How Real-Time Data Can Power Ultra-Personalized Retail
The pandemic drove e-commerce to new heights, increasing pressure on retailers to get ahead of changing consumer behaviors. However, delivering e-commerce systems that provide a competitive advantage is one of the toughest challenges facing retailers today.
The role of data — and the ability to offer personalized retail experiences — is increasingly critical, especially a requirement for businesses trying to compete with behemoths like Amazon.com. More than half (60 percent) of consumers will likely become repeat buyers after a personalized retail experience, up from 44 percent in 2017, research shows.
Here’s the rub: There are personalized offers based on what consumers bought last month, last week or even the day before. That’s no longer good enough. The ability to deliver a personalized shopping experience with relevant content at predictable sub-second latency is the difference between business success and failure.
Retailers must now take advantage of technology that can combine real-time and historical data — gaining a fluid, 360-degree view of their customers and the ability to immediately act on that information. This personalization is now informed by what consumers do in the moment; when they linger over a sweater on an e-commerce site or when they stand at an ATM, for example.
This new wave of data processing enables:
- More personalized experiences: The leaders of the digital economy — Meta (Facebook), Amazon, Apple, Netflix, Google (i.e., the FAANG companies), and others — respond in the moment to what consumers do online. They’ve built technology to do so, given their thousands of engineers. But any e-commerce retailer can do the same with new technologies that enable fast data analysis merged with insights from streaming data — on the same platform. That gives retailers the ability to act in the moment that matters, not an hour or day after a shopper has moved on. For example, say a shopper has a history of only buying Nike shoes. A retail website may feed the consumer prompts on Nike shoes and correlate that buying habit with other similar shoppers. Perhaps the data shows that shoppers who buy Nike shoes tend to buy T-shirts from another brand. That insight helps create a personalized experience that suits the shoppers’ buying patterns. Rather than peruse dozens of brands and items, the shopper finds what they want, faster. The retailer secures the sale by processing the right data in the moment — not after a purchase is made.
- Better inventory control: With a mix of fresh and historical data, retailers will be better informed to correct inventory data while people are buying. This will help reduce delayed deliveries and give consumers more accurate delivery dates, not to mention reduce costs of error handling when delivery promises are missed. Traditionally, supply chains are fed by separate databases — which do not combine historical with real-time data — and data is processed in batch, so the entire system is slower to adjust to real-time conditions.
- More revenue: Retailers could convert more deals and special offers if armed with critical customer information at the time of engagement. If that Nike shoe shopper had never bought socks along with shoes, a retailer that knows that might offer a special promotion on socks before the shopper checks out with their shoes.
The Real-Time Economy is Here
Speedy response and high availability are absolute musts, and making sense of the huge volumes of data generated by customers is critical. While not a retailer, BNP Paribas created an application that enables real-time customized loan offerings vs. sending an offer days after it was needed. The result? BNP Paribas grew offer conversion by 400 percent.
Consumers want what they want, when they want it. McKinsey found 75 percent of consumers tried new shopping behaviors during the pandemic. Of those who did, 39 percent left their trusted brands for new ones, with Gen Z and millennials leading the way.
This makes data most valuable the instant that it's born. Being able to act on that fresh data — while being informed as to how important it is based on historical data — is the linchpin equation of winning in a real-time economy. Retail will be among the leading industries to unlock this value; those that don’t will see their shoppers leave with an empty cart.
Dale Kim is senior director, technical solutions at Hazelcast, a real-time stream processing platform that lets you build applications that take action on data immediately.
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Dale Kim is the senior director of technical solutions at Hazelcast, and is responsible for product and go-to-market strategy for the real-time stream processing platform and the Viridian cloud-managed services. His background includes technical and management roles at IT companies in areas such as relational databases, search, content management, NoSQL, Hadoop/Spark, and big data analytics. Dale holds an MBA from Santa Clara, and a BA in computer science from Berkeley.