The Shift in Retail Data: Who Can Benefit?

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Jeff MorrisIn this special guest feature, Jeff Morris, Vice President of Data Monetization Strategy & Success at GoodData, explores a fundamental shift of the value of data within retail, and outlines solutions for various members of the retail community to utilize data for both internal and external use. Jeff is a veteran in the BI and marketing analytics space with over 11 years of technical product marketing and product management experience at Actuate, one of the leading open source BI vendors. He holds a degree from Syracuse University.

When we “data types” look back on the history of our practice, we’ll discover that we owe a huge debt of gratitude to the retail sector.

Retail brands have been on the leading edge of analytics for decades. Think about the last time you visited a brick-and-mortar store. The mix of products on the shelves? The result of analytics. The layout of the displays? Analytics. The music wafting from the speakers? You guessed it: analytics. For years, when it came to using data to improve the customer experience, no one did it better than retail brands.

And then our retailer friends hit a snag: the Internet. Instead of making trips to their local malls or big-box stores, customers were staying home and letting their fingers do the shopping. But smart retailers realized that their customers weren’t going away; they just weren’t going out. And this shift in shopping habits meant an opportunity to grow revenues by engaging consumers on multiple levels — guided, of course, by analytics.

Building the Blended Experience

While the rise of the web drove many retailers into extinction, those who stuck it out have sustained themselves with a blend of digital and in-store data. This blended approach allows them to get to know their customers better and pivot their businesses accordingly.

While online sales are important for retail brands, the in-store visit is still the holy grail. Impulse buys are more likely when the customer is browsing, and the opportunity to interact with store personnel fosters a greater sense of connection with the brand. Even e-commerce giant Amazon has come around to the power of the in-person experience, with plans to open the first of several physical stores in the coming months.

At the same time, the e-commerce side of retail offers a wealth of information around the habits and activities of shoppers: where they come from (email, social media, etc.), what other sites they visit, whether they are “searchers” or “browsers,” and much more. These insights can help retailers not only optimize their websites, but also fine-tune their in-store experiences.

To grow their revenues, retailers need to create a seamless brand encounter that keeps the customer engaged online while also enticing them back into the store. How can they accomplish this? The answer lies in their data.

Making the Local Connection

One way to foster that blended customer engagement is to embrace local connections and use geo-targeted analytics to the retailer’s advantage. The more successful retailers operate not only under the generic banners of their parent companies, but also as members of their local communities who serve the specific needs of their neighbors.

On the consumer level, collecting data around online shopping habits is nothing new. What retailers are discovering are ways to map that information back to customer demographics for local retail stores. They can combine data around weather, local events like parades or chamber of commerce activities, and customer demographics with point-of-sale and transaction data to get a clear picture of what their customers want and how they go about getting it, and to respond strategically. By using this knowledge to inform decisions such as offering home delivery, matching prices, or eliminating sales tax, the brand can build a relationship that keeps the customer coming back into the store again and again.

Localization in Action

For example, retailers serving certain areas of Florida may track weather data to discover that it rains nearly every day between 3 and 5 pm during the summer. They can use this information to plan tactics ranging from “teatime specials” at the in-store coffee shop to the placement of logo-printed umbrella displays near the checkout counters.

The drive to localize also highlights the need to get data into the hands of local management. As long as information is siloed with a few analysts at the brand’s corporate headquarters, it does no good for the store down the street. Retail brands need a system for empowering local managers with the timely data they need to make revenue-generating decisions — in a format that makes sense to them.

What’s Next?

Just as retailers are getting the hang of blending analytics for in-store and online, the rise of mobile devices and “the Internet of things” are now complicating the landscape even further, providing additional sources of data around the customer experience that must be understood and utilized.

How soon will it be that a consumer sees an alert on his smart watch saying that his local clothier has just stocked that jacket in the size that he was looking at online last week? And when he does visit the store, the retailer can offer such incentives as “we pay your sales tax” and throwing in a matching hat.

Fortunately, retailers’ decades of experience using analytics provides the perfect foundation for addressing these and other challenges — and exploring new sources of revenue for their business networks.


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  1. Certainly an insightful post on the growing importance of data in the optimization of retail performance. With the increasing amount of data being collected due to the IOT, it is important retailers not get lost in massive data sets, but rather find actionable insights from them. This will be a big challenge retailers will face the sources of data grows.