Each month, Basis Technologies’ Programmatic 101 series tackles a different facet of programmatic advertising—from best practices for buyers, to competitors in the space, to trends you should know.
With the introduction of programmatic media, brands and advertisers suddenly had access to an overwhelming number of options. Whether it be the rolodex of publishers to work with, the various data points available for targeting, or the sheer number of key performance indicators (KPIs) they could measure, there were a lot of decisions to make—and brands and advertisers needed help sifting through all the options.
Bid automation software was developed to answer this call for help. New algorithms and software were created to take on some of the manual work initially required of media buyers, quickening and streamlining the process of programmatic bidding.
Read on to learn what automated bidding is and what benefits it offers, and to explore a few examples of what automated bid management looks like in context.
Manual bidding is when a buyer sets the price of how much they are willing to bid on an impression. Generally, the buyer will use historical performance or will manually pull reports each week to assess eCPM, or effective cost-per-thousand impressions.
Automated bidding strategies, on the other hand, allow technology to decide how much a brand should bid to achieve their goal. The technology can ingest various data points in real time and use that information to update the bid price. That doesn't mean that bidding is out of the media buyer's hands, by any means—in fact, it gives them back the time they would have spent manually assessing reports to better control and optimize high-level strategies.
The biggest advantage of automated bidding strategies is their time- and cost- efficiency. With bidding automation, buyers don’t have to manually pull reports and analyze the data to confirm if their bid is set at the right price. Some automated strategies even ingest high-quality data that isn’t readily available to brands or advertisers—such as historical campaign performance, auction insights, or time of day—adding an extra layer of intelligence and depth to their decision-making.
Algorithmic optimization (AO) seeks out which inventory is performing best for each tactic included in a campaign. Marketers can set the algorithm to focus on either placement-level or domain-level data. After a learning period, AO adjusts the bid prices depending on how a particular domain and/or placement is performing. It can either increase the bid or stop bidding altogether on under-performing domains and placements.
Machine Learning Optimization (MLO) is a model-based solution that leverages artificial intelligence to optimize towards a campaign’s desired KPI. MLO analyzes data from more than thirty tactic parameters, at the brand level, and dynamically creates models in real-time that determine how much a tactic should bid on each impression based on the likelihood that it will result in the desired outcome—whether that be clicks, conversions, video completions, or viewability.
Bid Shading utilizes an algorithm to analyze historical bid data and determine a minimum price that is lower than the default CPM bid, while maintaining a high probability of winning the auction. Bid shading provides time savings and cost efficiencies that make it a no brainer for programmatic.
Bid Multipliers allows a single tactic to submit various bids based on how the parameter is performing. For example, let’s say a buyer notices that desktop inventory is leading to more conversions. Bid multipliers allow buyers to set rules so that when a desktop impression appears, the technology will automatically bid higher to ensure the impression is won. In the same vein, buyers can also decrease bids for inventory, domains, frequency, or creative if they are under- performing.
Put simply, automated bidding strategies leverage technology to help buyers make informed decisions about their bids. As a result, buyers can:
Curious to learn more? Check out our guide, Meeting the Moment with Advertising Automation, to learn about the many variations of automation, and how they serve to improve the lives of media professionals.