On August 5, the ARF Women in Analytics launched the 2026 Mentorship Program and discussed tips for growing and maintaining their connections. Attendees learned the methodology behind how their matches were determined and heard from past mentor-mentee pairs about their experiences and ideas for building a successful relationship.
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As AI assistants become fully embedded within e-commerce platforms, the question becomes: how are consumers actually using them? This MSI working paper analyzes the behavior of over 31 million users on a major travel platform to uncover who adopts shopping AI, when it is used in the purchase journey, and what consumers rely on it for. The findings reveal that AI assistants complement—not replace—traditional search, helping consumers navigate complex, exploratory decisions and reshaping how discovery happens online.
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At the Women in Analytics luncheon during the ARF Commerce & Shopper Intelligence conference, inspiring female leaders shared defining moments of bravery—how they silenced self-doubt, navigated opposition, and turned fear into triumph. Attendees had the opportunity to participate in an energizing conversation and build meaningful connections with like-minded peers.
Member Only AccessStony Brook University in New York asked 65,000 Facebook users to participate in a survey. They then analyzed 10 million messages. The results suggest that men and women speak different languages as soon as they start scrolling through their newsfeeds. The differences are predictable enough that an algorithm could determine a user’s sex accurately 90% of the time.
The experts found that women are more apt to use warmer and more agreeable language, while men are angrier and more argumentative.
However, women tend to be every bit as assertive as men.
/publications/article/277133/on-facebook-men-are-meaner-women-nicer.html?edition=93445
Member Only AccessStony Brook University in New York asked 65,000 Facebook users to participate in a survey. They then analyzed 10 million messages. The results suggest that men and women speak different languages as soon as they start scrolling through their newsfeeds. The differences are predictable enough that an algorithm could determine a user’s sex accurately 90% of the time.
The experts found that women are more apt to use warmer and more agreeable language, while men are angrier and more argumentative.
However, women tend to be every bit as assertive as men.
/publications/article/277133/on-facebook-men-are-meaner-women-nicer.html?edition=93445
Member Only AccessStony Brook University in New York asked 65,000 Facebook users to participate in a survey. They then analyzed 10 million messages. The results suggest that men and women speak different languages as soon as they start scrolling through their newsfeeds. The differences are predictable enough that an algorithm could determine a user’s sex accurately 90% of the time.
The experts found that women are more apt to use warmer and more agreeable language, while men are angrier and more argumentative.
However, women tend to be every bit as assertive as men.
/publications/article/277133/on-facebook-men-are-meaner-women-nicer.html?edition=93445
Member Only AccessStony Brook University in New York asked 65,000 Facebook users to participate in a survey. They then analyzed 10 million messages. The results suggest that men and women speak different languages as soon as they start scrolling through their newsfeeds. The differences are predictable enough that an algorithm could determine a user’s sex accurately 90% of the time.
The experts found that women are more apt to use warmer and more agreeable language, while men are angrier and more argumentative.
However, women tend to be every bit as assertive as men.
/publications/article/277133/on-facebook-men-are-meaner-women-nicer.html?edition=93445
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As generative AI tools increasingly influence product discovery and decision-making, subtle cues in user language can shape what consumers are shown—and how options are framed. This second phase of the sixth study in the Psychology of Gen AI series examines how implicit and explicit gender signals affect AI-generated product recommendations, revealing systematic differences in categories, brand repetition, descriptive language and price information. The findings raise important questions for advertisers and researchers about bias, brand visibility and the growing cultural role of AI in shaping consumer norms.
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