Jeremy Weisz 15:31
How long did it take to get everything together before you could actually go out and get your first?
Jan Kestle 15:36
Just about any more, a little bit more than a year, they were founded in August of 2003. And we launched our first major product release in 2004 in September.
Jeremy Weisz 15:49
Was that scary at all?
Jan Kestle 15:52
I was super excited, because I had worked with data and in the industry, and I knew that a lot of businesses were looking for a more integrated solution. Like it was a time when everybody knew that it was important to use data. And people had more of their hands on data because they have more CRM systems and point of sale data. But they wanted to combine their own data with data that would enhance it. So if you have data about your customer, you generally know what they bought with you, but you don’t know a lot of their demographics, you don’t know what their mindset is. So blending the data together was a big part of that first year of prep. But once we had, I would say the critical mass, then we knew a lot of people in the industry. And we knew that in financial services, and in retail, they were always looking for kind of advanced solutions. So I think our first customer was a bank, I think our second customer was a large telecom, that the focus was really on businesses, as well as government departments, that we’re trying to understand the small differences in their customer base. So it doesn’t have to be just one industry, but about 80% of our businesses, consumer focus about 20% could be considered b2b or b2b2c. But the best customer is someone who’s really trying to use segmentation and personification to get the right message to the right people using the right media and at the right time. And there’s a lot of granularity in that. So we grew up in retail, and finance, and telecom and not for profit. But we also got a lot of business from governments, because there were government departments who were starting to think about their constituents as sort of individuals instead of just everyone trying to understand if I’m looking at sending messages out about health or about safety, everybody’s not the same. And so the whole purpose of these kinds of data is to really understand the differentiation and get closer to people so that you can really connect with them and engage with them. And so I think those were the earliest customers. But more recently, I’d say in the last 10 years, we’ve branched a lot more into the relationship between food manufacturers, consumer packaged goods and the grocery stores. Because if you don’t get your shelf planning, right, if you go into a grocery store, that’s not in your neighborhood, you might notice that where I’m used to getting my six ounce peanut butter jar is not in the same place, because shelf planning is neighborhood specific. And so, helping the retailers and manufacturers understand neighborhood demographics, untapped potential. That was an area that we really focused on in the sort of the second decade, then we moved into things like energy, where I remember our customer at Ontario Hydro said oh, we’re so happy to work with you guys because we used to think of our customers as leaders. Now, we’re interested in promoting to people energy efficiency programs. What is the sort of the remote ways of controlling your thermostat when you’re out of town. And of course, that’s evolved now into working with the car companies to understand the demand for the vehicles. And then when you do that, you’re going to project the potential for E-vehicles into areas and then you also have to figure out who’s going to use the grid. And then is that grid going to be used for home charging? Or are you going to be using the electric vehicles for inter-city, and so does the grid home, can the grid sustain it. So you look at the consumer demand, what the manufacturers and the retailers are going to produce. And then that brought us into the energy sector, which is a very exciting sector, because it’s got a lot of location and transportation analysis. So the final, big evolution for us was to take the insights that we create for all these different marketers, and really help people figure out how to put them into action. And so I think for the first 15 years, we were really not selling and collaborating that much with media and agencies, I think, probably mistakenly, I kind of thought well, that we’re going to kind of park the data. And not that it was intentional, but the data is really valuable, we’re going to keep it and people are going to buy it directly from us. So what I learned over the last five years, and it’s you’d read about it in the literature is that the more that data can be out there and used by people for multiple purposes, then the more value it brings back. So what we did was we started to partner with agencies, where their customers were our customers. And there still is a divide between insights and activation amongst the brands. So you have teams of people working on market research and working on insights, and branding and positioning. But then the guy down the hall or the woman down the hall who’s planning that media stand, and that they’re somewhat removed, and then you’d have the agency and then when we had the digital agencies, we had a lot of disconnect. So the thing that’s changed for us in the last five years is we’ve really become the lingua franca, we’ve put our data, our wealth data and our prism segmentation system out there, so that the agencies can use it with the customers that we share, we’ve put it onto the media platform, so you can actually define a segment custom segment using our data, you can buy it through a DSP, or you can buy TV, we partner with Dell Media, where you can actually buy TV based on our segments from within their buying system, not just with Dell media, but also with Rogers and chorus and Canada and CDC and others. So our ideas have been the glue. So we provide the insights to the brands, and then we’ve made the data available to the ecosystem so that you can actually activate directly. Because, unfortunately, in the past, we could produce these great insights reports. And then what would happen is, someone in an agency would read them and turn them into a peer demographic. But we want to be able to reach and engage with customers and consumers around a broader thing than just demographics. So it’s been very exciting to expand into some of those, what we call the execution or the activation side and the next frontier, which we started on. But there’s still a long way to go is so after you do that, can you really measure statistically, what worked and what didn’t, if I drove by this board, and I saw this ad, that I walked into a store, and I bought something and we actually get a better measurement on the return on advertising spend. And the truth is that the data and the technology that are becoming available, are making it possible. The challenges. There’s the technology, and the providers aren’t always as rigorous. I’m a hawk on methodology. I say, don’t let technology trump methodology, you’re trying to use data to simulate reality to decide how you need to spend your money. And so let’s make sure that the data and analytics that are so prolific are being done with best practices so that the user gets a good outcome, people are going to pick a location or they’re going to drive out an Omni channel media campaign that’s going to spend millions of dollars and so we have to make sure that we’re doing the very best job of, you know, showing them who their right audiences are. Which medium works well, what kind of on the screen reach is how that can be optimized in your mmm so that you’re really spending your money in a way that comes back and what’s exciting is that the company’s ability to do a good job. And it’s limited because of a lot of technological constraints, but we have a growing capacity to actually measure the outcome. And so that makes me really optimistic that we’re just going to keep finding new frontiers.
Jeremy Weisz 25:15
There’s so much Jan that can be done with data. It’s incredible. I want to talk about the team and the evolution of the team. So I’m sure when you came out, and you’re studying math and physics, I’m not sure at that point, you’re saying, I’m going to a 300-person company, right. So it started off as just you talking about how the team evolved from you and where to go.
Jan Kestle 25:38
So because I had worked in previous kind of information company, I knew that the key components for someone who can really understand the data and how to model it and make it as accurate and link disparate data sources together, it’s a big challenge, because you can go to 10 different suppliers. And you can get a view of Canada and the provinces and the large cities, but to go right down into a neighborhood and bring all different kinds of data, I needed a methodologist, who really understood how to do that. And one of the founders who has passed away, but was a amazing pioneer in data science is Dr. Tony Lee. And he was a part of that founding team. He also had a younger methodologist, that he was mentoring Danny Human, who I started working together within previous company, and he’s the founder, and he’s like a 30 year veteran, and on this date of blending and about a great product. So really good quality on the data side, number one second key component, because as I mentioned before, I don’t think you can make a living just selling data, you have to put the data into a solution that makes it easy for people. So the second was to bring an individual who I had worked with previously as well. But who was innovative and thinking about, we were already in the Internet era, but we’re in 2003, we’re really thinking about software as a service. And we knew that if we’d license to commercial off the shelf software wouldn’t be able to do some of the things that our methodologist wanted to do. So we did a lot of mapping. But if you just put the data into a desktop mapping package, you couldn’t do some of the statistical algorithms that you needed. So I needed to bring a second component, which was a person who could conceptualize and actually code the software to deliver the data to the customers. And then the other two people were people who knew industries, and we knew we were going to focus on the financial sector in the retail sector. So there were eight founders, there was a IT person. And then the data methodologists had a person who was an actual data scientist who could augment the team. But we just rolled up our sleeves and started to build the products and think about the delivery of those products and think about the use cases. And for me, I mean, I was just so fortunate to have a great partner in terms of my investor, but also to entice those people who were known as leaders in the industry and people who are wanting to do it differently. Like we had kind of reached the maximum of what we could do in our previous engagements. And we said, we want to build that analytics space for the future, where it’s self-serve, where it’s configurable, and where it goes beyond just basic demographics and segmentation. Because our partner, our Environics research was the leading provider of information about psychographics was the first time we built a segmentation system that combine psychographic research with demographic behavior. And so the methodologists had to think about how to do that. And so I guess we wanted to take the kind of work we’ve been doing to the next level, and I can honestly say that I was able to assemble the A team. And I think, first of all, I shared some of my equity with them. So, I was in a partnership with the funder. And that was a really great experience, too, because we had a unanimous shareholders agreement and we had the two groups and we got to decide. And then within my group, I shared equity with the people who left other jobs and came and joined me.
Jeremy Weisz 30:01
That’s what I was gonna say like you assemble your dream team, it’s how do you attract that top talent?
Jan Kestle 30:07
Yeah. And some people have said to me, oh my god, you know, you’re so generous, you gave up too much equity. And I don’t agree with that at all, I believe that that whole team to lead teams are what makes companies successful and learning how to work as a team, and as we moved on from there, and we hired people, I think, we were competitive in the marketplace, we also do a lot of work, we still do a lot of work with universities and with associations in order to be a part of the thought leadership and help people to gain satisfaction, not just from their pay, but by being a part of a statistical organization that’s innovative. So I think, by being innovative, and motivating people to understand that data can make a difference to people’s lives to the well-being of our community, we attracted people who believe in that. And so we have a very low turnover rate, we have employees with long tenure, we have many employees in our company who’ve worked in this same field for decades, because they’re very passionate about using data to make people’s lives better. And I believe that at the end of the day, that’s what we’re trying to do.
Jeremy Weisz 31:21
Jan talk about, so I could see how you assemble this dream team. And you have software engineers, you have people in specific industries, you have data scientists, talk about people get to the best service, or product in the world, but we still have to sell it. So talk about the sales piece for a second. And I don’t know if it’s, these people were so ingrained in a certain industry that they had connections, how did you then get this in and sell this into the world.
Jan Kestle 31:53
So the sales leaders that we had were one original, and she and I went out and built the business one customer at a time. But the success we had that was Catherine and I in the beginning, I think was that we really understood. We weren’t that — we weren’t the super geeks, like we weren’t the modelers and the programmers. But we understood how these data could were different from what people were used to buying if they were used to buying survey data, or just working in their own CRM, that the sales team knew the secret sauce of the kind of methodology, and understood the industries that we were selling to. But at the end of the day, I believe that salespeople are born and you have to be willing to ask for the business. So one of my mentors, Bruce Carroll, who was one of the founders of the Geo Demographics business in the United States, he said to me, remember, nothing happens till somebody writes a check. And so I think the success of our sales team, and what we did was, we built the business for the first couple of years. And then when we knew we were really on a roll, and we hired that person, a person who was focused on retail, who had experience in retail, and we hired someone who had been in consumer packaged goods space, and then we hired someone from the government. So they kind of built it up that way. But the trick is that salespeople have to be completely enthusiastic and passionate and knowledgeable about their product, but they also have to know that it is going to make a difference. And you have to, it’s a missionary sell, you have to be convincing people that they need this, and that it’s going to help them. And then you also have to make sure that you do have back at the office, the servicing and support because when you’re selling complex products, we don’t want the bait and switch where the salesperson gets somebody in the door. And then the customer service team and the customer success team doesn’t, you know, actually make it real for the customer. So salespeople have to sell service people have to service and you have to know the problem that the customer is trying to solve. And you have to make sure that it’s actually working for them. We get like a very large number of our customers are on renewable contracts. And when we look at the dollar volume of what we had last year, in our data and software licensing, we get more than 100% renewed year over year. And that’s because we embedded in our software and our data licenses. These account managers and servicing people it’s not the training and will support this is on top of training and support are going to go once a quarter or once a month more often if the client will let us and say, okay, what are your strategic objectives? What are the business problems you’re trying to solve? What can you actually implement? What’s your budget? And then we say, okay, let’s try this kind of report or this kind of analysis. And let’s carry it through to the other end. So we’re not a consulting firm, but we will consult on your data strategy. So I think…
Jeremy Weisz 35:27
It’s like a value-add to the software platform.
Jan Kestle 35:30
The salespeople have to, it’s a heavy lift, it’s not a widget sell, but you do have to be willing to ask for the business.
Jeremy Weisz 35:40
Let’s talk through a few examples so people can understand. One of the companies or organizations is the Toronto Raptors.
Jan Kestle 35:49
Right. So we worked for MLSE for a number of years, mainly sports and entertainment. And especially in the early days, when before the Raptors won the championship, but it was bringing basketball to Toronto was always popular, but it wasn’t there was a little bit of competition, right. So MLSE owns the leafs and the raptors and now the soccer team or yeah, the soccer team. And I think one other, but it’s funny, sometimes people say no, you work for the raptors, why not the league, so you don’t have to do anything. So leave tickets in Toronto, right. So. But when the Raptors started out is really my best story is they very early adopted an approach to fan analytics. It’s completely widespread now in the NBA. As a matter of fact, the NBA gives an award and recognizes the contribution to fan Oh x. But before any of that happened, MLSE hired a person who had been a database marketing expert from a fashion retailer to analyze their customer database, and they hired us. And my favorite story was looking at the composition of their fan base. And understanding how to like Toronto is a very multicultural, very multi, culturally diverse community and has been for a long time, different from the US, we have twice as many cultural groups that represent a significant part of the population. And so what we did with the Raptors was we actually looked at the membership, the ticket, individual ticket sales and the membership. And we saw the growing interest from the South Asian, the Indian and Pakistani communities in Toronto. And so you worked at analyzing where the best potential was, and we helped them develop marketing campaigns for what was called Bollywood nights. So they actually had cultural events. Eventually, when we had, I think we had it was a cultural thing. So we had a light that appealed to the Spanish community. And we worked with them to really do a deeper dive on the demographics of who was buying, and look at the untapped potential and then looked at what the other behaviors and attitudes were those best potential customers and help them design sponsorship programs. So now, the Raptors are known for having a very progressive view on cultural diversity across the NBA. And I’m happy to say that the person who did that very early, bringing fan analytics using our data in our partnership won the first NBA award on using data to help drive the fan base. And she’s moved on to work another score, but it was kind of pioneering work before anybody was doing it. And very proud of that.
Jeremy Weisz 39:00
It’s really interesting. I mean, looking at data and giving your best clients and customers and attracting them with something that they’re gonna love. And it’s actually funny because when I picture the Raptors part of what I picture, I think his name is Na baccio he’s like a famous Toronto Raptors super fan.
Jan Kestle 39:23
He’s never missed a Raptors game. And he travels with them and he travels only the Canada basketball. And basketball is really growing across Canada in a lot of those growing communities. Canada is bringing a million immigrants a year because of the need for our labor markets. The vast majority of those immigrants come from India. And that fan base is hugely supportive base for the Toronto Raptors and for other sports as well, but it’s just a matter of understanding who your customers are when the gaps are. And because you can’t always just play to your strengths. If you think about beliefs, their seasons, tickets, a lot of them were purchased after the war, when immigrants came from Italy, primarily and other European countries to Canada and built our subways and went down in our minds, that’s the legacy of that period where we used to say that Toronto had the largest number of Italians in the world outside of Rome, it’s not true anymore, because that’s more distributed. But if you understand cultural diversity and where people came from, so a lot of immigrants I’m talking about now in the 50s, got leaf tickets, and so leaf nation had a very strong representation in the Italian community. But guess what, in order to buy leaf tickets, you have to pay, like the right to buy. And then the seasons, tickets are expensive. So sometimes those rights to buy memberships get left in people’s wills, to their children, and sometimes their children can’t afford to even pay for them, even though they were bought primarily by working class immigrants. So blocking the kind of turnover and who the client base is. This is, of course, leaving aside the corporate seats, which we’ve talked about, but understanding whether you’re a sports team or you’re a retailer, or you’re a not for profit, understanding that there’s a lot of differences and that one size doesn’t fit all. And when I was working in marketing in the 90s, we were all striving to how can we get a different message out. And I remember when Time Magazine, put out their first edition with different covers, was a very expensive thing that an organization similar to ours, really helped understand what motivates people to buy magazines, and they put different stories on the cover to see if they could properly target different messages to different people. Now, if you fast forward, that was expensive, very expensive to print. But now, because in the digital world, we can actually do different message different creative, different hot buttons, different wording in different messages, all this data enables us to really not only understand the diversity, and all aspects of diversity in our client base and in our potential client base, but it allows us to identify the untapped potential. And it allows us to get the right messaging with the right medium, to the right people, and even at the right time. So we’ve come a long way. And I sometimes think that people could be using their data, you know, more than they are now. So, we have this aspiration, to do what I just said, really leverage the data. And yet, we still go into many large organizations and one division has their customer data in one format, and another one has it in another, the C-suite are talking about AI and all of the great tools. But if organizations don’t really have a data strategy, which says from the beginning, I’m going to use my data to make a difference. And let’s, I think one of the dangers I see we didn’t ask me this button is organizations that are allowing the owners of the data, and the analytics and the insights people to set the data strategy. Actually, I strongly believe that a data set strategy has to come from the C suite from the CFO and the CEO, not just the CMO, figure out how are we using our data to the best advantage? Do we have silos? How can we break down silos to make sure that we’re really understanding our existing customers? If we do a lot of good work on our existing customers, to our BI teams, how do we make sure that we’re not neglecting the opportunity to get new customers like there aren’t that many organizations that have enough customers, so I see organizations that put all their emphasis on their internal data mining. So what our data can do is it can link from you know who your best customers are, that you already have to lose in the whole market. I like to call our data to denominator because you can use our data to measure your market share, find your whitespace understand how to market to your whitespace and at the end of the day, grow your business. So it just couldn’t be a more exciting time to be in the data business.
Jeremy Weisz 44:58
First of all, Jan, I want to be the first one to thank you. Thanks for sharing your journey, your stories, your lessons, and I just want to point everyone to check out and you can learn more. You can see if you’re watching the listening the audio, there’s a video piece we’ve shown their website throughout, but you can check out environicsanalytics.com It’s environicsanalytics.com to learn more. And Jan, I want to be the first one to thank you.
Jan Kestle 45:28
Thank you very much. I really enjoyed it. And I’m going to follow up and talk about how we can use your skills for our podcasts.
Jeremy Weisz 45:35
Awesome. Thanks, everyone. Thanks, Jan.
