EP45 - Actionable Data Strategies, Privacy, AI & Enterprise Leadership with Jan Kestle

How can marketers harness data-driven insights while navigating privacy and emerging technologies? CMA CEO Alison Simpson welcomes Jan Kestle, founder and President of Environics Analytics, to discuss why data strategies must be enterprise-led, moving from "so what?" insights to "now what?" outcomes. Their conversation covers privacy-compliant collaboration platforms that reveal which advertising works, how privacy compliance enables effective marketing, and AI's role in enhancing data interpretation.

00;00;01;23 - 00;00;06;09
Presenter
Welcome to CMA Connect, Canada's marketing podcast, where industry experts discuss how marketers must manage the tectonic shifts that will change how brands and businesses are built for tomorrow, while also delivering on today's business needs. With your host, CMA CEO, Alison Simpson.

00;00;22;17 - 00;00;49;16
Alison
Welcome to CMA Connect, the podcast where we dive deep into the world of marketing with industry leaders and innovators. Today, I am absolutely thrilled to welcome a true pioneer in the realms of data, statistics and marketing, Jan Nestle. Jan is the founder and president of Environics Analytics. With over five decades of experience, Jan has been at the forefront of using data and analytics to solve complex business problems and help shape the marketing landscape in Canada and beyond.

00;00;49;18 - 00;01;17;08
Alison
Jan's journey is nothing short of inspirational. From her early days at the Ontario Statistical Centre to founding Environics Analytics in 2003, she is consistently pushed the boundaries of what's possible with data-driven insights. Her contributions to the industry are massive and include developing the Prism Segmentation system and the Envision Business Intelligent platform, tools that have revolutionized how marketers understand and can reach their audiences.

00;01;17;10 - 00;01;42;08
Alison
In recognition of her groundbreaking work. Jan was honoured with the Canadian Marketing Association's Lifetime Achievement Award in 2022, truly cementing her status as a trailblazer in our field. In today's episode, we'll explore Jan's really fascinating career path and discuss a range of topics that are reshaping the marketing landscape. From the evolving role of data and predictive analytics, to solving business problems to navigating data privacy complexities,

00;01;42;10 - 00;02;01;23
Alison
we plan to delve into the future of data-driven marketing. We're also going to touch on AI, enhanced customer journey mapping, ethical considerations and hyper-personalized marketing, and the importance of data sharing and driving business success. There is no shortage of great topics that Jan can speak to, and it is an absolute pleasure, Jan, to welcome you to CMA Connect today.

00;02;01;26 - 00;02;20;00
Jan
Well, thank you very much and thank you for having me. I love working with the CMA. I think the CMA in the past few years has done an incredible job of having conversations that are very important to marketers and to the whole business community in Canada. So it's my pleasure to be with you here today, Alison.

00;02;20;02 - 00;02;40;21
Alison
And well, thank you so much. Jan, I don't use the term pioneer lightly. You've been a pioneer in data statistics in the marketing professions. The fact that you've been a leading innovator in all three is also a sign that you didn't really take a traditional path to your career. You also succeeded as a woman at a time that was, very sadly, very rare.

00;02;40;24 - 00;02;52;23
Alison
And I really find your story inspiring, both from a professional perspective and also from a personal perspective. I know many of our listeners would as well. So I'd love to start by having you share a little bit about your journey.

00;02;52;25 - 00;03;22;06
Jan
Sure. Thank you. Well, sometimes when I think about my long career, which I guess part of what makes me a pioneer is it goes so far back, is I think about three aspects to my career. First of all, as you mentioned, I worked as a government statistician, and then I worked as a sales and product development person and eventually a leader in a company that was well-established and owned by various players in the information business.

00;03;22;08 - 00;03;41;01
Alison
And then finally, in the latter part of my career, actually started a business and built that. And for some people, it's kind of like, well, how did you decide to go from a government job to leading and working in the private sector and then running a pretty interesting business in the private sector? Why did you decide to start your own business?

00;03;41;01 - 00;04;03;13
Jan
And always gives me pause when I think about it that way, because to me, it's been a continuum of figuring out how data and mathematics can really help simulate realities and help people solve problems, whether it's for business or social services or, you know, how we make Canada run better? I've always been excited about being able to use information to do that.

00;04;03;18 - 00;04;32;24
Jan
The truth is, I, I have a degree in applied math, which is kind of like a combination of math and physics, and I graduated in a class of four women out of 140 classmates. And it was difficult, even being, you know, a good student and high in the rankings of my class. It was difficult to to get jobs for women in those scientific, and honestly, in any business in that day and age.

00;04;32;24 - 00;05;04;18
Alison
So I was very excited to take on my first job as a survey statistician and editor in the provincial government. And that was when we didn't even have computers, never mind AI. We put data on spreadsheets that was actual pieces of paper. We edited questionnaires with red pencils. We shared our work with our partner across the table to do QA, and then we added up rows and columns in order to create data that went into large statistical outputs.

00;05;04;18 - 00;05;31;18
JAn
I always say I got really good training on thinking about what the data are telling you and what's missing and what makes sense by being right down in the trenches. Eventually, in that particular role, I moved on to doing a lot of negotiating for the priorities for Ontario and the federal provincial context. But eventually I was leading that organization, and I, I enjoyed very much the work that I got to do in the policy side.

00;05;31;20 - 00;06;03;28
Jan
But my choices for a new challenge were to leave the statistics and the data part of of the work and move into more of a government policy job. But I had the opportunity to go and work for the first company in Canada that took data and demographics and created customized versions of that that businesses could use. Compusearch was the pioneering company in that field, and they'd been around for about 15 years and mainly working for retailers.

00;06;04;00 - 00;06;27;07
Jan
But they started to branch out and they recruited someone who really understood packaged goods, and they understood someone who worked in the financial sector. And they came and recruited me as someone who worked with governments and not-for-profits. And I joined, actually only took me like five days to make a decision to leave my pension and all that government work and take this opportunity.

00;06;27;07 - 00;07;02;23
Jan
And I went into sales and I'd never been in sales. I'd done a lot of business and relationships, but my first job there was selling the data that they had produced back to the government sector, and that was exciting. And led me to an opportunity to work on the product side, is how do we take data and turn it into information, because many organizations can't really invest in building data and doing the work from the ground up. You actually have to turn data into a product or a service that can be actionable, that can make a difference to a community or to a business.

00;07;02;23 - 00;07;26;00
Jan
So I went from being the statistician to being the sales person and understanding my customer's needs, and then going into product, and then eventually to president of that organization during its largest period of growth, which was the time period when people got computers on their desks, desktop computers, we went into desktop mapping. It was just the very beginning.

00;07;26;02 - 00;07;52;06
Jan
It wasn't even the CRM era. It was what we call database marketing. When we were starting to mine data and combine the kind of data that brands had about their customers, quite limited, with the data that we had about postal codes. So I always think, you know, all these parts of this journey really enabled me to think about how to make data help people make good decisions.

00;07;52;08 - 00;08;21;26
Jan
And eventually Compusearch was sold a few times and, and, you know, kind of went the route of what often happens with little businesses when they become successful. They they kind of became a part of selling software. And you know, a more bundled solution. And a few of us had a lot of feedback from our former customers to say, we really want people who focus on the data and the customized solutions or implementing the standard solutions in a customized way.

00;08;21;26 - 00;08;52;21
Jan
And so, you know, the story is pretty well known. I decided to start a new business basically replicating building the data, but also helping people understand how to use the data. And that's what we started Environics Analytics. Our funding came from the traditional market research company, Environics Research, that had built a niche for itself, not only measuring political polling and consumer research, but measuring people's values and psychographics.

00;08;52;21 - 00;09;25;26
Jan
And so they had this idea of bringing those two worlds together. And so Environics and ourselves are a little team of former geo demographers started Environics Analytics. Twenty years, 300 people, you know, a thousand customers and lots of databases and lots of data development. I still feel like we're continuing that legacy of help people take data and turn it into information that enables them to take action that can actually make their organizations more successful and ultimately make people's lives better.

00;09;25;26 - 00;09;49;15
Jan
I think we know data for good. If you use data for health care or education or, you know, not-for-profit, that's considered data for good. But I also think it's data for good if you can get the right messages to the right people at the right time, get the right products on the right shelves, you know, organize people's busy lives so that they can really make good decisions and the data are there for them.

00;09;49;15 - 00;10;18;22
Jan
So that's a bit of a long story, but that's kind of how I went from one data-driven organization to another and ended up building Environics Analytics. And then of course, the last phase of that story was when my partners and I sold the business to BCE, and we're now a Bell- backed company, but we still run an independent company where Bell is a customer and treated on a level playing field with other customers.

00;10;18;24 - 00;10;38;11
Jan
But the great thing about that part of the journey is that they saw the need for Canada to really have a vibrant data business, and they have invested and supported us to continue to offer data and analytics services to the Canadian business community and their customers.

00;10;38;13 - 00;11;01;21
Alison
Their story is so inspiring on so many different levels. There's so much curiosity that you've experienced, there's so much openness to try new things, so much innovation, and having a real vision for where you could take your skills next so I'm inspired on all of those levels. I also love the fact that for someone who started their career before computers existed, there can be a lot of biases around that

00;11;01;21 - 00;11;09;29
Alison
you have to be a digital native to really get technology and to be visionary about technology. And you are such a wonderful example of that as total BS.

00;11;10;05 - 00;11;16;07
Jan
Before we had desktop computers, I have to say we have computers! 

00;11;16;10 - 00;11;38;11
Alison
Good clarification. Well, and you continue to be very tech savvy and leverage technology and being very future focused. So when I think about some of the stereotypes that exist about you have to be a digital native to get it and to lead it, but you're a great example that that's simply not true. And then being one of four woman in a class of 140, to what degree did that experience,

00;11;38;11 - 00;11;43;26
Alison
What skills to that experience give you that you think have benefited you throughout your career?

00;11;43;29 - 00;12;07;00
Jan
Well, throughout my career I've always been sometimes the only or one of the very few women in the room. Even still now, because being at the executive level in businesses and being in businesses that tend to be more tech- oriented, we just have not, we've had progress, but we haven't had enough women get into those jobs and into those levels.

00;12;07;02 - 00;12;31;02
Jan
I think the thing that, you know, you have to have a voice, and I think the most important thing for young women to know is, first of all, now I think they are far more welcomed and appreciated. I think that men in business understand the role that diversity brings to the table. So we talk about diversity in terms of of women in leadership and women in, in certain roles.

00;12;31;02 - 00;12;59;01
Jan
But I think organizations that are committed to promoting women and, you know, fighting racism and fighting all different kinds of discrimination against special populations are really understanding that the talent pool that they're unlocking is tremendously valuable. And you're not leaving half the population or a third of the population, or 10% of the population on the table, by just hiring and promoting the same kind of people.

00;12;59;01 - 00;13;43;16
Jan
So I learned by doing and and, you know, when I think back of the people that I highlighted and promoted in those different organizations, I'm proud of the fact that we always believed in diversity and equity and inclusion. And sometimes it was harder than other times to implement. One of the things that we do a lot of work on now is in the educational community, where we go into universities and we go into colleges and even into high schools and into women's organizations and into STEM  organizations to help people understand how exciting and interesting these jobs are.

00;13;43;18 - 00;14;09;10
Jan
Because sometimes if, you know, you're a high school student and struggling with math, or you might be really interested in geography, you might have no idea what the options are in the data and analytics space for you. You might think it's only coding and IT, but we're hiring a lot of people out of engineering. And you know what, the engineering classes have switched and the engineering classes are sometimes you know the majority are women.

00;14;09;10 - 00;14;21;18
Jan
So through the years, times have changed. But we've also, as a team, stuck to that objective to make sure that you're using the whole talent pool and that you're using it effectively.

00;14;21;20 - 00;14;37;13
Alison
It's another great example of how have you've led and positively impacted the profession. So a huge thank you for that. Now, with your wealth of experience, I know our listeners would absolutely benefit from hearing your views on the pivotal role that data, statistics and software have in helping us solve business problems.

00;14;37;15 - 00;14;57;24
Jan
I remember about ten years ago we did a roadshow. We also like to get out across the country. We would do a breakfast and we'd go to Vancouver and then we'd go to Calgary, Edmonton, Winnipeg, you know, stop in Quebec City and Montreal and then on to Halifax and other places and actually do a roadshow on the things we were doing.

00;14;57;24 - 00;15;28;18
Jan
And I remember being really struck, I want to say about, 2016, when every journal with you Reading Inc. magazine or even Forbes or the Harvard Business Reviews, all the articles were about data-driven decision making. And I used to make a joke to say, well, what the hell else would you do, right? But I also know the extent to which we were selling people sophisticated models to find the best locations for retail sites.

00;15;28;20 - 00;15;54;25
Jan
Or we were looking at, you know, early media, mixed models and channel optimization, looking at ROI across, you know, online and offline advertising. We were doing all this great work, and yet so many people were still using gut feel and the way we used to do it, that inertia and changing to new ways of doing things, is really hard for people.

00;15;54;28 - 00;16;21;17
Jan
And so once more data became available, everyone was talking about using data. What I saw was a lot of organizations investing in data and investing in technology, but I was also very worried throughout the 2000s with a real backlash, which says, well, we're spending all this money on data, we're investing in technology, but is it really making a difference?

00;16;21;19 - 00;16;54;09
Jan
And I think we now are at this stage where there's a lot more recognition that it does make a difference. But I think that it's pivotal if an organization has a data and analytics strategy, and it can't be something that comes from the ground up, even from the marketers, as much as the marketers I 100% support marketers have a role, but the IT department, the actual data scientists, they can't really develop a data strategy.

00;16;54;09 - 00;17;15;17
Jan
It has to come from the business strategy. We have to know what are the problems we're trying to solve and the extent to which even C-suite, the CFO, the CEO, the COO will collaborate with the CTO and the CMO and we say, look, here's all the data we have. Here's what we need to do to harness it. Here's the data we don't have.

00;17;15;17 - 00;17;37;06
Jan
Here's how we can get it. Here are the problems we're trying to solve. But then the other key question is, to what extent can we take this great insight and actually put it into practice? Because the last thing that you want to do if you're in the data business, is produce interesting studies and have people say, so what? You want people to say, now what?

00;17;37;06 - 00;18;04;26
Jan
And the thing that's made a big difference is people's recognition that you design your data strategy and your analytics strategy around your business goals, understanding, how much can you actually go and implement, and how can you tell whether or not it's working? And I'm very excited about the extent to which we see that enterprise perspective, the silos in different parts of organizations being broken down.

00;18;04;26 - 00;18;17;20
Jan
We're not there yet, but I think there's a recognition that there's an enterprise play for data and analytics, and that organizations will do much better when they have a strategy that's led from the top.

00;18;17;23 - 00;18;37;08
Alison
That makes infinite sense. And I love the pivot from instead of it being so what to now what? That strong action orientation is critical for any business and critical for any marketer in order to build the business and build their career. So how do you see the relationship between data analytics and consumer trust evolving in the coming years?

00;18;37;11 - 00;18;58;22
Jan
Well, when we're talking about data and analytics, the biggest application that we work in with marketers is really understanding consumers and creating consumer insight. You know, as much as we produce all this great third party data that comes from external sources and is anonymized and aggregated and delivered at the postal code, and I love the data, it's extremely useful.

00;18;58;22 - 00;19;25;28
Jan
Biggest asset that brands have is the data that they have about their own customers. You know, we now start talking about first party data. And it's, you know, the first party data is the secret to customer engagement and eliminating churn and getting the right message and tailoring the product and the customer journey. And it absolutely is. The data that any organization has about its customers is extremely valuable.

00;19;26;00 - 00;19;50;25
Jan
But those customers give you that data with an expectation that you are going to use that to make your relationship with them better, to make their service more effective. And as we you know, often ask them, can we use your data to partner and to do other things in the data and analytics space that help us do that for you?

00;19;50;25 - 00;20;20;17
Jan
And so when it comes to privacy, my view is that, you know, the principle based laws that say that you do what a reasonable person would reasonably expect, that you have a consistent transparency and consent framework, that you tell people what you intend to do with their data, that you have the right process for them to say yes or to opt out if they choose to.

00;20;20;19 - 00;20;54;29
Jan
But the key thing is that we can be successful and data- driven in Canada, and also honour the commitment that brands make to their customers or their members or their citizens and residents. If you tell people what you're going to do with their data, that you honour that and that you can be data-driven, I don't think there needs to be a contradiction between being, future-proof on privacy and being loyal and safeguarding consumer trust.

00;20;55;01 - 00;21;30;11
Jan
And I think that the examples where we've seen where so-called people use data are bad actors are very few and far between. And I work with hundreds of organizations in Canada who invest a lot of money to ensure that they're doing the right thing, whether it was, you know, in the early implementations of the current legislation, whether we went through CASL, when we were looking at legislative reform, I see businesses working really hard to make sure that they're doing the right thing.

00;21;30;19 - 00;21;49;24
Jan
And I don't think that privacy and regulation stand in the way of being data-driven for Canadian businesses. It's different. In the U.S. I hear from people all the time, oh, well, we can do it this way. We can do it that way. Why can't you do it this way in Canada? Well, how could we ever, you know, I don't want your your summaries of postal code data.

00;21;49;24 - 00;22;15;13
Jan
I want to know actually who went to this store and like that. The reality is, in Canada, we have great data and we have great ways of using third party and first party data. And as a statistician, I assert that we can get an excellent statistical result from the kinds of data that we have that are privacy-compliant, and that we're doing our jobs to make sure that the data that consumers give us are safe.

00;22;15;16 - 00;22;34;19
Alison
And your point around Canadian consumers are increasingly expecting this level of knowledge and personalization and are comfortable sharing their data when they know that they can opt out at any point, when they trust the brand and the business that they're sharing it with, and when there is a value equation and benefits for them.

00;22;34;21 - 00;23;01;20
Jan
The research really supports it. As you know, I'm active in the privacy lobby, and we got our ISO certification on privacy by design, which we did because we knew that the legislation was in flux and we knew that we had to adhere. There's no, it's no use in Canada to say, oh, we're compliant with a law that was passed in 2000 that the whole environment expects a higher standard.

00;23;01;20 - 00;23;36;29
Jan
And this CMA has done a great job of being a voice for that and helping to develop frameworks. And so between the CMA and other important organizations, industry associations, we're regulating and we're having that dialog with the government about what needs to happen. So I'm very optimistic, that Canadians feel good about that. But the truth is that, you know, you know, at Thanksgiving dinner or cocktail party when you say you're in the data business, we have to admit there's still a generalized public perception that this might not be a good thing.

00;23;37;01 - 00;23;58;29
Jan
And so the one thing that I also say is we have to be a voice for data literacy. We have to be a voice for the importance of data. And not just, you know, any data. The data have to be safe from a privacy point of view, but they also have to be safe from a methodology point of view.

00;23;59;01 - 00;24;32;24
Jan
You have to do the right thing. There's like there's a million different ways to create a model and and predict things and simulate reality and, and within the constraints of budgets and timing and privacy and technology., we also have to make sure that we keep our statistical standards high and that the quality of the data people have to be assured of, that people might spend 100,000 or even half $1 million on a research study and a big data and analytics project.

00;24;32;27 - 00;24;52;04
Jan
But then they're going to turn around and they're going to spend millions on bricks and mortar or media campaigns. So when we talk about data and what we should do, we talk about what's legal and what's right, what's ethical, but we also have to talk about what's the highest statistical quality that we can produce as well.

00;24;52;07 - 00;25;06;20
Alison
Great points. So Environics Analytics has been a leader and very good at anticipating where the profession is going. So I'd love to hear what future trends do you foresee in data driven marketing and how is Environics Analytics preparing for them?

00;25;06;22 - 00;25;27;20
Jan
As I mentioned, the biggest trend we see is the desire of organizations to leverage their first party data, but not just to leverage it internally, but to combine it with that of a partner. So, for example, if I'm in the private equity space, I want to look at an acquisition and I want to look at maybe a merger.

00;25;27;20 - 00;25;52;15
Jan
So you might want to take two disparate databases with different sets of consents, and you might want to merge them together to look at the feasibility and the market potential. Or if you're in media, you might want to know my publisher or broadcaster understands who's seeing the actual impressions of an ad. Then I want to know, you know, the extent to which those people actually bought my stuff.

00;25;52;18 - 00;26;16;12
Jan
That might mean that you have to combine the exposure data with purchase data. And then, you know, and the final thing, there's lots of organizations in loyalty and marketing that are really looking to sell each other stuff. So we've been working for a long time at the need in Canada, given our environment, to enable organizations to have a safe collaboration platform.

00;26;16;13 - 00;27;00;10
Jan
And we've invested a lot in what we call clean room technology and identity resolution and data collaboration platforms that are technologically advanced, but that have a governance and an orchestration layer on top of them that are optimized for Canada. So there's global solutions, and we partnered with the leading global provider, LiveRamp of these technologies last year because they have Canadian brands and they have Canadian businesses that are part of global brands, but they wanted to be able to bring these technologies for data blending, aggregation, de-identification reaching, you know, customers through the adtech system with anonymous identifiers and so on.

00;27;00;11 - 00;27;39;14
Jan
They wanted to bring that to Canada, but, you know, they knew what they built for the U.S. would not work in Canada because we don't have those kinds of identity graphs in Canada. And they knew what they built for Europe, which was GDPR-compliant, was probably closer. But there were some nuances in the Canadian marketplace. We had previous they've been investing in building something that we called our EA vault, kind of a homegrown version of that, to help organizations safely blend data and bring our own data into that ecosystem and bring our own methodologies so that we could be confident about the statistical processes.

00;27;39;17 - 00;28;14;13
Jan
So we were very fortunate to enter into a partnership with them and bring their technology and stand it up. We have it all operational and we have a number of customers using it which are helping people blend data and go to market together, but it's also helping marketers measure outcomes, because we're not becoming that measurement bureau. We're helping two organizations bring and exposures and outcomes measurement into a safe place where we can connect them through permissioned identifiers, and we can anonymize the results.

00;28;14;15 - 00;28;38;28
Jan
So, you know the old adage, I know 50% of my advertising works, I just don't know which 50%. We can actually say, for these custom audiences, with these kinds of exposures, through this channel or this screen, this is the lift that you get in terms of actual sales, or walking into my store or combining POS data from another partner, we can actually say, this is what's working.

00;28;38;28 - 00;29;01;17
Jan
This is what you could optimize and do differently. And to me, this is like the future. It's a whole new business for us. And at the beginning it was like, well, we're in the third party data, now we're going to be in the data collaboration space. But now what we're seeing is people who are really interested in doing good analytics want to be able to measure whether or not they're targeting worked.

00;29;01;19 - 00;29;20;22
Jan
And so this is a breakthrough for us in what we call real outcomes, not just measuring reach and frequency, because that's an important part of measurement, but actually saying this part of my advertising worked, these kind of customers that you have, we don't have. If we partner together, here's what our potential is.

00;29;20;24 - 00;29;33;23
Alison
Now, AI is another both disruptor and enabler in marketing and in business overall. So how is Environics Analytics approaching the balance between AI-driven insights and human expertise when it comes to data interpretation?

00;29;33;26 - 00;30;00;24
Jan
So it's a great question. When AI, ChatGPT boomed, we started using a lot of AI tools for making ourselves more efficient. We've been doing machine learning, which is different from the more modern AI, but we've been doing machine learning for decades and developing cluster systems and taking survey data and projecting maybe a 50,000 sample survey to 750,000 postal codes, there's a lot of machine learning algorithms that help with that.

00;30;00;27 - 00;30;44;26
Jan
So but when the new like wave, which are really, you know, new tools and now the next generation of those tools became available, we knew that we had a great opportunity to leverage these tools to help scale. So we are definitely using AI to help write code as we redevelop our envision system. We are using it to do kind of replicable algorithmic processes and data development, but it's important that we continue to work AI tools and the sort of more traditional tools in parallel to understand if we're prompting right, if we're using the data properly. The outcomes from the AI are great if you ask the right questions.

00;30;44;26 - 00;31;10;10
Jan
And what we see is the opportunity to ask questions and then learn what more questions we should be asking gets great results. We are already now using AI to develop some of the actual data solutions, instead of the kind of storytelling and what we would call personification of segments. We went from office automation, code replication, interpretation and storytelling,

00;31;10;15 - 00;31;26;25
Jan
we're now actually exploring how we use it to develop the key databases and some new kinds of databases. But we're cautious enough that we have to see how those results compare to what we got when we did it the traditional way.

00;31;26;27 - 00;31;46;00
Alison
Now, Jan, I know from our earlier conversations that you're a big believer that data hoarding is out and data sharing is in. So how can data sharing in privacy- compliant ways help organizations and marketers in their measurement of results and better understanding which 50% of their advertising and marketing is actually working well.

00;31;46;03 - 00;32;10;13
Jan
You know, if you have a lot of data about your own customers, you can use that to build your business. But if you're part of an ecosystem in the real world and you want to understand, as I mentioned before, growth opportunities through, you know, acquiring or consolidation opportunities, or you want to look at the combination of how I targeted my advertising with who actually bought my stuff.

00;32;10;16 - 00;32;50;15
Jan
You definitely have to be willing to share your data. And I'm not talking about, you know, everybody taking their data and throwing it into some open marketplace. But I'm talking about having frameworks and processes that are more than privacy-compliant and that are affordable and accessible and also easy to use. So the two organizations who decide to share data and blend it and maybe create new data or create, you know, some kind of analytics of results, do it in a way that they can use the technology that we're building because it's expensive and it's complicated.

00;32;50;15 - 00;33;14;07
Jan
So when we embarked on the program that I mentioned before to build a data collaboration service for Canada with clean rooms and identity resolution, we went to the industry and we said, we'll sell you these services, but we also said we'll make these services available to Canadian partners, to ad agencies, to publishers and broadcasters. You can use our clean room as a back office.

00;33;14;07 - 00;33;37;01
Jan
We don't have to be in the forefront of all of that. The reason for that is because we know that, you know, we have productivity challenges in Canada. We want to Canadianize  tools and technology so they work in Canada, but we can't go it alone in the technology space. So when we talk about data sharing, it's to enable all boats to rise.

00;33;37;01 - 00;34;06;26
Jan
Marketers, publishers, platforms, ad agencies. We want to be sure that there is a strong Canadian foundation for data blending to take place in a way that's safe and effective, and that's, you know, the thing that I'm really committed to right now is in building for Canada a collaboration space that other people can use in order to make their businesses more competitive in this tough environment.

00;34;06;28 - 00;34;18;24
Alison
And I know you're in early days of building this, I think you're about a year in now, so without giving away any client confidences, can you share a good example of partnership that's driving results and delivering on the vision?

00;34;18;27 - 00;34;44;23
Jan
Well, we have manufacturers who are asking us to combine, say, connected TV data. So who who had their TV turned on when my ad was actually showing, based on the anonymized connected TV data. So can you combine that with also where the exposures took place in a programmatic campaign? So now we've got, you know, TV data combined with programmatic data.

00;34;44;25 - 00;35;08;20
Jan
And then can we get point of sale data from a retailer and actually show the reach across different channels and different screens and show how different targeted audiences responded? Like how many times do they need to see my ad in order to drive them into the store to buy? How many people go in the store and do they buy, not buy?

00;35;08;20 - 00;35;39;10
Jan
So really true authenticated attribution multiscreen reach and frequency. And then a measurement of did they buy? We actually are doing those projects right now. We're also helping customers who want to understand how to add partners into loyalty programs. If I have this customer base right now, who am I missing? What other kinds of partners in business offers would make my program more attractive?

00;35;39;13 - 00;36;16;16
Jan
And you see the whole loyalty space changing with convenience and gas and grocery and then all these, you know, consolidated. So we are helping these organizations understand how they can improve their reach and their membership by understanding what the synergistic partnerships can be for them. We have, I would say, five marquee customers, big relationships with customers and those kind of major consumer marketing industries who are working with our collaboration and identity solutions through the EA Vault and LiveRamp.

00;36;16;16 - 00;36;38;20
Jan
And then we also done over 30 smaller projects, because people can take all this technology and do it themselves or they can come and ask us to do it for them on a managed-service basis. And so we've done big things and small things in the past year. We've been very excited at the adoption. Of course, we'd like it to be faster.

00;36;38;22 - 00;36;59;09
Jan
I know that the LiveRamp people are saying in the U.S., despite some of the kind of challenges that they've had, you know, really great growth in the past year, I heard somebody say that people at TMU wrote an article a little while ago calling Canada the Hesitation Nation. So we're trying to say, jump in, start collaborating,

00;36;59;09 - 00;37;16;03
Jan
blend your data with someone else's, do this outcomes measurement, so that we can actually, you know, show you how it works. Doesn't have to be a big project. You can start small. And that's kind of what's the most exciting thing that's happened with us in the last year.

00;37;16;06 - 00;37;24;18
Alison
That's a great update on the Hesitation Nation. Overcoming that is also going to be a key part of us for improving our level of productivity as a country too. 

00;37;24;18 - 00;37;42;18
Jan
Well yeah, people say, well, what do you think about the tariffs? What, you know, what are your customers during what should they do. Well, they should understand what data they have. They should understand the problem they're trying to solve. They should think about sharing their data with partners. They should look at, you know, segmentation, targeting and measuring the outcomes.

00;37;42;18 - 00;38;11;09
Jan
In other words, we just need to we have the tools. And in our business, both Compusearch and Environics. We've always had an opportunity to help people in tough times as well as in good times. And so we think right now with what's, you know, many Canadian businesses are facing, that having really good data and analytics solutions is a big part of making our businesses more resilient and more competitive.

00;38;11;09 - 00;38;26;09
Jan
You know, we're going to look for other partnerships. We're going to look to other parts of the world. But right now, data and analytics can really help businesses get the right product message idea to the right people at the right time and see whether it's working or not.

00;38;26;11 - 00;38;44;07
Alison
Well said. Now, Jan, you're incredibly busy and you've been very generous with your time. So I just have one more question and it's a bit of a pivot question before you go. I would love you to close by sharing the top advice that you would give to our listeners who aspire to follow in your footsteps.

00;38;44;09 - 00;39;05;16
Jan
Well, if you want to follow exactly in my footsteps, you got to be really good at math. But no, leaving that aside, the real thing I think that makes a difference is passion. Figure out what you want to do and make a contribution. People who are very successful in their careers know that what they're doing, whether it's big or small, is making a difference.

00;39;05;18 - 00;39;29;15
Jan
So as an entrepreneur, you run into forks in the road, you have decisions to make. There's no bad decisions. You have a plan. You have a passion. You're driving towards something. You have funding and customer problems. And people say, no, no, no, we can't do that. You know what? You just don't give up. Never give up. Having a passion and finding a way to make it work.

00;39;29;17 - 00;39;56;06
Jan
Obviously you live in the real world, so you have to respond to to pressures. But we can't just change what we're doing. Well, we used to do it the old way. Well, no, we can't do it that way. We have to find a way to implement the things that we think need to be done and tear down the roadblocks and the inertia and the silos that prevent us from realizing what it is that we think will make a difference.

00;39;56;11 - 00;39;59;13
Jan
Because we have to make a difference. We have to show up.

00;39;59;15 - 00;40;14;17
Alison
Well, I think back to the beginning of the podcast where you shared that you were one of four women in a class of 140, you learned very early on that you knew what you wanted to accomplish, you were passionate about it, and you were always going to find a way. And that clearly has served you incredibly well throughout your career.

00;40;14;24 - 00;40;17;16
Alison
So Jan, thank you so much. I've thoroughly enjoyed our conversation.

00;40;17;20 - 00;40;22;20
Jan
Thank you. Thank you, Alison, for having me here today.

00;40;22;23 - 00;40;35;10
Presenter
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