Predictive behavioral analytics and related use cases with Peter Dorrington – E10

Episode released on: 22.02.2021

CX Goalkeeper with Peter Dorrington – S1E10 is about predictive behavioral analytics and its "real" use cases Customer Experience Goals with the CX Goalkeeper

The CX Goalkeeper had a smart discussion with Peter Dorrington

LinkedIn Headline: Co-Founder and Chief Strategy Officer at Anthrolytics Ltd, Founder of XMplify Consulting and Event host/moderator for the Executive Leaders Network

Key learnings:

  • Experience Management is the intersection among Customer Experience, Employee Experience and Partner Experience
  • ·Predictive behavioral analytic is the combination of data science and behavioral science (e.g., from neuromarketing, behavioral economics)
  • The analytical approach tries to answer the following question: why people do the things that they do, what they are going to do next and what an organization should do with that
  • PBA tries to understand why customers, which look identical on paper, with the same stimulus react in a different way
  • It is about understanding the way we make decisions: what we need, how we feel, what we want, how we get influenced, how we process experiences and what was our history
  • PBA monitors and predicts how customers are feeling to prepare the reaction after a change
  • You, as a company, can tell to me “no” but you, as a company, you need to treat me in the right way (e.g., empathy)
  • Challenges organization faces, e.g.,
    • Misunderstanding that more data is better
    • Data we hold about customers is incomplete or out of date
    • Covid-19 changed customer behaviors definitively

How to contact Peter :

His Books suggestions:

  • Predictably Irrational, Dan Ariely
  • Thinking Fast and Slow, Daniel Kahneman

Peter’s golden nugget:

“Be prepared to demonstrate and quantify the value you bring to an organization. Use the business language to convince business. ROI is key.”

““Analysis without actions is academy, actions without analysis is anarchy” @pdorrington on the CX Goalkeeper Podcast

“Be prepared to demonstrate and quantify the value you bring to an organization. Use the business language to convince business. ROI is key.” @pdorrington on the CX Goalkeeper Podcast

Thank you Peter.

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Gregorio Uglioni 0:03
Ladies and gentlemen, welcome to this new edition of my webcast. I am extremely happy to have Peter with me. I Peter.

Peter Dorrington 0:14
Hi, Greg, how are you?

Gregorio Uglioni 0:15
Thank you very well. And let’s start. Perhaps as you know, I would like to see you introduce yourself. Because if I’m reading everything through, it’s, it’s not really so nice. And therefore, what I can say is: I met Peter, I met Peter, during the CX World Games 2020. It’s quite a lot of great insights. We are still in contact months after that, we finished the customer experience World Games, and I am more than happy to have you here. It’s a great pleasure. Thank you. Peter, could you please introduce yourself?

Peter Dorrington 0:49
Yes, thank you very much, Greg. And you’re right, the CX World Games was a great exercise. I learned a lot from it. And I hope that I was able to share a little bit. So let me firstly just give you a moment of context about myself. So that will help people understand why I talk about things I do at heart. And by training. I’m a scientist and engineer. So I like understanding how things work and building new things. But over the last 20 years or so that’s roughly half of my career, I’ve been working in the field of data and analytics, mostly with customer data. So I’ve seen the evolution of what’s now commonly called predictive analytics, machine learning and AI, really, into quite modern genres. And I’m still extremely interested in that. But most recently, I’ve been looking at the world of experience management. So experience management is the intersection of customer experience, employee experience, and partner experience or otherwise known as business to business customer experience. And my particular focus is on analytics in that field. So I don’t design experiences. But I help organizations understand what their customers are going through on what kind of experiences they need. Now, it’s been an extra ordinary year this year. And one of the things I did apart from starting my organization was spend a lot of time doing additional research, I’ve been writing quite a lot, and basically getting the word out about the state of play of CX, and where it’s going to and what we’re going to need for the future. So at heart, somebody who’s used to using numbers a lot, somebody who’s an engineer is building things like building solutions, very focused around data and analytics.

Gregorio Uglioni 2:47
Thank you, Peter. And what I want to say and want to share, you have also great newsletter, every time that I receive one, one email from you, it’s full of insights, great content, and therefore I think also the audience could get some advantages if they would join your newsletter. And I think we are here to discuss about this topic analytics and data. And we all know also the market that you are an expert on predictive behavior analytics. What is exactly behind designs? Could you please explain a bit? What what you understand and and what are perhaps also the use cases?

Peter Dorrington 3:27
Yes, certainly. So. And predictive behavioral analytics is a new combination of data science, which we’ve had for well over 40 years, really, since the emergence of computing and behavioral science. So those people that have heard of behavioral economics or neuro marketing, those kinds of tools, specifically, predictive behavioral analytics, as the name would imply, is an analytical approach. And it’s designed to identify why do people do the things that they do? What they’re going to do next, and what an organization should do with that. But at the heart of it, we’re trying to answer one fundamental question. Why do apparently similar people behave differently when presented with the same stimulus? So why is it that we run for example, a marketing campaign to two people who look like they’re identical types of customers? And one responds to the campaign positively, and one doesn’t? In order to do that, you really have to understand what motivates people. How do we make the decisions that we make? And that’s where the predictive part comes in. Because what we’re trying to understand are the causal influences of behavior, so that we can predict what a customer will do in the future. If we understand why they do what they do. We can start to look for those triggers, and then we can anticipate what they’re going to do next. So it’s slightly different from the kind of work we’ve done with a lot of customer experience, which is often retrospective, where we look back over things like Voice of the Customer programs, we look at surveys and analyses and focus growth groups and try to draw some kind of model out of that a forecast. Because what we’re doing is running lots of complex models which use both quantitative data. That’s the things that are easy to measure, like price, and the last time somebody purchased something, and qualitative data, and a lot of that qualitative data is subconscious. So how do people feel about what what it is that they’re doing? Because there’s no doubt and all of the research shows this. The way we make decisions is a complex mix of what we need, and what we want the influence of other people the way that we process information and decide what it means and what we should do, as well as our individual history. And it’s that last one that was particularly difficult to deal with, if you don’t have an understanding of how a customer or an employee has ended up where they are today, either journey prior to this is extremely difficult to figure out well, what do they expect for tomorrow. And that’s predominantly how they’re going to judge what happens today. So a customer has an expectation, whether we meet that expectation or not determines whether they think that was a good customer experience or not, irrespective of the fact that it may have been exactly the same experience for two customers, but they had different expectation. So a combination of data science and behavioral science,

Gregorio Uglioni 6:47
it’s really, really interesting. And I think you make that you did really something that make that understandable, because somebody could think about having a strange head with a lot of cables, try to understand what what happens into into our head. But But I think it was really a good a good explanation. And job by side for sure. It’s also linked about the perceptions of people, and the BA our behaviors change. And therefore it’s really extremely important to understand that and to get all this information. It’s all about data signals actions. Are we already there? Can you perhaps make some tangible example that people can understand what, what what’s really behind behind behind the science.

Peter Dorrington 7:38
So let me see if I can come up with a use case for you. And I’ve done research on this using a lot of consumer data. So I’ve actually had a privilege to have access to data. So I’m going to take take a notional example. Imagine a Consumer Bank, a high street bank, a retail bank, and they’ve got customers that have current accounts, and one of those customers goes unexpectedly overdrawn. So the bank does what the bank does, and it sends out a penalty notice to that customer, you are fined 15 euros for going overdrawn without notice. Now, you don’t need to be a genius to anticipate that most customers probably won’t like that, you know, that’s for them is a negative thing. Okay. But this is the big question. What is the customer likely to do as a result of receiving that penalty notice? Are they going to forgive us? Or are they going to cancel their account Storm Away and go to one of our competitors? So we know the event? We know the customers? What we’re trying to do is figure out what the customer is going to do with that? Well, the answer, unsurprisingly to that question is, it depends. And this is what it depends on how the customer already feels about the bank today. So when they receive that penalty, notice, if they like the bank, if they trust the bank, then they’ll likely forgive the bank and say, Okay, well, it’s one of those things, it was probably my fault anyway, and move on, it will have a very transient negative effects. Yes, I don’t like it. But it’s not going to change anything fundamentally about my relationship with my bank. But if a customer already dislikes the bank quite a lot, this might be the final thing that tips them over the edge when they say that’s it. I’ve had enough, you know, I’m going to leave this bank. Now the challenge there, of course, is knowing how does the customer already feel? And that’s where predictive behavioral analytics really comes into its own. Because what it’s doing is monitoring the things that people value. So what do customers actually care about, which is not always what they say they care about, but they care about enough that it influences their behavior. And then we can look at the history for each of these customers. And so we can say Okay, at this moment As a customer who is feeling very positive about the bank, and so if he receives a penalty notice is unlikely to change his perception of us. Greg, on the other hand has a negative impression of the bank. And this is going to be yet another episode of disappointment, which could trigger him to leave the bank to churn. So, the big challenge for most organizations is, as I say, is not knowing what they did. They know they sent the penalty notices, and they know to whom they sent them. They also have a historical record of what they’ve done with that customer, but they have no easy to use measure about how that customer is likely to be feeling, especially if they’ve not talked to that customer recently. And this is a challenge for a bank or a telco or a utility with hundreds of 1000s or millions of customers, you’re not in active conversations with most of them most of the time. So you don’t have something like a survey that says This customer is currently dissatisfied or satisfied. Or you know that this customer has given us a star rating of five or a star rating of one. And it’s giving banks or utilities or these organizations this ability to monitor and predict how a customer is currently feeling so that you can then look at the impact of what we do. So you can imagine, the customer journey extends beyond one simple episode or one series of interactions. It’s a lifetime journey, which is also why this appeals to certain types of organizations, if you’re only doing drive by selling that is I’m going to send you one thing, and then I’m never going to hear from you again, and you’re never going to hear from me, you’re not going to care about this, you know, it’s what you’re going to try and do is make sure you get that initial sale. But if you’re an organization that is built upon a model of we want recurring revenues, customer lifetime value is important. This stuff is critical. Particularly as at the moment, we’re all feeling senses of heightened emotions, for one reason or another, you know, COVID is an obvious one, but people are worrying about jobs, about their health about a whole range of different things, which means their decision making is skewed as well. So it’s having some very interesting localized effects quite currently, as you know that this is actually changing the way that customers view their relationships. And then the way that we need to view customers. So use case, we do something to a customer, it affects how they feel about us based upon how they were already feeling about us so that we can make a better decision about what to do next.

Gregorio Uglioni 12:43
And I think this is really important, because I am working in the financial industry in Switzerland. And basically most of the functionalities of a bank account or credit card, or payment, men are getting commodities, and therefore, nobody’s really willing to pay. And we see quite a lot of new entrants also in the financial industry that have extremely low prices, because their target is to get additional market share and not additional revenues. And and therefore the always the big question is, if we would introduce a new fee, or a company will introduce a new fee, our are going to customer to react, are they so sticky? As we are thinking and stay day, will they stay with us, or are then going to leave and then we lose the complete customer value over the life cycle of this customer. I think this this is a great point and is extremely important, also in Switzerland, that it’s something that we are facing now. Also due to the different crisis, today’s the pandemic, but tomorrow in six months, it’s about the economic crisis, because we will have major issues. And I think these these information are really key. The question if now it’s what you’re saying, it’s, it makes completely sense. But as usual, we need you’ll need quite a lot of data. And with this huge amount of data, how can companies really connect all the different systems because they have quite a lot of legacy? Legacy getting this information together in order to create? Let’s please let tell them Magix to get this information about the customer.

Peter Dorrington 14:23
Yeah, so the data question has been around ever since we started working with computers, there has always been where do we get the appropriate data? So let me just talk about some of the challenges that organizations face and then I’ll share with you some of my insights. So firstly, there’s often a misunderstanding that more data is always better is often not the case. Often you can have too much data or the wrong kind of data in which case your insights can be confusing. So for example, there’s a well known phenomenon called the spurious correlation. One example of This was somebody found a correlation between the amount of margerine consumed in the United States and the divorce rate. So if you feed all this data in, the challenge that you get is it finds relationships that don’t actually really exist, it just happens to be a correlation. So adding more data can sometimes confuse things, it can certainly make processing more difficult because analyzing all that data requires a lot of compute power. Secondly, that the data that we actually hold, particularly about customers, is often incomplete or partially out of date. Now, this is a particular problem at the moment, because what’s happened is that COVID-19 has caused a big reset of, for example, customer behavior, we know customers are behaving differently, because of the concerns that they have, or the things that they’re thinking about, or the simple reality of their life. So the challenge there is, a lot of the historic data that we’ve gathered over the last five to 10 years, is now partially obsolete. So we’ve got a lot of data from pre COVID, which doesn’t reflect what’s going on right now. So we’re going to have to find new sources of data or new ways to generate that data. And one of the ways that we can do that is the byproduct of what I call the dash to digital. So many organizations. Let’s take retailers, for example, when lockdown occurred, they couldn’t sell through bricks and mortar stores. So they had to get a digital presence as quickly as they could. One of the byproducts of being digital is it generates massive amounts of data. There’s lots of operational data, there’s lots of functional data. But you also have the ability to gather a lot of consumer and behavior data. So we have a tsunami of new data coming into organizations or organizations talk about a data lake, meaning that they collect a lot of data from a lot of sources, but now they’re dealing with a data ocean, there’s so much data coming in that, again, that it’s trying to work out how much of that data is important. The other challenges with all this data, even though there are a huge range of tools available for analysis of data, many organizations don’t have the tools or the skills to drive insight out of data. And this is a complaint I hear a lot from organizations. I have a lot of data, but very little insights. What does all this mean? I’ve got these I’m seeing trends in the data. I’m seeing peaks and troughs. But what do they mean? What can I do about that data. So there you need to be able to take not only the analytical tools, but the acumen of people who understand customers or operations or the business to turn that data into a story or a narrative that the business can act upon. And that’s the final thing, that you’ve got to have an organizational structure, which allows you to act on insights. So I talked about A plus A equals a gun, there are two versions of this. So when you have analysis, without action, that’s academia. When you have action, without analysis, that’s anarchy. So what you really need is both there’s no point having an insight if you don’t act on it, if you act without insights, that is to go on your instinct, particularly in a world, which is changing very rapidly, your experience might lead you down the path. So those are some of the challenges about dealing with big data. And we’ve been talking about big data for decades in reality. But what can a new organization do about this? Well, firstly, be very clear about what you need the data for. So understand what business challenge or opportunity are you trying to solve for? And then decide what data you need for that purpose? And where it’s going to be coming from? So is it something first party data data that we have? Is it data that somebody else has? Is it data that we can impute or infer from something else, then we get to the bit, which is where most of these programs start to really get into difficulty, which is when you start to aggregate that data in order to analyze it, or in order to work on it. Now, one example of that we used to do things called data warehouses. There’s a lot of work going on about well, do we need a data warehouse now with a lot of structure and a lot of format and we re up all the tables? That question I think has not gone away. But I think there is absolutely a place for that. But now we have some of the mass storage techniques which say, well, we can just store lots and lots of data because data is cheap. Rather, storage is cheap. We can have terabytes, petabytes of data for just a few dollars. The challenge of that again, is you end up with a data explosion. So a lot of organizations are trying to implement what’s called a customer data platform. So a customer data platform is not replicating all the data that you have in your operational systems or from third parties. But it brings together data from those sources, specifically about customers that you can act on. So it might be the customers purchasing records and details about their address, those kinds of things, you don’t replicate the whole, for example, your ERP system, just to get the customer tables. The challenge with this is that many organizations embark on a customer data platform that fails, because they try to do too much in one go. So they tried to get together all of their customer data at one time to answer all of their customer problems in all other kinds of operational environments. And it’s too much for many organizations. So my insight there would be to ensure success of a customer data platform, have focus, focus on one project or one challenge at a time, and then build out as you address more and more challenges. So if your challenges in marketing, start your customer data platform, around marketing, and don’t worry too much about including service and operations and business metrics, and all of those things, until you’ve solved the marketing problem. Now, that does have an implied requirement. Whatever approach you use for your customer data platform has to be extensible. So you have to be able to extend it to include new data as you need it, and to come up with new agile solutions. So it’s often about too many organizations think well, because storage is cheap, we’ll just have a massive amount of data in a vain hope that somehow insights will spontaneously appear from within that data. And it never does. Or they go on such a big program of change to become customer centric with their data, that they never finished a project that it just goes on and on and on. Because the systems are constantly changing, particularly over the last year with COVID, where we’ve had to rip up a lot of our operational systems and replace them with other things. So you don’t want to be locked into a system, which is very big and very difficult to maintain.

Gregorio Uglioni 22:02
I think it was quite a lot of great insight in the last four minutes, what you explained is the complete principle of agility, it means to find the next piece of value added that you can provide. And based on the fact that you want to take the biggest one, you did the example with marketing, you want to create the biggest value added for the company and for the customer. By using agile methodologies. I think this is the basis of all the innovation that we are doing now. And this is the key success factor, start small, create the first success, and then continue building on the other data. And also not only in the content in the from a business point of view, but also from a technological point of view, Don’t f if I use your words, there are three OCN, of Siena of, of data, but focus on small pieces in order to deliver the content. And I think this is key. But if we think forward, then what can we achieve in future with with designs? And perhaps do you have best in class example of company that are already leveraging that?

Peter Dorrington 23:16
Okay, so, yeah, let’s take what can be achieved. So when I first started looking and researching predictive behavioral analytics, which is a technique I had to invent, because nothing was there that the big question was the same year Why do customers behave differently? They look the same, but they behave differently. And it was to try and find a model of human motivation. So what does that mean? It means using things like language management and your understanding what language means understanding the operational data that we were seeing, and looking at the relationships between them so that we can understand what is it that people genuinely care enough about, that it influences their behavior? So yeah, at what point does a customer become angry enough that they cancel their account or a customer becomes so delighted that they’re receptive to a new marketing campaign? Historically, we did that by looking back over prior examples and building models, which were a prediction or a projection into the future. And that was on the basis, typically of quantifiable data, the things that were easy to measure, like the customer was take this make this up the customer’s age, or perhaps their gender or where they live. And these are all things now, which we don’t use so much. But at the time, these were thought to be great predictors of behavior. And they are they do do a lot. But when you start to think about language, people start to tell you little stories that you can then identify what they really care about, even without asking them directly, and why they care. Now, why is that important? Because what that enables you to do is identify a relatively short list of things that you as an organizer Do that customers care about that form part of that continuum of history and experience. And then you can start to predict what happens if you do one of those things. So let’s take that penalty Notice again, say the bank knows that they sent it out, they know which customer received. Now with predictive behavioral analytics, they have a sense of how the customer was feeling before they received it, and how they’re likely to feel afterwards. And therefore what they’re likely to do. That means you don’t have to wait for a customer to become dissatisfied, before you can intervene. You can say, well, we know we’re going to send this penalty notice out, but what we’ll do at the same time, is take another action, which helps to alleviate some of the unpleasantness of that for that particular customer. So we’ll take some of the sting out. Now, this was actually shown very recently in some research I was involved with which looked at the role of empathy in customer service. And one of the really surprising findings, and it was a very strong finding, was the number of customers who said, I don’t mind if you tell me No, as long as you do it with empathy and compassion, which means you have to understand the customer. So what they were saying is your operational process is definitely only part of my overall experience. And if you treat me in the right way, I’ll take that I’ll accept that and move on, and it will still be considered a good experience. So now what we can do is we can do those projections at the individual customer level for every customer for an organization, because we’re focused on what is it that we’re actually doing with this customer? What is their history, and where does it go. So let me give you an example of where that’s important.

Continue along that financial services line from now we’re in a position where we can start to anticipate if a customer is entering a danger zone where they’re at risk of churn before they’re symptomatic. So they’re not actually looking at competitor sites, their behavior hasn’t really slowed down a lot, what often an indicator of churn is when economic activity ceases, or becomes much less, we don’t have to wait for those symptoms, we can actually say we know what the causes are. So when we see the causes, we know that that’s going to have an effect on the customer. And we can take an action appropriate to that as say, to either build on the positive elements of a customer experience, or to defray the negative expense. So that’s the big win, you know, it’s that being able to predict for every customer, what your actions will do. Now, who does it best? Well, realistically, I haven’t seen anybody do an end to end solution to this because it’s relatively new. So there are bits of it that are already out there. And many organizations have done a lot, but they haven’t fully plugged it into real time marketing yet, or, you know, they haven’t built in the kind of analytics, you need to do dynamic segmentation. But the kinds of companies that are doing best and have deployed the most are to use your word, those commoditized industries, particularly business to consumer, because they’re looking to compete on something other than the traditional mechanism of price, or availability. So what they’re able to do so we can offer a better experience, we can manage the cost. And we can deliver something which is unique that you can’t get anywhere else, and much more human. So that tends to be we’ve talked about retail financial services, but telcos companies, utilities, companies, those organizations that do for example, high value retail goods, let’s take automotive at the mat, I think this is going to be an industry, which is going to be desperate to find ways to rebuild relationships next year, where it isn’t the one sale that it’s important. It’s a long term relationship where there will be ups and downs, and bumps in the roads, occasional moments of delight and occasional moments of disappointment. Those are the organizations which are desperately interested in this kind of technology, because it allows them to compete on not the features of their product, but the features of their experience. And that’s really what we as consumers are very clearly telling organisations we want. We want to be treated like a unique individual. That’s a human, not just an account number.

Gregorio Uglioni 29:26
Thank you, Peter. I think this was really, really interesting. And there are quite a lot of examples where we could leverage this solution because at the end, it’s what you are saying the most important is not to lose the customer, avoiding that they will churn and if we can leverage the science in order to keep the customer and to predict and implement measures before some before something happen. It’s always better to prevent instead of doing something later when it already happened. I’m seeing that time is flying It really, really is extremely interesting what what you are explaining? And therefore my last question on the content. And afterwards, I would like also to learn a bit more about Peter. And the last question is, we spoke about a lot about customer and customer experience, but it’s perfect. Do you have also example about perhaps improving the employee experience or employee predictions? What’s your view on that?

Peter Dorrington 30:27
This is why I was focused on Experience Management rather than one discipline, like customer experience, or employee experience. The reality is, almost every organization on the planet is composed of people. So we are customers and employees, we’re business partners. And we all have human motivations and desires. So this is absolutely relevant for employee experience as well, we can look at the things that employees value or don’t value, the things that upset them that might cause them to look for a post elsewhere, or what makes a loyal a highly motivated customer. So it absolutely is relevant. Particularly because there is an absolutely measurable direct link between employee experience, and customer experience. So we know that typically employees that have a great employee experience go on to deliver great customer experiences, when we give them the environment to do it. So that’s the other thing is, one of the things I’ve noticed over the last year, with so much change so quickly, is that some of the things aren’t so great about employee experience that people have to work in an environment, which is not good for them, you know, that’s not ideal, they’re under a great deal of pressure, customer experience has been suffering. So I think next year will be a year of reinvention where we will not only be bringing in, in event innovation into customer experience and journeys, because we’re not in a stable state, yet, we’re still on our transition to the new normal, whatever that will be. But I absolutely can see that employees are going to be a critical part of that journey.

Gregorio Uglioni 32:00
And I think this is this is again, an example. I was discussing, also in the in the last weeks with with some colleagues about not only customer churn, but also employee churn, if you can identify your top performer, and then ensuring that they’re not leaving, and you can, again, do something before they leave, and then you can keep them and then you can continue. It was really great having this discussion. But now it’s really time to get some insight also, on you, Peter, the situation COVID situation is we are all aware of the COVID situation. My question would be, how can you ensure to have a proper life work balance? And I said, I said life work balance and not work life balance? Because I think we’re all human. And we need also to keep an eye on on what we are doing outside of business?

Peter Dorrington 32:53
Yeah, thanks. That’s a great question. And one I’ve actually had to face numerous times in the last few months talking to customers. So we’re still figuring it out. You know, there have been phases when we first went into lockdown, it was on you and all of us were really focused on trying to solve the technical problems, you know, people having to work from home with laptops and environment wasn’t great. That quickly, we actually solved that. And I think everybody involved in the transition should pat themselves on the back, it was amazing what we achieved in a relatively short time that it started to feel normal. Okay, it wasn’t brilliant for some people, if you’re having to work off an ironing board in your kitchen, like an understand that’s not great. But this has gone on far longer than we expected. And now we’re coming out the other side. And it’s really beginning to be a strain for many people, not everybody, it’s typically a particularly affecting the young and those that are live alone, because for them, their social life was work. And we’ve not been able to replicate every part of that. So what do we need to do? Well, we need to develop some new competencies for management, do some of the things that we relied on walking around or serendipity in a meeting people in corridors going and talking to the person next to you. Clearly, those are more difficult to replicate in a hybrid or a work from home environment. But it’s not impossible. It has to be done though by design. So you mentioned employee experience a little earlier, when you understand the employee journey from the way that you acquire them, your recruitment to the company on board them, train them, motivate them, that’s changed, we have to look at some of these motivations and stresses and we need to do it by design. We can’t rely on emerging spontaneously from what’s going on. So one of the things I would really say to any leader or manager is firstly, make the time for your colleagues, not just your line reports, but for your colleagues. Do the check ins have those periods where you don’t really talk about work? You’re just showing that you’re genuinely interested in caring about employees and their challenges. I think that it will get better. We will come out of this into something which won’t be an exact replica of what we had in 2019 2021 will be a little different. But we do need to actually put some effort into developing those competencies. It’s in the same vein, as some people are natural presenters, they can stand up in front of an audience and deliver a really great and powerful speak, but others have to really work. And they have to train, we’ve had managers perhaps have been very used to having those close relationships with their team, who are now having to deal with geography, and having to deal with collaboration tools, like zoom or teams or any of the others, they need a bit of a help as well. But if we get it right, I think we’re going to come into a really exciting an interesting decade of innovation at the customer side at the employee side, but also for the wider ecosystem of how we deal with our suppliers, our channels, partners, that really interesting this period, because if nothing else, COVID has been a catalyst for change. A lot of things that happen this year, we’ve been talking about for a long time. Now we’ve had to do it. What we have to do next is make it great. And that’s the bit that I’m really excited about about what comes next, how do we turn something which is now a de facto reality, into a great experience for all of us.

Gregorio Uglioni 36:16
I think this is the strategic mindset that we are also feeling from from your side, about what we discussed, if somebody would would like to deep dive some some topics to understand a bit better, how is the best way to connect with you.

Peter Dorrington 36:31
So there are several mechanisms, you can use my website, so, and that’s x m p l i f y . c o . u k . I publish everything on there, I keep copies of the blog, which you’ve kindly said you’ve subscribed to. So that’s one way you can look for me on LinkedIn. If you do a search with Peter Dorrington, or one word, it’ll come up with me as the first answer. Or follow me on Twitter, at P Dorrington. And finally, if you aren’t a reader of my customer . com, I often publish there as well, my longer articles will often appear there. So website, LinkedIn, Twitter, or my customer . com,

Gregorio Uglioni 37:11
let’s say everywhere, but I think that’s the way in this digital world out to connect with all the big people because the only way that we have I’m coming to the last two questions that I really like to ask even if it’s we are speaking since it seems a while, what’s your preferred book or the last book that you will read?

Peter Dorrington 37:30
So I read very widely, particularly like looking into the future, but I can definitely recommend Dan Ariely his books. So if you haven’t come across, Dan, he writes fantastic books as an introduction to behavioral economics. So he addresses a lot about why people do what they do. He’s also extremely humorous. So I know there’s Daniel Kahneman, all the others who write about behavioral economics and human psychology motivation, but Dan is practical, easy to read humorous is the most accessible. But I also read widely from my peers and colleagues in the world of CX. Because there’s some great thinking going on out there. And yes, I’m everywhere. But that’s because I genuinely believe that we should share our insights and knowledge. If we’re to build a better world. And we want to do it quickly. We can’t do it by one person doing one brick at a time, we need to find ways to do that. So Dan Ariely particularly predictably irrational the first book that I read of his is absolutely wonderful, but his follow up books are great as well.

Gregorio Uglioni 38:34
This is exactly what I extremely like, and it’s really what motivates me during this webcast and other things in the customer experience worlds. Because I think everybody’s willing to share, you are investing your time. And and I think that’s, that’s really great. And all together, we can create something better, something new. And my really last question is, let’s phrase it like that. Do you have the last golden nugget that you want to share with the audience?

Peter Dorrington 39:05
So I think I’m parting thoughts. I think the world of CX for CX professionals and a discipline is at an inflection point, at the end of 2019. There was already a lot of commentary about where are we going with CX? Does it need to change? I absolutely think that it does. But it’s not a doom and gloom prediction. I’m actually very optimistic. And I can explain why. Firstly, we’ve got a whole new tool bag of things to play with. We’ve got new data, we’ve got new tools, we’ve got new insights and new techniques. The second thing is that this year, in the same way that many organizations had to dash to digital, they had to implement systems very quickly. didn’t have enough time really to get the best out of those systems. Next year, I think will be an opportunity for not only innovation, but to go back and double down on the things that were really good or find new ways of using These new channels, these new techniques, so I’m very optimistic about the future. I can’t call what’s going to happen with COVID. I don’t think anybody without a crystal ball would do that at the moment. But for those in the CX profession, I’d say be prepared to change, be prepared to look for those new opportunities. But one final thing, be prepared to demonstrate and quantify the value that you bring to an organization. Economically, many organizations are going to find budgets are restricted next year. So they’re going to want to see very solid business cases. So we need to go back and use the language of the business use to convince them that CX is something that not only is it good investment, because of things like reputation and getting recommendations, but actually makes hard business sense. It impacts the bottom line in a very tangible way.

Gregorio Uglioni 40:53
Thank you, Peter. As usual, I am not commenting on the last question, because it’s really up to you. It was great inside. And I really think that the topic around return on investment. It’s it’s a key success factor of every transformation program, every transformation initiative. Peter, thank you. Thank you very much for your time. It was really great to have you on board. And also thank you very much to the audience. It was great to have you again. Thank you very much.

Peter Dorrington 41:25
Thank you very much. Thanks. Bye bye now.

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