We’ve all seen it. It’s at the bottom of the page as you shop for shoes; in a pop-up window before you leave a retailer’s site; part of a sidebar display while you’re completing an online transaction: “Recommended for You.”
Offers for related products take many forms, from “Customers Also Bought” to “Shop the Look.” In essence, these recommendations are designed to encourage customers to view—and buy—additional items. But in today’s hyper-personalized retail reality, they are rarely enough to incentivize customers to the point of conversion. That’s because, as a form of customer engagement, they are “faceless”; in fact, they represent a soon-to-be outmoded form of personalized engagement.
Before a multitude of past and present customer data points were right at enterprises’ fingertips, businesses had to make educated guesses when it came to understanding their customers’ interests and needs. To do so, they bucketed customers into personas based on the few data points available for each shopper. So Greg, a 44-year-old father of two who works as an antique shop owner in Westchester, New York, could fall into the same persona segment as Ben, a single 35-year-old medical student in residence at Mount Sinai Hospital in Manhattan. While both are 35-45 year old men living in New York, their individual shopping habits and interests are likely wildly different—and unaccounted for by their assigned persona.
What these marketing personas do allow enterprises to do is target customers based on wide generalizations, such as “Products You Might Like.” But now that customers expect to be known and catered to on a 1:1 level during each unique customer journey on which they embark, generalized product-based recommendations are no longer enough to spark conversion.
Luckily, persona-based marketing is no longer the only solution for enterprises seeking personalized customer engagement. Thanks to advanced customer data collection via Customer Engagement Hubs (CEH) with powerful Artificial Intelligence (AI) and Machine Learning (ML) capabilities, enterprises are now able to fully understand, predict, and react to their customers’ needs and context in real time. This is a new stack that allows businesses to analyze each customer’s behavioral patterns, track their in-the-moment browsing activity, see their local time and weather—and strategically deploy the engagement most likely to resonate with their current priorities. Not only does this intelligent technology give each customer a “face,” but it also makes any and all customer engagement efforts feel much more personalized than “Related Products.”
By leveraging an intelligent CEH, enterprises can move beyond faceless product recommendations and explore opportunities to engage customers around:
Special Offers: “James: Order in the next 5 minutes and receive $5 off!”
The draw of saving money effectively incentivizes customers to checkout in a timely manner.
Other Customers’ Activity: “Sarah, 12 other people purchased this hat today.”
Sometimes shoppers need a little peer-encouragement to build confidence in a product and make a purchase.
Timely Opportunities: “Your store cash expires tomorrow, Lauren. Shop now and save!”
Time-sensitive reminders are a convenient and easy way to inspire customers to action.
Limited Stock Updates: “Hurry, Bill—only 5 left in stock!”
By creating a sense of urgency, enterprises can rapidly take customers from casual to converted.
With the right CEH solution, retail enterprises can move past the over-used “Related Product” offers their customers encounter across the web, and instead begin to effectively engage each and every shopper on a 1:1 level, transforming personalization into individualization.
ZineOne’s AI-Powered CEH uses real-time customer data stream processing to personalize every experience according to individual customers’ needs and interests. Learn more about ZineOne’s innovative approach to normalizing, analyzing, and personalizing engagement through a single intelligent platform.