Today, we’re going to talk with Matthew Blakstad about the meaning behind a sometimes buzzy marketing term: differentiation. We’ve heard it used over and over again to describe those fundamental attributes that make an organization unique, but we don’t often talk about what it means to achieve differentiation. How do you quantify that? How do you make sure what you think are your key differentiators actually are, rather than just some wishful thinking on the part of your C-suite?
[00:07] Welcome to It’s Worth Doing Right. I’m your host, Olivia Hayes: resident creative pragmatist, and the Head of Product Strategy at Accomplice. Today, we’re going to talk about the meaning behind a sometimes buzzy marketing term: differentiation. We’ve heard it used over and over again to describe those fundamental attributes that make an organization unique, but we don’t often talk about what it means to achieve differentiation. How do you quantify that? How do you make sure what you think are your key differentiators actually are, rather than just some wishful thinking on the part of your C-suite? Today, we’ll investigate and find meaning in what can otherwise be a flimsy marketing term by speaking with someone whose research has taken him deep into this topic.
[00:38] My name is Matthew Blakstad, and I’m a writer, researcher, and communicator specializing in the needs of low to middle-income people.
[00:45] So to get started and get a lay of the land, let’s talk about differentiation. When people ask you about differentiation, especially when they’re talking in terms of “market differentiation”, what do they mean by that? How do you define differentiation when you’re speaking to people?
[01:04] It depends who you’re talking to. I think a lot of people, even people within brand organizations, have a feature-driven view of differentiation. They’ll be wanting to know what the item is that you’re going to show on the trade stand, and what the thing is that the sales guys always talk about first. So in a sense, they’re not talking really about differentiation. I think they’re talking about cool stuff. For me – I tend to come at this more from a research and data perspective, so I guess I’m a bit biased – I tend to think of differentiation more in terms of an axis, or axes. Or in terms of variables. When you’re looking out into the customer universe today, you’ve got this huge, insanely large set of variables that you might considerably look at. And for me, the trick with differentiation is not so much to understand what feature is going to be important, but to understand along what axes and variables you know. For your customer, you need to really demonstrate that difference.
[02:04] So in your opinion, the definition of differentiation isn’t necessarily marketing, or cool stuff, or things that might catch people’s attention. It’s much more pragmatic than that.
[02:15] Well, it’s pragmatic, but I think it’s also more strategic. If you think about the journey that any customer or target segment of customers has with your organization, made up out of a number of touchpoints along that customer life cycle. If you’re going to drive your strategic goals, you’ve got to be thinking as an organization along every one of those touch points. What are the measurable things that we can do at each of those points that are going to make a differentiated impacts on that customer, and give them a positive service or product experience? So if you really want to create that through-line from corporate strategy to product strategy into a positive differentiation, it’s got to be driven through the data strategy. That, I guess, is my focus.
[03:04] When it comes to seeing your organization or product with true perspective, teams can often fail to identify a very important part of the equation: gaps and blind spots. It sounds counterintuitive, but there are ways to identify your known unknowns and they are critically important to understand.
[03:19] Right. I think the big one that I see, and I know I’ve been guilty of this myself, is unconscious bias. So looking out at the world of the customer through the lens of being inside an organization and being close to product development, particularly R&D? I think the big mistake, and it goes back to my point about the cool feature, is to anchor on the things that matter to you and to the people like you who you’re working with. And this is where you get onto the importance of research. I mentioned that in the context of the trust element. I might think that some cool feature for quickly making an impulsive saving in an app environment is a really cool feature. But unless I have overcome that customer barrier around lack of trust, then that feature really counts for nothing. So I think that the big blind spot that people have is to see the world through their own eyes, instead of the customers’.
[04:11] Can gaps within that known data be valuable? Are there ever instances where the gaps in that data can tell you more about your consumer than if the data were present?
[04:24] If you’re going to think about gaps in the data, I tend to think you need to look in a number of different places. There’s a lot of different ways in which data can exhibit gaps. Just to name some that I’ve been thinking about a lot recently, one of them is what I would call known data bias. There’s probably a better phrase for this, but it’s essentially what I was saying before, which is which is you tend to see the world through the eyes of your own organization and the way in which your organization kind of captures data on the world. So you differentiate between individuals on salary and age, let’s say, because those are variables that you’ve captured, and that’s what your data is driven by and how it’s structured.
[05:01] When you’re thinking about savings behavior, you do get a huge amount of predictive effect if you think about the combination of age and income. But if you’ve got a 32 year old on $62,000 earnings, they might be single and childless, or they might be married with three children and a dog and a very large mortgage. Who knows. So you’re probably more interested in disposable income, or even something much more subtle, related to savings, behavior and consumption. And that takes a lot more work to generate. But because you’ve been viewing the world through the lens of your known data, it can be quite hard to see beyond the profiling that you’re doing on your customers. So that’s one.
[05:38] Another one, which is really prevalent in retirement saving and long-term saving, is this problem of stated intention versus real world behavior. So people will very often report in data that you’ve captured through survey techniques for instance, that they will make a choice or take an action. But that tends to bear very little relationship towards their actual behavior when you’re dealing with low-touch and low-engagement products, like savings accounts. Generally when we get people into retirement accounts, or long-term savings, we tend to use defaults. We tend to auto-enroll people now. We tend to apply all this behavioral science. And the challenge of that is that they tend to behave in a very passive ways, and they tend to take whatever defaults they’re given. That kind of leads to another problem with the data, which is that people tend to look very similar through the lens of how a product provider might see them. But that’s only because you haven’t yet got beyond that initial default interaction, and haven’t started to engage them more as an individual and less as one of a potentially quite large population.
[06:43] The final one I think is really crucial, and I think we all fall foul of, is actually very often that the data may not be missing, but they may not be recognized as being valid or valuable data points. They could be kind of hidden in plain sight, almost. The figures that get thrown around, and I’m not sure if these are true, but they feel quite valid, is that organizations tend not to take into account about 50 percent of their structured data, and about 99 percent of the unstructured data. I think that that’s very truthful. And in a way, this is supposed to be the strongest example I’d give to your question of areas where gaps or perceived gaps in data can really reveal quite a lot of clues.
[07:21] So just to give you one very quick example of something I’ve been working on recently with some academics is to look at transcripts of conversations with customers. These might be web chat sessions, for instance, where you’ve got very easy access to the transcripts. Very often what you might do with those as a brand is apply a wrap code to them. So you might say, you know, this was a query about this, this was a complaint about this. And you categorize them, and count them, and correlate them with different customer experiences. But very often, we’re not looking at the rich content of those conversations. In a big data world and with the kind of analytic tools that we now have, the kinds of things we’re now testing is can you use textual analysis to understand the emotional content of those conversations? Can you generate a richer profile of the nature of that interaction that might teach you something about what the customer needs to see next? And that’s a fascinating example of where the data was just sitting there, but we maybe haven’t seen it as having a use, or an application. So I guess my answer to your question is yeah, I think when you look across the external and internal gaps in your data, very often you’re finding much richer indicators of customer preferences than you get if you just look at the sort of traditional “top-10” set of data points that we all tend to anchor on.
[08:35] So digging into your known unknowns is imperative, but how can organizations do that? Are there research methods that can be used to start investigating in a way that leads to insights that are actionable? Matthew had some thoughts about those constantly evolving methods.
[08:48] Research techniques that can overcome that, particularly with harder to reach groups of customers, are really emerging and evolving quite rapidly at the moment. I think there are some really interesting avenues where you can try and strip the sense of a professional coming in and judging the individual, which is a killer to getting any kind of honest dialogue or honest response about their financial circumstances. One technique that I think is really interesting is online panels – qualitative research, but with moderators and conducted online and with anonymization. You can have Reddit-style threads where people are chatting quite openly, and often getting quite punchy with each other – and moderators coming in and prompting. So it’s very conversational. It’s not like quantitative data gathering, but because it’s done in that online environment which is quite familiar to people and where they are very used to sharing in a relatively unmediated way, you can get so much more honest responses.
[09:41] Another one that I think is really interesting is peer to peer research, which is where a professional researcher will engage a group of individuals from a target segment and have the conversation with them, but then effectively train them to be able to go out and have further conversations with their own peers. And so the conversations that you’re then observing with permission, of course, are actually much more human-to-human than they are professional to human. All of these techniques, which I think are becoming more and more common, are about taking that judgment out of the interaction and trying to understand people in their own terms.
[10:18] In theory, we all know that we should be doing this kind of consumer research. But what does this process actually look like in action? As we’ve talked about in previous episodes, there can be a lot of internal resistance to this kind of discovery. So what are some examples of how this actually looks in the wild?
[10:32] The most effective providers that I see in this space, some of the bigger US firms particularly, developed this down to a fine art. Essentially what they do is they create these journeys where each touch point – each proposition to those customers that they might take a positive step, they might take an active step and engage – each one of those, effectively, depending on the customer response to that prompt, will drive them down a different path in terms of what’s next for them on their journey. Each of those interactions is designed to capture some data, and to measure the extent to which that individual is responding to particular prompts. And so for instance, things that are less urgent and more gentle that are prompted to one group of customers, some people will respond and that will teach the provider how to continue to interact with them in future. Whereas others who don’t respond might find the next set of touchpoints that they have with the provider is much more urgent. So what you’re basically doing is building that data gathering into what can be these relatively small sets of regular interactions, or even irregular interactions, over the course of that journey. And provided you’ve got a robust enough system for tagging and annotating that dataset and building that richness based on their responses, you are over time filling those data gaps, I guess. But unless you’re designing those things into the journey and the way that I’m describing, you know that the risk is that all those interactions get lost. Effectively every time you’re going back to the customer, you’re starting again from scratch with that very generic view of who they are
[12:00] Because you have been in a space that has so many kinds of constraints to it, are there lessons from the world of research on savings behavior that can be applied to understanding how consumers might engage with other brands?
[12:14] I guess I’d say humility. I think having worked in product categories that have much higher consumer touch, a much higher level of emotional engagement than a net promotion, the thing you learn when you’re offering a retirement income to somebody is that you can’t assume you own the touchpoint. You can’t assume you’re anywhere near the top of the customer’s hierarchy of needs at any given point in time.
[12:39] I was wondering if in the last few moments you could tell us a little bit about the current projects that you’re working on.
[12:44] Interestingly, I’m coming at the whole question of people’s relationship to data in a very different way because in my spare time, when I’m not helping organizations with data and research, I’m also a published novelist. My second novel, Lucky Ghost, just came out here in the UK Just a couple of weeks ago. Sadly not out in the States yet, but there’s a couple of stories already out called Sock Puppet and Lucky Ghost. If anyone has any way of accessing them, in particular, if they’re not in the US or Canada, then do go and give them a look. So the first one, Sock Puppet, is about – funny enough, this may sound like a familiar narrative – it’s about a female politician whose emails were hacked, and then a bunch of fake online voices start exploiting this inflammation against her. And then as she falls, this kind of brash entrepreneur figure kind of rises up to power. I don’t know if that sounds at all familiar to us, but that was, that was published in early 2016. So you have a prophet!
[13:44] But the reason I’m writing these stories, and hopefully the reason people are responding to them, is about what we were saying in our conversation earlier. The realm of data, and the way in which we interact with online services and with each other through online platforms, is so much part of our emotional makeup and who we are as people. And yet the creative arts tend only to engage with technology and with people in tech in a pretty surface way. I just think it’s really interesting to explore stories hopefully in an entertaining way, but to explore stories which bring that to life. The most recent book is Lucky Ghost, and the one before that was Sock Puppet.
[14:27] Well, congratulations on that. And is there any chance that we might be getting that in the US anytime in the near future?
[14:32] Well, I’m working on it. We may hopefully have some exciting news to announce soon about a TV deal. So fingers crossed on that, but at the moment, it’s just in the pipeline.
[14:47] Often when brands think about differentiation, they treat it as a marketing exercise, placing a storyline on top of their products. In reality, it’s a discovery process. And organizations often forget to audit for their gaps in knowledge. It’s understandable. It’s uncomfortable to think about what you don’t know about your brand. But not digging into those spaces can often leave you with only half the story. Getting a full perspective requires bravery, and a powerful flashlight as you go looking for answers.
[15:14] Thanks for joining us for today’s conversation. If you’d like to learn more about our family of agencies or give us feedback, visit us at itsworthdoingright.com, or drop us an email at firstname.lastname@example.org. And remember: if it’s worth doing, it’s worth doing right.