Dynamic dual adjustment of daily budgets and bids in sponsored search auctions

• We frame the dual adjustment problem of daily budgets and bids in sponsored search.
• We propose a CRL-based strategy, and present an iterative numerical solution.
• Our strategy performs better than two baseline strategies.

As a form of targeted advertising, sponsored search auctions attract advertisers bidding for a limited number of slots in paid online listings. Sponsored search markets usually change rapidly over time, which requires advertisers to adjust their advertising strategies in a timely manner according to market dynamics. In this research, we argue that both the bid price and the advertiser (claimed) daily budget should be dynamically changed at a fine granularity (e.g., within a day) for an effective advertising strategy. By doing so, we can avoid wasting money on early ineffective clicks and seize better advertising opportunities in the future. We formulate the problem of dual adjusting (claimed) daily budget and bid price as a continuous state — discrete action decision process in the continuous reinforcement learning (CRL) framework. We fit the CRL approach to our decision scenarios by considering market dynamics and features of sponsored search auctions. We conduct experiments on a real-world dataset collected from campaigns conducted by an e-commerce advertiser on a major Chinese search engine to evaluate our dual adjustment strategy. Experimental results show that our strategy outperforms two state-of-the-art baseline strategies and illustrate the effect of adjusting either (claimed) daily budget or bid price in advertising.