Portfolio Bidding For Paid Search


In the world of paid search, there is often a parent company that owns two or more Google AdWords accounts bidding on similar keywords.  For instance, Alamo, National, and Enterprise are all owned by Enterprise Holdings Inc., and their respective paid search accounts bid on the same keywords.  How does the parent company maximize paid search revenue in this intricate scenario?


Lead Horse has developed a sophisticated portfolio bidding approach coined MAP.  MAP stands for Master Account Portfolio and the “master account” goal is defined as the overall paid search goal for a company that possesses multiple, individual accounts bidding on the same keywords.  In order to maximize revenue and avoid auction based CPC inflation, a master account portfolio (MAP) bidding approach should be tested.  This bidding approach has two primary components; ‘Revenue & ROAS Maximization’ and ‘Bid Spreading’.

Revenue & ROAS Maximization

The primary component of the MAP bidding approach is revenue & ROAS maximization.  This approach considers all of the shared keywords across both accounts at once, assigning bids so that, on average, the group as a whole maximizes a goal while meeting certain constraints.  Examples of constraints could be a specific ROAS or cost per action (CPA), whichever is the most appropriate for the overall paid search goal.  The advantage of a MAP bidding approach across both accounts is that it treats each keyword appropriately with respect to the overall paid search goal. For example, let’s assume the overall or master paid search goal across both accounts is an average ROAS of 200 percent.  Given the overall goal of 200 percent, it might turn out that one keyword within  Account A can drive significant revenue at a ROAS of 180 percent, while the same keyword within the Account B drives the same amount of revenue at a ROAS of 220 percent. As long as the overall paid search goal ROAS of 200 percent is met, the MAP portfolio solution will declare success. This approach generally provides higher value from a set of shared keywords across two accounts compared to separate optimization strategies within individual accounts.

Bid Spreading

The secondary component of the MAP bidding approach is bid spreading.  In order to avoid CPC inflation occurring within the auction based landscape, bid spreading by ad positions across shared keywords should be considered.  For instance, if the keyword ‘paid search’ currently resides in position one in Account A and the same keyword for Account B currently resides in position two, the master account runs the risk of Account B pushing the average CPCs higher for Account A.  Quality Scores play a significant role in this equation, but let’s assume the Quality Score is equal for both instances of the keyword ‘paid search’ in this scenario.  A weekly report (adjacency report) can be automatically produced each Monday to inform the Media team of instances across the shared keyword list wherein ad positions are adjacent. Based on performance indications, the Media team can decide whether or not it’s beneficial to adjust max CPC bids in one account in order to produce a position spread between the two accounts.  In this scenario, a competitor’s ad maintains the position in between the master account’s ad results.  If warranted, creating the position spread by adjusting bids eliminates the risk of auction based CPC inflation.

MAP in Action

For the first round of MAP implementation, select a bucket of keywords based on the following criteria:

  • High volume
  • Above average performance
  • Equal QS between both sets of shared KWs

Focus on the revenue & ROAS maximization component of MAP first.  Once the ideal bids for the maximization of blended revenue and ROAS have been identified, test the bid spreading component.  Are there instances of shared keywords that could benefit from bid spreading through the reduction of avg. CPCs by eliminating occurrences of ad adjacencies?  By utilizing the MAP bidding approach, companies with multiple paid search accounts can maximize revenue and efficiency.

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