Calculating and pushing keyword bids in real time is one of the most talked about paid search bidding strategies. However, this approach is easier said than done. Accounting for consumer behavior and collecting enough performance data minute-to-minute or hour-to-hour is difficult and often results in sub-optimal bids. Today we’ll discuss the challenges that online marketers face when attempting to calculate bids in real time and the requirements that need to be in place to execute a successful bidding strategy.
Consumers often click on multiple ads before converting. In many cases, they conduct research using several different search queries over the course of days, weeks, or in some cases months. Often times a consumer will validate their decision with one final burst of research just before converting. This delay between the initial ad click and subsequent conversion is known as conversion latency. To measure this latency, advertisers rely on tracking solutions that identify each unique user and follows them through the conversion process. The conversion and any associated revenue can then be attributed across each touch point; for example, the three paid search clicks that occurred prior to a purchase. Achieving this level of visibility enables automated bidding solutions to calculate accurate and optimal bids for all keywords that contributed to the conversion.
Display Retargeting vs. Search
Real-time bidding was born in the world of display retargeting, where advertisers bid to deliver ads to specific users based on information collected about them, such as which products or pages they've previously viewed on a company’s website. In this scenario, an automated bid, calculated and pushed out in real time, enables advertisers to target their spend and impressions towards consumers who are more likely to convert. The likelihood of a conversion is estimated using large datasets; the behavior of thousands, if not hundreds of thousands of visitors to a website is analyzed before implementing an optimal bidding strategy. This is a fundamental difference when approaching real-time bidding in display versus search, where the amount of data collected for display retargeting is significant enough to inform bidding decisions in real time.
Real-Time Bidding in Paid Search
In paid search, the data collected for a keyword in real time is minimal. Calculating keyword bids based on such a small dataset is extremely risky as these bids often leverage an insignificant amount of data that doesn't account for consumer behavior.
To address this challenge, Marin developed a patented bidding algorithm that utilizes Bayesian Estimation to minimize the risk of bidding on low volume keywords. Additionally, to account for consumer behavior, Marin allows online marketers to exclude performance data from bid calculations across a custom rolling date range. For businesses that experience high conversion latency or a large proportion of latent conversions, this means optimal bids based on accurate and complete performance data. Finally, Marin analyzes time of day paid search performance across multiple weeks in order to identify significant trends. These trends are used to inform daily and hourly dayparting recommendations, which can be implemented with just a few clicks.
Execute a Sound Strategy
When making bidding decisions, online marketers must ensure that they have a complete picture of performance. For paid search campaigns that are subject to conversion latency, this means sacrificing real-time bids in favor of implementing rolling date exclusions across a significant amount of performance data. Search marketers that execute their bidding strategy with this in mind are positioned to calculated optimal bids that maximize keyword performance.
For more information on Marin’s bidding solution, please contact firstname.lastname@example.org.