AI is here, and we’re told it will make our lives easier in many ways – such as with automated bidding tools. And it can. But it isn’t as simple as flipping a switch. Which automated bidding strategy should be used for which type of account? What kind of monitoring and testing should we do? What can we do if we aren’t getting the results we expected? Based on our extensive experience with automated bidding tools, here are a couple initial questions to consider:
- What is the goal for the account? Different goals lead to different strategies
- What is the historical performance of the account? The algorithm needs data to perform well
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Read on for more details about (un)Common Logic’s experience and best practices with regard to automated bidding strategies.
Automated Bidding Strategies: The What & Why
Automated bidding strategies have become less a possibility and more of an inevitability recently. The balance of control between advertisers and platforms has gone back and forth over the years as Google continues to move away from manual bidding. In their eyes, being able to optimize bids at nuanced levels like keyword, device, or location have become inefficient uses of time that could be better spent developing higher level strategies. Google will set the bids for you, using machine learning and algorithms to reach a desired goal. While this seems tempting at first there are several factors that need to be thought through before reducing or eliminating manual bidding and rolling out automated bidding strategies. What should you consider? Let’s start with the basics and then discuss strategies and tactics around automated bidding.
To begin, there are six different automated bidding strategies to choose from depending on the desired outcome. Each strategy will automatically set your bids:
- Target CPA – The user sets a target Cost-Per-Acquisition (CPA) that the algorithm will work towards while capturing as many conversions to stay more or less around the desired target
- Target ROAS – Similar to target CPA, but instead the user sets a target Return on Ad Spend (ROAS) that the algorithm will optimize towards
- Maximize Clicks – Within your set budget the system will work to capture as many clicks as possible whether or not they could result in a conversion
- Maximize Conversions – Similar to Maximize Clicks, except the system will work to capture as many conversions as possible, but it can come at the cost of drastically increased CPAs
- Maximize Conversion Value – Also similar to Maximize Clicks and Maximize Conversions except it tries to capture as much revenue regardless of the efficiency as long as it remains within the set budget
- Target Impression Share – The system optimizes to show your ad at the absolute top of the page, on the top of the page, or anywhere within a Google search result.
Enhanced Cost-Per-Click (eCPC), while considered by many to be a variation of automated bidding, is not included in Google’s definition since the user can still implement several manual adjustments.
Each automated strategy uses the historical performance of the account or campaign, typically ranging from 14 to 30 days, to adjust their bids and define a user profile that is more likely to complete the desired outcome. In order to use any of these strategies, the campaigns will need to be launched using manual bidding to collect that performance data before they are eligible to switch over to an automated form.
With that covered, let’s talk about best practices for running automated bidding strategies.
How to Choose the Right Automated Bidding Strategy
I attended a workshop a few years ago when automated bidding strategies were really starting to gain ground, and I remember the Google representative saying that the ideal budget for running an automated strategy was an unlimited one. Now, in the real world this is entirely unrealistic, but it speaks to the designated outcome Google wants and what we need to consider before implementing an automated bid system.
Like a snowflake, each account is wholly unique in its performance and structure. What works for one account doesn’t necessarily translate to others, but in my experience most accounts are working towards a common goal: growing lead or revenue volume efficiently. Target CPA (tCPA) and Target ROAS (tROAS) are the most popular forms of automated bidding because they’re geared towards that goal.
When an account manager sets a target for tCPA or tROAS, the algorithm used to automatically set bids is primarily concerned with reaching that designated efficiency goal. If a campaign’s daily allotted budget is too small to evenly serve ads throughout the day, it hinders the tCPA and tROAS algorithm’s ability to test various auctions and adjust bids accordingly.
When the tCPA or tROAS algorithm is evaluating if it should bid on a user, it is not concerned about the cost of that bid, it’s concerned about the target CPA or ROAS. , The result is that, based on how likely it thinks a user will convert, the algorithm will adjust the aggressiveness of the bid to ensure that it captures the user and optimizes towards the CPA or ROAS goal.
Meanwhile, the Maximize variations and Target Impression Share strategies are designed to work within the constraints of any budget. That means if a campaign is limited by budget, these strategies will still work to maximize volume. The major drawback is that often this comes at the expense of efficiency. The strategy will bid as aggressively as possible to capture volume, which can lead to higher CPCs andCPAs, and lower ROAS.
Another big consideration is historical performance. Google now stipulates that you don’t need a certain conversion threshold to use the tCPA or tROAS strategies, but the best practice would be to have a minimum of 15 conversions within the past 30 days. The more data, the better, since these algorithms use past performance to dictate bids. If your campaigns have low volume then using a Maximize Conversions or Maximize Conversion Value strategy could help bolster performance to allow you to switch towards an efficiency automated bidding strategy.
The next question is what user action the automated bidding algorithms should optimize towards. For e-commerce it’s typically a purchase and for lead-generation it’s filling out a form. We only want to count people who have completed this action, we usually don’t want to count people that added something to their cart but didn’t complete the purchase or only partially filled out a lead form. The algorithm will then optimize towards the conversion action that we want counted. The algorithm builds a user profile around customers that completed the desired action, and when the algorithm is evaluating bidding in another auction it will bid more aggressively on users that fit within that profile. If you have secondary conversion actions outside of the primary completion objective, you have to specify that those secondary conversion actions should be counted for the system to optimize towards them.
While each strategy has its pros and cons it doesn’t mean that a strategy should be discounted because of a preconceived notion. I recommend you test a strategy on a small number of campaigns and review performance before deciding if it should be rolled out across other areas of the account.
When & How to Optimize Your Automated Bidding Strategy
With automated bidding strategies it is all to easy to set it and forget it, but that should never be the case. These algorithms fluctuate, and since you can’t adjust your keyword, device, or location bids anymore it begs the question of how we rope them in.
In testing tCPA and tROAS, we need to start with a realistic goal that the algorithm can work towards. For example, if your desired long-term goal is a 200% return and the campaign’s current ROAS is 80% you’ll run into large fluctuations in performance if you try to achieve that large an increase all at once. Large spikes in cost, no or low spending, or volume tanking are all possibilities if you set unrealistic targets. Typically, I look at the last 14 – 30 days of performance and use that as a baseline. Remember, since these strategies use that time range to set bids this is the most accurate representation of what the algorithm will think is possible. If I’m switching from a manual strategy to tCPA or tROAS, I will usually set the target 5 – 10% below the baseline since it will initially enter the learning phase, the next consideration for testing.
For the Maximize variations the levers we can pull are limited. You can freely adjust budgets on campaigns since the algorithm will use the average daily budget to determine spend, you can set maximum bid limits to ensure the system doesn’t bid higher than your specified cap, and you can set the ad scheduling to show during certain times of the week, but that is the extent of . The lack of levers means controlling spend is a much more difficult task, especially since these strategies can quickly increase the daily cost putting accurate spend pacing in jeopardy.
Target Impression Share works in a similar fashion, but allows you to choose between aiming for the absolute top of the page, on the top of the page, or anywhere on the page of a Google Search result. You can then adjust an impression share target and the system will automatically set your bids to show your ads the total possible amount of times at that target. You also have the ability to set bid limits. Similar to the Maximize variations, Target Impression Share can quickly increase the daily cost of campaigns, making spend pacing much more difficult.
With these bidding strategies, if efficiency metrics are a primary concern then I would not recommend starting with them and instead testing tCPA or tROAS.
When launched, the automated bidding strategy will enter a learning period for 1-2 weeks in which it tests the boundaries of bidding. Who is likely to convert or what types of auctions should it bid in are questions the algorithm seeks to answer during that time. While in learning, you should not adjust the goal or make any major changes to the campaign or you risk extending the learning period further. It can be tempting to make a change since performance will be scattered in this phase, but once it is out of learning you should see more stability. In a recent test we conducted for tCPA we set the target at $600. in learning, the CPA was around $1100, but after that initial learning period, we saw the CPA drop into the low $600 range.
Once the campaign is out of learning, changes still need to be made sparingly because you risk the campaign reverting back to a learning period or undergoing large changes in performance. The max adjustment for tCPA and tROAS targets should only be between 5%-15% at a time. Goal adjustments should also only happen about once a week, and if the campaign is “Budget Aware” it should not be combined with large shifts in the average daily budget. Despite the buzz around machine learning these algorithms are sensitive. It’s best to make a change, wait a few days for stability, and then make an additional change if needed.
If you’re running any promotions, there are additional considerations to keep in mind. Last Spring, we switched over to tROAS for a majority of Non-Brand campaigns for one of our e-commerce clients. We typically run multiple offers throughout the month, and as we entered the busy summertime, we noticed fluctuations in performance that didn’t correspond to the strength of the offer. The following chart shows the type of promotions we ran during June:
The strongest offers were at the beginning of the month with a Buy One Get One, but if you notice on June 17th, we see a sudden spike in spend and revenue trending down after switching to a 30% Off deal. Even when adjusting targets to be stricter to lower the aggressiveness of bids with the weaker offer, spend still increased. The reason for the decline in performance is the historical data the automated bidding strategy was using. As more of the 14-day lookback period was dominated by the improved performance from the BOGO, when we switched to the less enticing 30% Off, the algorithm believed users were still going to convert at a much higher rate and therefore bid much more aggressively.
After this realization we worked with the client to restructure the promotional offers to avoid these large shifts in offer strength. Steadily ramping up and down the promotional strength allowed the algorithm to normalize performance and reduced the fluctuations in spend that we experienced.
If you rarely run promotions or have specific promotions where you see a jump in performance, you can use seasonality adjusters to push the algorithms to bid more aggressively. These adjusters are based on the CVR increase you might expect running that type of offer. For example, if you only run a Black Friday promotion each year and saw that your CVR increased by 20%, you can tell the system to spend more during that time by setting a 20% seasonality adjuster. The time period that you specify with the adjuster won’t be considered in the lookback period for automated strategies, which reduces the risk of performance fluctuation following the offer. It’s recommended though that you only use a seasonality adjuster for a maximum of four days. Typically, we will use a seasonality adjuster during the first day or two of a strong promotion to help prime the algorithms.
Automated bidding is the future, for better or worse. While it removes a level of control that advertisers have become accustomed too, it can also help improve account performance by taking into consideration several factors that humans aren’t privy to. In the coming years, as automated bidding becomes the new normal, advertisers will still need to be wary of the espoused oasis Google claims because, while machines have the ability to help, they should not dictate our actions. Constant monitoring and testing is needed to ensure these strategies help grow accounts sustainably and that they fit your desired direction, but automated bidding can become a strong asset for accounts when executed with that understanding in mind.
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