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Gloria Jeans, Russia’s largest retailer, has a secret: It can predict the future.
As you may have guessed, predictive analytics can predict what will likely happen in the future such as how global news and consumer buying habits will impact your business. To do that, you need a predictive analytics system that takes data you collected in the past, applies machine learning to it and presents you with a forecast.
When I talked to Timothy Kasbe, former COO of Gloria Jeans, he told me the company uses its own data, social media data and other publicly available data including weather data to improve its business. I asked him what effect that had and heard some interesting things that the company can teach the rest of us.
#1: You Can Forecast Revenue More Accurately
All businesses have some kind of data they can use to understand historical trends. Some call that “doing business through the rear-view mirror.”
Gloria Jeans used to do that exclusively. Using predictive analytics, it’s able to tell what’s happening now and what will happen next week, next month and two months from now with varying degrees of certainty.
For example, by 1 p.m., it knows what its revenue for the day will be.
#2: You Can Better Match Supply with Demand
Retailers are getting better at balancing supply and demand, but there are still back orders and clearance sales.
Imagine if you had to predict what will be popular several months from now or a year from now. Welcome to the fashion industry.
Gloria Jeans uses predictive analytics to do three things: avoid placing the wrong order, make sure it doesn’t order too much of a certain thing and avoid sellouts. To do those things more effectively, it uses lots of different sources of data including weather data and oil futures data.
#3: You Can Forecast Consumer Buying Trends
More businesses are supplementing their own data with data about competitors to compete more effectively. By doing this, Gloria Jeans avoided a huge mistake.
The company had placed an order for Winter/Fall 2015. That particular order placed a heavy emphasis on knitwear for women’s fashion. However, competitive data, verified by some sleuth work, indicated that its competitors had moved from knits to woven fabric. Luckily, it was able to change its order in time.
And That’s Not All
Predictive analytics are used by organizations in virtually every industry whether it’s predicting the next flu outbreak, preventing a security breach or predicting how many customers will walk through a mall, store, or zoo.
The advantage is taking smarter next steps because you are able to anticipate the likelihood of events more accurately than you could do if you were only using historical data or making educated guesses.
When should you consider predictive analytics? When you want to outthink your competitors or when the competitive pressure becomes so obvious (everyone else is doing it), that you have to do it to stay relevant.
Don’t worry. You’re probably not in imminent danger yet. But if your company grows the way you hope it will, your competitive landscape will change.