Lifestyle Segmentation: An Interview with Jan Kestle, Founder and CEO, Environics Analytics

JK: Compusearch was about 50 people when I joined. It had been founded by Bill Goldstein, and Mike Williams had just taken over as VP and was going to become President. And Mike had a similar background to me, he'd worked in the government statistics side, but he was responsible for like taking what Compusearch had built and taking the show on the road to other industries. So, he recruited me to come there, and I joined in sales, even though I'd never really been involved in sales. But I'd never seen selling data as really sales, I always loved the fact that you can solve business problems and society's problems with statistics. But that's when I first really got exposed to this geodemographics thing.

SS: Just go back to Bill for a second. He founded the company, right? I mean, did he get his cue from Jonathan Robbins at Claritas, or did he independently come up with the idea that "Hey, we can leverage this census data for business decision making?

JK: So, there were, you know, three or four companies that kind of pioneered geodemography at the same time. In the UK, there was CACI and CCN Marketing, which eventually became Experian. And the person behind that development of geodemographics was Richard Weber, who is also quite...he's still active in teaching and well-written on the subject. And then Jonathan Robin and Bruce Perro and Robin Page and others started Claritas in the U.S. around the same time. And Bill started Compusearch in Canada. And there was, you know, cross-collaboration as has happened in the industry through all that time. So, I think it was the concept of, was started, they say, in the University of Chicago on a paper written in this School of Human Ecology that if we know where you live and we understand a lot of the characteristics, even though it's not perfect, we can make a reasonable assumption about who you're likely to be. So, there was a discussion, but for the most part, Bill read those academic papers, understood, and still understands data very well, and, you know, Canada's often a bit of a laggard in terms of how you can get your hands on data. But as an aside note, Bill went to Ottawa, and he went to Statistics Canada and was the first private sector business who actually bought census data. And so, he had to pioneer that arrangement where Statistics Canada made its data available to the business community as well. And he started off by writing - and he was a programmer as well – he started off by writing code that enabled putting a dot on a map and drawing a circle and adding up the enumeration areas we called it then, census data. So, he first got the demographics to look at really who lived in a trade area. And then all along the same period of time when those products and methodologies were being developed in the UK and the U.S. he brought in Tony Lea and others to really develop the cluster algorithms that allowed us to look at something that created the first lifestyles. And that was before I joined. (11.29)

SS: And Tony was instrumental obviously in developing those methodologies and algorithms. Was he, again, looking stateside and taking his cue from Robin's work or other people's work, or did he develop on his own, his own approach to creating these lifestyle segments?

JK: They developed on their own, but they read the literature. And it wouldn't just be Robbin’s, it would be, you know, Richard Weber's work and the University of Chicago. But they also, you know, when you build a segmentation system like that and when it's based on cluster analysis, there's a lot of art that goes into it, and, you know, which variables you select, and how you ensure that you're not using two co-directional variables like, you know, what is the correlation between income and age? And if we put both in, is it gonna differentiate or is it gonna, you know, [inaudible]. So, it's what Tony really did a lot of work on because he was an operations research person and a quantitative geographer, and he really pioneered the sort of weighting schemas, and how you create the segmentation system by using relatively small number of variables and adding the weights to them. But, you know, through that whole period of time after I joined, we had lots of dialogue always with Claritas, and with CCN, who were doing that pioneer work in other countries. So, I'd say it was more of a collaborative exchange than just one person and one method, but what happened was there was a whole small niche. I don't know if you wanna call it market research, it isn't really, it's marketing research that the lab called this geodemography but there's only ever been a handful of companies, you know, a couple, two or three at the most in Canada and, you know, two or three in the U.S. and two or three in the UK that did it. And then, you know, some of those companies were absorbed into larger entities and did some global work. But the real pioneering work of creating a geodemographic segmentation system was done in Canada, the U.S., and the UK.

SS: Yeah. So, I wanna fast forward a bit here. I joined BPMSI if you will, in the early '90s, and you were very well established at the time. By the time I left the company was going through the process of being sold and then eventually, you know, Compusearch itself morphed over the next few years, you left the company. In 2003 though, you decided that you were gonna reboot the company as Environics Analytics, a decision that would've been still pretty risky at the time. What led you to that decision? You had this period of time where you'd left the company, this interregnum if you will, what was the business opportunity that you saw that you wanted to re-enter the market with your own company?

JK: Well, as I said before, I think that the concept that if you understand where people live, you really know a lot about them and you have to employ that carefully, but it really works. It's almost the secret sauce. And so, you know, Compusearch grew from things like segmentation systems to using similar kinds of estimation techniques to take sample surveys of 20,000 people, 10,000 people. And if you have the behavior of 10,000 people and you're making an estimate of what that's likely to be for 750,000 postal codes, there's some pretty cool methodologies going on. And so I'm very proud of the fact that through the '90s, Compusearch extended way beyond demographics and the lifestyles and the site segmentation system and worked with now [inaudible] data used to be PMB and Numeris used to be BBM. And we really were able to take survey data and project it to the ground, creating a whole new perspective that could be combined with customer data, and could be looked at local markets. And we had expanded, as you know, into the work that you were doing in CRM. We'd also expanded into the U.S. but by that time, Compusearch was owned by Polk. And that was a great partner as well because the relationship between demographics and what kind of cars people buy is really exciting if you're a statistician and a modeler, but Polk decided for its worldwide strategy to divest of its consumer marketing assets and really focused on its automotive work. And then that left Compusearch a little bit of an orphan. And so one of the last things that happened before I left Compusearch, was I arranged for the buyer of the full consumer marketing assets, Equifax to negotiate to sell Compusearch to MapInfo. And the reason why that was an appropriate home for Compusearch at the time was MapInfo had a very strong market share in Canada. And, you know, that kind of desktop mapping really needed data. So the marriage between, you know, the great data that Compusearch had about Canada and helping MapInfo extend its market share. And I, of course, had the opportunity to stay on and continue to grow, but I also was sort of enticed away to do something a little different. So, I went to actually run a small business venture that the Blackburn folks had invested in that was, you know, in the direct marketing business and I was gonna bring the knowledge I had there and suffice it. It was exciting, I was gonna have equity, it was gonna be a, you know, leverage the internet, because we're talking, you know, 1999, 2000. So, I was really excited about going and doing that, but the truth is it didn't work out, and I'd only had my government job for a long time and my Compusearch job for a long time. I'm not like a job hopper, so I learned a lot because I was on the board. There were three different investor groups who didn't always see eye to eye, and eventually, you know, I got fired, which was a, you know, kind of an interesting experience. But fortunately, I had a decent arrangement that I could, you know, really figure out what I wanted to do. I'd been enticed to go there by Bill Goldstein who was doing work for Blackburn on looking at business opportunity. So, it was kind of an interesting story, but I never have any regrets. (18.22) But what I felt when first of all I stopped working in that job, and I had some run rate on my hand, I had some time to figure out what I wanted to do, I'd stayed in touch with a lot of people, clients in particular, and I got a few gigs, like doing some analytics work for them and helping them. But a lot of people were saying like we're still, MapInfo is great, but the, you know, the data in the mapping package aren't really capturing some of the opportunities that we can do when we bring geodemography and CRM and bring all that together. So, I really felt like there was unfinished business and an opportunity. And so there was a core group who agreed with me both on the client side and on the, you know, former staff side and we decided, you know, to put together a business case. And I spoke to quite a few people, some former customers, you know, investors in the U.S. and the UK industry, and, you know, I was looking for one million bucks. And when you think back to that time it doesn't seem like a lot of money, but I gotta tell you, trying to raise $1 million in 2002 to, you know, restart a business like that, well, it wasn't easy. So, it was really great for me when Michael Adams, who owned Environics Research, stepped up. That was after more than a year of presenting and having banks and key information businesses take me all the way to the altar, but then never quite get there. So, the idea was we needed to put a small team of people together. We were going to base the business around a solid segmentation system, but what was really exciting about doing it with the Environics Research partnership was that they had the psychographics. They had been, you know, very well known for more than a decade at providing their social values data. So, if you remember "Sex in the Snow" and the...[inaudible] where Michael used their, you know, social values to really understand Canadians and understand the differences between Canadians and Americans, we've always had the challenge of how do you use demographics as a surrogate psychographic, or, you know, now there was the opportunity to really look from a research and methodology point of view of how can we combine this segmentation system, which is primarily demographic with, you know, this attitudinal and really mindset data. And so, you know because you're an expert in the field, that you can't, you know, that a sample survey of psychographics, same as any other survey, you can't project it into 50,000 dissemination areas or 700,000 postal codes. So, Michael invested in the Environics team and his partners and I recruited a sort of a founding team. And what did I do? I went for someone who really understood how to sell and work with clients. I went for a software development expert, and I went for a data development expert, and I went for an IT expert, and assembled a team of five, six people whose names you would all know from the Compusearch days. And then it grew a little bit as we got our funding into about 12 people. And we just rolled up our sleeves and said the first thing that we have to do is build a segmentation system that's gonna take us to the next level. And so, we built the segmentation system by using a lot of still census data, and of course, what we do is we try to bring the census data up to current by doing estimates of the key census variables, and all of the stuff that Tony Lea and Danny Heuman, you know, worked on that system, so, you know, how can we really use those data? But we also built a much more granular segmentation system based on geography and demographics. And then we grouped those nano-segments, the ones that were under, into, you know, the larger groups based on their psychographics and attitudes that we could get from the social value statement. And so, you know, when you create a segmentation system, a cluster system, there's hundreds of good solutions and there's, you know, we have now the technology's there that you can create a solution in one day and you can run it against a thousand different variables overnight, and you can really see empirically is this system giving you more lift? Because when you build a system like this, you're looking for the magnitude of small differences. You're looking for high highs and low lows. And you don't set in advance, I want roughly this number of segments and they need to be this big, but you don't say, I want this many in Quebec, or I want this many to be, you know, culturally diverse, you actually allow the data to speak. (23.54) So, there's many different solutions, and if you then optimize that granular solution with ones that really help you see the relationship in values and attitudes, like how do people feel about the environment? Who are the early adopters of technology? Who's really interested in conspicuous consumption, ostentatious behavior, you know, there's like 150 values propositions that we, you know, optimized our PRIZM solution. And then interestingly enough, why did we call it PRIZM? Well, PRIZM was the first system used in North America. It was developed by Claritas, the folks that you mentioned, and it was, you know, really a recognized brand. And it's a great brand because when you're looking at things, you're looking at it through different lenses, you're seeing different, you know, [inaudible]. I mean, PRIZM is great, it belonged to Claritas, they had never come into Canada, they'd always partnered with us in the Compusearch days. So, I went to them and I said I'd really like to license the brand. And we would work on which segments are similar in Canada and the United States because people always want a North American or a global system. But that's a contradiction because it's basically a granular geographic system. So, in Canada, you know, we have, at that point in time when we had 25% Francophone, but they all lived within a geographic area. You don't have that; you have that language diversity in the U.S. but it's distributed. Affluent Canadians tended to still live downtown. You didn't have those gated suburbs of living the affluent people. We have 10% of the population in a larger land mass. So just naturally, if you're gonna allow the data at the small spatial scale to create a categorization, you can't have one that works. Like we had this discussion for years, well, let's build a North American one, but it's just dumbing down, you're getting rid of your differentiation by doing that. But what we did was we built our Canadian system independently, and then we actually looked at the behaviors of all the segments in Canada and the U.S. and we twinned them. And so, we were able in the launch of the first product to say this segment exists in both countries. Interestingly enough, so we thought, you know, using the PRIZM brand has recognition with some companies, and it's a great name, and we had this partnership, we still have that partnership with them for working on both sides of the border. But the reality is that PRIZM as a brand is mostly associated with Environics Analytics, not Claritas, because Claritas is not known in Canada. So, we worked really hard, it took us a year to build the first PRIZM system because not only do you do all that math, but you have an artist to draw the sketches, and you have an expert, namely Michael Weiss, who was one of the sort of pioneers in what I'd call the personification of segments. (27.16)

SS: And in 1988 wrote a wonderful book called "The Clustering of America" which really shone the spotlight on this concept of geodemographic segmentation and visited, which is the interesting part to me, visited all of the different segments personally to talk to people to see, is this real? Is this true? And it's a fascinating sociological study.

JK: So he was hired to help us write up the descriptions of the first PRIZM system when we launched it and eventually joined us full time and became our VP and head of marketing and wrote amazing articles. And he's retired now, I wish he wasn't, but he has the ability to make these systems come alive. So, that's our story. So, we built PRIZM at the same time we built, you know, current year demographics. We linked PRIZM to social...we included some of that social values fabric in PRIZM but then we also linked it to the annually updated Environics Research social values survey. And that enabled us to say, you know, here's... and see, I'll just take a slight diversion and say, you know, why does anybody need something like PRIZM when they have so much customer data? Which is the age-old question and these funny things that are happening in the marketing community where you have people saying, "Well, all we need is CRM or all we need is custom modeling, and we don't need these, you know, silly old postal code things anymore." Well, the truth is the more data you have, both at Compusearch and in Environics Analytics, the more data that are available in customer databases, in the digital world, the more people use our PRIZM system. It's not that one is substituted for the other. Of course, if you have one-to-one data and you can execute that way, then that's what you do. But for most people, they don't have enough customers. So, they like to use a tool like PRIZM, obviously for acquisition. But it only starts there, you know, you can also use it for your internal analysis to understand your share of market, your share of wallet, to understand your sponsorship opportunities because PRIZM sits at the center of what can be your [inaudible] customer data, the median measurement of the day that's, you know, really the choice in the market. It connects to location, so you can think about it for merchandising and because it connects to values, you can use it for creative and message targeting. It's easy for people to understand. Because we've done the heavy lifting of looking at that multi-dimensional demographic characteristic, it allows a lot of researchers who can do more custom things to work on top of it. So, if a brand builds beautiful custom segments around lifetime value, or, you know, churn, then if you link those custom segments from the customer data, or from a survey into PRIZM, then it can help you apply the learnings from your internal data or your custom survey to the outside world. So, our experience has been like the more data that are available, the more people use our third-party data because it links things together. It becomes the lingua franca that allows you to have a much broader view, not only of your own customers but, by the way, there's still a lot of organizations that don't have customer data. (31.00)

SS: Well, and you described it beautifully. I call it the triangulation of on-the-ground data, geodemographic data if you will, and the values, I want to get into that in a second, the behavioral data that you're describing, but as well, the emotional attitudinal data that often explains that behavior. It links them quite neatly as you're describing it, you know, it's more of a holistic approach, if you will, to profiling a population. And that's one of the things that I think people miss, and Weiss brings it out brilliantly in his book, is that it's really a snapshot of our identity as a country. We're a mosaic, if you will, of communities and neighborhoods and the system you've built really reflects that quite neatly. And let me ask you about values because it seems to me really key today, like people seem to agglomerate if you will, largely around values. And we're seeing that - it's, of course, led to a certain amount of divisiveness in society, obviously. Is this just a social media illusion or is your own analysis showing that, yes, values are becoming a key driver today of people's decisions, of who they wanna do business with, who they wanna buy from, you know, whether it's, you're talking about ecological issues, or political issues, or ideological issues, are people starting to really wanna hang out, if you will, with people that share their own values? What are you seeing?

JK: I think that values have always been important in really connecting with your customers and your prospects. Because we don't only work for businesses, by the way, we do a lot of work for governments and not-for-profits, and people who are really trying to get to the hearts and minds of Canadians. So, there's sort of two aspects I'm hearing in your question. One is to what extent do we see values changing? And the Environics Research work which is reflected in our work tracks pretty nicely over time, you know, the shifts and it's always interesting to look at it in the context of Canada and the U.S. So, you know, the latest work shows that yes, while there is more polarization, there's also a lot of continuation of the different, more traditional kinds of value sets in Canada. So, the jury's out a little bit, I think the next year or so will be interesting. But what I see when I look at the data is still the things that our age, I shouldn't include you in my age, but older people, you know, still see that Canadian value of appreciating the cultural mosaic, not so much that, you know, that kind of xenophobia. Yeah, more tolerance. We still see that. And we see the social justice movement that's, you know, really taken the world in the last three years, and we see evidence of that in the values of Canadians. But we also see shifting in terms of prioritization of technology, just this sort of that's natural. And so, I would say there's reason for concern and monitoring, and then the whole role of social media is kind of a different issue, but in terms of the actual research we see some change, but mostly similar patterns, especially when you compare Canada to the U.S. But in terms of our customers, you know, you and I lived in an era where if only we could get the different message to different people in a cost-effective way, what a great world. And so I don't know if you remember when Time magazine did an experiment in the '90s where they sent a pre-magazine to different addresses in the U.S. with different covers to see what the difference in the covers would mean to their acquisition rate. And they did, you know, it's the best research they could to do that. And we were also excited about it but it was so expensive, right? So, you'd think now when the cost of getting the right message to the right people in the digital world was almost nothing, then you'd have way more different content. And I get frustrated that by the extent to which the differentiated content is sort of like we ended up doing in direct mail or at least you get my name right, and, you know, maybe a little message because I'm a woman of a certain age. But the opportunity right now to, you know, through programmatic, through, you know, even our connected TV, digital out-of-home home to get different messages to different people, it's not being leveraged nearly to the extent that it could be. Now, I think there's reasons for that. I think, first of all, you know, if you were gonna try...I remember some people in the direct mail business using our 67 or 68 segmentation system and trying to have 67 different messages. Well, nobody can afford to do that, and also things are moving so fast that you can't do it in a timely fashion. But if you do a large segmentation study of your whole customer base or the whole market and you look at their media preferences, and you come up with, you know, you've basically got 10 different kinds of customers and you decide you're gonna focus on three of those in your campaigns for the next year, you can really truly well look at what kind of message is gonna resonate. And so, we've done a lot more work with agencies than we used to do in the past, but mainly it's been on the activation side where we've allowed our data to sit in DSPs and in DMPs and with agencies so that our customers can actually activate against the insights that we give them. But the next frontier for me is to go back to our friends in the creative side of the agencies and to our customers, and saying, "Let's really leverage the values." If you've got three segments, surely you can write three different messages and three different creatives, and you can have that consistent across the customer journey. So, I've got great reinvesting in our use cases and marketing and promotion of social values, because I think its time has really come. Also if you know everybody's talking about ESG. So, there are many, many brands that I think are very sincerely … I don't think this is just, you know, checking off a checklist. I think that people in business, and consumers, and citizens are really concerned about the environment, and about social justice and equity, and about the governance and regulation of businesses. (38.09)

SS: And about corporate citizenship or good citizenship.

JK: Yes. And so, that's, you know, so when we're working with a customer and, you know, we can help them understand for their customer base, is it the E or is it the S or the G? Is it environment, social justice, or corporate citizenship? We can help them understand what resonates more with their customers by bringing the values into the segmentation. And, you know, this is being required by businesses to demonstrate their investment and their adherence to understanding what needs to happen in terms of environment and other programs. So, I think it's a very important thing to leverage the relationship between customer data, you know, market data, the segments, and the values and attitudes in order to...like we know the customers, they're really in charge. It's not a matter that we're pushing advertising and messages at them and that's what's determining, they're deciding what they're gonna read, what they're gonna see, and we have that moment to connect. And so, you know, one of the great things about something like a PRIZM system that we have, and in Canada the pioneering work we did 20 years ago to combine PRIZM with values, I think it’s time. And because we're also like, you know, one of the things we said before we started the formal thing was, you know, this old geodemographic thing, I think that to the extent that we've been successful and I don't wanna, you know, blow my own horn, but I think it's because we've adapted it to the modern world. So, being able to use it in the digital world where, you know, physical address may not be the way in which you target, it may be programmatic, it can be eyeballs, it can be, you know, your IP address in your set-top box, it can be a whole lot of other things in a digital world, but there are ways with a high degree of accuracy and reliability to link our data into those digital ecosystems. So if you can actually, you know, have an omnichannel campaign with a consistent target group with a persona of different types of people, and you can bring the values in, and you can actually reach across that great divide and buy your media, and activate, you're gonna get a better result. So, that's why I'm excited about the future is because we've taken this old methodology and adapted it, given the fact that there's new data, and that there's new requirements, and there's new media, and, you know, the next frontier is, can we actually do that and then do the decent kind of attribution and measure the result that you get from different media, and different messages, and different campaigns. I think it's still tough because one of the reasons why it's tough is because the software and the platforms are still a little bit fragmented, that big part of it, but the more we can bring that together and the more we can bring a solution for Canada, so we can go from insights to activation with, you know, really targeted messaging, and then measure the result and take that back full circle. That's...hey, I mean, you know, you're a pioneer and that's what we've all been doing. (41.47)

SS: But to your point earlier, I mean, you know, we carved out our careers on that promise of precision marketing, if you will, more personalized marketing, more targeted marketing. But, I mean, today, really, isn't the whole concept of analytics to drive business strategy and not just business performance, per se, but really to serve as a bridge between the various data sets that you're using and the strategy to grow the business. And I wanna talk about that. Because a lot of what you're discussing here is, let's face it, at the high end of the maturity scale. A lot of companies aren't there yet. Your company has to do a lot of teaching, mentoring, being a sherpa, serving as that bridge. Are you largely for most of your clients leading as opposed to following? I mean, surely it's gotta be a very, very tiny group of companies, say, that see the world the way you're describing it.

JK: Well, I think it's quite varied. I have a few things that I say in most of my speaking engagements, which is data and analytics strategy has to come from the C-suite. You were saying it before me. So, you know, it's so important because there certainly is a lot of investment, there's a lot of commitment, there's a lot of understanding that data and analytics make a difference. But honestly, I think especially, you know, in tough times coming out of pandemic, recovery, there's a lot of work being done, but if it isn't put in that strategic context of … what are the key business problems we're trying to solve? What kind of data do we have? What kind of analysis makes sense? What's within our time and budget? What can we implement? So to me, there has to be a much more strategic, at least support. And, you know, we started here about getting the CMO a seat at the table. The CMO has a seat at the table, but the CEO and the CFO have to insist that organizations use data more effectively. So, what does that mean? It means breaking down silos, it means getting across the enterprise, it means having a common understanding of how you're gonna implement the results. It's very frustrating for us when we work with a great client and we do a big segmentation program, all the things we've been talking about, and then two months later, some other department crops up and says, "Well, I wanna use that custom work for this." And we say, "If only we'd known that at the beginning." So, first, the answer to your question is, I think there's some real bright lights, but they’re still small numbers. And we are doing something that, you know, you and I tried to do in 1999, in 2000, which we're standing up a consulting group, but we're not at all in any way gonna do general business consulting. We are going to be a strategic data and analytics consulting team - three people, an experienced person from our team, a very experienced person from data and analytics at a big agency, and a very experienced person from consumer packaging. And we're gonna announce this in the fourth quarter of 2022, and these people are available to come in and work with understanding the main business challenges and the business objectives, put the stakeholders together, help the teams of people identify the roadmaps and the blockages. And then we will do the things that can help accelerate that agenda because sometimes there is a strategy but the priority of even getting the data out of the database … if IT departments are much more aligned, but there's just aren't enough resources to do everything. So, we think that, you know, C-suite-led, executive-led enterprise data and analytics strategy for large and medium organizations needs more help. Our customers are the executors of programs and campaigns, and they rely on their BI teams and they rely on their IT teams, and there's a small number of companies in Canada that have data science. You know, we have 100 data scientists, but most of them are deployed around working on our products, but we're gonna offer that help. So that's one thing. The second thing is that I think this climate around privacy and the changing in privacy is causing some, you know, some headwinds in some ways, and I really don't think it has to. I don't think there's any reason why we can't be data-driven in Canada and completely stay true to the trust that individuals and consumers have when they give their data to government, and to information companies, and to brands. We can follow the principles of consent, transparency, you know, using the data in a way that a reasonable person would reasonably expect, we can do that. The laws are being amended to reflect the modern age, but there's way too much of a bad narrative out there that says businesses using data are all bad actors. The banks and the insurance companies, and the telecoms, and the retailers, and the businesses and all businesses in Canada have spent millions and millions of dollars in the past 20 years complying with PIPEDA, complying with CASL, and making sure that the work they do internally is consistent with the laws of the land. Companies like ourselves, we only use data that's consented and permissioned, and we are already retooling our systems so that we can be sure that we don't use any PII and that the data that we received are already de-identified or anonymized if required to. So, I think the first point is it needs C-suite support. It needs enterprise strategies, but the data and analytics community has to recognize that the implications that, you know, people are asking for, "Tell me what you're gonna do, keep my data safe, make sure I can't be identified." That shouldn't stop us from being data-driven. (48.40) So, I'm excited that we're gonna finally have legislation, even if it's not perfect, but you know, if the legislation passes the way it's set out now, it will hopefully bring us into GDPR equivalency. We have to understand we need the government to make all the regulations and issues around, you know, de-identification and anonymization really clear. We have to make sure that statisticians and methodologists are in charge of how we manipulate data to use it, we have to make sure that data is still of the highest quality. But I am on kind of a mission to encourage the data community, to not kinda wince before the lash and say, "Well, we can't do anything with data." Because there's so much we can do. And so, we're working on, you know, increasing our capacity to use mobile movement data, which is, you know, everybody's antsy about that. But taking the signals that are passively collected from phones and from phone connectors and using those data in 100% privacy-compliant ways to really help with things like transportation plan, healthcare delivery, social service delivery, but they also help with getting the right products on the shelves, with getting the stores in the right place, with understanding the mix between online and offline. And for Canada to be competitive in such a tough global market, we need to be data-driven. So, no contradiction between getting data and observing, you know, the legitimate and reasonable direction of privacy laws. We have to make sure that all those regulations are enforced equally between, you know, the users of data like the brands, between information companies like ourselves, and in terms of all of the digital platforms where we have, you know, Canadian players and we have offshore and we have global players. And I'm not gonna come off as a raving nationalist, but we need, you know, a Canadian ecosystem that, you know, where brands can share data. A lot of people are talking about first-party data, so where, you know, the data of an advertiser and a publisher can be shared in a privacy-compliant way to understand, you know, what the best activation paths are. So we're working on that. We're building a clean room that's built for Canada that will enable the blending of, you know, disparate data, whether it be two advertisers to an advertiser and a publisher, bring in our third-party data, but make sure that first-party data can be blended inside a clean room in a secure environment that's built for purpose, that's for a limited period of time, where the data are all used consistent with the consents that exist so that organizations can understand their co-marketing opportunities, their sponsorship opportunities. (52.08) So, it's kind of like going from the organizations that are coming to us, and there's seven POCs going on right now. So, this is where there is a broad understanding and support for analytics, where people are prepared to do something different. So, it's early stages, but, you know, we started building this Canadian clean room, which uses some external tech, but it uses some of our own matching algorithms, our own data quality, our data enhancing. It combines as I said two organizations' data in a totally privacy-compliant way that's governed by a statement of work that deals with consent and brings our data in, and it allows us to develop audiences, execute audiences with partners, and then measure the result. The fact that I've got seven projects going on right now, and they're all PLCs, but they're helping us understand what we need to invest as we modernize our platforms so that, you know, we can be competitive. And, you know, some of our customers operate in Canada only, and some of them operate globally. And I think I was a little, you know, thinking ahead for a number of years because I kept talking to some of the big global platforms in social media or in CRM. And that, well, you gotta adapt Canada is different, you know, we don't have that unit record data we have a different [inaudible]. And, you know, the truth is that everybody's crying about GDPR, but we've lived in an environment very similar to that and coped in that environment. But anyway, you know, I was trying to get Canadian solutions in ad targeting and ad tech and social media. But what I really came around to to is we have to build; we have to adapt the Canadian solution and make it fit into the global solutions rather than trying to get global solutions to do something special for Canada. Why? Because Canada is a tiny market and why would they? So we're investing with a ton of partners in an ecosystem that will allow, you know, data to be blended, third-party data to come in, APIs, other kinds of platforms so that we can assure our advertiser customers, that there's a Canadian solution that works for them in ad tech. And, you know, I'm not a Pollyanna, but I'm not any longer feeling like, "Hey, we can't get it done in Canada just because we're small." I think it's a partnership game, we have to decide what we're gonna do, and we have to pick the best of breed partners that we're gonna work with, but there's a lot of willingness to do that. You know, we work closely with Bell and Bell Media, but we also work with Corus and Rogers. And we also, you know, Telus is a customer, so, you know, we are building something. And in our partnership with Bell, they're not restraining us at all. And in fact, they're investing and they're encouraging us to build a solution in third-party data and in data blending, and in links to advanced advertising that works for the whole marketplace, for all of our customers. And so, I'm very excited about that and it is tough, you know, I mean, the other side of the coin is I can still go in to a big customer and they still don't have their customer database. (55.42)

SS: So, I wanted to ask to pick up on that a little bit because I was gonna ask this earlier, but who do you put in Canada anyway in the pantheon of advanced data users, that is applying, you know, analytics to business decision making in the way that we were describing earlier? I think of, just to name one company, Shoppers has been amazing using its Optimum program to drive promotions and merchandising and drive purchase behavior. But they’re a select group of companies, there aren't that many that are sitting on these big databases that can be really leveraged, but who would you put in that pantheon sitting here today?

JK: I could name four significant retailers who are really...they may not be as far along as LCL, but they're rarely doing some innovative work. Certainly a bank or two, you know, and telecom. It needs people to have the resources to do it, but we also see, you know, credit unions who are, you know, “we try harder”, we're small and we're trying to compete with the big banks. We see some people taking the lead who never really had that history in data and analytics. So, in the energy sector, you know, we did have a case study with Hydro One where they said, you know, we used to think of our targets as meters. Well, now we understand we have customers. And we've done great work in the energy sector helping those organizations. We've actually had some federal government departments and municipal departments think about citizens and residents as, you know, the same way as brands think about consumers. So, you know, our work with municipalities and government used to be a lot around demographics, but a lot of it now is, you know, different targets and how do we reach them? So, the shining lights that I'm seeing are coming, you know, really a lot more from those organizations that, you know, have really decided that they wanna be data-driven. And some of them have some resource and they want help, and others often have no resources. For us, we have to be prepared to work with people in different ways, but I'm optimistic that there's a change out. What would I say? What I would say, we have 1,000 customers, would there be 50 that I could say are on the path? Yeah. And what could I say, maybe there's 20 who are amazing. So I don't wanna … but the other, you know, 850 are starting down that path. People are curious, they believe in data, and they're really open to trying to solve business problems differently. But I think it's our job to advocate and help not only businesses. And kind of circling back a little bit on the privacy side, we have to help Canadians understand why data make their lives better. And we all haven't done a good job of that, you know. We have to really explain to people. Yeah, there's some dangers and there's some pitfalls, but if we do not leverage data and use it in new ways and, you know, need to figure these stories about Cambridge Analytica or keep following people around on their cell phone, most of that is not based on any real fact. Most people are using data responsibly and we have to participate as experienced, you know, learned data community in helping your average Canadian understand why data are so important to their future. When the census was canceled and then reinstated, it was amazing, every cocktail party, every bar, every party, every coffee, I had people talking to me about it. And it made me realize that if we explained the issues properly and really helped people understand, you know, we can counter the negative narrative which is out there, but we have to do it. We have to take responsibility for it, not just as analytics people, but as Canadians. This country is small, it sits in an interesting place in the global economy, it's got a tremendous track record on it. So the social safety net and yet we can never sit back. We have to be the advocates for data making life better and being used responsibly. (1.00)

SS: And in the interest of people, to your point, the public perception is soured on use of data and for good reason, given its use as an intrusive ad vehicle. But the organizations haven't quite, and we were talking about this earlier, figured out that - it's not just about personalization. It's about how do I deliver a better service, a better offer, how do I make life easier for people using the information and knowledge I've accumulated from them. And I think that's where organizations are really falling down. That's back to the, are we competing on analytics here or are we competing on insight? If we're competing on insight, yep, we can change the playing field dramatically. Just a final question, we're almost out of time here, Jan, this has been amazing conversation. You've been a pioneer in this business and the knowledge you have and understanding of this business is amazing. Do you see your business as, going forward, as an accelerant of a lot of these trends we're talking about, as a change agent, as an enabler, in other words, making it easier for companies to actually maybe, outsource some of this very complex work that needs to be done? What's your vision for Environics Analytics going forward?

JK: Well, we see our role as an accelerator and an enabler. But our plan is to continue to build more data faster, data that is, you know, weekly and monthly, you know, it's not super real-time, but weekly and monthly, instead of annually and quarterly, that's number one. Number two, to have platforms that connect to the ecosystems that people can use, so it's easy for the people and the brands who actually are doing the marketing and the executing can do things quickly. And then invest in, you know, new technologies like clean room and understanding identity and, you know, but starting from the business problems people are trying to solve. How we deliver that? We have models where we can give people the tools and they can do it themselves; we have models where we can do simple or complex projects for them, and everything in between. And the answer is, how we're gonna grow and accelerate this, is we have to be able to understand what a client has the capacity to do in terms of insights and what they wanna execute. So, we are on a bit of a mission. We do wanna lead people to think about strategy and we're, as I said before, we're investing in, you know, more data production, we're investing in better platforms. We have a great system called ENVISION, which is used by over 2,500 analysts and users and it's great, but it's 11 or 7 years old, so we're rewriting it so that it's, you know, moving to the cloud, so that it's modular, so that it connects to everything else. And we're also, you know, investing in people who can help organizations implement the data strategy. And, you know, I think what the next frontier is for us is to help people actually measure and know what works and what doesn't.

SS: Well, this has been, as I expected, an incredible conversation, Jan. I've always been hugely impressed by your intelligence and expansive knowledge of every aspect of this business. So, thank you very, very much for today's conversation.

JK: Thanks, Steve. It's been great.

That concludes my interview with Jan Kestle. As we learned, lifestyle segmentation is still very relevant today, maybe more so than ever. That’s because over the past half century people have increasingly chosen to live in distinct enclaves, drawn together by a common world view, a social phenomenon known as “The Big Sort”. People prefer to live in communities of like-minded neighbours, where they feel instantly at home. As marketers learn to become more data-driven, more skilled at segmenting their audiences by their values, more adept at personalization, at engagement, they will be less concerned with how their target market looks and much more interested in what they believe. You can find past episodes of this podcast on CustomerFirstThinking.ca where you’ll also find articles, strategic frameworks, video and more on the transformation of marketing. In closing, a big shout-out to my friends and colleagues Justin Ecock and Shak Rana for their contribution to making this podcast happen. Until next time, thanks for listening.