A Tale of Two Channels: How Digital Ads Perform in AI Recommendation vs. User Subscription Channels on Platforms Like Twitter, Google News, and TikTok
The JM Buzz - A podcast by Journal of Marketing
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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
