Consumer Insights

Nokia On Wisely Analyzing The Cognition Of The Crowd

Crowds draw crowds, and for retailers, drawing a crowd means drawing dollars. In an interview with PYMNTS’ Karen Webster, Shelley Schlueter, who heads Nokia’s analytics marketing ops, delves into how Nokia’s Cognitive Analytics for Crowd Insight can offer companies real-time knowledge about who wants what and when — and maybe even why.
 

Brick-and-mortar retail and foot traffic do not always go hand in glove — as declining foot traffic over a period of years now proves.

Human behavior is a mystery at its core, and perhaps even more so in retail, where the best laid marketing campaigns of firms go awry and astray, with goods piling up on shelves. Technology, via big data, and even nuanced information about who is doing what, can help solve some of that mystery.

But, not all.

To that end, Nokia will in the third quarter of 2017 will roll out its new platform, Cognitive Analytics for Crowd Insight. Using machine learning and the big data generated from all of the world’s mobile operators, the software is designed to track subscribers and their movements, to give retailers, in this case, information on where the crowds are and where they are headed — and even what they do once they get there.

In an interview with PYMNTS’ Karen Webster, Shelley Schlueter, head of Analytics Marketing at Nokia, stated that the software app, still in a trial phase, has the power to sharpen competitive analysis to help retailers tailor campaigns designed to bring more feet into their stores.

Schlueter stated that, as is specific to retail, “what we do with our [Crowd] Insight is give retailers the information and the context that they couldn’t get before by accessing a continuous flow of data from network operators.” Upon receiving that continuous data flow, she continued, Nokia anonymizes it. The data is combined with other points of information, such as GPS data, demographics — even social networking information — to get a better picture of whose feet are going where and why.

“When we pull all of that together, it gives a continuous movement of those different segments.” As a result, a retailer is able to see where 18 to 25-year-old males — a group that the retailer may be looking to attract as a consumer base — are going.

The genesis for Nokia Analytics — and the drive to make analytics a service — stems from Bell Labs, said the executive, which wanted to apply analytics beyond what she termed “traditional models.” This has been data that has always been collected by operators, though Schlueter said that “what we are supplying them is software that can go on what they’ve already got … it is [comprised of] extra algorithms” that can allow users to see finite locations and get better segmentation from crowds — and across all networks.

More traditional analytics offerings are limited, in a relative sense, to being application-based or geolocation-based or do not take a big enough sample size to be truly representative. They often also take a “snapshot in time” rather than offering a continual real-time data flow, maintained Schlueter.

The retailers’ (or other users’) understanding of consumer behavior that develops from Nokia Analytics’ data, said Schlueter, can be as granular as understanding how long someone spends in a store and how many times they might be visiting a certain location, including the patterns of how consumers move from one store to another as part of their shopping journey.

This allows a firm to compare locations within a portfolio of stores, discovering which sites are grabbing more foot traffic and where customers might be lingering. Such insight can help improve results at a situational level and, of course, positively impact the company.

Schlueter noted a real-life trial case that had occurred with one local shopping mall, where the establishment was looking to garner more business from the people in the surrounding neighborhoods, and where the ambition was to “be the go-to place for all our locals.”

At the time, traffic was evenly distributed between people far away and people close by (who were, in fact, traveling to malls that were far away). Using Nokia Analytics, the company looked at traffic patterns of local neighborhoods and adopted targeted advertising — including one that invited shoppers to bring friends in return for a discount voucher to be used in the mall. As a result, traffic increased by 40 percent in just a couple of weeks. One of the insights that they uncovered was that mall advertising was primarily done inside of the mall — not where they lived, a mix which has now been changed.

Transportation industries also can benefit from data analytics on this scale, said Schlueter, who said that city planners can find where they might need to locate new bus stops according to consumers’ and commuters’ needs. Actionable insight may be found in terms of maintaining a bridge or “what roads do I need to target when I am doing road work, because I have more traffic coming through and those roads degrade faster?”

Thus far, she told Webster, the Nokia Analytics offering is geared chiefly toward operators who then can sell the interface to third parties — rather than being available to, for example, a payments network that might want the same data. A Nokia “monetization office” works with operators to help determine which use cases of the software application might be most appropriate.

What to do with all the data?

Once captured and examined amid the constant flow, said Schlueter, with awareness of who is doing what, where and when, “it’s up to the retailer to determine the why.”

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