Finding the perfect holiday gift can be stressful—for shoppers and retailers alike.
According to Deloitte’s 2018 Holiday Retail Survey, total retail sales are expected to increase up to 5.6% this holiday season, with online sales alone rising 17 – 22%. As the holidays draw nearer and shopping becomes increasingly hectic, retailers struggle to individually engage the hundreds of customers pouring through their doors and visiting their online stores; as a result, many customers leave empty-handed.
Fortunately, Machine Learning (ML) models are making holiday shopping easier on everyone. ML empowers retailers to help shoppers find just what they’re looking for, boosting sales and driving customer satisfaction. When incorporated into an intelligent Customer Engagement Hub (CEH), ML models can help shoppers:
When it comes to assisting online shoppers during the holiday season, going beyond standard product recommendations (like “Related Items” and “Chosen for You”) is key. Instead, intelligent customer engagement leverages ML for a personalized, humanized approach to both suggest and incentivize in ways designed to resonate with a customer in real time. Consider the following example:
Jack is shopping online for a Christmas gift for his girlfriend. He visits the website of a jewelry store where he also purchased a gift for Valentine’s Day. The store’s CEH immediately recognizes Jack as a returning customer, and its data stream shows that his only other purchase was a necklace in February.
The CEH’s ML model pulls in additional environmental data, such as the date and time, to rapidly determine Jack’s current mindset: it is four days before Christmas, and he is browsing products that suggest he is looking at items specifically for his significant other. Using this deduction, the CEH is able to push Jack a personalized message designed to assist and incentivize him: “Welcome back, Jack! Check out our Sapphire Earrings to match your recent purchase of our Sapphire Necklace. Order in the next 20 minutes and have them in time to put under the tree!”
This message not only identifies a relevant product of interest for Jack, but also encourages him to convert quickly by creating a sense of urgency that is relevant to his situation and current time constraints. ML makes this real-time customer engagement possible.
Just because a shopper pays a visit to brick-and-mortar store this holiday season doesn’t mean that ML technology can’t be used to engage them. In fact, CEH solutions equipped with ML models are able to enable intelligent customer engagement across any and all available channels—including in-person. Here’s an example:
Abby is preparing taking her family on their annual ski trip over the upcoming holiday break. She visits her regular ski supply store and heads toward the children’s section to find ski jackets as early Christmas presents for her children. The store’s CEH recognizes that Abby is in the store as soon as her smartphone connects to the store’s Wi-Fi. With additional location-sensitive Wi-Fi beacons placed around the store, the CEH can even tell that Abby is now browsing through children’s coats.
As Abby leaves this section of the store and heads toward the register, her phone buzzes with a notification from the store’s app: “Abby, use your loyalty points and receive up to 20% off on children’s winter accessories.” Prompted by the reminder, Abby chooses matching hats for each of the coats she’s selected, pleased with her savings and her gifts.
Again, the CEH’s ML technology optimized Abby’s in-store customer engagement by delivering an offer that was personalized to her and helped her find the gifts she needed—all in real time.
Not only can ML models intelligently engage customers online and in-store this holiday season, but when deployed by the right CEH, they can also create seamless experiences across channels. This drives enhanced customer engagement throughout the entire holiday shopping season, whether a customer is on a retailer’s website or in the store:
Garrett bought a shirt online to wear to his office’s holiday party, but wasn’t pleased with the product when it arrived. He visits the department store in order to make a return. The store’s CEH kicks into action when Garrett’s smartphone connects to their Wi-Fi, and tracks his progress via Wi-Fi beacons as he makes his way to the Customer Service counter. When his return transaction hits the CEH’s customer data stream, an ML model recognizes an opportunity and crafts a personalized message to deliver to Garrett.
As he leaves Customer Service and walks by the Men’s Apparel section, the store’s app pushes him a notification: “Garrett: Men’s ties are 50% off. Use store credit from your recent return to stock up.” Thinking about his holiday party next week, Garrett decides to take advantage of the sale—and his store credit—by purchasing a new tie in-store.
Though Garrett’s experience began online, intelligent ML effectively created a multlichannel holiday shopping experience that resulted in a sale and kept the customer satisfied.
ZineOne’s AI-powered Customer Engagement Hub utilizes Machine Learning to optimize the holiday shopping season for customers and retailers alike. With the ability to deploy personalized, cross-channel offers in real time, the ZineOne CEH is the ultimate customer engagement tool for the holidays—and every day.