Welcome to Let’s Talk Loyalty, An Industry podcast for loyalty marketing professionals. I’m Paula Thomas, the founder of Let’s Talk Loyalty. Today’s show is hosted by my colleague Amanda Cromhout, the founder of Truth, an International Loyalty Consultancy firm based in Cape Town, South Africa. If you work in loyalty marketing, make sure to join Let’s Talk Loyalty every Tuesday, every Wednesday and every Thursday to learn the latest ideas from loyalty experts around the world.
Let’s Talk Loyalty is inviting you to come and join us to talk all about loyalty. We want to know what are the biggest challenges you face to capture the loyalty of your customers as we approach 2023. In partnership with Collinson, Let’s Talk Loyalty is planning a live session on LinkedIn to talk about creating customer loyalty in the year ahead.
I’m inviting all of you listening to share with me the burning questions and key topics you’d like to hear us cover in a live discussion. Simply drop me an email. It’s paula@letstalkloyalty.com. Then we’ll pick the most popular ideas and questions and talk them through on our Let’s Talk Loyalty live event this November, powered by Collinson.
My email address again is paula@letstalkloyalty.com. Please do send over your questions and ideas and then join us as we talk loyalty live together for the first time.
Amanda: Hello and welcome to Let’s Talk Loyalty. Today we have a very special discussion with Dr. Shorful Islam. Shorful is the CEO of Be Data Solutions and comes into this industry with a PhD in psychology. I’ve had the pleasure of working with shortfall and his team in Indonesia many years ago. And I was absolutely fascinated by the quality of insights and data analytics capability of his company at the time called Stream Intelligence.
Through our discussion today, we hear about how he applies his psychology experience to work through the data representation of human behavior, which we all know in the loyalty world, this is an imperative. In our discussion, we also focus on how data and automation can only get you so far when you’re dealing with humans, you need humans to understand what we are strategically all trying to achieve. Simply put, it’s one of those interviews which could have gone on for hours and hours. I hope you enjoy the show.
So Shorful, welcome to Let’s Talk Loyalty. It’s absolutely fantastic to have you with us.
Shorful: Thank you, Amanda. It’s nice to be here.
Amanda: Yeah, it’s really, I’m really excited about today’s discussion because for a couple of reasons. Number one, I’ve known you for many years and always enjoyed the work we’ve done together.
But secondly, I know what’s behind, um, today’s discussion and I’m super excited that we can share that with the listeners of Let’s Talk Loyalty. So I think, um, as we want to start off with a traditional Let’s Talk Loyalty question, Please share with everyone what is your favorite loyalty program.
Shorful: Um, that, that’s a really good question.
I mean, obviously when you work in the loyalty space, you, you, you sort of look at a lot of loyalty programs, but my favorite has to be the Qatar Airways Privilege Club. Um, I’ve always loved like, um, you know, airline loyalty programs, but I, I found the Qatar one quite nice, especially the execution on the coms and then, then all the sort of things you could get with Airpoints.
Amanda: That’s great. I think I, um, in my, in my question, I also answered airline and actually a previous, um, recording I did, the response was also a frequent flyer program. So obviously they’re doing something right, so even though they pioneered loyalty, so me many years ago, it’s great to hear we’re still all loving them.
So yeah. Lovely. So, Shorful, I’ve known you for many years in the data environment and I’ve had the privilege of, um, our company Truth, working with your company and your, uh, brainchild amongst data analytics. And that’s why I really wanted to talk to you today. So I think what would be super useful for the listeners is to understand a bit more about you, you now the CEO and founder, um, of your business Be Data Solutions.
Tell us a little bit about the role and about the company Be Data Solutions.
Shorful: Yeah, so we, you know, Be Data Solutions is, um, a data company. We do everything from data strategy, data engineering, data analytics, and data science. Um, and essentially, you know, if clients have data problems, that’s where we come in and, and help.
And you can imagine a lot of clients who have loyalty programs, um, you know, get these loyalty program up and running. They’ve got great welcome programs and CRM program, but then they need sort of help with the data side, whether it’s, doing the analysis and providing insights or whether it’s just moving data around connecting sources that they haven’t connected before.
Um, so that’s, that’s essentially what we do at Be Data with, like I said, end to end data, uh, solutions provider.
Amanda: Great. And it’s a slightly different company than the company, I, um, worked with you on some of your clients way back. So I think what would be useful for us to understand a little bit about. If I think about your background, so for all of our listeners, the reason I so wanted to talk to Shorful today, he’s actually Dr. Shorful and his doctorate comes from a degree, a master’s and a PhD, all in psychology. So you can imagine how this really resonates to us as loyalty practitioners because at the end of the day, customers and psychology, we need to understand that. So I think Shorful, talk to us a bit about that, cuz you also obviously recognize that, that connection and how has it brought you to where you are now really leading the field in some data solutions for your clients?
Shorful: Yeah, so as you mentioned, my, my background is in psychology and I have a PhD in psychology and one of the first things, or one of the things I did in my PhD or my thesis was, To predict why children have accidents. So I was always interested in data being a representation of human behavior. Okay. And so the idea was that you would, um, look, look at all this data and infer human behavior from it.
Then obviously with a lot of the advanced statistical techniques, you can actually then predict it. So that was what my PhD was about how do you predict why children have accidents? Um, and if you’re a parent, I’m, I’m sorry to say, but you know, it’s about 60% of the reasons why children have accidents is based on how you supervise your child when they’re with you.
Really? Oh wow. It is. I was slightly over 60%. Um, but how I got into it, so I obviously worked in the NHS and government, so NHS being the UK’s National Health Service. And you know, originally I went in to do sort of like, you know, the primary type research where you do surveys and stuff, but I was more interested in the data that was collected by systems already. So in the NHS for example, they had a patient administration system. So if you came in, you’d, you know, tell the receptionist why you’re here, you know, the, the details about yourself. And I was interested in analyzing that data. And then I moved into government where I, I sort of looked at similar data, so, you know, benefits data, council tax, uh, national insurance, data, that, those kind of stuff.
And then I moved to the commercial sector where I got introduced to loyalty data. And what was fascinating is that it is, um, you know, from a psychology point of view, you’re, you’re actually looking at these data points and trying to, you know, map behaviors and predict behaviors, but then also infer intent, right? So the idea is that you look at these behaviors and you say, Well, why are they doing these kind of stuff?
And that is brilliant for loyalty, right? Because if you start seeing people doing certain type of behavior, you start going, Oh, maybe their, their mission or their purpose for doing that behavior, Let’s take like a, a Starbucks or a McDonald’s. You know, you might find people in the morning buying coffee and stuff and going into work, so you realize, well, you will see that behavior consistently, let’s say four or five times a week, uh, during the weekday.
So they’re buying breakfast as they’re going on work. So there’s a mission, there’s a, an intent and a purpose behind that. So that’s really why I’m, I’m sort of in the space that I am, because even though I’m analyzing this data and everybody thinks, Oh, you, you must have a computer science background or a math, or a stats background, um, I, I don’t actually see data.
As the end goal. Well, I see data as a representation of the behaviors that, uh, humans and people do. And, and like I said, in loyalty program, that’s brilliant because you actually can track someone across, uh, multiple timeframes across multiple, uh, products and services. Um, and as a psychologist, that’s quite interesting because then you’re trying to infer, well, you know, what are the different types of behaviors that you’re seeing?
What are the different types of groups that you’re seeing and, and you know, what are they doing? And ultimately how do you, um, Predict what they’re going to do to make sure that the loyalty program’s more successful.
Amanda: I absolutely love that the fact you started off with predicting why children have accidents and putting the blame 60% of it onto the parents through to predicting and looking at my behaviors about how I’m going to show, get my coffee at Starbucks.
So absolutely fantastic and there must be a correlation between that as well. Starbucks usage said, Children’s accidents somewhere in there. Um, That’s incredible. I, you know, I did actually, uh, remember you talking about being involved in the NHS. I didn’t realize you then went into the public sector before the commercial environment, so, um, I can absolutely see how that pathway has brought you to mapping behaviors for customer loyalty and then inferring intent.
What lovely, like way of describing it. I think then if we are looking at inferring intent, you know, if we all look at our customer database, we’ve all got, uh, distribution of customers. Some are super profitable, super loyal, and others are what the sort of rudimentary description of the long tail of a database.
What, from your point of view, let’s talk about like the most loyal customers. What are you seeing there that will be interesting to understand, the most loyal, the top end of the, of the distribution curve.
Shorful: Yeah, so, so that is really interesting. So whether it’s the top 1% for like, you know, really high value, um, sort of law luxury products or whether it’s the top five, 10% for more sort of mass products, I’ve always found that that top tier of customers, so, you know, usually they’ll generate, you know, if it’s like the top 1%, they’ll generate a 25% of your revenue, or the top 10% will generate 50% of your revenue.
You, what I found interestingly, um, Is that they’re always, you’re always able to stretch them. That’s the first thing. So whenever I work with the CRM team, you know, the loyalty team and, and they’re, they’re talking about, oh, which segment should we sort of try trying to, you know, get more of the share of wallet.
That group, even though they spend a lot, seem to still have more money to spend with you. And, you know, it doesn’t matter, loyalty program I’ve worked across and, and you know, Amanda, I’ve worked quite a few. Yeah. I always find that that group you can always stretch, you know, as long as they’re not maxed out in terms of physically being able to come to you.
Um, but you can sort of stretch them. So if there’s more expensive upsell products, for example, or if there’s more basket or products that you could sell in that same interaction, um, Then it’s possible to, to stretch them even in food. I mean, that’s another interesting thing I found. Like you’d think that, oh, you wouldn’t be able to stretch them cause how much more can they eat?
But what they end up doing is, you know, you can start bundling things and they start buying bigger bundles or they’ll start buying, um, larger, uh, you know, for food packages or, or like meals. Those kind of stuff. So it, it is interesting that, that I see, I see that, that that group and, and you would, and one of the things when I first got into sort loyalty and analytics in loyalty for being a psychologist, I was very skeptical about can humans actually be loyal to an entity, to a brand, you know? But if you take the behavior itself, if you take away the underlying psychological emotions around being loyal, the behavior suggests that people are loyal. You know, you incentivize them to come or you give them a, or even I found even non incentivizing these sort of highly loyal customers, uh, still gets them to come. You just need to communicate them with the right message at the right time. Um, and you know, a lot of the analytics we do, you know, now leverages sort of machine learning. So we’re able to really customize, you know, what gets sent to which, um, segment, even sub segments within those highly loyal customers.
Amanda: That last piece, I mean, everything you said was super interesting, but I think that’s really powerful for everyone to understand. You don’t actually have to always incentivize your most loyal customers, just communicating well or engaging at the right time because everyone falls into that trap of having to throw margin at at, at the group.
But also I have, you’ve said something quite different than I’ve heard in other circles, so it’s lovely to hear that even the very top segment, there’s a way of stretching some level of spend or contribution. So that’s really great to hear, even as you say, as bundling or upselling. So let’s, let’s flip this on its head a little bit.
What do you see at the opposites end of the distribution curve? Let’s have a chat about the, the less loyal customers.
Shorful: Yeah. I mean, they’re an interesting bunch and you can, and I always look at that and I’ve always seen them as like the, the easiest way to like, Get more revenue in a loyalty program.
Cause you know, they’re really infrequent, right? So we, we look at loyalty program, whether it’s someone like a department store where you might get visits once every 3, 4, 5 months. Or whether it’s a, a, a, you know, in terms of sort of food and, uh, restaurants and stuff where you might see someone once a month or something.
They’re really hard. There’s multiple groups in that. So there’s one group who will join and I think they joined because. It was something that was being promoted at the time, but they have no loyalty to your brand, so they just happen to be your brand. There happened to be a loyalty program and they joined and I found that, that you can identify that group cuz no matter what you do, and I’ve seen people really, really incentivize, like even make a loss with that group.
They don’t come back cuz then that’s not part of their natural, um, sort of behaviors to come back. But then you do see those people who are sort of less frequent, which suggest that they are probably, you know, shopping elsewhere for similar products. And if you can incentivize them correctly. I mean, I’m always wary of, uh, providing too much incentives because then they’re, they’re like what we call like coupon hunters and bargain hunters coming in, you know, uh, because they’ve got a coupon, they’re gonna come in.
Uh, but there, there is a group who, you know, with the right sort of timing of the message, You can reactivate them. Um, and, and we have found that with some, some of the, some of our clients, So one of our clients is sort of fast food and we’ve, we’ve sort of seen people historically have a behavior and then it sort of, um, so they become very infrequent and, you know, they, they, but just the time to remind them, understanding why they used to come to you, just time to reminder can reactivate them.
Um, but yeah, but it, I mean, I find that, that that group can be as big as 60% in some loyalty programs. Um, you know, where you will only see them once, uh, within your active window, whatever that active window is. Um, and actually, you know, you’ll be very hard pressed to get 50, you know, probably about 50% of them to reactivate easily, uh, without some sort of really, um, uh, lu, um, attractive offer should we say.
Amanda: But even that, that’s a loss. Like, wow, 50%. Like I think any of us would be super excited to see a 50% response rates on a campaign.
Shorful: I know, I know. I mean, it is not necessarily the campaign, but across a window of time of activity, you know, trying to get them back. But yeah, you’ll be, you know, And also whenever we do like, the modeling. So our clients will always say, Well, you know, who should we target and what kind of stuff? They seem to be the easiest to model for like incremental revenue, like I said. Cause if you’ve got massive beta people who you only see once in your active window and you say to yourself, If I can get 20% of them come back one more time, that’s an increase of X amount.
Yeah. In revenue. And it’s like quite big, like five, 6% increase in revenue for some of these clients. But they are really hard to get back. And also because the data from, from an analytic point of view, because they’ve only like visited once or very infrequently, the data points are very few. So therefore it’s really hard to model their behavior and understand what their intent or mission or purpose is, is we are shopping with you.
Amanda: Of course. Yeah. And actually we see so many clients of ours who spend so much on their, their direct marketing budget without proper analytics, that they actually send every SMS to every single person on the database. And as you said at the start of this question was, so many customers do actually sign-up once and actually have no interest in your brand whatsoever thereafter.
And you could be wasting such a lot of marketing money on that, on those campaigns. Yeah. So I can immediately, even if it’s not an incremental performance, it would be in cost savings if you, if we could just dig underneath the, the skin of all of this. Yeah. So you mentioned inactive and how, you know, some of them like a 50% response rate or over time.
To get them activated. What are the secrets have you got of how to activate inactive, so to speak?
Shorful: Yeah, so I mean, the first secret, I have to say, that you have to do sub segmentation within that group. So a lot of the time, you know, we’ve got, Oh, here’s our inactive people. Let’s do a rescue program or something to bring them back.
They’re not a homogenous group, okay? So even with the minimal data points that we have, we are able to identify multiple groups within them. Um, like I said, there’s that one group who just sort of fly by, should we say, you know, you very, you know, even if you didn’t SMS them, you’d save quite a bit of money cuz they’re not coming back to you.
But the rest, you know, you’ll find lots of different groups. Like I said, you might find one group where they have competitors, so you, you might see them that their behavior might manifest itself in sort of, uh, a, a regular sort of routine, but irregular in terms of when they come to you, and you’ve just gotta infer that, okay, you know, they, they, they probably do buy a similar product, but they’re buying it elsewhere, but you’re seeing them infrequently.
How can we trigger them to come? And that’s about sending sort of right time message to make sure they come. Others you might find just based on like how much they spend. So if they’ve like, you know, come to you and they spend quite a huge basket, but they’re very infrequent, then you might assume again that they, you know, it’s not that they haven’t got the money to spend, that they’re spending it elsewhere.
So therefore, how do you get them to come back and spend with you. So the secret really is, is to segmentation, to really understand your customers. Don’t treat them as homogenous groups. I think, you know, in the past there was a tendency to just have broad groups, and they were very good when you’re doing sort of marketing personas and stuff.
But when you’re actually executing sort of the, you know, campaigns to CRMs type stuff, you know, with modern technologies and stuff you can use, you know, AI, machine learning and stuff to sort of hyper target groups and see how they respond and then use that data to then, you know, re-target and make sure you get the optimal response rates.
Um, but if, but my piece of my secret source really is about really understanding that those groups of people don’t see them as one homogenous group.
Amanda: Yeah, absolutely. As you say, there is a place for the broader groups, maybe in strategic positioning in the business and marketing personas to help the businesses understand. The types of typologies, but as you say, when you get to actually wanting to actually change behavior, um, I like your secret source there, Shorful. I want to return to the discussion around psychology, actually. So, um, it, it’s a very fascinating but, yeah, so relevant as a background. So can you talk to us more from your experience having worked on so many different loyalty programs about the psychology behind the loyalty program?
Shorful: Yeah, I mean, it, it is, I mean, like I said, I was, I’m, I was, I probably still am a bit skeptical that humans, um, you know, are loyal to brands. You know, they may exhibit the behaviors that we consider to be loyal. Are you returning frequently, spending more, you know, engaging, being advocates, you know, promoting the brand.
So they may exhibit that behavior, but I wonder whether a human, like loyalty is to other humans, right? Whether that can truly be, um, attributed or, or assigned to a brand. But if you look across, I mean, you know, we, like I said, we’ve done department stores, you know, supermarkets, airlines, um, you know, fast food, uh, coffee shops, and you know, everything in between.
And, and you find that, you can infer why people come to a lot of those, those places. Like there’s discreet behaviors. It’s not like everybody’s random, like everybody comes to different, um, uh, brands for different, you know, different reasons and stuff. If you look within a brand or even a group of brands, you find that people do have these consistent behavioral, uh, clusters, I guess, you know, So they come for a purpose. They come for a mission and there’s always outliers, right? You know, So example, you know, you might, I might go, um, shop, you know, buy coffee every morning from my favorite coffee shop, but once in a while I might have a meeting earlier or later, so I won’t go to that coffee shop.
I go somewhere. So there’s always those outliers, but on the whole and at a group level, cause that’s what we analyze, but I don’t try and predict individuals. I try and predict what the groups do, at the group level, what you do find is that there are sort of discreet types of behaviors that, uh, people exhibit with brands.
And it’s always fascinating because whenever I do this sort of behavioral clustering with, uh, different brands, uh, as we go in, it’s one of the first things I, I advocate they do. Is, you know, you can’t really knight this discreet brands and they’re, well, you know, usually quite well, um, sort of represented across all of the entire customer base.
Um, and what it um, tells me about the psychology of it is that yes, there are some people who come to you, you know, and the loyalty program probably helps a lot with that. But there are some people, and, you know, going back to the point of not having to incentivize will come to you anywhere. The loyalty program, just a bonus.
Okay. And it’s about being smart enough to identify those that’s, are with you because of the loyalty program or other reasons, and those that are with you because of your brand strength. Right. You know, um, you know, and if you can do that, then it, it means that the way you communicate will, will, will need to reflect that otherwise, you know, if you start, um, you know, providing offer based, you know, messaging to people who are actually, they like your brand, it, it might come across a bit, you know, insulting because they think, well, I would’ve visited you anyway, but thanks for the offer, but I didn’t really need it. Um, so yeah. So I think, you know, one of the things that when you look at loyalty program, first, from an analytic point of view is try to, when you do these segmentations or if you do any analysis, it’s to understand what is the motivation for them wanting to come to this brand or this, you know, store or, or, or these services.
Yeah. And the reason that I, I get my analysts to think like that is because I say, don’t forget that data at the end of the loyalty program is a person, it’s not a machine doing this. Right? Yes. So those people had a reason, had a reason to turn. Um, so therefore you, you are a person, so you should know why did they, you know, come, come up with hypothesis of why they turned up.
And when you do that, it’s actually really good because then you can provide much more color to those, um, groups of people, those behaviors. Um, and then that helps the client understand their customers better, but also understand the purpose of their loyalty program as well, slightly better.
Amanda: I love what you’ve just said, like about the data point at the end of the day as a real live human.
And it reminds me, I’ve just come back from the Comarch user group in Paris, a conference with some phenomenal speakers, and my, one of my favorite takeouts was around AI is not a Artificial Intelligence. It actually stands for Augmented Intelligence in the sense that if you turn your equation on its head, you were talking about the consumer as a human person, not just a data point, but actually the human beings in your world, the analysts, they are also humans, um, and they’re working with machine learning, but the human beings and machine alone can’t do it. And I guess that’s the science and soul of what you’re doing, cuz you are the soul of what you’re doing is feeling the psychology and the um, you know, the human behavior versus the science, which is coding and looking just at the data points. What do you feel about that?
Like the augmented intelligence rather than artificial intelligence?
Shorful: I mean, I, I, I totally agree with that. I think there is, um, a danger where we believe that, Oh, we can leave everything to artificial intelligence. I think when you’re dealing with humans, you need humans to understand, uh, what other people want.
And, you know, it’s part of the marketing creative process, right? You know, if you’re coming up with a, a message or a, or a, you know, a creative or, or, or even like an offer, you know, the way you position that offer, I, I, I find it very difficult that you could leave it to a machine. I think, yes, The, the data and stuff, the, the analysis can provide intelligence, but that intelligence needs to be augmented by humans, by our thinking process.
Because don’t forget, one of the things I always tell, uh, my analysts and also the clients, is that the definition of success is determined by us, right? It’s not, the machine can’t tell you what good looks like, right? So your loyalty program, you have to define what good looks like. Is it that, that you want incremental revenues, that you wanna repeat behavior?
Is it that you just want people to be, you know, advocates, you know, you, we define that and then the machine is trained on that definition, uh, for us. So therefore, Absolutely, I, I totally agree that AI should all be about augmented intelligence. You know, the data can only get us so far and, you know, I call it, you know, maybe descriptive and inferential, uh, sort of analytics where it describes stuff and maybe it can predict and infer what will happen next, but how it happens and why it happens and, you know, should it happen, should be up to us as humans.
So, on the other side of when we provide our Insights or our data and analytics to our clients. I always emphasize this is the starting point. This is the, the facts from which you build the, the creative, the, the, the CRM plan, the, you know, customer journeys. This is not the end goal. The data is not the end goal.
It it, it is a component of the foundation to how you build out the loyalty program. Um, and all its constituent parts. And you know, the other thing I’d like to add there is also, If you do go down the route of AI and people say, Oh, we can send million per mutations of emails and stuff, what are you actually optimizing?
Because don’t forget, a lot of AI and stuff really, really rely on historical behavior. Now if, if I’ve come in and I’ve, and I’ve, you know, let’s say a department store, um, and I’ve always bought, work clothes over time I change, I become a father. I, you know, I become a father. I became a husband. Yeah, I may shop differently, but if you’re using historical data to always predict what I do, you, you’re gonna be optimizing me for the the past me, not the future me. And I think here, this is where humans come in. Cause we have that understanding. We look at it and go, hang on, this guy’s reaching his thirties, you know, early thirties probably gonna have a child. We, we see that across our customer base anyway. Maybe we should test something. Let’s, let’s test selling in baby clothes or something.
Who knows? Yeah. . You can see I’m not a creative, so I, I don’t with good ideas. Um, but, you know, but, but that’s why I think it should be augmented. You need people in that equation. It can’t all be that left to machines.
Amanda: Yeah. Absolutely. Absolutely. I love that. Thank you so much. Now you, from what I know of you and having worked with you, we started our sort of, um, our working relationship, both was speaking at a conference in Malaysia and then we worked on a, one of your clients in, in Indonesia.
So very much in, in Asia. But back to your role in the UK, you’ve done an awful lot of charity work, so well, When I say charity, not charity work in the old fashioned sense, but a lot of your thinking to help the charity sector, which uh, absolutely fascinates me. So please tell us about that, cuz I think it’s a new angle.
It’s perfectly logical when we talk, when we think about it, but I haven’t really heard it unpacked before, so please share that with us, Shorful.
Shorful: Yeah, so I mean, you know, I think when we met five, six years, probably longer, um, ago, you know, we, we wanted to help the charity sector and one of the, like the angles we thought is why not use the skills that we have that we deploy to help businesses make more money to help charities make more money?
That being data and analytics. And so what we did is, you know, so what we did is we approached charities and say, look, you know, you know, use the skills that we have to help you because we are clearly commercial companies, large, large commercial companies pay us to help them make more money. So I’m sure we could transfer that skill and, and I appreciate there are some nuances.
Obviously they’re not profit driven. They’re, you know, Donors donating and, and there’s all corporate image or this, uh, sort of an image they have to maintain. But I think the principles are still the same. If you think about it, they’re looking at, um, donors who donate money and then therefore using the analytics to understand, you know, like I said, The, the data is something that a human has done.
So if a donor donated because of a chord and you see them, uh, repeat, donate because of that chords, then you can start inferring things. You can start, you know, um, inferring their intents, so that’s psychology behind it and say, Maybe this type of donor donates when there’s a disaster. Right? And then you might find another type of donor donates when, uh, it’s helping children or another type of donor, helps during a certain time of year. Yeah. Like, you know, religious, uh, uh, periods. And so therefore you, you, you can sort of take the, the data that, um, charities collect and start building sort of similar, uh, Types of analytics that you would in commercials in the commercial sector.
Um, and that’s what we, what we have done with charity. We’ve tried to help them, uh, not only just do the analytics, but also sort of get them to build sort of similar teams. Um, so, you know, like have data analyst look at data as an, uh, a valuable asset rather than just, Oh, we’ve got, uh, you know, a thousand, 10,000 donors on our database.
We’re just gonna email them because we’ve got another campaign. Look at it and say, Look, we’ve got 10,000, a hundred thousand whatever donors on my day to database. Can I group them? Can I segment them? Uh, what can I understand about them? Can I predict anything about them? And so that’s what we’ve tried to get charities to do.
And some charities, like all organi, like organizations, so some are very forward thinking and they’ve adopted and that others. Let’s put it not so forward thinking. Yeah. And, and it’s been a longer journey, but what has been interesting is that you sort of find that the, the skills or the approach and the analytical approach are very similar.
You know, I’ve always found that yes, data has nuances, but data is data. Right. Um, And if you collect good quality data and charity seem to do collect good quality data, uh, whether it’s for audit purposes or, or whatever regulatory requirements, um, you can do quite a lot of analytics on it. And I think that is a massive opportunity for donors because you can imagine, you know, as, as, people’s cost, as cost of living rises, people’s incomes are squeezed. Yeah. So therefore, being smarter with who you target, how you target, and even the type of messaging you sent them, you know, could be very powerful. So yeah. So that’s what we’ve been sort of trying to help in the charity sector, sort of pro bono work that, that we do.
We, we sort of go out and, and use the skills that we have in, in the team to help help those um, organizations.
Amanda: I love that. I mean, obviously cuz it’s giving back, but it’s just so logical what you’ve said, so it’s, it’s fantastic to hear and lovely to hear your companies doing that for, for a sector that I’m sure doesn’t often get that kind of approach.
So it’s just lovely. Thank you for sharing that. Um, what I wanted to ask, without you giving away anything you can’t give away, cuz I respect that cuz obviously you’ve got client confidentiality, but if you think about all of your clients over the many years you’ve been working. So specifically in the data in the commercial sector, how many of them would you say are doing using data well versus, not at all, or not at all well, um, if you put it in percentage terms, I think it’s quite interesting to see how the marketplace is in terms of good data usage.
Shorful: Yeah. I mean, so this is obviously, you know, biased by the, the clients that I’ve worked with. But, you know, I mean, I, I’ve also met a lot of clients in this space.
I would, I would put that number around 15%, maybe 20 who do it well, you know, so they, they’ve got their data, they’ve got it all connected. It’s, you know, uh, the systems work really well, and going into those clients is really, really a nice experience because it’s just literally, Oh, yeah, you want access to data, Here you go.
Here’s a login. Um, yeah, we have all our, we have all, we have all our definition, but like I said, they’re about 15 to 20% and they. It’s not that they’re always the big ones. I mean, I have met a few more ones, but they are usually the, the larger companies, if that makes sense. Yeah. Because they’ve, they’ve already got like an enterprise data warehouse.
They’ve got that, the governance in place and everything. So when they must have, when they launched the loyalty program or the CRM program, they, they, they’ve had that thinking so they’ve been able to deploy it. But saying that, Well, you know, there is probably around 60% of clients who are okay. Okay.
There’s like huge room of improvement. They’re the bulk of the clients that we, we come across disjointed systems. Um, You know, not, not connected at all. They’ve not done the basics in terms of analytics. Um, I mean, you know, some of them are just doing reporting. You know, they’ve sort of taken loyalty and just translated what they’ve done in sort of the corporate world of just management information and business intelligence and applied it to, uh, the loyalty program where they’re just reporting on the loyalty program rather than using that data intelligently.
But like I said, that’s about, uh, About two thirds of them. I mean, just like, probably less two, two thirds. But yeah, around that, around that number. And they’re the clients who usually come to us because they, they are stuck, if that makes sense. So they’ve, they’ve got like a team of analysts, but because they kept doing it the way they’ve done it, they, they can’t sort of break out of that.
Um, and they sort of come to us and then we see probably another. I would, I’ll split this group up the bottom tier. So there’s 10% who really, like, they’ve got their data. It may not be connected, but they’re enthusiastic. They just don’t know what to do with it, you know, they dunno what to do with it. And they’re usually companies where they’ve like, you know, everybody said, That do a loyalty program.
So there’s an loyalty program and they’ve got data sitting in the loyalty pro provider’s platform. It doesn’t speak to, you know, other systems. And we’ve gone in and the, the enthusiasm is there that they want to do something with data, but they haven’t really started. But then you get the probably less than 10% and they’re still around, who still don’t know what to do with data.
I mean, even if they run a loyalty program, I, I, I am really, really shocked. So they’ll, they would’ve got an agency just to like, you know, do the loyalty stuff, you know, the marketing, the crm, and, and report. campaign reporting and stuff, but they don’t really take it seriously. And when you, when we do engage with those clients that, that, that sort of 10% or less, It is, it is mainly an education journey.
Yeah. You have to educate them of why they need to use data. If you go in and start doing all this analysis, you know, there’s, there’s an initial excitement but it doesn’t embed cuz they don’t have the culture of data. Um, and so I find that whenever I work in these organizations and I ask them what have they done historically, where are they now?
And typically they just have financial reports and performance reports. Um, and like I said, they have agencies supplying them, everything else. I realize that if we go in and do anything, it won’t stick. It’ll be a one off. So I sort of try and work with the senior management, trying to embed a data, data informed or data driven culture in them so they can see why they need that data.
And it is a case of literally saying, Look, if we were to build this report, This is why you would need this number. This will help your marketing team acquire more people. This will help your loyalty team, you know, target the right people. This will help the, you know, the, the loyalty team, um, get repeat visits and it’s literally about breaking and then showing this is where ROI would come in and you could measure it all through.
Um, but yeah, so that’s how to, that’s how I do it just on that, that big bulk of clients who do it. Okay. There is a subset of those clients, probably, I would say if it’s 60%, they’re probably around 15% , quarter of that. Yeah. Where they have really good pockets of excellence. You know, like there’ll be a team who do it really well.
Like they, they might have an analytics team or product team who do it really well, and then their loyalty team aren’t doing it well. And it’s really interesting because you can see that the, the company can do stuff really well, but they haven’t translated that skill over. Yeah. Um, and, and, and, you know, and you feel redundant being asked to come in and help the, the, the marketing or the loyalty team when you know that they’ve got a really capable, product analytics team who are doing these fantastic things within the organization,
Amanda: But listening to you Shorful what it sounds like, particularly in that 15% subset of the 60% and then the, the lower tiers, if we call it tiers, your role becomes much more of a change management role. So less of the data analytics, but much more around change management, which at the end of the day, I feel is, a lot of the role we have to play with clients to, to really help them through this cuz it, you know, as super as a data analytics team might be, or, or even the loyalty team, if they can’t actually get it to stick with senior management or buying groups. If we’re talking about retail or product houses, if we’re talking about financial services, it actually weren’t stick for the long term.
So, I totally hear you and I totally understand that.
Shorful: Yeah, And, and, and you’re right. I mean, in terms of like the, the change management, it, it is, you know, as an, an analyst or or running an analytics company, do not underestimate that. I mean, a lot of the time, like, you know, my analysts say, but if we do something amazing, they’ll, they’ll, they’ll love it and they’ll, they’ll use it. And I’m like, but again, this is going back to the psychology and I deploy my knowledge of psychology even when I’m working with clients. So I’m just giving away a bit of secret here that my, with my clients that I do sort of look at it from a psychology point of view is that, don’t forget the client themselves are humans, right?
They, they have. They have priorities, they, they’re busy. You know, your work may be 1%, 5% of everything they have to do. Right. You know, um, you know, especially if you’re presenting to the C-suite, the directors, you know, the CMOs got other things to worry about. It’s not just gonna worry about what, you know, analysis you’ve done in your, in the loyalty program.
So you have to, you know, understand that they’re also people and they have, you know, priorities. They have goals, they have ambitions, and how can we help them do that. And, and that’s when, you know, when, when we do go in to do the change management is to understand that, is to understand if we did this piece of work, would it help you?
Okay? Because if it doesn’t help you, then what can we do to help you? Right? Because it may be that it, it is, you know, the starting point is not doing the analytics and the loyalty or building some kind of sophisticated propensity model. The basics might be that the CMO just wants to know every day how many customers are using the loyalty program.
And it could be as simple as that. And he could be like, If I get that number, at least I’m reassured that the loyalty program is a value, rather than go and build some propensity model that he, he can’t see an immediate benefit to him or her.
Amanda: Yeah, absolutely. Absolutely. And I think that’s listening to you how you have got a rare skill here, Shorful that you’ve got this science and soul. Cause most analysts maybe don’t have that insight that you are bringing in from your psychology background. So, uh, all kudos to you, It’s great to hear you talk about it again. I mean, that’s how I first met when you were talking about it at a conference. Um, last, uh, not quite my last question, but, um, What would you, if you wanted to guide a, a listener to the show, the Let’s Talk Loyalty show, how can they really take their data to the max?
I give them a golden nugget. You’ve given us a few secret sources along this discussion. Give us one more golden nugget, how to really maximize their data usage.
Shorful: Yeah. So I mean, there is no like one thing, but if, if I was to say a process that they could use to maximize their data is, the first thing is if you are using your data, you should always have done an audit on the data because you’ll be surprised that there are so many, uh, nuances in the data that it’s not agreed across the business.
So even if you take some, I mean, I’m working with a recent client and we’re taking revenue as, as a value, this might three or four, uh, variables in their database that says revenue actually says revenue on it. Um, and, and they’re all calculated differently because they’re used by different parts of the business.
So the first thing is, you know, when you, when you look at your data, try and do some kind of informal, You don’t have to be formal. It can be an informal order of the data and make sure that the business agrees on those terms, because if you don’t agree what the numbers mean, whatever you produce afterwards is meaningless.
And you find that with loyalty program, like people talk about, Oh, can we measure incremental revenue? And if you don’t understand what revenue is, how are you gonna calculate incremental? What is revenue? Is it minus refunds? Is it including discounts? Is it excluding offers? You know what is revenue? And you’ve gotta agree that, and you find that these are the things that are really overlooked.
And if you get that right, A, especially if you’re the analytical team or if you’re the person running an under team, you get a lot of credibility and trust because people know that you’re thorough, you’re robust, you pay attention, uh, to detail. And then the second thing I always say is, If a lot of businesses look at their data from a product lens, how many of this have I sold?
How many of that have I sold? Or how many stores have sold this? So it’s very much from their business lens, product lens. I always say, Look, to do data, well see it from the consumer lens, from the customer lens, right? So try and connect your data based on a customer, not based on the business’s view of the of the world, which is typically products or services. Uh, and you’ll see, you know, if you go into companies and you look at their reports, a lot of it’s around, We’ve sold this many products, we’ve sold this many services. I always say, No, no, re, re re-look at that data and look at it from a customer. And I think as soon as businesses start, start to do that, They will get maximum value from their cust uh, from their data.
Because don’t forget, a product is only the, the, the number of products sold is, is, um, an artifact of the customers buying that product. A product doesn’t sell on its own. A customer has to buy the product in order for the product to increase in number of counts. So if you don’t see it from the customer point of view, you don’t know how that product is being generated.
And you’ll find interesting things. For example, you might find there’s a subset of, uh, customers who only, buy this product. So even though it looks like this product is really a high selling product, it could actually be a very small number of customers to buy it. And so therefore you’ve got a massive customer who aren’t buying that product, and you’ve gotta ask yourself why.
And secondly, if that group who’s buying that product, if they move on, I won’t be selling that product anymore. Um, so understanding it from a customer lens, I think. Is where you drive most the value. But I think, but I think, like I said before, you need to do those audit type exercises to make sure you are all aligned.
You know, We use to talk about single source of Truth, Amanda during in the days that we needed the single source of Truth so that we were all aligned on it. Um, and then secondly, see it from a single, you know, a customer lens. It doesn’t necessarily mean that you need a single customer view. Sometimes it’s very hard to create single customer views, but just to see your data from a customer lens, I think will give you maximum value from your data.
Amanda: Love it. I mean, um, I remember in my, in my retailing days, the absolute success of getting the, corporate board KPIs to include a customer KPI rather than just product sales. KPIs was probably one of the most fundamental days of my retailing career. I loved it so much when the board went right. We’re gonna actually report on a monthly basis on customer KPIs, not just sales, product sales.
So thank you for explaining that to everyone. Shorful, I could talk to you all day long. I’ve utterly enjoyed connecting with you again and utter enjoyed this discussion. So unfortunately we can’t talk for too much longer cuz our time is running out. So as I bring a, a discussion to a close, is there anything else you’d like to share with the listeners of Let’s Talk loyalty.
Shorful: Um, yes. I mean, like I said, I mean, you probably talk to people who are the loyalty practitioners, and I’m more from the data part of view. So I, I what I would say to both sides, the data and the practitioners side is that get the, the analyst to actually be part of, um, what you do with the customers. Okay?
So whether it’s how you design the loyalty program, how, whether it’s you execute the communications, what offers you give, because I think a lot of the time analysts are used just to generate the number and they go, Oh, here’s the number. And then the people who are the practitioners take that number and, and do it.
I think having an analyst in the room or in in the discussion, uh, can be very beneficial where I have been able to, you know, have analysts involved in that discussion. They provide a different perspective, uh, and I think a lot of businesses, especially if they’re running loyalty program, could benefit from that perspective.
So don’t just treat your analyst as someone who’s gonna provide you numbers. Try and think of your analyst as someone who can also provide ideas and inputs to your actual program itself.
Amanda: Absolutely. Absolutely. Well, thank you very, very much and thank you for making the time to talk to us. I’ve thoroughly enjoyed it.
Thanks, Shorful.
Shorful: No problem, Amanda, I, It’s been a pleasure to talk to you too.
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