In 2013, Google and Bing have introduced bid adjustments that allow advertisers to better target key demographics. In addition to bids, the advertisers submit bid adjustments for search query features such as geographical location, time of day, device, and audience. For each search query, the respective bid adjustments are multiplied by the bid to reach an effective bid. We introduce the Bid Adjustment Problem (BAP) where an advertiser determines base bids and bid adjustments to maximize expected revenue subject to an advertising budget. We formulate the BAP as a mathematical program, however the multiplicative nature of bid adjustments leads to computational challenges. Under practical assumptions, we show that the BAP can be modeled as a mixed integer program. In addition, we show that the mathematical formulation can be reduced to two types of subproblems. In the base bid subproblem, we assume the bid adjustments are known. In the adjustment subproblem, we assume the base bids and bid adjustments for all features but one are known. We develop the Iterative Adjustment Algorithm where we create and solve subproblems in an iterative fashion to obtain a feasible set of base bids and bid adjustments. We perform computational experiments on data generated based on forecasts provided by the Google Keyword Planner. We compare the revenue obtained from the algorithm with the upper bound obtained from the mixed integer program, and demonstrate the algorithm provides near optimal solutions. We show that using bid adjustments leads to a significant increase in revenue, especially in the presence of high revenue-per-click variation across features.