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Qloo raises money to expand its AI-powered recommendation API – londonbusinessblog.com

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Policymakers have cracked down on platforms that collect consumer data and have introduced rules that require companies to be transparent about the details they collect and use for commercial purposes. For example, last year Virginia passed the Colorado Data Protection Act, which requires companies to obtain consent before processing sensitive information, disclose when the information is sold, and allow customers to opt-out. California, Colorado and the European Union have similar frameworks, while others states and to land consider the same.

Some marketers argue that these safeguards have made it more difficult to suggest or predict what products customers might want. But Alex Elias says it doesn’t have to be that way. He is the founder of Qlooa platform that uses AI to help brands analyze customer preference data to make recommendations, including recommendations for entertainment and physical goods.

“The regulatory and platform environments surrounding privacy have severely limited identity-driven ways of understanding consumers. This has had seismic impacts across industries ranging from technology to consumer packaged goods, and has led many companies to collect their own first-party data, which poses significant risk,” Elias told londonbusinessblog.com in an email interview. “At the same time, consumer tastes are becoming more fragmented and detailed in their profiles, with the increase in media and music consumption making it more difficult to reach consumers.”

Elias, along with Jay Alger, Qloo’s chief operating officer, created Qloo to solve these twin problems, Elias said. “Qloo can illuminate audience preferences in a big way, the data can improve sales efficiency, increase conversion and thereby increase profits,” he said. “Most companies that have an interest in understanding consumer taste at a more granular level can benefit from Qloo.”

In a vote of confidence from investors, Qloo today secured another $15 million in funding as part of a Series B round led by Eldridge and AXA Venture Partners. It brings the company’s total revenue to $30 million, including contributions from well-known celebrities such as actor Leonardo DiCaprio, Elton John and Starwood Hotels founder Barry Sternlicht.

Qloo claims its API correlates with more than 575 million “primary entities” — including movies, books, restaurants and songs — to make predictions of consumer tastes for “dozens” of enterprise customers, such as PepsiCo and Elton John’s music company Rocket Entertainment. . The API also powers TasteDive, a social media app with a built-in entertainment recommendations engine for movies, TV shows, music, video games, and books that Qloo acquired in 2019.

According to Elias, Qloo does not use any personally identifiable information, keeps all requests “ad hoc” and refuses to store the identities of customers’ customers. Data is ostensibly anonymized and encrypted, and the platform’s data handling is “fully compliant” with regulations, including the GDPR and the California Consumer Privacy Act, Elias said.

The details are a bit vague, but at a high level, Qloo uses a knowledge base of customer preferences to refine algorithms that yield product recommendations and insights. For example, Qloo can create “taste affinities” for entities (e.g., music artists) overlaid over a geographic region, revealing the tastes and trends of particular cities and even neighborhoods (such as which musicians are popular in Downtown Brooklyn). The platform may also generate descriptions about the tastes of entity groupings or entity comparisons, such as the differences in music taste between a Nike sneaker customer and Vans customer.

Armed with Qloo and its integrations with Snowflake, Tableau and other existing data platforms, customers can better solve problems such as driving sales, reducing ad spend and choosing store locations, Elias said.

Image Credits: Qloo

“[Qloo’s] AI is tuned to a wide range of parameters, allowing end customers to dynamically adjust the algorithm’s weighting, based on how ‘new’ or ‘expected’ they would like the taste correlations to be for the end consumer,” Elias continues. “For example, a streaming client in Asia was able to prioritize regionally specific results over globally popular recommendations and tailor the algorithm accordingly.”

Elias admits that numerous companies have tried to crack personalizations and recommendations through AI, including Dynamic Yield and RichRelevance, owned by Mastercard. But he sees the Qloo platform as complementary to competitors that use more general recommendation engines, such as Amazon Personalize, Azure Recommendations API, and Google Cloud’s Recommendations AI, because it supposedly brings “out-of-the-box knowledge” that they sometimes lack.

“Qloo stands out for its deep dataset and existing knowledge base across domains including music, products, travel and more. This enables Qloo to help customers achieve significant personalization with minimal context delivery,” said Elias. “Qloo is also a privileged competitor to traditional, more expensive insights tools, such as focus groups or tailor-made surveys, as it can quickly and efficiently provide ad hoc insights based on much larger panels, e.g. ‘What movies will people in the Upper East Side like Lululemon likes too?’”

Series B proceeds will support the launch of Qloo’s latest product, Elias says — a “lightweight” version of the platform that offers subscriptions to a visual interface designed for less tech-savvy users. In addition, the money will support product development, expand Qloo’s 30-person team by more than 30% in the coming months, and expand the company’s sales channels.

Elias hesitated when asked about the income. But he said Qloo has so far managed to break the economic trend, thanks in part to recovering demand in sectors such as travel and entertainment.

“Two years after the pandemic, Qloo has seen an increase in demand for its services, leading to unprecedented revenue and API usage,” said Elias. “Fundamental tailwinds, including a commitment to privacy, a focus on revenue growth and broad adoption of AI, have more than offset headwinds from the macro environment and broader technical valuations. Qloo has achieved a very low burn rate and is approaching profitability.”

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