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