Charles Tyrwhitt

Personalization based on each visitor’s behavior

Charles Tyrwhitt looked at a range of recommendation providers, then selected Episerver Product Recommendations. The solution was trialed on the website over a period of three months, with results showing that it would deliver an estimated additional £1 million in annual sales.

“Basing recommendations on what other customers have done is not good enough, because we know that no two people are the same,” says Jennie Blythe, Head of Web Development and Trading at Charles Tyrwhitt. “Episerver’s technology understood this, with individual insights replacing standard-issue algorithms.”

Episerver Product Recommendations uses more than 70 algorithms, as well as hints and filters to analyze a customer’s entire engagement history as they explore the website. While the technology is complex, implementation is easy, injecting product information into the website using a line of javascript. The solution works as soon as it’s plugged in, and it also enables the company to customize the best algorithm for a particular website placement.

Episerver holds quarterly review meetings with Charles Tyrwhitt to ensure that they are delivering maximum value. “We knew that out-of-stock items were impacting sales performance, so we wanted the technology to come up with a solution to tackle this,” Blythe says. “The team took it in their stride, customizing the product recommendation engine so that it supplied appropriate alternative product recommendations that would both satisfy customers and secure what would otherwise have been a lost sale.”

Personalized alternatives to products that are out of stock

Today, when customers come to the Charles Tyrwhitt website, they not only receive product recommendations that complement their desired purchase but intelligent suggestions if their initial choice is unavailable.

Episerver delivers quantifiable, bottom-line benefits: the solution increased average order value by 36 percent in six months; and it increased average units per order by 24 percent in six months. Optimal recommendation positioning of the basket delivered 28 percent of recommendation revenue, and 6 percent of recommendation revenue came from the out-of-stock feature.

“In essence, product recommendations bring our customers a personalized shopping assistant, mimicking the in-store retail experience,” says Blythe. “It means that customers enjoy a more satisfying online experience and get the products they really want, while we optimize sales and build a trusted relationship with individual consumers. It’s a recommendation service we can recommend.”