Omnichannel retailing

Intel, Google, Adobe — all of these tech giants have listed artificial intelligence, known as AI, as one their newest ventures — and for good reason. In a market dictated by divisive consumers, retailers and fashion apparel brands are charged with providing customized content and omnichannel experiences.

This is where companies such as Monetate come in. Last week, the personalization platform announced the introduction of its Intelligent Personalization Engine — a solution that aims extend customized experiences at scale. Here, Lucinda Duncalfe, chief executive officer of Monetate, expounds on shifts in the market that necessitate the use of AI, how the technology can benefit the entire retail chain, and best practices case studies.

WWD: What was going on in the retail market that demanded the evolution that resulted in Monetate’s latest platform?

Lucinda Duncalfe: In 2015, it looked like people — and technology — would be ready for personalized experiences, since brands face an ever-rising bar of customer expectations and an increasing set of competitive pressures. They have typically approached these challenges by using segmentation and testing approaches to deliver and improve customer interactions, but unfortunately, while segmentation approaches are valuable in some applications, they don’t deliver the level of personalization customers have come to expect.

In 2015, we decided that we would use the very best of Monetate’s expertise to create the future. We would enable brands to create meaningful experiences for individual customers, across every touchpoint. That meant we would build an AI engine that could decide, in real time, what experience each individual customer should get; open our system to pull in all data, wherever it sits, about that customer; provide that decision to every touchpoint, whatever system is managing it, including the web; create a full feedback loop so the engine and the marketer can learn from the results.

WWD: Retailers’ departments are still siloed — how can they address this? How is this affecting their success?

L.D.: Retailers’ internal silos are impacting their success by creating poor customer experiences, which translates directly into revenue loss. The only way to fix the problem is by starting at the customer — truly focusing on their needs and desires — and working back into the organization. The organization has to serve the customer.

 WWD: How will the adoption of AI into business strategies affect success for both the back-end of retailers and also the front-end experience for shoppers?

L.D.: The adoption of AI has benefits for everyone in the retail chain. First shoppers are more likely to have a positive experience because the right AI models and implementation will provide them with the best possible experience.

Internally, AI enables marketers to do things they’ve always wanted to do but never could: provide that right experience. Deeper inside the organization, marketers will be able to learn more about their customers than they ever have, because they’ll have a huge expanse of new data to analyze and learn from.

WWD: Who is using Monetate’s software well?

 L.D.: The testing phase began several months ago and 15 brands have participated including Club Monaco, JD Williams, Office Depot, National Geographic, Toms Shoes and Talbots. The results we’re seeing are beyond our hopes and certainly our expectations. We worked in tandem with clients to deliver results such as: 73 percent increase in e-mail sign-ups and 3.3 percent decrease in homepage bounce rates. 

WWD: What should retailers and brands consider when considering various AI platforms?

 L.D.: The collection of millions — or for some brands, hundreds of millions or billions of individual decisions — is not inherently interpretable. It is difficult to look at what was done for billions of individuals and somehow see the themes and the insights. And since the idea of making individual decisions produces individual output; a pile of answers that isn’t inherently interpretive, brands should ensure that they have some way to interpret this information.

In our case, knowing that marketers are never comfortable with a black box system that they can’t understand and learn from, Monetate knew that we had to find a way to get some insight out of that data. What we ended up doing is actually pointing a second machine learning system at the output of The Engine in order to interpret the results to provide marketers with insights they may not otherwise be able to access.

More on Artificial Intelligence from WWD:

Exclusive: How AI Predicts the Biggest Trends of the Season

Salesforce Broadens AI Reach

Is AI Worthless Without a Central Focus?

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