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Armed with a tablet device or laptop, a Piinpoint client such as Mr. Sohi can view a prospective site, pull up a map on the screen and isolate variables with a few clicks: traffic in the neighbourhood, construction, and available real estate all help to identify a target market and predict sales. (Piinpoint)

Armed with a tablet device or laptop, a Piinpoint client such as Mr. Sohi can view a prospective site, pull up a map on the screen and isolate variables with a few clicks: traffic in the neighbourhood, construction, and available real estate all help to identify a target market and predict sales.

(Piinpoint)

Where should you set up shop? Data firms help business owners find the best spots Add to ...

When Parry Sohi was starting to scout new locations for his tutoring company Scholars Education Centre three years ago, he had trouble finding the data he needed to make his decision.

“Here I am, opening up a location in 2012, with 2008 data. It’s almost irrelevant,” Mr. Sohi, director of franchise growth for Barrie, Ont.-based Scholars, said of the Statistics Canada census information available at the time.

Mr. Sohi needed a good grasp of local demographics: the number of children in a neighbourhood; the number of schools nearby; household disposable income and area landmarks, such as community centres. When Kitchener, Ont.-based location analytics startup Piinpoint contacted him about participating in a pilot project, he got the data he had been looking for, rolled into a single platform, allowing him to find optimal locations in Southern Ontario beyond Scholars’ flagship centre in Thunder Bay, Ont.

By manipulating statistics on an interactive map, Mr. Sohi – who is about to open the company’s 12th pre-Kindergarten-to-Grade 12 tutoring centre in Mississauga – has significantly reduced on-the-ground investigative work and travel expenses, because he has the tools he needs on his computer screen.

“[Data] is playing a primary role, because you can quickly identify the best places to locate,” he says. “It definitely puts us in a very good position to be more confident.”

It’s a strategy that’s more commonly used by larger retailers with a broad geographic reach. Often, they’re using sophisticated programs to scout new store possibilities, analyzing data models that examine everything from how much money residents make to where competitors are on any given street. Small businesses like Scholars are only beginning to adopt the practice as they aim to expand.

When they do, they’ll be contributing to a global mobile, retail and location analytics industry worth $7.3-billion (U.S.) last year, according to research and consulting firm Frost & Sullivan.

Armed with a tablet device or laptop, a Piinpoint client such as Mr. Sohi can view a prospective site, pull up a map on the screen and isolate variables with a few clicks: traffic in the neighbourhood, construction, and available real estate all help to identify a target market and predict sales.

Piinpoint says it compiles data from a variety of sources, including Statistics Canada, Google and Environics Analytics. It also retrieves its data from municipalities and from Piinpoint clients themselves.

Piinpoint co-founder Jim Robeson says small to mid-market firms are the biggest source of client growth, but their needs are different from the more ubiquitous chains like Tim Hortons, Piinpoint’s largest client. “With a small business opening up your second location, the amount of data you’d have at your disposal internally is probably somewhat limited.”

Indeed, many large chains have proprietary systems in place to gather and crunch numbers on their own, says David Bell, a Vancouver-based senior consultant in the retail consulting division of commercial real estate firm Colliers International. “Wal-Mart will track and do a very complex analysis because they have an endless supply of data flowing through their constant sales.”

It’s perhaps for that reason that emotion becomes a bigger quotient in decision-making for entrepreneurs. Gaby Kassas, a resident of the Centre-Sud neighbourhood of Montreal for 14 years, wanted to play a role in helping the area shed its undesirable reputation.

She had her heart set on a commercial space for a café, but the landlord was cool to her as a first-time proprietor. With a waiting list of prospective tenants, the landlord enlisted the services of Potloc, a Montreal startup that crowdsources the sentiment of residents toward the opening of a store.

The Potloc campaign collected 1,000 votes in favour of Ms. Kassas’s Café Sfouf, enough of an affirmation to convince the landlord to lease her the locale. The café, which just passed its one-year anniversary, was cash-flow positive just six months into its operation. The site where it stands, incidentally, had been empty for an eight-year stretch until the landlord bought it two years ago and restored it.

Potloc’s approach is distinct from the numbers-driven location data relied upon by many large retailers. Instead, it tries to gauge demand by going right to consumers. The questions are simple: Where do they live, and what kind of business do they want in their neighbourhood?

The startup’s approach also attempts to sustain initial, perhaps fleeting, excitement by using social media and promotions to build a community. In the case of another Montreal café that recently opened, Potloc, which has now expanded across Canada, sent an e-mail to 600 backers and website users who lived in close proximity with a voucher to claim a free espresso.

When James Boudreau, a commercial real estate broker, scouts locations for clients in Waterloo, Ont., the numbers go hand-in-hand with instinct. He does much of his data analysis manually, when he’s not driving clients around the city to see locations first-hand. “There’s a lot of gut involved in it. It’s a lot of understanding your product and market.”

Opinion varies as to how readily available is the right information – Mr. Boudreau said he’d use it more if it was easier to find – but Colliers’ Mr. Bell said small business will gradually demand more of it, as well as smarter software to crunch it. “Just tracking data is one step, but understanding what that data means is a whole other level.”

In an age when corporations analyze every click that Web consumers make, companies also scrutinize the habits of shoppers away from their computers, in the name of finding not just the best physical location, but also an optimal design and operational strategy for the locale.

Firms such as San Jose, Calif.-based RetailNext install sensors on store shelves to collect point-of-sale-data from unsuspecting shoppers, providing clients with powerful information on customer behaviour.

James Brayshaw, a U.K.-based vice-president of location intelligence at data management company Pitney Bowes, which entered the location analytics game when it acquired New York startup MapInfo in 2007, said the applications of such technology go even further than retail business, from disaster response to agriculture.

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