Way back in December 2014, an eternity in the retail industry, we wrote in this space about predictive intelligence noting that, “In today’s retail environment, where digital footprints are created from every customer interaction… personalization MUST be more than “Hi [insert first name].”
“The next generation of personalization requires predictive intelligence that doesn’t just react to a customer’s actions, but proactively predicts what a customer wants before that customer even knows it.”
Fast-forward about 18 months, and forward-thinking retailers are deploying just this type of individualized personalization – with great results.
Skis.com, an innovative 36-year old merchant of ski and snowboard gear and apparel, was an early adopter of Predictive Recommendations from Demandware, which leverages advanced machine learning algorithms to suggest product recommendations for both known and anonymous shoppers.
A shopper who views a jacket, for example, will be automatically presented with alternative jackets, as well as cross-sell items that speak to their broader needs, like hats and pants, or the right poles to compliment their skis. These recommendations happen automatically and in real time which, given the enormity of the Skis.com product catalog, is a huge time saver for their merchandizers. Instead of manually setting up product recommendations or worrying about segments, merchandising teams can now focus their time on more strategic tasks.
The results were impressive; Skis.com ran an A/B split test for three months during the fourth quarter, which represents the majority of its peak selling for the year, and saw an increase in the number of orders per session, conversion and average order value. In total, Predictive Recommendations from Demandware drove more than 7% increase in overall revenue per visitor.
Read the full Skis.com case study.
For Avenue Stores, the third largest specialty retailer for women’s plus-size clothing, a literal five-minute effort yielded 15% lift in revenue per email.
Email is a crucial way the company communicates with its shoppers, and in fact is a top revenue driver to its site and stores. Avenue knew that if they could make their marketing emails uniquely personalized to each shopper, it could increase sales and customer engagement.
So it launched Predictive Email from Demandware. Because the Predictive Intelligence Engine is embedded in the Demandware platform, Avenue simply leveraged Demandware’s knowledge of its customer data, products and order history to enable the engine without a time consuming data integration. And execution was simple because Predictive Email works with existing email processes, allowing Avenue to keep their preferred email service provider.
Avenue split their email list so half the recipients received the regular marketing email and half received an email with four product recommendations. “We simply cut and pasted URLs, provided by Demandware, into our email template. There were no additional resources required on our end,” says Kristin St. Peter, Director of ecommerce.
The results were immediate: a 15% increase in revenue per email across all 11 emails they sent, driving Avenue to expand from an A/B split to sending recommendations to 100% of its email database. Avenue trusted the engine to create 1:1 personalized email experiences and eliminated the effort involved with assigning specific product recommendations to different segments.
Read the full Avenue Stores case study.
These are just two examples of the power of personalized, relevant engagement. With Predictive Intelligence, retailers can consider a customer’s data contextually to better understand them as an individual versus part of a broad demographic group. By predicting individual customer behavior, marketers can customize a shopper’s journey and match it to desires that the customer may not even have known she had.
Consider this the evolution of relevancy – retail brands leveraging the newest machine learning and data science to go beyond segmentation. In this new world enabled by predictive intelligence, brands can personalize content for each consumer and offer truly tailored shopping experiences.