What is Customer Science? Is This the Next Wave of Change?
by Colin Shaw
What is Customer Science? Is this the next competitive battleground?
One of this newsletter’s successes has been in engaging and educating people on behavioural science and how you can use it to improve your Customer Experience. I am starting to hear an exciting new phrase that could be a catalyst for change in the next few years and become the next competitive battleground in business: Customer Science. I thought it was critical to bring to your attention and get your views on it in the comments below.
So, what is Customer Science? Like any embryonic change, Customer Science is evolving and emerging, like Customer Experience did over the years. In my view, Customer Science is the fusion between technology (mainly AI), behavioural science, and data. These individual parts are not new, but their integration under one concept is— and it can make a massive difference in customer-driven growth.
Customer Science is the fusion between technology (mainly AI), behavioural science, and data.
Let’s break it down a bit more, starting by defining science. Professor Ryan Hamilton is the only scientist I know. Fortunately, he is also the co-host of our podcast, The Intuitive Customer, and we discussed what Customer Science is on a recent episode. Professor Hamilton explained that science is a rigorous identification and measurement of phenomena, leading to understanding it systematically and then presenting it with a causal link or a theory. Science doesn’t say we know for sure that we will be right, but it does say when we get new information, we know more than when we started. I agree.
So, I ask you, is your company good at the rigorous identification of customer experiences in a systematic way that you can explain with a theory? I am sure most of you will say no because most businesses don’t. When I look at organizations today, the vast majority have neither rigor nor an in-depth understanding of customers and their experiences. Moreover, most organizations do not look at a rational, emotional, subconscious, and psychological level of a Customer Experience, which is vital to understanding customer behaviour. These organizations can go into incredible depth using science in other areas like products, finances, operations, and so on, but not customers. Therein lies the problem. Without this in-depth understanding of customer behaviour, organizations can’t interpret the data or adequately train their AI.
For example, I talked to a client and asked him if he had any client research in a specific area. He said he did—from seven years ago. I thought, “Blimey! That is a lifetime ago! The world has changed drastically in the last seven months, let alone seven years!” When I asked to see data on how a customer feels and which specific emotions they were evoking with their present experience, my client did not have any data on these areas either. Without this information, how can a business claim to have a rigorous identification of Customer Experiences systematically or explain customer behaviour with a causal link? However, that doesn’t bother me nearly as much as the fact that they don’t even realize it is necessary. But I digress…
As I mentioned before, Customer Science is an embryonic idea. When you google Customer Science, it doesn’t yield many results. There are a few out there, but not an overwhelming amount. Customer Science is not fully defined; it’s more of a fusion between technology, Customer Experience, and behavioural science.
It reminded me of 2002 when I first started talking about getting into Customer Experience. Back then, Customer Experience(CX) was still solidifying as a concept. I spent most of my time in the first five years educating people on what CX was and what it meant. Moreover, it morphed into all kinds of areas and focused over 18 years as different opinions surfaced and changed the conversation. Customer Science is here now, at the beginning of this journey into meaning.
“Nothing is more powerful than an idea whose time has come.”
Data Collection is the First Step Toward Customer Science
A vast reservoir of Customer Data exists for science to rigorously identify and explain. Take Amazon, for example.
I love Amazon just as much as I love Apple (which, as many of you know, is a lot). I have a couple of Echo Dots in the house, shop on the site and the pantry, use their entertainment options, etc. This means that Amazon knows when I get up and when I go to bed, what I like to listen to, what I want to eat, what I like to do with my free time, and everything else.
Amazon can consolidate and aggregate all this data, apply what they know about customer behaviour, and understand my buying behaviour. Amazon knows what I buy when and how much and, perhaps most importantly, they can see where they can influence my behaviour. The difference between Amazon and many other companies is using this data to understand their customers at a behavioural science depth. They have a division called Amazon Science. What I love about Amazon Science is this division is all about “customer obsession.” Also, they refer to their team as “our scientists.”
Is your company customer-obsessed, and do you employ scientists to look at customer behaviour based on the data you have collected? Again, the majority of you will answer no. However, what would be possible if you used that data for segmentation by customers’ buying habits, hobbies, values, and spending amounts, too? It would be a powerful package.
In other words, with all the technology and the data, you can apply the rigor of science to it to explain why people are doing things, and then employ those findings to get people to buy more. Voila! Customer Science.
Reinventing What Already Exists
Do you remember what life was like before the iPhone? I do. You could take a digital picture, talk or text on the phone, listen to digital music, check your email, and shop on the internet, just like you do today. However, we did all these things on separate devices. The iPhone brought them all together, and the advantage was the integration. The consolidation of functionality makes them more potent than they were as individual devices.
It’s the same idea of consolidation with the concept of Customer Science. The term is a repackaging of existing theories—Customer Experience, AI, technology, cloud, 5G’s potential for data collection, Customer Experience Management, and the behavioural sciences’ psychological concepts—in a new way. It’s not wrong to repackage, either. If making an old idea new again makes people excited about it, and the old theory is a sound and important one, I am all for it.
Another significant influence on the idea is that organizations feel pressure to change. The marketplace has changed drastically over the pandemic. The increasingly widespread adoption of cloud computing, digital transformation, and 5G fuel the fusion of these ideas and encourage organizations to embrace them. Customer Science presents an opportunity to test our theories against our data using increasingly advancing tools to explain customer behaviour and improve what we do.
We aren’t there yet. There is no Customer Science lab hard at work, optimizing the way to foster customer-driven growth. The concept is nascent, little more than a label at the moment. However, it’s a label with potential, and perhaps even an idea whose time has come.
The iPhone was also an idea whose time had come. Apple did pull the existing technology together to address a saturated phone market with a unique and advanced approach. Perhaps, in time, Customer Science can do the same for business.
The INTERPRETATION of Data is the Next Competitive Battleground
We all know we have masses of data. The issue is not the collection of data but instead the interpretation of data that is the issue. Behavioural scientists can look at data and interpret customer behaviour to see patterns. The Apples and the Googles of this world recognize that there are patterns within the data. They also know if they look at the data differently with more advanced thinking, they can uncover what the customer really wants. Most organizations do not have this capability.
The evidence of this opinion lies in the company’s segmentation. Data enables improved segmentation. When you look at people’s psychological attributes, like their personality characteristics, lifestyle, interests, and social classes, you can infer something about their behaviour. However, few organizations do this. Most segment their customers into large, medium, and small, or other such basic segmentation, which isn’t nearly enough differentiation, and certainly doesn’t include any rigor or explanation.
When you look at people’s psychological attributes, like their personality characteristics, lifestyle, interests, and social classes, you can infer something about their behaviour. However, few organizations do this.
The opportunity is in science. Firms that have taken a scientific approach, as Amazon does with their data. Amazon has developed theories about customers and tested them to see if they work. Google runs thousands of A/B Tests every year to see what works and what doesn’t. They collect data, develop theories, test the hypotheses, and then develop new ideas based on their findings.
I am baffled by companies that invest millions of dollars based on a hunch rather than research. Sure, you can invest in whatever you want; it’s your money. But even if you do succeed, it’s more luck than anything else—and one day, your luck is going to run out.
However, if you can predict what the customer wants by examining the data through a behavioural sciences lens, you can see what customers want. Moreover, you know what they want even when they might not know themselves.
Unfortunately, many firms don’t have the data to examine. If you count yourself among them, our Emotional Signature® Research determines what emotional engagement you have currently with your customers and how you could gain more. Moreover, it’s research, not a “hunch.”
Customer Science is the Future for Customer Growth Analysis
It is also important to remember that the world is a messy place. With a non-scientific approach to business, which almost all companies take, it can be challenging to know whether your efforts were successful or not. Sure, there are outliers where a business makes a small adjustment to the process and ends up with 300% growth, or, to the contrary, a 50% loss in sales. However, it’s more likely that sales and the Net Promoter Score (NPS)® go up by five percent, but your competition also went up or down a little bit, or the market got more competitive or any other host of influences. With a non-scientific approach, it is difficult to know what change affected sales the most.
I am hopeful that if business embraces the idea of Customer Science, it will result in much more fundamental changes in the approaches that people take to managing Customer Experiences, as well as cultural differences about how we make decisions and evaluate success. Perhaps most importantly, it gives you the rigor and to identify and measure whether what you are doing is creating customer-driven growth—so you can keep doing it for more.