An Auto-Coupon Framework for Advertiser Retention in Online Advertising

The online advertising platform serves as the intermediary between advertisers and users to facilitate the product trading process and generate profits. While the vitality of both the user and advertiser community is crucial to the long-term development of platforms, compared with the enduring efforts to improve user experience, little attention has been given to methods for attracting or retaining advertisers. To address this oversight, in this work, we investigate the first intelligent solution to the advertiser retention problem in online advertising. Motivated by advertiser surveys and data analytics, we first define a set of short-term ad performance metrics as the key retention metrics we need to optimize for advertisers. In consideration of the desirable properties of a retention framework, we propose an Auto-Coupon module to distribute impression-level virtual coupons to selected advertisers during the bidding process. We decompose the resulted retention optimization problem into two stages, and adopt feedback control as well as reinforcement learning methods to realize personalized coupon distribution. The offline experiments demonstrate the effectiveness of our methods in boosting the ad performance of churning advertisers, and the large-scale online A/B test further justifies the improvement in advertisers' willingness to stay in the platform.