Interview: Andy Berks, digital product director, ASOS

ASOS’s digital product director Andy Berks talks about how the online retailer has used AI and machine learning technologies – and why the needs of the customer have to come first

The tail end of 2018 saw online retailer ASOS release Fit Assistant, one of several digital services the brand introduced in the last 12 months.

Fit Assistant joined the number of recommendation services launched by ASOS, focused on using machine learning and AI to improve the customer experience, including personalisation features such as Your Edit, which shows products it thinks a customer might like; Style Match, which shows customers products similar to those already bought or looked at; and curated carousel “You might also like”.

But ASOS’s digital product director, Andy Berks, points out these new features are not just digital for digital’s sake.

“It’s also a part of a bigger thing,” he says. “It’s one feature that’s solving one customer problem, or trying to make one part of the customer experience better.”

Technology for customers, not for business

Many retailers are focused on adopting third-party services such as Metail in a bid to reduce the number of customer returns, a customer behaviour that can end up costing retailers a lot of money.

But Berks says the idea for Fit Assistant came out of customer needs rather than the business problem of reducing returns, highlighting the need to implement new technologies to meet a consumer need, not just because you can.

Introducing a sizing service may well drive down returns of items that don’t fit, but as Berks points out, returns are “many things” as well as just ill-fitting garments.

Instead, services introduced by the retailer are focused on “minimising potential disappointment” as well as making the customer journey more effortless.

“We’re a business, so obviously the things we do hopefully lead to us benefiting and growing as well, but we do that through ensuring the customer problems are solved,” he says.

“If you looked at it through the lens of returns, you might not end up making good decisions for the customer: you might end up doing things that help you. But if you always look at it through the lens of the customer there may be benefits down the road.

“We always start from a customer perspective, it will lead us to a good place.”

A data-driven retailer

Much like other retailers focused on offering a personalised shopping experience, many of ASOS’s recently launched services are underpinned by data and artificial intelligence (AI).

While Berks says AI is a “buzzword” used to describe a set of technologies that have been widely used for a long time, he also admits ASOS has been “experimenting” with AI as people currently understand it for the last few years.

“Over the past six to 12 months, we’ve really stepped up our efforts on that,” he says. “I think we’ve reached a point where we’ve started to see the value it can drive. We’ve started to release things like Fit Assistant.”

Now the digital retailer is investing more in AI and machine learning, “both from a scientist research AI and engineering technology side, to make AI something that can power the whole experience”, says Berks.

Both roles are equally important in delivering relevant services, as there is a “danger” a capability could be built by engineers without the ability to actually change the customer experience at all, due to a lack of data science knowledge.

“That’s really an engineering challenge as much as it is a data science challenge,” he says.

As an online retailer, ASOS has always been considered by the industry as “digital first”, and with upwards of 18m customers at the end of 2018, Berks points out the “huge technology ecosystem” needed to serve its market – as well as ensure features are scalable, responsive and resilient.

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ASOS has been working towards developing its teams to give them “engineering disciplines and processes” so they can work towards developing and scaling services that solve customer problems, as well as ensuring data is readily available to its scientists to either develop technologies that solve a customer problem, improve the customer experience, or “start to influence a part of the business in such a way it previously might have struggled to do without the use of those technologies”.

While many retailers are just discovering how they can use data for customer experience and personalisation, ASOS has been using data from day one, and much of its business relies upon it.

“We’ve always used data across the business to empower our decisions,” says Berks. “That’s part of who we are as an online retailer. These technologies just allow you to do those at a greater scale.”

For many of its services, such as Fit Assistant, the retailer took an agile approach to development and launch, running a beta across its web platform to see whether there was an improvement to customer experience.

Once a benefit was proven, the service is scaled, all feeding back into keeping customers happy.

Making sure services fit customer needs

Proving a service will be beneficial to customers can be a challenge in itself, especially when the focus is on delivering a good customer experience as opposed to measuring progress by profit margin or increased sales.

When launching Fit Assistant, Berks says it was obvious that helping customers choose the right size the first time would improve their experience of the brand.

“We stock between 800 and 900 brands on the site and we shouldn’t expect the customer to know what size they are in each of those brands, that’s not realistic, so we can help then with that through this sort of technology.”

Other innovations, such as using voice-driven commerce, take more experimenting to discover what works for customers and what doesn’t.

The brand initially launched its artificially intelligent (AI) shopping guide, Enki, as a Facebook-based messenger bot, through which customers could use voice messages to chat directly with the retailer.

This evolved into using Google Assistant to allow customers to browse Enki using voice commands.

Berks points out these capabilities were not only tested and experimented with over an extended period of time, but are also powered by AI and machine learning technology.

Testing new features

Part of testing whether or not a new feature is scaled sometimes comes down to a community of 4,000 customers called Front Row, who fit into a number of different market categories who are asked through hosted discussions whether certain features would be beneficial or not, and which customers would use them.

“Then we do things through testing, so we’ll always try to get stuff out in front of a section of customers first so that we get real feedback, because you can’t always know until you get something out into the app or on to the website how it’s going to land or how customers are going to use it,” says Berks.

Sometimes services are tested through a random controlled beta experiment, where users are not told they are part of a beta to see how they react to, and use, potential new features.

This is especially true of features such as early versions of Enki, where the customer problem the service is solving may not be as clear as it is for features such as Fit Assistant.

“With something like Fit Assistant, the customer problem is very clear,” he says. “We don’t necessarily know what we put in will solve it at first, so we might have to iterate to make it perfect, but we’re pretty clear we’re solving a known problem.”

“Something like Enki, certainly in something like the Google Assistant ecosystem, is such a new space. It’s such a new, emerging domain that we don’t know exactly how we will be able to bring value to customers in that domain. It might not be what we originally think it is – customers might want something completely different from it.”

Curation and personalisation

Social media can be a huge channel for many retailers, allowing them to connect with customers and learn more about what the market wants.

Off of the back of consumer chatter over social media, ASOS ran with the idea of launching Boards, a service whereby customers can save and curate items they have seen on ASOS into collections, such as products someone wants to buy for an event.

“All these different touchpoints we have really helped us validate that it was going to be relevant,” says Berks.

This sometimes means some services are found not to fit in with the needs of the customer, which is why iteration and an agile approach to new products is important.

“Our product managers are always sending time looking through those different sources of data trying to find what the customer is telling us,” he says. “If you create something and you get it out early and you try and get moving fast, customers will tell you if it’s something useful or not, and especially in the voice ecosystem they’ll ask the questions they want you to answer.”

Sometimes it takes a few iterations of a technology to develop something that meets the customer needs, underpinned by ASOS’s focus on its customers to ensure they are tackling the right areas in the vast sector of retail technology.

“If you’re customer-focused, you should be starting to solve those problems early, because you should be spotting where the opportunities are,” says Berks. “You can be powered by technology, but you have to also believe in the power of technology to be something brilliant and solve problems for the future. You have to have that spirit of experimentation.” 

This was last published in January 2019

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