Jan Kestle heads up Canada’s largest marketing analytics company and is a longtime leader in the field of geodemographic segmentation.
As a nation, we often describe ourselves in terms of what makes us different. English versus French. East versus West. Affluent versus poor. Urban versus rural. Elites versus working class. Progressives versus conservatives. But those differences are never quite as stark or binary as they appear. We are in fact more of a cultural mosaic – a community of communities – defined as much by our lifestyles and values as our demographics.
A lifestyle is how we choose to live. It is the totality of our habits, interests, attitudes. It is how we think – what we believe – how we spend our time – what we cherish most. And those lifestyle characteristics are remarkably correlated with where we live, for the simple reason that we like to live where we feel most at home. Where we live, in short, says something about who we are. Our next door neighbours may look different, may be slightly older or younger, but chances are they probably share many of the same values and beliefs. They probably watch the same shows – buy the same products – vote for the same political party.
That simple calculus is known as geodemographic segmentation. It is based on the premise that “birds of a feather flock together”. Marketers have been using lifestyle systems for more than half a century to benchmark their customers against the population, find look-a-like prospects in the market, select media channels, craft tailored brand messages, pick the best retail site location and much more.
The concept of geodemographic segmentation was initially developed by a social scientist named Jerome Robbins in the early 1970s. He took the first computer tapes of the U.S. Census in 1970, classified the demographic variables into five domains (social class and affluence, family life cycle, mobility, ethnicity, housing style and degree of urbanization), and found the key factors that accounted for most of the variance between neighbourhoods at the ZIP code level. He then grouped the zip codes into distinct, homogeneous clusters. It was a eureka moment. By knowing the ZIP code someone lived in, you could reliably predict their lifestyle (and by extension, their media and product preferences).
That revelation led Robbins to start up the company Claritas in 1974 for purposes of commercializing his cluster segmentation model which identified 40 distinct lifestyle segments. Later the company launched its PRIZM system (an acronym for Potential Rating by Zip Markets) which became an instant hit with marketers who had begun to recognize that America had become a highly fragmented society. As niche marketing grew in importance, the demand for geodemographic tools soared.
In Canada the counterpart to Claritas was a company called Compusearch, also founded in the 1970s, whose geosegmentation model became very popular with marketers. In 1993 the company lured Jan Kestle away from her senior role as head of the Ontario government’s Statistical Centre and soon after appointed her President. After Compusearch was sold, Jan left the industry, only to return in 2003 to form her own geodemographic company, Environics Analytics, in partnership with the Environics group of companies. Today the company is owned by Bell Canada and is the leading supplier of geodemographic products and tools in Canada. Jan has become the doyen of marketing analytics in Canada, presiding over a dynamic team of 200 data scientists, software developers and marketing specialists.
I started by asking Jan what first drew her into the world of geodemography.
Jan Kestle (JK): So, I was a mathematician by training, applied math, actually, so kind of on the math and physics side. But when I graduated, the first job I got was with the Ontario Statistical Center. And it was a great opportunity in retrospect to really have a great foundation in data and how data are collected. And my job there, statistics is a federal responsibility so in the province it was quite limited, but my first job there was to actually take survey data and work on edit and imputation. Now, of course, these are paper surveys and, you know, a crayon that you edit with, so we’re kind of talking the Stone Ages. But what I learned through that was really how, you know, there’s kind of a flow and a pattern, and when you diagnose data, and you can see missing values or you can see outliers, you have to look for them. And that was kind of job one.
Then my second job was working on the census, and at that point in time Ontario did a lot of value added on top of census data. But of course, the census data came in printouts, very little technology transfer, so I had to spend a lot of time understanding the concepts of the census, which was a great foundation for what came later. And then finally my last job there was actually leading about a 50-person Ontario statistical team that was called The Focal Point. And that job, I had to do two things. I had to understand from 27 Ontario government ministries what kind of data they needed for a wide variety of programs. So, climate equity, police services, daycare, healthcare, economic development, economic accounts. So, I did that in sort of leading the consultation within the Ontario policy areas of what kind of information they needed, and then I had to go to Ottawa and sit at the table in the federal-provincial negotiation for what the National Statistical System was gonna build.
So, I always say that when the Compusearch team came along and asked me to join because they were expanding, they had started a business that was primarily focused on retail and helping retailers understand who lived in their trade areas with demographics. And they were extending from, they’d gone from retail into automotive, and they wanted quite smartly to look at, you know, consumer package goods, financial services, government. So, they asked me to come and sell their data back to the government sector.
Stephen Shaw (SS): And who was “they” at the time, Jan?