AiThority Interview with James Alger, Co-founder and COO of Qloo

James Alger, Co-founder and COO of Qloo chats about the latest uses of AI in customer engagement, AI-powered personalization, concerns about data privacy and more in this catch-up…

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Hi James, welcome to our AI Interview Series. Tell us about yourself and your journey as a co-founder at Qloo Inc. What inspired the platform?

I spent 20 years in advertising and marketing, including as CEO of creative digital and marketing agency Deepend. As I conjured up campaigns for brands like Estee Lauder, ESPN, MTV, Toyota, and United Healthcare, I saw the critical role audience insights played in connecting with consumers. And yet, methods for extracting these insights were very analog, time consuming, and expensive — focus groups, surveys, and the like. I knew technology could help brands understand consumers’ tastes and interests, allowing them to continuously keep their finger on the pulse of culture.

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It was around this time that I met Alex Elias, who was at NYU law school studying internet privacy. Alex was focused on how companies like Spotify, Expedia, and Netflix were dominating their respective sectors because of the incredible customer data assets they were amassing. Together, we committed to building a unified audience intelligence system that could reveal and predict consumer tastes across different domains like movies, music, hotels, restaurants, fashion brands, and more. This would give businesses the power to understand their customers’ unique preferences and allow them to reflect those nuances in marketing campaigns, in the digital customer experience, in the products they launched, and more. Twelve years later, this mission is still the guiding light within Qloo.

What is the role of AI in redefining consumer engagement protocols for businesses in the IT sector?

Personalization is critical to satisfying the needs and expectations of today’s customers — in fact, 76% of consumers are more likely to consider purchasing from brands that personalize the user journey. AI is an incredibly powerful technology for delivering advanced personalization, and Qloo’s Taste AI goes beyond a company’s first-party data to deliver experiences based on consumers’ full spectrum of interests and preferences.

When considering how to use AI to redefine consumer engagement protocols, first assess the areas of your customer journey where personalization could be most impactful. Then, select the right AI technologies and partners that can address those opportunities, all the while remembering to strike the balance of personalization and privacy.

In the era of increasing concerns about data privacy, Qloo’s completely anonymized consumer data is a significant differentiator. Talk about how Qloo ensures data privacy while still delivering high-quality insights to its clients.

Since Qloo’s inception 12 years ago, our AI models have never used personally identifiable information (PII). Rather, our entire system is trained to find the connections between lifestyle entities (notable people, places, things, and interests) and anonymized consumer signals that are completely void of PII. Because of this very intentional and privacy-centric design, Qloo’s Taste AI is fully compliant with leading regulations like GDPR and CCPA in California, as well as future regulations that stipulate the use of consumer data.

What are some of the top challenges in AI-powered personalization today, and how is Qloo positioned to overcome them?

Currently, the data brands use for personalization tends to come from two main sources: that company’s first-party data (which includes information like customer demographics, website interactions, feedback, transaction history, and past purchasing behaviors) and external data from third-party sources. While these data sets can reveal insightful details about a customer, they’re limited in what they can predict about tastes and preferences across categories. I travel a lot for work to places like Chicago, Las Vegas, and Miami, but when I log into my Delta app to get inspiration for a personal holiday, the recommendations are extremely biased by these past, business-related transactions. Qloo’s Taste AI, in contrast, considers a user’s full spectrum of preferences — the stores they shop at, the TV shows they watch, the restaurants they prefer — to make taste-based recommendations that truly reflect a customer’s personality. In doing so, Qloo is powering more advanced personalization that puts a user’s individual tastes at the center of the experience.

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What proven practices help organizations overcome the common challenges in implementing AI-powered personalization solutions

Qloo has been around for 12 years — I could fill a library with our learnings! One that is top of mind is that personalization is not a one-size-fits-all endeavour. Some brands want to inspire discovery and support curiosity, which requires recommendations to focus on more obscure suggestions. Others, like a big box retailer, want to drive repeatable product sales, so their recommender systems need to optimize for the most popular suggestions time after time. Qloo’s Taste AI, accessible via API, is designed to support all personalization goals. During the implementation period, we work closely with our customers to run controlled experiments to test for success and allow us to refine the model to meet their needs.

Highlight some of the emerging technologies or trends that you believe will have a significant impact on the personalization space in the next five years.

This year, Qloo worked with many forward-thinking companies to integrate LLMs into their customer experiences — from offering advice to shoppers on which shoes to buy, to building out customized travel itineraries. We believe these NLP-first experiences will become increasingly popular over the next five years. However, it’s essential that brands train and ground these models in accurate, factual consumer data to deliver the personalized outputs that today’s consumers expect. Qloo’s Taste AI is a critical ingredient in this equation, acting as a third-party oracle providing validated, structured data and inferential capabilities that dramatically enhance the precision of LLM outputs.

Beyond LLMs, we are closely monitoring how Apple Intelligence will raise consumer expectations for on-device personalization. Just as the launch of the iPhone back in 2007 ushered in the mobile-first era, we believe that Apple Intelligence will welcome the personal AI era. Hardware manufacturers and app developers will need to quickly embed AI models into their products to bring a new level of curation and recommendations to every action a user takes.

Finally, I think a lot about how noisy the content landscape is about to become as AI-generated music, films, and more flood this already crowded ecosystem. Consumers will need new ways to navigate this influx of options, making personalized recommendation systems more essential than ever.

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