Jun 18 2019
Data Analytics

How Retailers Use IT to Discern Buyer Intent and Optimize the Shopping Experience

Data-driven technology also helps physical stores compete with nimble online merchants.

Brick-and-mortar retailers must rely on far deeper insights than simply forecasting a new style trend or reviewing sales figures after a holiday shopping season. 

Stores should leverage an increasingly predictive pool of data that examines the frequency and duration of customer visits, peak traffic hours and social media chatter, among other key findings that can be collected in real time. 

The greater challenge: turning that information into business-boosting best practices.

But the effort is crucial for physical outlets to distinguish themselves and compete with e-commerce giants well-attuned to the power of data, according to a CDW white paper titled “Smart Retail Powers Data-Driven Insights.” The white paper urges retailers not only to harness data-driven insights, but also to invest in robust back-end IT infrastructure that supports them.

Such tactics, the report notes, can result in increased upsell opportunities, higher customer engagement and a more seamless connection between online and in-store transactions.

Digital Transformation

Why Retailers Should Collect Data

Ensuring a good customer experience involves more than personable salespeople and a clean showroom. Anticipating what shoppers want — and meeting those needs instinctively — can be achieved with data culled from a variety of sources and tools.

That strategy, Deloitte’s “2019 Retail Outlook” report notes, also means communicating the value of data collection to customers.

After all, 73 percent of shoppers visit a store with a specific purchase in mind, according to the National Retail Federation. And 58 percent say the ability to find what they want quickly and easily is one of the most important factors in selecting a retailer.

Consumers in search of a particular item, for example, could be aided by radio frequency identification tagging technology that can track availability and precise location. Another RFID benefit: the speed and accuracy to buy online and pick up in-store, which are a hallmark of omnichannel retail.

Deployment of artificial intelligence and machine learning can automate tasks such as ordering when stock of an in-demand product is low. On a greater scale, predictive analytics may help inform current and future market trends to advise on issues such as staffing and sales targets.

And the addition of data-mining (yet functional) in-store tools such as interactive kiosks, mobile payment acceptance and self-checkout machines are among digital transformation efforts retailers can use to close a substantial sales gap identified in a 2016 Cisco study — namely, that hundreds of billions of dollars are at stake and can be lost due to subpar customer experience.

Still, discerning a buyer’s intent can be tricky, says Sucharita Kodali, a Forrester retail analyst.

The largest pain point? “Anytime someone comes into a store and can’t find what they’re looking for; no retailer is capturing that” moment to avoid future missteps, Kodali tells BizTech.

Brick-and-mortar merchants, she notes, might take a cue from Revolve, an online clothing company whose trendy stock is heavily informed by analytics. New items are created based on hundreds of details — such as fit, garment length and style of buttons — tied to the site’s top-selling goods, and customers receive push notifications when stock of a hot item is running low.

MORE FROM BIZTECH: Read more about how to get the most out of retail data in 2019.

Technology to Support Retail Data Analytics

Major retailers with physical locations spread across multiple states may have different needs than those with a regional or local presence. 

In either case, the CDW white paper notes, retailers should evaluate technologies that could be implemented not only within the next 9 to 12 months but also as far as five years down the road. Compared to sporadic IT triage, it’s a more effective way to identify long-term problems and opportunities. 

That process starts with boosting network solutions to ensure bandwidth for handling information from customer-facing mobile apps, smart cameras and a wide range of Internet of Things endpoints. It also involves assessing data center and cloud needs — likely a hybrid of outsourcing some duties and keeping others onsite.

Another crucial factor: having the proper end-user solutions (such as mobile point-of-sale devices and barcode scanners) for floor associates to provide quick, convenient service. 

It’s important to evaluate where technology resources will be located and how store managers can reach out for help. In any case, robust security solutions — including next-generation firewalls, incident response systems and frequent testing — are mandatory to ensure the safety of customer data and to sustain trust that allows data collection to continue.

Getty Images/ hakule

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