Diane Keng launched two startups before she turned 18, before joining Apple and Symantec. Now the software engineer is the co-founder and CEO of Breinify, a technology company that makes strategic predictive personalization easy across any channel, content, and customer for retailers and FMCG brands.
At Shoptalk Fall in Chicago last week, Keng talked to Inside Retail’s Amier Larter for an episode of the podcast Retail Untangled about trends in implementing artificial intelligence in the retail space – and the challenges companies face with planning, resources and legal considerations.
“The biggest theme that we’re hearing across the major retail and consumer packaged goods brands is that AI-powered personalisation is key for this upcoming year,” says Keng.
“We’re hitting this new trend where people don’t really know what they’re doing with AI, and they just know that it is required now. So, many retailers have been sitting on this treasure trove of first-party data, and now they realize that they don’t know how to use it well.”
Companies considering introducing AI-driven solutions are often split over their approach, she adds. “Some are focused on working with their existing technology and trying to activate it internally. Some focus on bringing in external help that can overlay many things.”
But at the core, she says, it comes down to how customer companies know the AI is effective. She has heard people talk about that struggle on stage at Shopify: all the different solutions are being promoted as much more powerful – “but how do you tell that it’s doing things better than before?”
Keng says businesses looking to adopt AI must have the ability to be very data-driven because it is critical to understand whether or not the technology is improving revenue.
She says that while personalization is not new, the advent of AI is opening the way to do much more. However, the introduction of piecemeal solutions over the past few months has seen many companies adopting software with some sort of AI component inside that doesn’t work across platforms or replicate the customer experience inside email and other channels. “You end up with very different groups of silos of data and experiences”.
The increasing adoption of AI functions within software tools and services is driving increased demand among marketing teams to conduct faster testing to speed learning.
“Many individuals have great ideas; they are just unsure of how they will perform. They are bottlenecked by the ability to run a test. Those tests can be as simple as a layout on a site or something a little bit more complicated. For example, how do you define similar items? Is it similar because it’s the same price? Is it similar because that’s the same category or color?”
At Shoptalk, executives from Wendy’s and Dermalogica spoke about implementing a data and AI strategy across their organisations. Larter asked Keng the best place for a company to start.
“One of the big themes we always recommend is this concept of ‘crawl, walk, run’. Sometimes, some brands get overzealous. They plan to buy a solution that does everything, but nothing works until you’re fully integrated. And so you blink, and then nine months have gone by, and they haven’t launched their first experience.”
The correct path, she advocates, is to figure out the main problem the company wants to solve. “Think about the funnel. You’re working so hard and paying for all these kinds of ways for traffic to get to your site, but then the customers are reaching a dead end; they’re not moving forward, and they’re not engaging in the way that you would like them to, whether it’s a checkout or some sort of enhancement of their experience. So, where is the actual problem? Is it getting them to a second page? That’s straightforward. Or is it the fact that they’re not fully converting?”
Keng stresses the significant cost to retailers of failing to adopt AI components of data-driven technologies.
“If you’re behind now, your business won’t be thriving in two or three years. Given that revenue right now is gold for every single brand out there, I think people know they need to jump onto this bandwagon, but strategy is still a big missing part.
“One of the big things we always come across when meeting new brands is that they don’t know where to start. They feel like they need to get this whole big concept in place, whether it’s a CDP and marketing cloud or all these things, before they can decide. But the truth is, the world’s still moving, even while you’re implementing other tools. So how do you find a happy medium where you can still leverage the power of AI to do good but at the same time build up infrastructure in the background?”
That is a challenge Breinify has solved, she concludes. “We know that to be able to jump that hurdle and overcome the obstacle of using AI, you’ve got to make the integration and setup as easy as possible, where it doesn’t rely too much on the engineering teams for you to see the value.”
Listen to the podcast to hear Keng talk about optimizing recommendation solutions for online shoppers, the importance of dynamic categories, and the future of generative AI.
