With billions of customer behavior data points, transactional histories in the trillions of dollars, and pinpoint segmenting, AI turns ROI best-guess calculations into data-driven predictions, uncovers new customers, adds science to the art of targeting, and more. Learn how major retail organizations are realizing massive ROI with machine learning when you catch up on this VB Live event!  

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Most customers won’t look further than the top of a search engine result, so snagging them means optimizing your organic search results to one-up your competitors, and directly impact your bottom line. For Eugene Feygin, SEO manager at Sears Parts Direct, that means leveraging artificial intelligence. A recent internal case study made the investment a no-brainer, he says: for $20K in development, their calculations found, they’d see a return of $2.4 million.

“With machine learning technology, we’re able to calculate how much revenue lift we would see from a potential shift in rank,” Feygin says. “We’ve been pushing a lot more aggressively just because the market is changing so dynamically and there’s a lot more competition coming in. What we’re focusing on is how to serve up the right information to ensure we capture and retain that customer.”

The big SEO challenge has become Google’s RankBrain technology, which has profoundly impacted search engine results pages (SERPs). Google is using its own machine learning to serve a greatly expanded number of results. It used to be just 10 blue links and a couple of paid links, Feygin explains. Now it’s maps, images, news, quick answers — more than 40 different types of results that can appear on a page, and they change dynamically depending on who’s searching, where, on what device, and more.

Let’s say you’re on a mobile device and you type in “office supplies.” Staples wants to show up first in those results, but now they’ve been pushed down the page because Google shows a map with a variety of local listings, and Staples’ chance of getting clicked on has dropped drastically.

But for the user, that kind of personalization has become de rigueur, always served up and fully fine-tuned. Customers are increasingly sophisticated, and most understand the kind of personal data they’re giving up to companies — and in return, they have high expectations. In other words, for example, if I’m giving up my location data, I better get an accurate area map.

That’s what Google is nailing, and that’s what companies need to dig out of.

A company also needs to pin down their objective ranking to drive revenue, Feygin says. But with 40 different SERPs that constantly change and evolve, it’s difficult to pin down what position you’re at, and that information is essential. If you know what position you’re at, then you can figure out what your clickthrough is, or how many people are going to come to your site, and then if you know what your average conversion on that page is, you can estimate how much revenue you’ll have.

“It’s challenging for me to plan out properly how we’re doing from an SEO perspective and how we’re driving quality leads to the site, to quantify our results and determine how we’re performing ” Feygin says. “Which is why we use machine learning — without that it would be impossible.”

Beyond search results, SEO also touches almost every part of the website, Feygin says, and they use machine learning to optimize search results and ensure that a customer finds exactly what they’re looking for, even dynamically updating pages to serve the most relevant results.

Machine learning also allows them to analyze a customer experience and optimize it on the fly. They can add a metric that defines what the KPIs are for a successful customer visit in stages, and as criteria are met, the customer’s journey is designed.

“The challenging part right now is that we’re in an age where there’s so much data coming in from so many different sources, so how do you act on it in real time, proactively?” Feygin says. “Say exit rates are dropping off. Or you have too high a bounce rate on some page. You need machine learning to tame that information, because it gets out of control very quickly.”

To learn more about how machine learning is helping leading brands attract new customers, engage consumers, and turn browsers into buyers, catch up on this VB Live event.

Don’t miss out!

Access this VB Live event on demand right here.

Attend this webinar and learn:

  • How AI helps retailers curate content, offers, and experiences to revolutionize customer acquisition and engagement.
  • How companies can leverage AI to uncover new markets
  • The role of AI in data-powered email marketing (e.g. empowered segmentation), voice and visual search
  • The future of AI in retail: What’s next?


  • Rob Roy, Chief Digital Officer, Sprint
  • Eugene Feygin, SEO Manager, SearsPartsDirect.com
  • Jaimy Szymanski, Industry Analyst & Founding Partner, Kaleido Insights
  • Rachael Brownell, Moderator, VentureBeat

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