AI democratizes the recommendation engine

Tech has its share of dropouts that have made it look like champions – Larry Ellison, Steve Jobs, Bill Gates – it’s an impressive list. They grew up to reshape the world. As adults.

Add Founder and CEO Olivier Edholm to this elite group. Now 19, he is also going his own way. At 15, her summer internship at Klarna turned into a software development job while finishing evening high school. At 16, he left for Singapore to start a new chapter.

The Y Combinator alum completed a $17 million Series A in February and is wasting no time.

People on a passionate professional mission are often like that. In Edholm’s case, his breakthrough was knowing that 35% of Amazon’s sales come from product recommendations, yet few (if any) competing e-commerce players have recommendation technology for products. compete.

Speaking to Karen Webster of PYMNTS, Edholm explained that the “related product” widgets served by Amazon “use a form of artificial intelligence, and the AI ​​today needs a lot of data to work properly. Most e-commerce merchants don’t have as much data as Amazon,” he said.

“We are democratizing the ‘related products’ widgets you see on Amazon, [providing the ability for] great recommendations for all e-commerce stores.”’s disruptive innovation creates a recommendation engine that any e-commerce site can integrate with “to deliver recommendations without so much data, and through that to create very high quality recommendations and provide much better value”.

In the absence of a pool of data on a given shopper or group persona, recommendations use AI to better understand consumers as they purchase based on real-time observations.

Combining data about what a shopper is browsing and purchasing with the right product information allows online users to understand “e-commerce products on the site almost the same as if you were going to the physical retail store. and ask someone for advice. he said.

“That’s the level of understanding that we try to translate into what we do, and with that, although it’s very technical, you can demand a lot less data and provide better service.”

See also: 100% of the most successful online merchants offer product reviews and live on-site help

Recommendations without computer pain

Even after two years of digital transition, some merchants still need to be convinced that technologies like AI can have a transformative effect on their e-commerce business.

As a workaround, allows e-commerce sites to use its Recommendations-as-a-Service platform for free, running a live A/B test showing sales with and without recommendations.

Edholm said, “When we measure the number of purchases generated through referrals, we saw a 2-4x increase, which is a lot.”

Continuous measurement of conversions tells the retailer which recommendations are underperforming and adjusting, constantly evaluating the revenue recommendations created.

This not only generates additional sales, but also reduces the pressure on IT.

“E-commerce merchants have a lot of issues with computing resources,” he said. “What we’ve figured out how to do is create an integration technology and experience where we can integrate without any IT resources from an e-commerce merchant’s perspective. That translates into an experience where you can get this live on your site very quickly and see that it works.

If the merchant does not see improvement against the recommendations within two months, walks away.

“There are a lot of learnings you accumulate once you work with traders, and there are a lot of quick wins you can have. If you can’t properly display recommendations intuitively for the user in the right place on the website, there isn’t too much value. We come with both.

With a client list currently including “regional parts of Staples and Office Depot” as well as big brands in the Nordic region like Björn Borg Group and Ideal of Sweden, works with as much data as its partners can provide, so let its AI solution monitor and make suggestions.

For example, he said, “We can get margin data for all of your SKUs. [stock-keeping units, bar codes] and help mix recommendations to push, say, higher-margin or lower-return products. This would be especially useful for clothing sites where returns are a major issue requiring a solution.

“You can figure out which items have the lowest returns and then push them,” he said.

See also: 90% of acquiring banks say using AI to monitor merchant transactions improves efficiency

Next step: Research

Strategically using an AI-powered recommendation engine can help businesses understand which sales approaches are working, which aren’t, and how to quickly remedy the situation.

Edholm said “a lot of [merchants] think they need to push more stuff. We help with [that by identifying] which generates the most revenue by helping the customer find the product they want. »

Specializing in businesses with high SKU counts, primarily operates in the office supplies, fashion, furniture, home decor and jewelry industries, he told Webster.

In 2022, the priorities are to move beyond product recommendations to research.

Edholm said the company’s search engine is currently in beta with several clients.

Expanding into search gives a broader view of the individual searcher and feeds “that into recommendations and vice versa so the experience becomes much smoother. And from an e-commerce merchant perspective, you can work with [fewer] suppliers.”

With the sheer volume of size, color, style, and two dozen other considerations going into the consumer’s calculation, product recommendations are very complex, and Edholm finds that e-commerce merchants are crying out for a platform solution. simple and effective form.

“Our customers are very explicit about what they want from us,” he said, “and we work hard to make sure we can deliver it, because it’s a complex product.”



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