Jan 21, 2022
Data handling is everyone's concern at the moment. But do you know the difference between 2nd, 3rd, 1st and now zero-party data? A recent term that's starting to appear everywhere, zero-party, or trusted, data refers to data the customer intentionally and proactively shares with a brand.
Host Will Francis chats with digital expert Clark Boyd about how brands can treat this new form of personal preferences and information usefully and responsibly.
Will: Welcome to "Ahead of the Game," a podcast brought to you by the Digital Marketing Institute. This episode is a big Q&A, where we explore an area of marketing through a leading industry expert. I'm your host, Will Francis, and today, I'll be talking to Clark Boyd, a digital marketing expert and author based in London, all about zero-party data. It's important now and in the near future for all companies on the internet. Clark has worked around the world helping brands like Adidas and American Express shape their digital strategy. He trains digital marketers through a variety of institutions and programs, including those at Google, Cambridge University, Imperial College London, and Columbia University. Clark, welcome back to the podcast. How are you doing?
Clark: Hi, Will, good to be here again. How are things?
Will: Very good. Thanks. Yeah. I feel even in the time since we last spoke, much has changed in the fast-moving world of commerce and digital and what have you, and there are new concerns on the horizon. One of them is data and how we handle it, like customer data, and yeah, there's a lot of talk of zero, first, second, third-party data. So I'd love you to really give us the lowdown on that today. Does that sound doable?
Clark: More than doable. Yeah, yeah. Looking forward to it.
Will: Good, good, because I wanna really kind of clarify that whole topic, really. I mean, let's just start at the start. What is zero-party data, and then how does it relate to first, second, and third-party in a nutshell? And then we'll kind of dig into the detail.
Clark: Yeah, it's a good place to start. I'll begin with just almost dictionary definition of zero-party data. It's so fresh. I don't think it's in the dictionary yet, but we'll get there. So zero-party data is a term that was coined by Forrester just a couple of years ago but has really taken off. And it is data that a customer intentionally and proactively shares with a brand. So to think about how that might work, it could be a preference center that a brand has where think of a newspaper, for example, where you can select which topics you're interested in and then it will send you newsletters based on that. So expand that out and start thinking about, say, a home decor space where you could say which styles you like, which may have something like, say, almost a Tinder-style functionality where you can swipe on styles you like or styles that you don't. That's the idea with zero-party data. It's a layer up from the other types that we're used to and that we will discuss where the customer is actively involved and is knowingly sharing data with a brand, and the value exchange is, "You give us some data, and we'll give you a better experience. We'll show you through recommendations or through style guides" or could even just be personalized emails that you will get a better service based on doing this.
Now, the name zero-party data...it's also called trusted data. You may hear those terms used interchangeably. But zero-party data is intentionally on that spectrum as opposed starting with third-party data that then moves into second and first. So third-party data, everyone will be familiar with, you will have used it quite a lot. It is collected by external parties, usually aggregated and then sold in data exchanges. So I could be on a newspaper website, and a third-party would be tracking my activity, and then I move to another website and they are still tracking me.
Will: So is that things like ad networks like Google's ad network and what have you?
Clark: Absolutely. So they are dependent on this kind of third-party cookie tracking, and brands pay a lot of money to access it because, well, you can see what your customers are up to, which other websites they go to, and you can place ads wherever they go. So Google and Facebook, two of the big players there, but you'll hear other companies like Creatio that really specialize in this space. Now, when I talk about zero-party data, it's something quite different to that where the customer is actively involved. I think marketers sometimes wonder, "Well, why is that important today?" There's so much data already, and zero-party data, which we'll get into clear examples of it and the process of how it works and everything like that, it can seem like a little bit of a gimmick. Customers don't want to be involved necessarily in this process. I see why the brand would want the data, but why would the customer actively give it up? But the roots to getting third-party data are closing off all the time.
So I've written about it multiple times on the blog, and many other people have probably explained better on the DMI blog as well. We're seeing the clampdown of these third-party cookies. We're seeing 96% of people on iOS 14 now opting out of app tracking now that Apple is giving them the option to do so, and it's seen as quite invasive. A lot of marketers are... Actually, Creatio I just mentioned a moment ago, they conducted a survey recently, and they found that vast majority of marketers are really relieved that Google is delaying its removal of this third-party cookie tracking until 2023 it looks like now because, of course, they're trying to find other ways to connect with their customers. I would offer a couple of points on that based on what zero-party data gives us as opposed to third-party.
The first is that third-party data was never particularly accurate. It's really a strategy of throwing a lot of mud at the wall and hoping something sticks. And sometimes it does, sometimes it doesn't, but the tracking is set up and the reporting is set up to make it often look like correlations or causations, I would say. And we believe that this is working because we show so many ads that people are bound to have seen them somewhere, and we, therefore, attribute their eventual purchase in some way to having seen that ad. Now...
Will: So just because of the sheer scale of the activity that we base on third-party data, i.e., a large-scale remarketing ad campaign. Yes, there's a lot of misfires, i.e., we're reaching a lot of people who aren't, in fact, looking for a holiday, but we are operating at such a scale that it looks as if we have gained a competitive advantage because those campaigns seem to be doing quite well. Is that what you mean?
Clark: Yeah. And there are studies that show that remarketing...in that example that you gave actually, Will, the study did look at travel and found that that kind of remarketing works best, actually, when it comes to defending against people moving to another competitor. So, you stay top of mind through it because you're always present even if it's a subconscious effect. There's, of course, impact. It's advertising. It generally tends to work. We just don't know exactly how much and exactly where, but we can see from our results that it's having an impact. Now, that hasn't always been that well understood, and I run quite a lot of courses around remarketing practices and always survey people and ask them, creating my own zero-party data, I suppose, how they feel about the remarketing ads they've seen recently, and they never feel particularly good about them. And these are marketers that are wanting to learn how to do this. They see the benefit of it, but it does feel a little bit like the balance has shifted towards marketers who are being a bit more invasive towards brands that are reporting their own metrics and creating their own homework, you might say, and it's all been tilted away from the consumer.
Now, when things like this happen, we don't see like a flick of a switch and everything changes overnight. We move from this invasive tracking to a really enlightened approach to customer interactions. What we'll see is a little bit more of a rebalancing, and with zero-party data at the other end of the spectrum, that starts to sharpen people's minds. It gets them thinking more about, first of all, first-party data. So I haven't necessarily defined that one. We've looked at zero. We've looked at third. I might as well do first and second while we're at it.
Now, second-party data, the least used of these. Some people would say it doesn't even really exist if you wanna get right down into the nitty-gritty of it. But it's really another company's first-party data that you can acquire through a marketplace or a partnership. So first-party data, of course, can be pretty accurate. You're seeing exactly what these customers are doing, but it's not very scalable if you're going to go out and try and secure partnerships all the time, and whether it's, you know, really second-party or then counts as third-party is something people can argue over. So not that much used. The main ones to think of are first, third, and increasingly zero as well.
Will: Yeah. Okay. So to clarify, zero-party data is that which the customer or the web visitor has explicitly offered up to us. They've told them something specifically about us, and because of the way that we can frame those questions or kind of lead those responses, we can actually, like you say, answer some much deeper questions about what they need from us and why and how we may best serve them, not only in terms of products, but also in terms of content, right?
Clark: Yeah. So we do quite a lot of work, you know, some of the companies that I work with, thinking about how do you personalize the customer journey? How do you figure out what people really want when they come to our website? So think about an e-commerce website today and you go there with a rough idea that you're in the market for a sofa. You've maybe seen one. You're trying to find it. There are very few tools that will help you get from point A to point B. You have potentially hundreds, thousands of very similar products on there. And we use a lot of assumption, I think, about customers try and get them to that point. The whole idea with zero-party data would be that you could then ask them even to submit a photo of something they're looking to find. The good example and one that shows just how important this actually is if people at this stage are thinking, "Ah, I'm sort of intrigued, so I'll listen a bit more, maybe hear a few more examples, and so on," the big one to look at here to understand where it's going is Google. They've launched this multimodal search, which a lot of other companies are doing too, but when it's Google, people pay attention, and you can, yeah, search using photos, so visual search, but you can add text on top of it where you can say, "I'm looking for styles that are a little bit similar to this."
Now, you may even think these companies with all the data that they have and all of these really clever algorithms in the background and big data and so on, it sounds blindingly simple to just add a text box and say, "Tell me what you're looking for here." But what we've been trying to do with third-party data is really overcomplicate this quite often and make those assumptions for people when they're okay with providing a little bit of information to get to a much better result.
So zero-party data could mean, as you say, Will, you could go to the website and they could ask. Say you're launching a new business. You're on a bank's website and there are lots of products and services there. You land on there. They don't really know anything about you. You've maybe opened an account, haven't done too much yet, and they could ask you what your business goals are for the next two years or what you're hoping to achieve in the next 12 months. But that's a very different experience if they're listening to your answer, and you could send them to personalized content paths and draw together different articles that would fit what they're looking for. That's much better than the customer not necessarily knowing, and the best questions when it comes to zero-party data encourage introspection, their discovery-based questions. They will actually force the consumer to think a little bit more and sharpen their focus, and ask, "What is it I'm looking to do here?" You know, you may be looking to open an account, you may be looking at some of the resources, "Well, what am I thinking of doing in about 12 months' time?" And then you can channel them the whole way through the website.
Now, it can be a popup that's quite invasive. Brands do that when they're quite confident that this is central to the experience. So quite often it might be retail. The big example people have used actually for this is Tide detergent. That's what Forrester used to show that this was happening a few years ago where there was just a popup on the app that said, you know, "How many people in your family? What kind of detergent do you like?" And then they could move you through and give you the right product. What most people do is have this just as part of the experience, and it's very much optional because you need the customer to be engaged. You need them to be interested in sharing information in order to bring them on board. But it's a good example you use, Will, where it doesn't have to be really sharply focused on retail e-commerce, you know, my example of looking for a sofa and you can share or even just click on styles that you like, and then the brand does the hard work in the background and recommends five things for you. It could really just be channeling you towards content that if you're hiring the funnel, as we might like to say, you're not really wanting products force at you like remarketing would do. You may be just wanna find out more about the products and services and you might come back. You might not.
Will: Yeah. I don't know if you've ever explained this about, you know, email lists to clients or people in the past where, you know, people are really fixated on numbers, same with social media followings. People are really fixated on big numbers, but actually, it's surprising how small you can go if the quality is there. And actually, you know, it's better to have zero-party data on like 100 customers than just a load of anonymized third-party data on 10,000 people because you can really do something with that quite detailed information about 100 people, you know.
Clark: Yeah. I think I've been talking to people who maybe are a bit longer in the tooth with these things that these things have been around marketing 20, 30, 40 years. And you say, Will, it's interesting what we're seeing at the moment with digital marketing where a lot of the things people are talking about are the things that we were talking about in the '80s and '90s but that have gone out of fashion. So because we've had access to all of this data and because really the main thing has been setting up tracking, creating lots of data, and not always thinking about what we're going to do with it later on, it's there and we'll maybe get someone to mine the data for insights. And the challenge with zero-party data is if you don't ask for it and if you don't think very carefully about the way you're crafting your questions and what you're asking customers to share with you, first of all, either they might not share it if they don't see why it's important, but also you won't get anything that you don't ask for. You can't just track them, watch them, and that's it, and hopefully, later on, if you show them 50 ads they'll buy your product. This is really about understanding that a process of inquiring, of discovery about customers, you need to know if you're going to take their attention on the website for even 20, 30 seconds, can you ask the fewest amount of questions that will give you the richest amount of information and then understand what you are going to do with it next?
Now, part of that is very qualitative in the sense that it is about trying to understand people, it's knowing your own product, it's knowing how you might be perceived in the market, all of these things. So I'll share a couple of examples of this sort of thing, people who do this well in just a moment. But the other side of this that I think is often overlooked and maybe because it's dry and it's where I spend most of my time on this side of the equation, I guess, is if you haven't got your data inventory set up in a way that can match to these kinds of preferences, then you can't actually respond anyway. So you could do all the listening in the world, but you would still just channel people through the same sort of e-commerce website if we want to run with that example. If you haven't got your data inventory categorized based around these kinds of preferences so there's more qualitative sense of understanding customers, then you won't be able to give them the right results that they're looking for anyway. And I actually think it takes people quite a while to start thinking in these terms because there's an art to asking these kinds of questions, and it doesn't have to be just questions. I'll give a broad range of examples on that. But it could be you're asking them to submit a picture or something like that or just take a few boxes.
But the harder bit, actually, is getting the data inventory matched up because we don't really think about that. We think a little bit like we used to think with customer data, "Well, it's there. We've captured it. We've got it. We've got all of that information." But if you don't understand the end-to-end process of how these results are served up and how they function, and it's not always something marketers have really thought about too much, then you're not going to be able to, I suppose, serve that new kind of customer desire that you are building up. So the starting point is actually those two in tandem. It's working together. It's coming up with...I like to start with even just what-if questions? So what are the things we'd love to know about our customers but we can't see it from our data or...? Often, we're too afraid to ask. We don't want to ask them these kinds of questions, or we think they wouldn't answer. But it's so easy to set up tests at the moment as well. You can just set up an AB test and look at whether people do engage with this kind of thing or not, and then you can fine-tune it from there and try something else and, you know, let them decide along the way. But once you've got an idea of what that might look like, then start working on the doll, but really important part of...we might call data augmentation where you're applying new tags to your data so that it can be served up. So if people do respond that they like mid-century furniture over modern furniture or whatever, you can start giving them the kind of website experience that they would be looking for on the other side of that.
Will: Yes, no, that makes sense. As well as the obvious quality benefits, personalization benefits to zero-party data, there also seems to me to be a big compliance benefit because you have a bit more control over your data, and it seems to be maybe a bit more easy to be compliant with GDPR and stuff like that.
Clark: Yeah. So that's one of the main benefits, really. Again, it's not the most glamorous one, but I think everyone will know just how important it is and something you can't overlook or have as an afterthought. So it should really be compliant by design. I mean, you are putting data into a customer profile. You need to know who the customer is, where the data is stored. It's pretty transparent and one of the big benefits for brands can be that it engenders a little bit of trust with customers as well. So if they can see that you use their data thoughtfully and that you remember who they are... The only way that you can do all of those things is if you have rather neatly stored customer profiles within a data platform that, you know, if the customer were to ask where all of their data is, you could zip it up and send it to them pretty quickly. So, it really should be compliant by design if you take this approach.
Will: Yeah, absolutely. Hello. A quick reminder from me that if you're enjoying our podcast series, why not become a member of the DMI so that you can enjoy loads more content from webinars and case studies to toolkits and more real-life insights from the world of digital marketing? Head to digitalmarketinginstitute.com/aheadofthegame, sign up for free. Now back to the podcast. Well, in the webinar that you recently gave, which is...you can go and find in the DMI hub, you talked through a few brand examples of zero-party data, which I thought were really interesting. You've already mentioned Tide. Give us some other examples that you've dug up from the world of brand marketing.
Clark: Yeah. There are quite a few going around at the moment. A lot of people are trying out different approaches here, which is I think pretty useful because they're all over the place, really. The first I would offer up is a Canadian bank that's just for entrepreneurs and small businesses and go onto their homepage. They're called BDC, and they just ask, "What's your business goal?" I was referencing that a little bit earlier without mention them. When you do select a different business goal, you can offer up more information. So, you know, if you wanted to share your annual turnover and that sort of thing, you can, but you don't have to. It will take your first response and then direct you through the website to wherever you want, and then it can ask you if you want more advice, if you'd like to speak to someone, or if you'd like to use the chatbot. So it's kind of the idea of the omnichannel but brought to the website itself, omnicontact. You can reach out to us in whatever way you want. We'll only ask you to click a few boxes and then we're here if you need us a little bit more. So it's a really good example. They're worth looking out there as well.
A company that has, I think, maybe put this more to the center of their whole value proposition really is Yelp. So Yelp is completely redesigned. It's mobile experience to frame it around this kind of zero-party data. So you can go in and just tap a few buttons to say what you're interested in, and if you're using it to find restaurants for, say, this evening, you can just put in the kinds of food that you like, and, crucially, it will show you in the results that it serves up which bits of data that you give it it used to give you your results. So that level of transparency is really clear. You can understand why you're getting the recommendations. I think quite often, people find these a little bit creepy if they make the assumption, you turn up at Yelp and it has bought a whole lot of data about you and assumes that you're a pescatarian and that you've got five people in your family and shows you those recommendations. They might be completely accurate, but you wonder how they've arrived at those. But what Yelp does is just makes it a simple, I suppose, survey to begin the whole value exchange, and once you go through the results, it'll just show you what you're getting but also why it has recommended those for you.
Will: Yeah, I like that. And I think it obviously works really well for food, but I could see how that could be applied in lots of different ways. And you're right. Yeah. I've seen the screenshots. You go to...you know, there's a welcome splash page and you literally tap like kind of dietary requirements, lifestyle, whether you've got kids, and then everything is served to you, like you say, through that lens. But what's really nice, you're right, is those results highlights the things that you've told it. So it highlights if you ticked like, yeah, pescatarian, it highlights that it has pescatarian options at that restaurant. And so, there's a clarity on the part of the customer. It builds trust rather than kind of nags at it because we don't know where that insight came from. And it makes us as a customer feel in control of something that's really working for us. I really like that.
Clark: Yeah. And I think we can expand that out a bit because you're definitely right in pointing that out that what happens quite often is people see these examples and then will go, "Well, yeah, but I don't run a restaurant business. What should I take from this?" And so, well, there are clear principles here I think that go beyond that because, if you think about the kind of information we often have on customers through third-party data exchanges, they might tell you something about the person overall, but they don't tell you about what that person wants from your particular brand in that particular context. So to Yelp, in this instance, it doesn't really matter that the person, say, earns a certain amount of money or drives a certain car because that same person, whether those numbers are high, low, small, big, they will want different things at different times and they will know that better than anyone else. That's the whole point of zero-party data is trying to make it simpler for the customer and make it obvious to them why they should share the information to giving the brand richer information that is contextual and meaningful right there. Because think of the assumptions we make quite often. I mean, I speak from experience, I suppose, of getting lists of people to target and we see that they absolutely meet our criteria and we just reach out to them wherever. But in that moment, they may not be interested in that. They may not be in the mood for, in Yelp's instance, that kind of food at the moment. Yes, they have a typical preference for eating steak, but tonight they want a pescatarian recommendation, and you will never make that assumption. The hard bit, of course, is getting that information from people depending on what you do, but if we think about that bank that has re-engineered its paths around just asking what your business goals are, that seems kind of worthwhile. It's a simple question. It's not too much to ask, but you can see the value in doing it straight away.
There are good examples as well with...so Threads does this really well, and I think, you know, Stitch Fix and other people like that as well. What I like about the way that they've done this is they...so Thread, in particular, I'll mention is, well, U.K.-based, but I think they're in the U.S. now as well, and they do men's wear and women's wear these days, but they started with men's wear. And the idea was...actually, they did an AB test between men's wear and women's wear to see which one to go with. They found that men, in particular, found the process of shopping either online or offline for clothes to be a little bit painful. They call it the choice paradox. There's so much to choose from that you end up choosing nothing. The amount of time that men were willing to spend browsing before making a purchase on their e-commerce store really wasn't that long and they left without making a sale at all.
So they started thinking, "Well, what are the little bits of information that we could know that we could guide them to? What's the problem that they have? Why are they looking for so long without finding anything?" And they started doing things like you just tap the styles that you like, and they actually say at the beginning of that survey, "We don't know anything else about you. You can just browse this website however you want. If you do give us more information, we will give you better recommendations. We feel confident that you'll feel better about it, but if you don't want us having any information about you, then just browse the website. It's just like any other retail website you would find, but you can also submit a picture of yourself and do all of these things." And then they have an AI stylist that will recommend things to you. So their average order value is far above the industry average in that space, and, you know, they've sold a minority stake to H&M. I believe you're after that kind of technology. But we see it in spaces as well with things like wine.
Actually, a company that I mentioned on the webinar but they have since launched a brand new product in this space since then. It's this app called Tastry, so T-A-S-T-R-Y. And I stumbled across them because I was writing a piece of technology in wine. So I thought it's just a fascinating sort of space. I think when you look at the inventory for say a retail website, you can categorize that pretty clearly. Yeah, there are fashions and styles and so on, but you can quantify a lot of it. But something like wine, so much of it is the culture. It can seem quite elitist. It's very subjective, really hard to pin that down. I found this app that's been doing it for years, and they're now in Walmart, on iPads and things in the U.S, and they've launched a new product, actually, which is about AI recommendations for wine. But how do they know what you like?
Well, it's not as simple as just asking someone, as people often don't actually know. And they might say a certain type of wine. So I prefer a Sauvignon Blanc over a Chardonnay or whatever. Maybe they haven't thought about that too much. They just have had one before that they liked and didn't so much like the Chardonnay. But what this does is it uses the science of taste, whatever the scientific name for that is, I'm no expert, but those just ask you simple questions like, "How do you feel about the taste of licorice? Do you enjoy a chocolatey taste in your coffee?" And it builds together a taste profile on the individual. So you just have to...A bit like the Yelp example we were just discussing. Just tap a few buttons. Now, on the other side of this, and this is the key part of that inventory, the company was founded by a chemist, and she has broken down all the compounds in the wines to figure out what makes them taste like certain things.
So you could identify the mixture of compounds that give off a licoricey taste if that is, for some reason, something that you're interested in, and it could match that up to people. So that, again, is, you know, offering value way above the normal index for that sort of space, and it seems to be going really well. But it's all about that thoughtful design. It's asking, as you mentioned, Will, actually, this notion of small data rather than just lots and lots of it but no idea what to do with it. Starting with a really clear angle. Why would we wanna know if you prefer the smell of chocolate over the smell of licorice? I mean, it seems like a very trivial question, but if you've got the chemistry behind it, now you can do something with it. And the principle applies, I think, to everyone. If you think about your company...and we should as marketers, I mean, we're supposed to be this voice of the customer. We're supposed to understand exactly what they want. We can build that in to what we're doing no matter what industry we're in, from banking to wine, to Yelp, to Tide, every company out there is trying to do this.
Will: Yeah. That's really interesting. I'm actually looking at their app now, and it's fascinating how they use all these inputs. And actually, it's quite a fun thing for the user because you are asked things. You take a 30-second palette survey. So they ask you like, "How do you feel about the smell of flowers?" Yeah, little of those things that you mentioned. And then they give you this kind of feedback, this kind of, like, taste analysis profile and then sort of suggesting wines and maybe even pairing them with dishes and things like that. Obviously, there's a bit of science going into that. We don't all have that faculty, but I think the wider point is that, yeah, if you can do it for something so kind of nuanced and subjective as wine, you can probably do it for any business. Even in a very simple way like we saw with BDC where they literally just point you to a relevant section of the website based on one question on the front page or, yeah, a very scientific survey into how your palette works. So I think there's room for every business to explore that.
Clark: Yeah. Something I've definitely thought when I was first looking at this was that it can be a little bit of a gimmick. You know, is everyone just gonna add a quiz that you take when you start and, you know, it's all just like a BuzzFeed recommender engine or something like that? And I thought it does seem just like a bit of a gimmick. And it was only when I started finding businesses like this and actually rethinking some that I was already using like Thread to realize they're all doing this in very different ways. And if people are looking at it from the perspective of, "Well, even now those examples seem really clear, but my company is in, you know, the oil and gas sector or whatever..." That's the one I always use because I get asked about it a lot. That's always the question that comes up. What we can also think about is, well, first of all, is there information that you genuinely want from customers, to begin with, that should be...? You know, is there something that you would love to know that could really help you give them a better experience and make more sales? If you're starting from that vantage point, then, yes, you should go ahead with this, but there's a full range of things that you can do.
So you can begin with something like the wine app. Like you say, well, it sounds...it's kind of fun. It's something that you do. It's why people take BuzzFeed quizzes, for example, when there's actually not that much in it except it tells you something about yourself you didn't know. And that's great with wine. You know, you can find recommendations, find new ones you would never have known. But at the other end of that scale, what a lot of companies are doing to get zero-party data is they're just offering something really commercial and clear in exchange. So you get a reward or you get access to content that other people don't get, or you'll get a 10% discount if you let us know this because... You were just mentioning the BDC example.
And a bit I hadn't really mentioned is that even though they're just asking a question and then sending you through the website, so they don't need AI or anything crazy for that, it can all be quite manual, think about what they're learning as well about their customers. You know, say 25% of people select X as their business goal when they come to the website and we didn't know that. We thought it would be much lower or much higher or whatever. That's insight that you can use to reshape the website. You can make it better. You can think about your product and service more broadly than you did before.
Also, companies like Shell. You mentioned the oil and gas example. They use it for just customer insights. You know, "What do you know about our company? What do you think about our current strategy? And why would you really wanna help companies like Shell?" I'm not them, to single them out as the example, but, you know, what's really in it for the customer to answer these queries? "Well, here's a discount next time you go to buy one of our products," and then it makes a bit more sense. So it can be at the gamified end of the spectrum, and that's where we focus a lot of attention. That's where a lot of people are getting a lot of value out of this. But for companies that still think, "Yeah, look, we still have things we'd love to know about our customers, and we can still make things better for them, but they might not wanna help us out." Well, that's where a 5% or 10% discount can easily come into it as well.
Will: Well, absolutely. Yeah. That's right. So do you think we need to incentivize with, you know, discounts, offers to get that data?
Clark: Well, part of it. So this is where... We talk about these things. I think it was TechCrunch that said, "Zero-party data is the biggest buzzword in marketing at the moment," which tends to send alarm bells ringing in my head, anyway, when we get to, you know, biggest buzzword status for anything. But what we need to do is to put this into a broader strategic context. There is pretty much no way that adding in some zero-party data elements to your website is going to solve your bigger customer acquisition challenges or something like that. It has to be understood as a layer to what you're doing. And if we think about say customer acquisition, actually, and how we prospect and get people to the website, it might seem like zero-party data only kicks in once they arrive. I mean, what can you really do otherwise? You're getting into the space of remarketing or something if you start just chasing people around.
Where does it then fit in strategically? Well, yes, of course, it should be focusing more on the website experience and so on. But if you are offering something in exchange, whether it is recommendations or a personalized approach to what you do or if it is access to different content or 10% discount or whatever way you want to phrase it, that's something you can start using in your marketing messaging as well. So when you're reaching out to people using these other tactics, you can use the value proposition from your zero-party data strategy in the marketing messaging in order to attract them as well. So I wouldn't overlook it as something that you can use within that full digital marketing cycle, even if it is only ever really going to kick in once people come to the website and start interacting with you.
Will: Now, in the webinar, you talked about this idea of modern brands becoming participation brands and that becoming quite normal where, you know, as consumers, we are less...just passive, and we actually get a bit more involved in brands and their products. Just explain to me what that means and just give us an explanation of that trend that you see emerging.
Clark: Yeah. So this again is something that is kind of classic marketing theory that is coming back to the fore or maybe something we overlooked with all of the shiny toys we've been playing with in digital marketing. So there's a company called Iris Worldwide. They have a participation brand index, they call it, that they've been running for a few years, and they find that if customers are involved in participating with a brand, so you could think about a really obvious and big example like Lego where they can, you know, put their ideas on the Lego Ideas platform and it's all fantastic and people love getting involved, that has a big impact on their brand. But the company that has had a psychological effect named after itself is actually Ikea. I call this the Ikea effect where people value their furniture from Ikea more than other furniture that is of similar or higher price because they personally were involved in building it. And it's very strange because, of course, you think you would want something that is already made. It turns up and you pay less for Ikea because, yeah, you have to do it and it's a bit of a chore. But people then value it even more. And a lot of companies are applying this in the digital world.
The Iris Participation Brand Index finds that there's a much higher link between customers thinking about a brand quite often and participating in the brand in some way. Now, what I find intriguing, I wouldn't say we've completely joined the dots on this, it was something I was just thinking about myself given that it's this deep and not always that well understood psychological phenomenon, is that if we do think or we have reason to test the assumption that people being involved in developing the experience with a brand, whether it's as physical as something like Ikea or whether it's just engaging with them on social media or shipping their own preference center on the website if there's some link between that and being top of mind and customer loyalty over time, why wouldn't we explore it? So it's something that's worth testing.
I think it's a bit of an assumption and a hypothesis at the moment that if customers are involved in this zero-party data creation and collection, they are more likely to say spend more to recommend the brand. And we see it. The examples I've given, I chose them because, well, I have done a bit of research into them and it seems to be working for them. Something like, say, Tastry, it gives you better recommendations, so it makes sense that you would spend more. But people do seem to spend more time on it. They do seem to come back more often, and it's hard to disentangle that and say, well, that's down to the Ikea effect or that's down to better recommendations. We don't know what exact percentage of that it is, but if it's working, well, just, generally speaking, that's gotta be something worth looking at.
Will: Yeah. I mean, I'm using it myself actually as we're talking. Yeah, I'm just looking through it. And I think there's something...there is an interest in psychological effect because, even though I'm looking at a list of wines in an e-commerce setting, I feel like I'm the only person seeing that list of wines in that order. Whether I am or not, I don't know. But I feel like I am. I've not consciously thought about it. It's just the way that it instinctively feels to me and probably other people who use it. So it feels like something somewhere has decided that they are wines I should like. That is inherently more interesting by an order of magnitude than whatever featured wines are in the store that week because that's nothing to do with me. And then, of course, there's this extra element here where you can go into a wine and you can see its flavor profile, how that relates to your apparent palette preferences based on the little quiz that you gave, which, again, feels in some way that... Everything puts you at the center, I think, and makes...even though...if I don't get around to buying wine, there's something fascinating about looking around the wines all in the context of my palette profile. So I'm not sure all our listeners can just go ahead and replicate that. I'm sure it took them a long time to put that together. Just small things, I think, small ways to make people feel like they're being served something a bit more relevant than what everyone gets shown, if you can think of any way to do that and, you know, based on a useful question you've asked them, then that can only be a good thing.
Clark: Yeah, yeah. It is interesting. I guess it's that point of like we know that this exists, this has been something that's been tested. As I mentioned in the webinar, it was really given a name when it started with cake mix where they found that people just adding an egg into cake mix was enough for them to make it feel like the cake that they ended up making was theirs and therefore they were much happier and they went back and they kept using this cake mix brand. Others were using dried eggs and you could just add a spoonful of water and you were ready to go, and they were much less successful. So we know and there have been plenty of studies about that and then about Ikea and then a little bit about some in the digital age but it's quite nascent. The question is what's the...? Taking in a cynical marketing term. What's the least interaction you can have for the maximum impact? You know, where does this effect die off? So I think it obviously does happen if you're really involved in say with Lego. You're submitting product ideas and everything. You're getting really involved. You're co-creating the brand there in a very active way. But does it filter right down to the level where if you are just on a website you're submitting your preferences and then it feels like it knows you a little bit? Does that give you some element of this psychological effect? So hasn't really been tested too much yet, but I feel like it probably does, to some extent, because you don't wanna give up that information every single time you go to another website. Why not just come back to the one that already knows you and that has given you the recommendations you need? Worth testing, anyway.
Will: Absolutely. Yeah. Well, give our listeners some practical tips that they can go away and try immediately to start preparing for or even gathering zero-party data and making use of it in their own businesses.
Clark: Yeah. So there's a bit of a step-by-step process for this, and it really does start with thinking of those questions that you would like to ask and then thinking of the way that you would go about asking them. So it can't just be clicking on things. It could be submitting something, and you can use this as just a bit of a pop-up. Depends which...for example, if it's e-commerce, there are lots of plugins that you can use on places like Shopify. I think I've shared some recommendations at the end of the webinar that we can put along with this recording as well. And you can ask some questions to people. There are tools and tips and things to help you get that bit done. The next bit is, though, how you use that information and tie it to your website experience.
Now, it's not as difficult as it sounds. If you're thinking, "Well, it sounds like I've gotta go and develop my whole AI preference engine or something myself," of course, these also exist as plugins. Now, there's a lot of this that's right there. The bit that you need to focus on is that strategic side of what are the things that you would like to know? And then if you did know that, what would you do differently? How would you use that information within your flow? So start with those questions. Typically, then it's used on the website to shape a bit of that experience. Then in emails, then to add as a layer of data to a CRM or a customer data platform or something like that, and then it's used in marketing messaging as well. So that's a bit of a step-by-step process for it.
But tips in terms of going about doing that and being a bit more strategic, the first thing would be and this's pretty obvious but it has to be done is really think of putting the customer in control of the experience. Put yourself in their shoes and understand that your brand is a tiny, tiny part of their day. They live a varied life. They have a lot of things going on. They are on your website, and that is a great thing. You do not want to, in any way, put them off that experience. So put them in control. Think about that little sense, as you mentioned, Will, looking at the wine app of it being something you're engrossed in. You know, gamify it a little bit more. Make it feel like they're making progress through it.
The second thing is...and maybe it's...well, you might wanna do this one first, but it's second on my list is start by defining a hypothesis around your customers. So, we make a lot of assumptions, and that is what we've done a lot in the third-party data days. In an era of more and more quality first-party data and then on top of that zero-party data, think about a hypothesis of how customers would want to access your products or services, just your value propositions that you're putting out there and how you would go about testing that. So instead of just thinking, "Okay, zero-party data is the biggest buzzword in marketing. We need to do this. Let's add a popup on our website," break it down a little bit and come up with a testable hypothesis so you can verify or falsify about your customers, and then AB test there.
Third is offer something in exchange. So that can just be recommendations. That can be better content. It can be more effective emails, but don't make that...here comes that word again. Don't make that assumption on the customer's behalf. So you might think... And I've fallen into this trap and I've worked at companies where I've had that, I suppose, epiphany of thinking, we are really not customer-centric here. We think we are but we're not where we've gone. "Well, customers would want to do this because we'll give them more targeted advertising." Wait. Has a customer ever woken up and thought, "You know, what I would love today is more targeted advertising"? I mean, it's literally never happened but it's easy to think as a marketer because you know what you do and you know you do it well that someone would actually want that. So if you think, "Well, they're going to get more personalized emails in exchange for this," I wouldn't be so confident that that's what they're wanting. So make it clear, make it really obvious, and say upfront, "We're going to ask you for a bit of information, and here's what you'll get in response. And here's what we're going to do with your data, whether it is deleted or whether it's stored in your profile to recommend things to you in future." Make that data exchange or that value exchange really clear.
The next thing, fourth out of my five, I'll try and give you five. So my fourth one is feed that data then into marketing campaigns that you use. If you really are offering something that other people can't, whether it is going down the road of a personalized or tailored experience or a curated experience with recommendations, or if it is just an access to new content that's coming out in the next three weeks and that they'll get a BView a week before, anything like that can be used as a way of prospecting and getting people to your website. It's unique. It's something other people aren't doing. And the fifth one, which comes back to the beginning again, is do keep adapting the approach to this over time and be really thoughtful from the outset about what data you want to capture. So we are... People ask me a lot, "Is this completely different from first-party data? Why do we need to think of it as something different?" And the big thing is because it's such a different mindset, and we need to train ourselves to get into this way of thinking if we're going to do it properly.
We have, I think, a second nature now that we set up our tracking and then we look in our dashboards and everything is fed in there and we can see what's going on. But if we haven't designed this properly and if we aren't adapting to it over time, we won't get the data that we need. So it's not about first-party data-style mass data capture. It's about us really thoughtfully designing the experience for people so that we can then get the information that we need. So those would be my five off the top of my head.
Will: Thanks. That's great. That's more than enough for us to keep us busy, yeah, as we go about kind of I think taking zero-party data more seriously and starting to really kind of use it in our business as well. Thanks, Clark. I really appreciate your time in explaining that. I feel a lot clearer on what it is and how we can use it. Just one last thing. Where can people find you and connect with you online?
Clark: Yeah. So by this stage, you think as a marketer I'd have a better way of doing this. So one good way, always seek me out on LinkedIn. I'm always chatting to people there. So if you search for Clark Boyd on there, there aren't too many of us, you'll see me pop up straight away on my newsletter "hi, tech," which there's a link to in my bio on the DMI blog as well. That's still going, over 100 editions now, so we'll keep going with that.
Will: That's great. Yes. I can recommend that myself as well. Go and subscribe to Clark's newsletter. Well, Clark, thank you so much. Once again, that was very interesting, and I hope to chat to you again soon. Thanks a lot. See you.
Clark: Brilliant. Thanks, Will. Bye.
Will: If you enjoyed this episode, subscribe wherever you get your podcasts, and for more information about transforming your marketing career through certified online training, head to digitalmarketinginstitute.com. Thanks for listening.