How To Tell Stories Using Data And Statistics

SUMMARY

Details how to use data and statistics in story telling by providing six golden rules for success.

·      It can be argued that the central equation underpinning success in the modern knowledge economy is: analytics + storytelling = influence – since marketing is the best established of the “moving businesses”, the equation has particular resonance in marketing.

·      Data-driven storytelling fundamentally an act of empathy, it requires the narrator to imagine what it’s like to be in the shoes and mindset of the audience.

·      Six golden rules for data storytelling are: keep it simple, yet smart, find and use only relevant data, avoid false positives, beware the curse of knowledge, know your audience, and talk human.

·      Statistics can power stories and numbers drive narratives, but only if as a storyteller you are able to resist the seductive lure of data to tell the story for you.

NEED TO KNOW

·      The storytelling potential of data transcends marketing. Data-driven storytelling offers potential right across business and society.

·      Lightly peppering narratives with just a handful of well-chosen, killer statistics is the key to data-driven storytelling success.

·      Use data in messaging with caution. The more information you deploy in arguments when you’re looking to convince others and to change people’s minds, the more likely the audience is to resist.

·      If you put all available data into the mixer and look for relationships, you’ll find a connection and be tempted to conclude it’s important, when in fact it’s a false positive. Correlation is not causation, and connections in complex systems are very rarely caused by single factors.

·      Don’t fall into the trap of using all data available. Brand storytellers should start with the reason or purpose they’re looking for data in the first place, and then choose the right data.

·      Effective data-driven storytelling is fundamentally an act of empathy and human understanding. If you can use your research, data, and statistics to show you understand those you’re looking to influence, your story is much more likely to prove effective.

INTRODUCTION

The world of brands and brand marketing is full of data. Media data, customer data, marketing performance data. Data that reveals customer behaviour, data that attributes marketing inputs to business performance outcomes, and data that explains who’s seen which commercials, where, and how often. The sheer volume of data in marketing means that practitioners are faced with a delicious paradox: it has never been more challenging to make sense of all the data that swirls around a brand, and at the same time it has never been more possible to do so.

The storytelling potential of data transcends marketing. Data-driven storytelling offers potential right across business and society. We are all, as writer Dan Pink says, in the “moving business”; the business of persuading others to take action and respond to the stories we tell. Psychologist Daniel Kahneman has crystallised what we know about human decision-making. We make decisions fast using what Kahneman calls System 1 thinking, based on our emotional response to situations and stimuli. We then justify our decisions using slower, more cognitive thought processes, using System 2 thinking. This is where the rationality of well-presented facts, numbers, and data has a critical role to play. The most powerful and influential stories are those that appeal to both our emotions and our intellect. It can be argued, therefore, that the central equation underpinning success in the modern knowledge economy is this:

ANALYTICS + STORYTELLING = INFLUENCE.

Since marketing is the best established of the “moving businesses”, the equation has particular resonance in marketing.

WHERE TO START

It’s a common misconception that using data and statistics to build more powerful and purposeful stories is about collecting and deploying as many numbers as you can to make your point. Yet social psychology 101 tells us that the more information you deploy in arguments when you’re looking to convince others to take a course of action – and particularly when you want to change people’s minds – the more likely the audience is to resist. In recent years, this has been labelled the “Project Fear” phenomenon, after the large number of data sources and data-driven scenarios the Remain campaign deployed in the run-up to the 2016 EU Referendum in the UK.

Using data and statistics as the rational underpinning to well-balanced, emotional, and above all human stories is much more about the judicious use of a small number of well-chosen data points to support an appeal or call to action. This makes data-driven storytelling fundamentally an act of empathy. It requires the narrator to imagine what it’s like to be in the shoes – the mind; the mindset – of the audience. And to realise what it feels like to be assaulted with number after data point after statistic. Browbeating the audience into submission not only doesn’t work; it’s actively counter-productive. Lightly peppering narratives with just a handful of well-chosen, killer statistics is the key to data-driven storytelling success.

ESSENTIALS

1. KEEP IT SIMPLE – YET SMART

When you’ve thoroughly researched and understood a brand, a market, or an issue, the temptation is to share your depth of knowledge and understanding. Market researchers are particularly guilty of wanting to show all their workings out – all the data that underpins the insights they bring to their clients – but they’re not the only breed of communicators who fail to keep it simple. Clients don’t pay agencies to take them through their workings out and justify why they reached the conclusions they reached. Consumers don’t want a complete rationale of the thinking – and increasingly the data, numbers, and statistics – that underpin a campaign. Both want to know what they should do in simple, simplified terms.

Keeping it simple – yet smart – avoids the storytellers no-no of exposition or backstory. How much better as a storyteller to have the audience hanging on every twist in the story and begging for more – the next storyline, the next episode – than to be bored into submission. Building simple yet smart stories with data as the rational underpinning isn’t necessarily easy to pull off. As Mark Twain, Oscar Wilde, and Winston Churchill are all said to have said: “I would have written you a shorter letter but I didn’t have the time.”

2. FIND AND USE ONLY RELEVANT DATA

There are so many different sources of data available to brands and companies today – their own data, third-party data, and publicly-available data sets – that it’s hard to choose data that is relevant to the story you’re looking to build. Brand data can include: first-party customer journey data, social and news media content, sales data, employee attitudes and behaviours. Third-party and publicly-available data can include: analyst commentary, Government demographic data, wealth and health data, weather data, academic and peer-reviewed research, crime statistics. You name it, it’s probably been counted, analysed, and shared online.

The trick in finding and using only relevant data is to identify and deploy the corner of Big Data – what the Small Data Forum podcast calls “little big data” – that’s relevant to the hypothesis you’re trying to test, and not just what happens to be available, convenient, or easy to use. Brand storytellers should use the principles set out by writer and TED Talk favourite, Simon Sinek, and “start with why”; start with the reason or purpose they’re looking for data in the first place, and then choose the right data.

3. AVOID FALSE POSITIVES

The trouble with there being lots of potential data sets available to those looking to build brand narratives is that, when you put them together, you’re likely to find connections between different variables that are most likely meaningless. There’s every possibility that, if you’re looking to prove (rather than test) a hypothesis by putting all available data into the mixer and seeing where there might be a relationship, you’ll find something and be tempted to conclude you’ve found something important when in fact it’s a false positive. Remember the statistician’s acronym GIGO: Garbage In, Garbage Out.

As creatures, we’re hard-wired to look for relationships between variables – and the simpler the connection the better. We find it satisfying to conclude “the Gulf War was all about oil” or “Brexit was caused by unjustified fear of migrants”. Trouble is, complex connections in complex systems are very rarely caused by single factors. They’re also unlikely to be directly causally-connected. More often than not there’s a hidden third cause that you’ll have completely overlooked if you haven’t first kept it simple and second found and used only relevant data. Remember also that correlation is most definitely not causation.

4. BEWARE THE CURSE OF KNOWLEDGE

When you know a lot about a subject, it’s very hard to imagine what it’s like not to know what you know. This is called the Curse of Knowledge. In his book The Sense of Style, Harvard psychologist Steve Pinker calls out academics, government officials, lawyers, financial advisors, and many of those in Big Pharma to be among the worst afflicted by this condition.

Like using too much or irrelevant data, the Curse of Knowledge is a significant turn-off for an audience. They feel talked down to – patronised – and ignorant in the face of an expert not prepared to come down to their level. Unsurprisingly, this approach – such a common failing among those who use data and statistics in their storytelling – is also counter-productive. It’s as unengaging as showing your workings out and convinces almost no one.

5. KNOW YOUR AUDIENCE

Effective data-driven storytelling is fundamentally an act of empathy and human understanding. If you can use your research, data, and statistics to show you understand those you’re looking to influence, your story is much more likely to prove effective. The dictionary definition of insight is “a profound or deep understanding of someone or something”, and if you can use data and statistics to get under the skin and into the mindset and motivations of your audience, they’re very much more likely to be receptive to your message. The case studies on Sport England and Dove, below, show how data-driven insights can spark social and market change.

6. TALK HUMAN

It’s a curious truism of corporate behaviour that many companies and brands – including some of the most successful B2C brands, and many B2B – speak a dialect that is very unlike the way that people talk. Those mandated to talk on behalf of a company – a growing pool of individuals in our social media age in which many more voices matter – often adopt a pretentious, jargon-rich way of speaking that explains little and convinces very few. When data and statistics are at the heart of the story you’re looking to build, this problem can be made worse. Corporate and brand storytellers should resist this temptation and use numbers to support not dominate their narratives. They should use a range of emotions and talk in that rarest of corporate dialects: human.

CASE STUDIES

BRITISH HEART FOUNDATION – STAYING ALIVE

The British Heart Foundation wanted to raise awareness of how to perform hands-only CPR to restart someone’s heart if they’ve had a heart attack. Using black humour and football hard man Vinnie Jones – just featured as a gangland villain in Lock Stock & Two Smoking Barrels – the BHF managed to balance the rational with the emotional in a core campaign ad full of both information and entertainment. One of the most important pieces of information they needed to convey was the pace with which first aiders need to pump the chest of a heart attack victim, which is a couple of times every second. The planners on the BHF campaign found and used relevant data – that the Bee Gees’ disco classic Staying Alive is played at 110-120bpm – and used that as the incongruent (and memorable) soundtrack to Jones’ infomercial.

SPORT ENGLAND – THIS GIRL CAN

From early in secondary school onwards, right up until after retirement age, girls and women take part in less regular sport than boys and men; there truly is a gender exercise gap. Sport England wanted to bring about sustainable behaviour change in girls and women. Their research found that one of the main drags on women’s participation in sport was the fear of being judged by others – of sweating, make-up running, flushed cheeks, wearing unflattering Lycra, being seen out of control. 85% or women who don’t exercise say it’s for fear of being judged. And they were right – at least partially. 85% of those who see women exercising do judge them – and they do so entirely positively. They admire real women really exercising. It makes them think “I should do that!” Hence the real women that have featured in every one of the campaign’s three ads, a campaign which has led 1.6m more women to take up exercise. Sport England truly does know its audience.

DOVE – CAMPAIGN FOR REAL BEAUTY

Another brand that knows its audience and that found and used relevant data as the rational underpinning of its campaigning is Dove and its 15 year-old Campaign for Real Beauty. Back in the early 2000s, most beauty brand communication was having a negative impact on women’s self-esteem and sense of self-worth. Cross-cultural research by Dove found that in 20 countries – from Thailand to Brazil, from Russia to India – just 2% of the world’s women would use the word “beautiful” to describe themselves. This gave Dove the legitimacy to take on the beauty industry on behalf of real women, to celebrate ‘real types not stereotypes’, and to feature real women – real beauty – in their category-redefining campaign. Once the data-driven insight informed new product formulation, too, sustainable conversations with teens, tweens, and women turned into growing and sustained growth and profit for the Unilever brand.

KEY TAKEAWAYS

In his introduction his book the The Signal & The Noise, Nate Silver observes: “The numbers have no way of speaking for themselves. We speak for them. We imbue them with meaning.” It’s a sentiment repeated by Cambridge statistics professor David Spiegelhalter in the introduction to his 2019 book, The Art of Statistics.

If you keep it simple yet smart with numbers, you’re off to a good narrative start. Find and use only meaningful statistics but avoid false positives when two data points or series seem to be connected. Beware the Curse of Knowledge, a fundamentally arrogant and inhuman way to share data and statistics that you know and understand but that your audience doesn’t. Data storytelling is at its heart an act of empathy that requires skilled practitioners to know their audience – to get inside their minds – and ultimately to talk that rarest of corporate and brand dialogues: human.

Statistics can power stories and numbers drive narratives. But only if as a storyteller you are able to resist the seductive lure of data to tell the story for you or – worst of all – be presented as the story itself.

HOW TO USE THE B=MAT MODEL FOR BEHAVIOURAL CHANGE

OVERVIEW

Behavioural change is one of the ‘holy grails’ for any marketer. Effective strategies for getting consumers to start, stop or change a behaviour are much sought after, and success can be elusive. As behavioural science has evolved, experts have developed robust frameworks and models to help apply behavioural science in a rigorous, systematic way, thereby effecting behavioural change.

In this best practice paper, we explore the B=MAT (now known as the B=MAP) model developed by Stanford Professor and behavioural psychologist BJ Fogg. The B=MAT model is used by practitioners to firstly analyse and then ultimately tackle behaviour change challenges. By understanding why or how behaviour can occur, practitioners can begin to understand existing behaviour and also what they need to change to build a new behaviour.

ESSENTIALS

The B=MAT model provides a structured framework and a common reference point for any behaviour change team to think about the behaviour they want change. It allows the practitioner to both understand what people are currently doing and looks at how it might move people towards a new behaviour. For example, whether people can be encouraged to better manage money and repay debt, or how they can be encouraged to commute by bike, bus or train rather than the car.

The B=MAT model (Behaviour = Motivation + Ability + Trigger)

The premise of the B=MAT model is that behaviour change is the result of three specific elements coming together in the same moment: motivation (M), ability (A) & an effective trigger (T).

The model implies that motivation and ability are trade-offs of a kind. If motivation is high enough, people will overcome barriers and deficits in their ability. If ability is high enough or the target behaviour is simple enough to do, people may overcome low motivation. The model name was changed in 2018 to B=MAP where P stands from ‘Prompts’ rather than T for ‘Triggers’.

This was to provide increased clarity around the model components, but Triggers and Prompts are, for all intents and purposes, the same. It was developed in 2007 by BJ Fogg, Professor of Behavioural Science and Director of the Stanford Behaviour Design Lab at Stanford University^.

Model strengths: Identifying how to trigger behaviour change.
Specifically:

• In-context executional ideas, in the form of triggers, to steer people to adopt a target behaviour or stop an undesired behaviour.
• Problems or gaps in persuasion and influence to achieve a target behaviour or stop an undesired behaviour.

Strengths of the B=MAT model: Good for...

• Identifying how to trigger behaviour change
• Generating executional ideas to address behaviour change challenges
• In-context persuasion - the ‘last mile’

Component parts of the model: motivation, ability and triggers

1. MOTIVATION
What might motivate us to carry out a behaviour? Fogg outlines three broad areas of motivation, broken down further into subtypes:

• Sensation – this is a very primitive, automatic type of motivation, with little thinking or reflection involved. Examples are hunger, thirst, sex, pain and other visceral responses.
• Anticipation – specifically, this might involve feelings of either fear or hope. Fear is related to loss such as the loss of health or looks or having to pay a penalty or fine; hope is a positive feeling related to the possibility of something good happening such as finding a partner on a dating website or saving money.
• Belonging – we may be motivated to gain acceptance by our peers and avoid rejection, particularly teenagers and young adults for whom peer approval is often a significant driver of behaviour.

2. ABILITY
Whilst we might have oodles of motivation, we still need to be capable of doing the new behaviour at a specific point in time or place. In this model, ability is less about skills and more about in-the-moment capacity to carry out the behaviour. Fogg believes that enabling a behaviour in the moment is not necessarily about teaching people to do new things or training them to improve. Instead, it’s more about making the behaviour easier to do and enlarging that in-the-moment capacity.

Richard Thaler, co-author of the bestselling book ‘Nudge’ often reminds us that if you want someone to do something, we need to ‘make it easy’ or simple to do. A classic example is 1-click purchasing - it’s easy to find on the webpage and no effort to do. In this case, ability is high.

Fogg outlines six different factors of ability, many of which relate to whether we have different types of resources available to us. The model acknowledges that different people will have different abilities: some will have time or mental bandwidth in abundance, others money or physical effort. Here are the six areas:

• Time: If our time is scarce – if we’re in a rush or busy with something else - we’re less likely to engage in the target behaviour.
• Money: Likewise, if money is scarce and we need it for other essential items, we’re unlikely to buy something.
• Physical effort or physical capability: if a behaviour takes a lot of physical energy, for example, walking several miles to buy a product, or is difficult physically, it’s unlikely we’ll do it.
• Mental bandwidth: Fogg calls this ‘brain cycles’ but at The Behavioural Architects we tend to prefer the term ‘mental bandwidth’ or psychological capability. How hard do we need to think about doing something? Do we believe we can do it? Do we have the mental bandwidth to engage with it at this moment in time or are we overloaded with other demands?
• Social opportunity or social deviance: Is the target behaviour approved of in society? Is it also already being done by others (that we know)?
• Habit/routine: Is the behaviour part of our existing routine or habits or could it be easily added to our routine? If it’s a new behaviour or a one-off behaviour, such as switching bank accounts or getting a flu jab, it’s less likely we’ll do it and we’re likely to just stick to our existing routine.

 

3.   TRIGGERS

The final area looks at what might trigger someone to do the behaviour, particularly if they have almost enough motivation and/or ability and just need a final nudge. Fogg highlights the importance of timing for this component, pointing out that the ancient Greeks even had a name for it: ‘Kairos’ - the opportune moment to persuade. He outlines three types of triggers or in-context cues which ultimately nudge someone to carry out the target behaviour in the moment. Providing the right kind of trigger can help get someone over the behavioural ‘threshold’ to achieve the target behaviour.

• Spark – a ‘spark’ raises motivation if someone doesn’t quite have enough motivation to do a target behaviour. What are the benefits of doing the behaviour? Can it bring them peer approval or pleasure? For example, highlighting the benefits of getting a flu vaccination by drawing on the emotions of fear could be enough to convince someone to make an appointment.
• Facilitator – a ‘facilitator’ raises someone’s ability, effectively giving them ‘a leg up’ to enable them to do the target behaviour. Making something cognitively easy to do is one example, making it free to do is another. Amazon’s infamous ‘one click’ purchasing is a typical example. Another might be to facilitate customer use of self-scan facilities in stores to avoid the checkout queue.
• Signal – a ‘signal’ or in-context cue works best when someone already has enough motivation or ability to do the target behaviour, but they just need a reminder in the moment. A simple example is a traffic light going green. Another is a study which reminded people to use a free coffee coupon by placing a toy alien on the counter where they paid for their coffee. The sight of the unusual toy reminded them that they’d been given the free coupon. A current example comes from social media; ‘micro-nudges’, as they are called, are salient, small animations on a social media feed designed to catch the eye of the user and encourage them to engage further.

For instance Instagram use micro-nudges to encourage users to add a comment or to tap on an image to view the tags, for example to see what brands of clothing a model is wearing.

To sum up, a behaviour change team can use the B=MAT model to find out what might be missing and preventing a target behaviour from happening, or conversely, what might be triggering an undesired behaviour such as smoking. Sometimes intuition or existing knowledge will be able to identify what’s missing. At other times in-depth research into the behaviour using the model may be required to unlock new insight in a structured way.

Aims and scope of the B=MAT model

• B=MAT is suited to identifying in-context, instant solutions for persuasion using in the moment triggers. For example, B=MAT might be used to improve an existing app or develop simple prompts or facilitators to remind doctors to use and apply existing knowledge, approaches or treatment.
• In the B=MAT model, motivation and ability are a trade-off and to some extent, a substitute for one another meaning that if someone has enough motivation and drive to do something, it might be enough to overcome any deficits in their ability and vice versa. For example, someone might be sufficiently highly motivated to eat a healthy, more varied diet, but lack cooking skills and experience triggering them to subscribe to a meal kit service making it much easier for them to make themselves healthy meals. This is a different relationship to motivation and capability to the alternative COM-B model. The COM-B model assumes that motivation and ability (capability) are equally necessary and can also feed into each other.
• In the B=MAT model triggers are seen as the ‘last mile’, the final element that might tip someone over the threshold to carrying out a behaviour and might involve only a small effort or in-context tweak to change. For example, take the issue of encouraging people to drive in a more fuel-efficient way. Using the B=MAT model, a simple and successful trigger has been to change the dashboard design in cars so that miles per gallon information is displayed by default. This is in contrast to the broader COM-B model, which might also consider this approach, but it might also consider broader strategic approaches such as encouraging people to replace their existing car with a more fuel-efficient car or develop a campaign to show how driving in a fuel-efficient way is socially desired.
• The B=MAT model defines types of motivation quite narrowly. It only really involves emotional types of motivation and does not consider reflective types of motivation. In B=MAT, automatic habits are classed as a type of ability that can help facilitate a behaviour rather than an automatic type of motivation.
• B=MAT specifically incorporates the idea of scarce resources, such as money, time and mental bandwidth within the concept of ability. All three of these factors could likely be significant enough barriers to doing a desired behaviour.

The COM-B model comprises of three components – capability (ability), opportunity and motivation. It is similar to B=MAT, but has notable differences, discussed in this second paper.

Behaviour = Capability + Opportunity + Motivation.

CHECKLIST

What type of behavioural change problem are you tackling? If you're looking for in-context, executional solutions then use the B=MAT model. If you need to develop a behaviour change strategy focused on filling in crucial gaps in people’s capability, opportunity and motivation, use the COM-B model.

Consider the following areas when applying the B=MAT model to your behavioural challenge:

• Motivation
• Sensation: Is a visceral, automatic response e.g. desire, pain, hunger, thirst motivating them to do the desired behaviour?
• Anticipation: Does fear or hope drive them to do the behaviour?
• Belonging: Are they driven by a desire to be accepted or to avoid rejection by peers or society?
• Ability
• Time: Do they have time or feel they have time to do the desired behaviour? What other demands are there on their time?
• Money: Does it cost someone to do the behaviour? Do they have enough money to be able to afford the desired behaviour? Do they feel it’s affordable or worth spending money on?
• Physical effort and physical capability: How physically easy is it to do the desired behaviour?
• Mental bandwidth: How hard do they need to think about doing the desired behaviour? Do they have the mental capacity to engage with it at this moment or are they overloaded with competing demands or stressors?
• Social opportunity: Is the desired behaviour being done by others? Is it approved of by society?
• Habit/routine: Are existing habits and routines blocking the desired behaviour? Is the desired behaviour part of someone’s existing routine? Is it new behaviour or a one-off behaviour, making it harder to do?
• Triggers
• Spark - Are there any salient benefits or motivating factors driving people to do the behaviour?
• Facilitator - Is there anything that makes it easier - or at least feel easier - for the person to do the desired behaviour?
• Signal - What in-context cues are there to remind them to do the desired behaviour?

SUMMARY

Enabling and effecting sustained behavioural change is not always easy. Fortunately, as applied behavioural science has evolved over the past decade, experts have developed robust, easy-to-use models to help apply behavioural science in a rigorous, systematic way and effect behavioural change.

In this best practice paper, we’ve brought one of the best behavioural change models - the B=MAT model - to practitioners’ attention, helping to make it accessible and provide guidelines for when to use this model. Our simultaneously published paper covers similar guidelines for the COM-B model. Applying these models will undoubtedly ensure strong and sound application of behavioural science.

CASE STUDIES

Below, we outline two case studies drawn from The Behavioural Architects’ work to illustrate how the B=MAT model can be applied.

1. Driving adoption and engagement of new in-home technology

Behavioural challenge: The Behavioural Architects worked with a utilities provider to better understand how they might encourage households to install and engage with new, in-home technology designed to track household behaviours and help people reduce wastage and save money.

Applying the B-MAT model: The B=MAT model offered a simple framework to help our client understand the critical components of this challenge. And, with its focus on how to identify and build the necessary triggers to enable a desired behaviour it provided us with a structure to pin actionable solutions against, helping us work out how to steer customer behaviour using the tool most easily available to the client - their customer communications.

First, we conducted research with the client’s customers via a 10-day online research platform followed by in-depth interviews with them. We engaged with a variety of households, from those with the technology already installed, to those considering it and those less convinced.

We then analysed our findings using the B=MAT model, identifying what might be driving or hindering motivation to install and use the technology, what might influence a household’s immediate ability to install and what the triggers might be to both install and ensure households are engaging with the technology and making the most of it.

• Motivation: Here we found that although the benefits of the technology are well known, they aren’t always enough to prompt adoption. The gains were apparent to households but not enough of a draw for many.
• Ability: Within this component, time and physical and mental effort as well as potential disruption to existing routines to get the technology installed, were typically significant factors for customers. For many there was just too much friction - needing to stay at home for half a day to let in the installation engineer or too complex and confusing a booking process. In addition, customers often lacked the ability and know-how to use the technology once it had been installed.
• Triggers: There were few ‘signals’ or in-context cues to remind customers to get the technology installed. In the home, reminders are usually out of sight, bills come only periodically, and renewal is only an annual event. Second, trigger types in the form of ‘sparks’ were also lacking - customers could see no immediate or attractive benefit to motivate them to arrange an installation and were more likely to put it off to a later date.

With this analysis and understanding in hand, we worked with the client to identify how to break down some of these barriers, more clearly highlight the benefits of the technology, facilitate installation so it was easier and less confusing, but also ensure they were reminded about the technology at more opportune times when the benefits and advantages of it were more salient.

We helped them craft these comms, to be timely, appeal to people's motivations and build ability by making it feel easier to get the technology installed. We also helped the client build more general understanding for how to apply the B=MAT model so that they could use it for other business challenges.

At its heart B=MAT helped the client re-think how they could tackle and solve this type of behavioural challenge by structuring communication strategies against the model.

Impact: Our B=MAT inspired recommendations have shaped the client’s communications strategy including leveraging key triggers across the customer lifecycle, testing different behavioural science inspired messaging strategies to drive up motivation and eliminating key friction points within the customer booking journey.

2. Encouraging customers to increase their digital engagement with a financial services provider

Behavioural challenge: Our client, a global financial services provider, had identified a large number of customers who were disengaged with their digital services e.g. had never enrolled or were no longer engaged. Our client wanted more customers to engage with their digital services to drive down calls to call centres.

Our challenge was to understand why these customers were not engaging with the digital services offered by our client i.e. the triggers, barriers and mindsets behind this behaviour to unlock ways to drive digital enrolment and engagement.

Applying the B=MAT model: After delving into our client’s existing data and insights, we developed a set of working behavioural hypotheses informed by the B=MAT model, focusing on specific motivations, abilities and triggers (or lack of them) behind non-digital behaviours. 

For example, the client suspected that customer concern over the security of their personal information stored on the app may be a barrier to digital engagement. Within the model, this would be classified as a motivational barrier driven by fear. People may also perceive that it’s faster and easier to call to solve their problem, meaning that they have reduced mental bandwidth to explore other solutions - a factor limiting their ability. Customers may also have an existing habit or routine to engage with the client using means other than a digital channel, again limiting their ability to engage.

Next, we tested these hypotheses via research with non-digital customers (who have never enrolled or are no longer active) involving online self-ethnography over 10 days to explore behaviours and mindsets in depth and face-to-face immersions with selected customers to deepen understanding and bring to life their mindsets.

We discovered that, contrary to our client’s expectations, ability was not the key barrier to engagement for the majority of customers. Instead, customers are generally pretty tech savvy, actively engaged digitally, with many online routines and habits and did see the benefits of using technology to manage their daily lives.

What was significant was people’s lack of motivation to engage. Many non-digital customers felt that the client’s existing digital services were rooted purely in simple transactional tasks, which hindered any deeper engagement, for example, in solving problems. Instead, customers often reverted to using the client’s call centre. Others had established non-digital routines to engage with the client’s services, so saw no need to digitally engage any further or needed additional services so rarely that they forgot they were there or did not see the benefit in enrolling for something they used so rarely.

Ability was a factor for one subset of customers. We discovered that less tech proficient customers can often fall at the first hurdle and give up after experiencing problems during the enrolment process. For some, security fears were a motivational barrier too, preventing greater engagement with digital services.

To drive digital adoption and engagement, we made eight different recommendations based on developing potential new triggers, including:

• Developing ‘spark’-type triggers to build motivation: We suggested evolving the user experience beyond simple tasks, identifying critical opportunities to communicate with the customer to build motivation and help customers realise the benefits of engaging digitally.
• Developing ‘facilitator’-type triggers to make enrolment easier: We also suggested streamlining and simplify the enrolment process to reduce dropouts by those who are less confident or accustomed to using digital services.

Impact: The client has used the insights and recommendations to develop a new data mining and communications strategy to better identify and segment targets for digital adoption. In addition, they shared the findings from the study with teams working on other digital initiatives (enrolment, paperless, online banking, marketing, and more). Among other things, the study provided these teams with a strong foundational base of knowledge that is helping to inform the work they do.

Further Reading

How to use the COM-B model for behavioural change

How to apply behavioural science to build more effective everyday communications

How to use behavioural science to build new habits

Seven key behavioural science-based concepts for optimising everyday communications

What we know about behavioural economics

Fogg, B.J. ‘A Behaviour Model for Persuasive Design’, Stanford Behaviour Design Lab, Stanford University, www.bjfogg.com