.

“We should really focus on making human-assistance

systems instead of human-replacement systems.”

I think we’re far better off thinking of autonomous

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

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

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

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

at others, and social problems arise from situations in which

people misjudge how suitable a computer is for performing

the task.”

Your Customers’Spending Habitsare ChangingTLF’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.

37%Saving

18%More food

to eat at home

24%Home / garden

improvements

16%Better quality food

to eat at home

12%Home

entertainment

11%Beer / wine /

spirits for home

£

42%Eating out

29%Holidays

abroad

31%Drinking in

pubs and bars

27%Clothes

19%Day trips

10%Public

transport

16%Holidays UK


What’s happening to jobs?

In short, they’re starting to disappear. In

May 3/4 of respondents had a job but now

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

likely to be travelling to their normal place

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

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

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

after paying for all the essentials by selecting

up to 3 categories that they were devoting

more money to. This really highlights the

growing propensity to save and shows the

top 6 categories receiving more of customers’

disposable income:

What are people spending less on?

Remembering that this survey took place

before any Tier 3 lockdowns or hospitality

closures, here are the main categories receiving

less of customers’ disposable income:



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.”

Live Life“I will be making up for lost time doing the things I haven’t

been able to do during lockdown.”


Nigel Hill

Chairman

TLF Research

Note that spending less far outweighs

spending more. For allocating more

disposable income, the top category was

saving and whilst 13% said they hadn’t been

spending less on anything, 28% haven’t been

spending more on anything.

Are the changes temporary or more permanent?

Customers’ behaviours are driven by

their attitudes and beliefs. During the

national lockdown we identified 3 attitudinal

segments:

Several months on in October, the diagram shows that the proportions in each segment

are still almost identical.

Whilst 24% can’t wait to Live Life to

the full again, this is now matched by the

extremely cautious Protect Lifers and the

Appreciate Life segment has cemented its

predominant position. So what does this

mean for future spending? With over 3/4

of the population still much less inclined

to have a hedonistic lifestyle it suggests

that greater propensity to save and more

spending on home life – investment in the

home and garden, food and drink for home

consumption and home entertainment

– are set to continue. Companies in the

hospitality, foreign travel and clothing

sectors will need to think very carefully

about their segmentation and targeting

strategies.

What about the future?

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

over the summer, whilst their expectations

for the future have stayed depressed into

October. However, the Sentiment Index

showed a divide between consumers who

are positive and those who are negative

about their own financial prospects. This

gulf is confirmed by this October TLF Panel

survey where 58% of respondents have no

worries about their finances compared with

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

worried about having enough money for

basics – rent/mortgage, utility bills and

food. Others are worried about not affording

non-essentials such as holidays, home

improvements or their ability to save, but

they are still expecting to have less money in

the future than they’ve had in the past which

will have a negative impact on many sectors

of the economy.

Appreciate life

50%52%

Protect life

22%24%

28%

24%

October

Live life

Customer Insight Magazine is createdand 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 [email protected] give him a call on 01484 467014

ABOUT TLF RESEARCHWe 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

USING ONLINE COMMUNITIES FOR QUALITATIVE RESEARCHOnline 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.

NPS BEST PRACTICEIf 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.

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.

FINDING & TELLING YOUR CUSTOMER INSIGHT STORYDo 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.

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.

TURNING INSIGHT IN TO ACTION: THE IMPORTANCE OF ACTION PLANNING

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.

USER STORIES & CUSTOMER JOURNEY MAPPINGThis 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.

CUSTOMER SATISFACTION INDEX: HOW & WHY TO USE ITA 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.

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.

FREE WEBINARS - WATCH ON DEMANDOur 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.

Sign up today at tlfresearch.com/webinars

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

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'.

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.

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