Optimal keyword bidding in search-based advertising with target exposure levels

Search-based advertising has become very popular since it provides advertisers the ability to attract potential customers with measurable returns. In this type of advertising, advertisers bid on keywords to have an impact on their ad’s placement, which in turn affects the response from potential customers. An advertiser must choose the right keywords and then bid correctly for each keyword in order to maximize the expected revenue or attain a certain level of exposure while keeping the daily costs in mind. In response to increasing need for analytical models that provide a guidance to advertisers, we construct and examine deterministic optimization models that minimize total expected advertising costs while satisfying a desired level of exposure. We investigate the relationship between our problem and the well-known continuous non-linear knapsack problem, and then solve the problem optimally by utilizing Karush–Kuhn–Tucker conditions. We present practical managerial insights based on the analysis of both a real-life data from a retailer and a hypothetical data.

► We analyze the problem of finding bids for keywords in search-based advertising.
► We present two different deterministic non-linear optimization models.
► The objective is to minimize expected total cost.
► At the same time, a desired campaign exposure level must be satisfied.
► We provide managerial insights by using real-life data and hypothetical data.