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Why Smart Data Strategies Will Drive The Evolution Of Retail

Forbes Technology Council
POST WRITTEN BY
Kathy Leake

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“More than 2,200 stores are closing in 2020 as the retail apocalypse drags on.”

Retail apocalypse? It’s a term that has come to be accepted as fact in the retail industry, as formerly high-flying chains have been forced to shutter stores across the country or go out of business entirely. With the rise of online, omnichannel (and perhaps even omnipresent, in the case of Amazon) retailers, brick-and-mortar locations have their backs against the wall.

But predictions of the apocalypse, retail or otherwise, are known for being overblown, and even this recent headline should be taken with a huge grain of salt. Retail is not dying, and I bet those who are willing to argue otherwise will be proven wrong with the long-term success of innovative, adaptive retailers.

Retailers continue to struggle with the challenges associated with the major shift to online retail; however, the associated challenges also present opportunities to those who can understand and adapt to the new ecosystem. This is the first in a three-part series on the challenges facing retail performance and what companies can do to ensure long-term survival and best take advantage of the industry's new landscape.

Data: Overhyped, Underused And Misinterpreted

To be fair, no one can claim that retail companies have ignored the need for data. However, in my experience, common data strategies fall into three traps:

1. Relying only on past performance to drive future assortment decisions.

2. Focusing too much on the details in the present and losing sight of the macro patterns.

3. Failing to look at exterior consumer demand signals to predict what consumers will care about one to two years from now.

For most companies, their first choice when leveraging data is to analyze their own past performance. This can provide valuable information; however, without the context provided by current market and competitive information, historical data is merely a single data point. Added market context enables comparisons and the ability to make informed decisions based on internal projections. Dependence on recent past performance is compounded when companies place a microfocus on their company’s, their department’s or even their own individual immediate well-being.

Retailers need as much data as possible — internal and external — in order to frame the current data and predict the future. Retailers need to know both what customers bought previously in their own stores and also what the external consumer demand signs like search and social media portend. The more information you can collect, the clearer the picture provided by your data.

Breaking The Cycle

Comprehensive data analysis is an essential tool to avoid these common forecasting pitfalls and faulty applications. Using consumer demand signals across social media, search patterns and market performance, a company can gather plenty of external data and easily make sense of whether a trend is growing, declining or stabilizing.

Definitive predictions like this can lead retailers to more profitable strategies: The right purchasing decision at the right time ensures enough of a popular product to sell through to the end of a season. This approach is simple to deploy once you have the right resources at your disposal.

Some noteworthy and forward-thinking companies have already caught on to the need for more robust predictive analytics. In the past two years, Nike has acquired three companies focused on data science: Zodiac, Invertex and Celect. These investments have helped Nike to more effectively plan its retail inventory and better anticipate the needs of its customers. It has moved beyond traditional data techniques and is leveraging a more detailed picture of the marketplace.

Similarly, U.K. retail giant Marks & Spencer relies on First Insight to drive inventory decisions; the predictive analytics platform uses online social engagement tools to project which products will result in the most success.

And, of course, the behemoth that all these retailers are up against, Amazon, uses predictive analytics better than any other company to anticipate, recommend and personalize the shopping experience. And to what end? Well, that’s why everyone else is left chasing and confronting this prophecy of the retail apocalypse.

The prescription for averting these dire predictions and overcoming bad data strategy is simple: widen the field of view. Retailers need to be both backward-looking and forward-looking, examining both internal and external data. By combining all these data points to garner a holistic understanding, retailers will be able to effectively predict what their customers want and when they want it.

The retailers that use predictive data as part of their decision-making likely won’t have time to worry about the retail apocalypse — they’ll be too busy serving their customers. Stay tuned for the second article in our series that reveals what else retailers can do to stall the impending marketplace danger.

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