Editor’s note: This is one of 17 case studies featured in INMA’s strategic report “Making Big Data Smarter For Media Companies,” released in December.

Canada’s Torstar is in the early stages of developing its data strategy. But it recognises that understanding and using data to drive revenue is increasingly a necessity for media companies, says Pam Laycock, senior vice president for corporate strategy and development at Torstar. 

“There is a culture of data that is growing; there are skill sets, I think, that need to be enhanced; and I think there’s a lot of supporting infrastructure that we’ll need to build upon,” Laycock says. “And then the final piece is actually having a bunch of this data and getting at it.”

Because data about its users currently sits in many different pockets throughout the company, Torstar does not yet have a holistic view of its consumers. Uniting that data and building a richer understanding is a short-term priority, but also a major challenge in practical terms.

“If you take it back to first principles, the more we know about our consumers, the more we understand their behaviours, their needs, their wants, their desires, the better we can serve them,” Laycock says. “And the better we can serve them, the better we can have them continue to interact with us. Therefore the stronger story we have for marketers is to make sure that we’re an amazing bridge between the marketer and those desired customers.” 

At the corporate level, Laycock is working to identify the “really amazing, data-centric people” already among the media company’s staff and to bring them together “because we’ve got some folks here who have done some really amazing things with data based on fairly grassroots tools at this stage in the game.” 

Researchers at the Toronto Star, for instance, have used common tools to overlay third-party data from vendors such as Environics Analytics against existing customer profiles. 

In the digital commerce division at Metroland, staff members have built a database of user click-paths, shopping cart data, e-mail open rates, and click-through rates. They have then used that information to create new predictive models they have used to refine e-mail marketing strategies and personalise the messages that go out to 3 million subscribers. 

“The skill sets that they have, and then the skill sets that the Star research guys have, are complementary skill sets, and they’re using tools that the others should know about,” Laycock says.

By identifying existing skills and available tools, and by better understanding existing data, Torstar can know its customers better, target them better, and help advertisers target them better.

“I think the interesting lesson is, don’t assume that the outside people have it all figured out right away,” Laycock says. “It will take a few iterations in order to refine your approach and your methodology.”

After four months of refining the predictive model, Metroland was seeing the sales lift it anticipated, but it was still at too early a stage to assess the return on its investment. 

Another Torstar company, eyeReturn Marketing, has created its own data environment, capturing ad traffic and click data for its advertising clients across Canada. It has also experimented with overlaying third-party data from Google and other vendors to help target ads. eyeReturn’s technology leader has helped Metroland’s digital commerce division build its own data environment.

By using existing assets and moving incrementally, Laycock hopes to avoid getting caught up in all the excitement over Big Data and making one of the easy mistakes: “You can create all these big repositories of all this data and then not know what to do with it.”

She is also concerned about capturing data in a smart way, gathering the information that will really help to market to users and not just gathering information because it’s out there.