Mar 17 2020

A Guide to Predictive Analytics in Retail

From customer behavior to inventory management, predictive analytics in retail can take a retailer’s data strategy to the next level.

The retail landscape is a competitive one. Between online marketplaces, direct-to-consumer enterprises and brick-and-mortar stores, businesses are clamoring for any edge available — and they’re using technology to get it.

Whether it’s using mobile points of sale, accepting payment via near-field communication or engaging digital signage to draw customers in, more stores are going digital to enhance the buying experience. But perhaps the most effective tool of all is data. 

From customer information to inventory tracking, retailers are collecting data at record rates. Determining how to best use that information is the job of predictive analytics. By looking at past trends, predictive analytics can determine what will likely happen in the future, arming retailers with the information they need to retain customers and meet their goals. 

What Is Predictive Analytics?

Data analytics continues to be a popular topic across industries, but predictive analytics could be the aspect with the biggest potential payoff for retailers. It combines data mining, statistical modeling and machine learning to take historical information and use it to identify the likelihood of future outcomes. 

For retailers, this uses past patterns to predict things like customer behavior or when a certain item will run out. It’s a turning point for an industry that has spent decades collecting information on its buyers from almost every angle.

“Retailers have been accumulating data over the past 20 to 30 years, from the moment we could scan in a barcode,” said Guy Yehiav, general manager for Zebra Analytics at Zebra Technologies at the National Retail Federation’s 2020 conference in January. “You need to do something with it that is actionable.”

MORE FROM BIZTECH: Four trends that will drive retail in the 2020s. 

Where Does Predictive Data Come From in Retail?

Customers are already being tracked in a number of different ways. Their personal information is collected at the point of sale, website “cookies” can track their movements online and one click can spur a seemingly endless string of ads for customers to buy products they’ve researched on other websites. Even rewards programs billed as giving back to loyal buyers are a way to track someone’s commercial life.

“The ability to collect data and process it, and how it impacts retailers, is pretty profound when the data is used correctly,” says Stacey Shulman, chief innovation officer for retail, banking, hospitality and education at Intel

That information doesn’t just come in digital form. Tools like enhanced video surveillance are allowing stores to track the physical movements of their customers, recording which items they’re engaging with and how. 

“Understanding when the customer walks in the door, and where they turn, we know most of them turn right. Knowing that is interesting,” says Shulman. “Knowing that they turned right and they walked past the hottest item of the season is really interesting. And then knowing that they picked up that item and picked up the item next to it and took it into the fitting room, well now you get to a different level of consideration and understanding of where they went in the customer journey.”

“When you can map that entire customer journey and understand their engagement levels and what they’re interested in, it’s really powerful,” she adds.

Experts discuss the evolving role of analytics in retail at NRF 2020.

What Is Predictive Modeling in Retail?

Armed with the right information, retailers can use all of that data they’ve collected to help predict human behavior. When put into the right models, the information gathered about customers can calculate the likely next steps in the customer journey, allowing stores to manipulate the experience to push certain items or encourage someone to buy more.

“The retailers are able to harness customer information and know, the next time you’re in the stores, I can immediately go, ‘You know, maybe you should take a look at this product or this accessory,’” said Ed Durbin, global director of retail end user computing at VMware, at the NRF conference. “Or maybe give them a 20 percent-off coupon.”

Finding that perfect discount is another way to use modeling as a competitive advantage. When writing in The Harvard Business Review about how artificial intelligence is changing sales, AI researcher Victor Antonio wrote, “An AI algorithm could tell you what the ideal discount rate should be for a proposal to ensure that you’re most likely to win the deal by looking at specific features of each past deal that was won or lost.” 

The same types of models can be applied to price-setting practices in stores, allowing them to set the price at a level where the item is most likely to be bought. It can similarly better predict sales, allowing managers to set accurate goals for stores.

MORE FROM BIZTECH: Read how retail is entering a second wave of digital disruption.

What Is Predictive Maintenance in Retail?

Retailers can not only gain new insights from predictive analytics, they can also keep their current systems running smoothly. Predictive maintenance uses sensors to track data from machines and items during production. Powered by the Internet of Things, the information can be used to forecast when something might need to be updated, repaired or even replaced before it breaks. This can reduce downtime, as retailers will know there could be a problem before it actually happens. Similar forecasts can also show when inventory will run out, allowing managers to stay fully stocked.

From inventory to production and customer experience, data analytics is becoming more and more crucial to the bottom line for retailers.

“The biggest thing with data analysis is really letting people understand that the changes they’re making are winning for us,” says Durbin. “It’s helping people understand new opportunities. People can see trends that otherwise might have been hidden. And now you can see that within the analytics.”

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