One of the big trends in online retailing is to open physical stores. Digital retailers including Allbirds, Away, Bonobos, Glossier, ModCloth and Zappos have all opened brick-and-mortar stores to enable customers to touch and see merchandise.
While there's tremendous value in feeling the softness of a leather shoe, the high thread count of sheets or the fabric of clothes, online retailers have an advantage over even the most seasoned and experienced retail employee — access to customer journey data.
And at no time was this more important than in the recently completed holiday shopping season.
Prom Dresses With Flip-Flops
Whether using product recommendations, online chat or some other method, the holiday shopping season provided smart retailers with a tremendous opportunity to upsell intelligently. A shopper puts a pair of Nike tennis shoes in their shopping cart, so let’s offer her socks or tennis balls. Another shopper buys dress shirts, so why not offer him some ties? The problem with these suggestions is that they’re obvious. And often, they’re wrong — I buy lots of dress shirts but haven’t bought a tie since my wedding a number of years ago.
Today, retailers (including physical chain stores) have access to customer data which can enable them to make more intelligent and profitable product recommendations. By analyzing the data, they’re able to go beyond the obvious to make better recommendations, which increase revenue and delight their more profitable customers.
Let’s start with the customer. For me, the sign of a good salesperson is when they make a recommendation which goes beyond the obvious. Recently, my wife and I were delighted at a restaurant when the waiter informed us that the artichoke ravioli we ordered wasn’t available but that the chef would instead make us an artichoke salad. From our conversation, the waiter understood that we didn’t want another pasta, instead offering us an alternative which we ended up loving.
Digital and larger chain retailers have access to lots of customer data, which enable them to offer their customers recommendations which will delight them based on the patterns they’re seeing across their audience.
A trend I saw last spring was with shoppers looking for flip-flops to match their prom dress. At first, this seemed odd to me until my better half informed me that after a few hours of wearing and dancing in high-heels, most women would be thrilled to slip into a matching pair of flip-flops. She’s seriously considering opening a store to sell white flip-flops for brides. And prom season is almost here again …
Today, using machine learning technology, it’s possible to uncover cross-sell and upsell product correlations which were nearly impossible to find even a few years ago. To start, retailers need intelligent segmentation to effectively categorize customers according to their journey, including their actual purchase patterns. Then, once a retailer has a greater understanding of purchase patterns, it’s easier to predict which products will provide a good upsell or cross-sell opportunity.
Beyond upselling, the same machine learning technology can also help a retailer predict which products to carry when (seasonality) and where (locations /online-offline) based on the identified customer purchase patterns.
Though predictive machine learning technology started with online retailers, there's no reason a tablet-powered sale associate can’t use the identical technology to improve upselling and cross-selling in the mall. Furthermore, store managers and chain store buyers can use the same predictive machine learning technology to improve product sales predictions and enhance inventory allocation across different stores and sales channels.
Last holiday season, I saw retailers make use of data for more intelligent upsell recommendations, which resulted in 40 percent to 60 percent increases in average shopping cart. Other retailers benefited from better product predictions which limited last-minute sales and improved profitability.
With the low margins and challenges coming from lower-cost foreign markets, retailers — both online and on Main Street — can use predictive machine learning technology to increase upsell and cross-sell opportunities beyond the obvious while also enhancing sales predictions.
Roei Livneh is the CEO of Curve.tech, a company providing businesses with machine learning-automated insights derived from existing and analyzed customer journey patterns.
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