Jarvis ML lands $16 million to help companies personalize their products – TechCrunch

Jarvis ML, a platform offering an AI-powered personalization engine to brands selling products, services and experiences, today announced it has raised $16 million in a funding round led by Dell Capital Technologies. In an interview with TechCrunch, CEO Rakesh Yadav said the new capital will be used to develop Jarvis ML’s R&D and sales and marketing teams to “accelerate product development and market penetration”.

As the pandemic has prompted brands to spruce up — or create from scratch — online presences, the value of personalization has become apparent. Accustomed to bespoke product recommendations like Netflix and Amazon, customers have begun to demand the same from businesses of all sizes. According to McKinsey, 71% of buyers now expect companies to offer personalized interactions, while 76% are frustrated when this doesn’t happen.

Some research, especially from customer analytics providers, unsurprisingly suggests that personalization is a worthwhile investment. Forty percent of consumers responding to a survey said they bought something more expensive than originally expected due to personalized experiences. But creating this kind of customization can be difficult from a technical standpoint.

This is why Yadav founded Jarvis ML in 2021. Former senior engineer at Google, where he led the development of the machine learning platforms behind Google Payments and Google AdsYadav sought to create a product that could enable companies to turn data into brand engagements, like marketing campaigns or personalized web experiences.

The pandemic has accelerated the shift in online consumer shopping trends. It also means online recommendation strategies are critical for businesses to adapt to this changing consumer paradigm,” Yadav told TechCrunch via email. Giant tech companies like Amazon, Airbnb, Google, and Facebook are using machine learning to delight consumers and restrict the independence of growth-stage and mid-market companies that end up being relegated to supplier or fulfillment roles in giant tech ecosystems. Jarvis ML enables these companies to leverage the data they already have to reduce their reliance on tech giants while scaling sustainably. »

A screenshot of the Jarvis ML website.

Yadav describes Jarvis ML as a fully managed “machine learning-as-a-service” solution designed to enable companies to quickly deploy a personalization engine to their products. The platform relies on algorithms to learn sales and inventory patterns in the data companies provide to it, also creating prediction, pricing and promotion models that allow these companies to personalize their websites, apps and advertisements as well as concierge services and customer service.

Biases of all kinds have been found to come from personalization engines. Often they result from a data imbalance: one customer group is under-represented in the data used to develop the engine. Last year, LinkedIn mentioned it fixed an issue that made its connection suggestions less accurate for people who used the service less often than others. Other research has suggested that, on e-commerce sites, recommender systems can process economically unfairly disadvantaged customers compared to customers who make a lot of purchases.

Yadav did not directly address the issue of bias, but pointed out that Jarvis ML customers “own their data” and that the platform leverages revenue from the “lifetime values, preferences and tastes” of different buyers.

“Jarvis ML is just a platform that allows this data to be leveraged into actionable brand engagements, like marketing campaigns or personalized website experiences,” Yadav explained. “By profiling cohorts of customers…Jarvis ML can provide highly relevant recommended products, services and experiences to maximize sales. Our system selects the best models from a set of models based on tastes and selects the best model that works for our client businesses. »

In the recommendation engine market — a market that could be worth $17.30 billion by 2028, according to Grand View Research — Jarvis ML competes with e-commerce-focused startups like Constructor and Richrelevance. Other rivals include Flybits and Monetate (which was acquired by Kibo in 2019).

But Yadav expressed confidence in Jarvis ML’s ability to grow despite the competition, pointing to early adoption by customers like Twiddy & Company Vacation Rentals. The startup currently has a staff of 21, which it plans to expand to over 40 by the end of the year.

Our products are easy to integrate and offer deep machine learning capabilities to help businesses generate more revenue… For example, a travel agency might automatically feature beachfront homes on its website to a family in Beverly Hills while offering more modest condos to a retired couple in Salt Lake City. Both are relevant to each customer based on the lifetime value context,” Yadav said. “TTechnical managers at the C suite level can drive results simply by leveraging the Jarvis ML JavaScript SDK and one line of code change.

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