Dynamic Yield Raises $31M To Drive Machine Learning For Retail Marketers

Dynamic Yield, a personalization platform for publishers and ecommerce brands such as Under Armour and Sephora, has raised $31 million in Series C financing, the company announced Thursday, bringing its funding total to $45 million.

New investors Deutsche Telekom Capital Partners and La Maison joined existing investors like Baidu to help the company expand its offices globally and ramp up headcount with 30 new data engineering, sales and marketing roles.

No investors took money off of the table, according to Dynamic Yield CEO Liad Agmon, who quipped that he would “buy more shares” if he could.

“It was a heavily oversubscribed round for us, so we’re lucky,” he said. “One reason we added Deutsche Telekom is Germany is a strong market for us and it opens a lot of new opportunities through their network to expand our product and fast-growing offices in Berlin and Singapore.”

Dynamic Yield, which described itself as an “ad server for onsite content” in its earliest iteration, claims it was early to the race to tackle web personalization for retailers. 

Its tool lets marketers onboard and activate data, surface personalized recommendations and run site and in-app analytics across devices – all from the same platform.

Agmon claimed Dynamic Yield is much more predictive than traditional A/B testing tools, which he said are limited because they simply reduce campaign performance down to “averages.”

And Dynamic Yield claims to have a richer data set compared to its competitors. Like Google Analytics and Adobe Analytics, Dyamic Yield tags websites and onboards first- and third-party data through a relationship with Oracle Data Cloud.

Dynamic Yield has also doubled down on machine learning, employing 70 data engineers across its global offices.

“The way we look at machine learning is for each individual user,” he said “We have an increasingly large set of data based on first- and third-party data, CRM and purchase history, and the ability to crunch all these data points and rank their importance to craft an experience in real time. This is where machine learning comes into play.”

While an advanced algorithm might help a marketer create custom audiences based on purchase history or loyalty, Agmon argued that its machine learning can help generate a different website or ad experience with unique recommendations for each individual user – each time around.

“We think there’s too much focus on analyzing revenue per session or determining what happens after I click on an ad, arrive on a website and make a conversion,” Agmon said.

Although maximizing ROI per session is important, consumer journeys are far less linear.

The reality is, consumers today are likely to see an ad for a product on Facebook, visit the website and browse, but transact three days later in-app.

“While we’re not an attribution solution, this is why attribution has become incredibly important,” Agmon said. “We’re creating tools for retailers and B2C companies to create continuity from the mobile to the desktop experience independent of how someone accesses them.”

 

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