Do you prefer social media posts from the sources you're subscribed to? Or are you more interested in the content recommended by AI algorithms? A new Journal of Marketing study shows content that is "recommended" for users has less consumer engagement but fewer ads they find annoying, resulting in higher click-through rates but lower conversion rates.

Read an in-depth recap of this research here: https://www.ama.org/2023/08/22/a-tale-of-two-channels-how-digital-ads-perform-in-ai-recommendation-vs-user-subscription-channels-on-platforms-like-twitter-google-news-and-tiktok/

Read the full Journal of Marketing article here: https://doi.org/10.1177/00222429231190021

Reference: Beibei Dong, Mengzhou Zhuang, Eric (Er) Fang, and Minxue Huang, “Tales of Two Channels: Digital Advertising Performance Between AI Recommendation and User Subscription Channels,” ⁠Journal of Marketing⁠.

Narrator: Elizabeth Ann Sismour

Acknowledgments: Sushma Kambagowni

Topics: advertising, marketing strategy, social media, digital marketing

The JM Buzz Podcast is a production of the American Marketing Association's Journal of Marketing and is produced by ⁠University FM

A Tale of Two Channels: How Digital Ads Perform in AI Recommendation vs. User Subscription Channels on Platforms Like Twitter, Google News, and TikTok