Imagine having a data-driven crystal ball to help make the kind of strategic business decisions that send organization-wide growth soaring.
Sound like the stuff of C-suite fantasy? Now it’s within reach of businesses large or small that invest in both the staff and technology to leverage the treasure trove of data they increasingly collect.
As competitive pressures mount across industries, thanks to new innovation and an influx of foreign competitors, Canadian executives are increasingly tapping the power of predictive analytics to forecast outcomes and client behaviour, then driving improvements in everything from staff retention and customer service, to manufacturing processes and delivery times.
The overarching goal: using data to build sustainable, long-term competitive advantages.
Gaining an edge was the goal when executives at the Canadian Automobile Association (CAA) – which provides services such as roadside assistance, insurance, travel packages and loyalty programs to members across the country – decided to tap predictive analytics about a decade ago as new players began to crowd its core market.
“There’s more competition in certain lines of business, like roadside assistance, so we realized we needed more touch points with our customers,” explains Jeff Walker, Ottawa-based CAA’s chief strategy officer.
“We had strong offerings in insurance and travel, but weren’t getting the mass [roadside assistance] penetration we hoped for. We were also conscious of not wanting to bombard our customers with too much stuff, so predictive analytics allowed us to target our [marketing].”
Specifically, CAA dug deep into its database of nearly six million members to predict buying habits and better understand the needs of its customers.
Analyzing household demographics and member behaviour, CAA analysts concluded that certain products such as roadside assistance might appeal to a family with two children in suburban Toronto, but not a single twentysomething living in downtown Vancouver, who might instead be interested in last-minute travel deals. The organization, an association of regional clubs, used those insights to personalize its monthly e-mail blasts and direct-mail marketing and keep members engaged by providing them with relevant, customized information and offers.
The result: “Clubs that started using predictive analytics were able to boost their renewal rates from the mid-80-per-cent range, to better than 90 per cent within about three years,” according to Mr. Walker.
While complex, at its core, predictive analytics is simply about identifying key business challenges and the metrics that matter most to an organization’s success, then collecting and analyzing historical data to achieve specific future goals such as optimizing pricing or maximizing profitability.
Those metrics can be anything from Facebook “likes” to customer purchase data to help retailers market to their customers, flight arrival and weather statistics to help airlines improve on-time departures, or data to help manufacturers minimize assembly-line downtime.
The tricky part, experts say, is defining exactly which metrics to monitor and building the team to effectively distill and analyze data – not to mention sifting through the reams of unstructured “big data” floating in the digital and social media ether that can help deliver key insights such as brand perception – before taking action based on their findings.
In CAA’s case, that meant building four-to-five-person teams comprised of information technology, statistics and marketing specialists at its regional branches to help target marketing efforts.
While Mr. Walker won’t disclose his organization’s predictive analytics investments, he says that generally speaking, a company can expect to invest “in the millions” of dollars upfront to build or purchase the software systems and staff data collection and analysis efforts, but notes that costs drop dramatically once systems are up and running.
“There’s no doubt competition is fierce, but I think it’s because the technology is there now and allows us to get a competitive advantage we didn’t have in the past,” explains Wayne Ingram, the Toronto-based managing director of technology for global management and technology consulting firm Accenture.
As Mr. Ingram adds, computer processing power has increased exponentially in the past decade, while technology prices have declined, making predictive analytics a viable option for more organizations – even some small to medium-sized firms.
The widespread availability of data is another major factor, according to Richard Boire, a partner at Pickering, Ont.-based predictive analytics research firm Boire Filler Group.
“Retailers years ago didn’t know their customer, but now they can collect data via their website,” Mr. Boire says. “The digital realm has allowed access to data that wasn’t there before.”
While noting that mining data to predict outcomes is easier for large companies with massive databases such as banks and telecommunications providers, he points out that companies of all shapes and sizes are jumping on the predictive analytics bandwagon thanks to the huge potential bottom-line benefits.
Just how lucrative are those opportunities? Mr. Boire cites one of his firm’s engagements with a major Canadian financial institution, which involved combing the bank’s databases to guess which credit card customers would be most likely to upgrade from a basic to a gold credit card.
He recalls that in just one campaign, the bank produced a net profit boost of $130,000 simply by using existing data to fine-tune marketing efforts to its cardholders.
It’s no surprise, then, that Mr. Walker is unequivocal when explaining how predictive analytics have benefited CAA: “It’s allowed us to stay ahead of our competitors,” he says. “It will continue to be extremely valuable because things aren’t getting less competitive in areas we do business and that’s one way we can hopefully stay relevant to our member base.”
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