In the not-too-distant future, personalization at all levels will be as common in ecommerce as the buy button. This is the story of a 134-year old brand with an ambition to become a truly digital business, not only competitive online, but adept at using artificial intelligence to better interact with customers and develop a more personalized relationship.
Marks & Spencer is a London-based retailer with 81,000 employees, nearly 1,500 stores worldwide and over £10 billion in sales in 2018. M&S International was an early mover in leveraging personalization, with Salesforce Einstein, to deliver personalized product recommendations and more relevant, personalized merchandising.
“Customers expect relevant content to be delivered in the right place at the right time, to make their shopping journeys easier,” says Ira Dubinsky, Head of International Marketing at M&S. “Personalization is a top priority for M&S and it’s brilliant to have reliable, scalable tools at our disposal to make it a reality.”
“Einstein has opened up a world of possibilities on personalization that just weren’t there before,” says Sam Sibbert, Head of Digital Product, International, at M&S.
In the Fall of 2016, mere weeks before the crucial Christmas selling season, M&S launched personalized product recommendations in seven markets: U.S., Canada, Australia, New Zealand, Sweden, Switzerland and Denmark. The technology underpinned its email marketing (Marketing Cloud) and commerce platform (Commerce Cloud).
According to Sibbert, brands need consistency across all customer-facing touchpoints to ensure customers have a relevant and seamless journey from the moment they receive an email to when they shop on the site. This is why Einstein needs to be opened up to all data collection engines. “Part of the aspiration of getting to omni-channel is that consistency along the journey for customers,” he says. “But any AI is only as good as the data you feed it.”
With a large product catalog spanning apparel for men, women and children, home decor, cookware and more, getting the most relevant products in front of customers is absolutely critical. Using out-of-the-box rules embedded with Einstein, M&S went live with personalized Einstein Product Recommendations in just a matter of hours.
Following positive results, M&S expanded its use of Einstein to Predictive Sort to showcase new products or those with high availability – so that customers could easy shop products they want to buy.
“We first started using personalized recommendations on the product detail page, but now we’re using it in emails, marketing ‘just for you’ messages with exclusive product recommendations,” says Sibbert. “And now, we’re testing how recommendations work on other pages like the home page, 4-star rated product carousel, or those based on browsing behavior.”
Now, eighteen months later, M&S has launched Einstein-based product recommendations and Predictive Sort in 24 countries.
“Personalization is such an integral part of our strategy. We don’t launch in any new countries without it.”
The results explain why:
Click-to-open rate (email) is up, and M&S is generating more revenue compared to manually merchandised emails. (Einstein Product Recommendations).
Average order value for customers interacting with Einstein Product Recommendations is higher than those who don’t.
Einstein drove an 11% higher interaction rate than its previous recommendation engine.
Conversion rate in the U.S. and Australia jumped around 30% (Einstein Predictive Sort);
Product recommendations on the homepage are boosting average revenue per visitor.
Personalized content offers are now “one of the biggest things called out in our customer satisfaction scores,” Sibbert says. “Our customers like the products we’re offering, enabling us to become more relevant, more often. It’s a massive change.”
A consistent, personalized experience – globally
As a brand with an international presence, M&S is acutely aware of the many variations in seasonality and consumer behavior by region. In fact, these variations created a challenge in delivering personalization on a global scale. An inherent benefit of Commerce Cloud is that, after testing, new capabilities can be rolled out consistently.
“Before, we used manual sorting rules, but now we’re personalizing by the person, not by the country,” says Sibbert. “Now, rather than spending time building basic rules, we can analyze what Einstein is doing, and tweak those rules, freeing up the team to work on other areas to improve our customers’ experience.”
“Our strategy revolves around developing a better connection with the customer and AI is helping is to enable conversations with our customers and make every touchpoint as relevant as possible.”
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