What do working out, putting money in a 401(k), and running an advertising campaign have in common?
They all require an investment—whether that be time, effort, or money (or all three!). And, like with any investment, people expect a return. Avid gym goers expect to get stronger. Folks investing through their 401(k) accounts expect those funds to grow. And brands spending money on advertising campaigns? They expect for that money to drive revenue.
Today, we’re digging into how marketing teams can maximize their return on ad spend (ROAS) by adjusting their target ROAS bidding strategy. We’ll explore what a target ROAS strategy is, when to use one, and how to fine-tune your target ROAS in different scenarios to maximize returns. Ready to become an ROAS rockstar? Read on!
Before we dive into what target ROAS is, let’s review the basics. ROAS is a metric that tells advertising teams how much revenue they are generating relative to their advertising spend.
How do advertisers calculate ROAS? Easy—they use the following formula:
ROAS = (Revenue ÷ Ad Spend) x 100
So, if you spend $100 on a campaign and it generates $200 in revenue, your ROAS = (200/100) x 100, or 200%.
Target ROAS is an automated bidding strategy offered by many programmatic advertising platforms. As the name suggests, it optimizes campaigns towards a “target” ROAS value, which is set by an advertising team based on historical conversion values.
For instance, if you are using a target ROAS strategy and set a target of 300%, machine learning will automatically optimize your bids towards the placements that are most likely to generate $3 of revenue for every $1 of ad spend. To be clear, this doesn’t mean the technology will only bid on placements that will generate $3 in revenue, but rather that $3 per every $1 of ad spend is the average revenue target it will optimize towards.
Target cost per action (otherwise known as cost per acquisition or CPA) is also an automated bidding strategy, but it optimizes bids towards a different goal than target ROAS. Target CPA takes the amount you’re willing to spend per a specific conversion or consumer action, and bids with the goal of achieving the highest number of conversions or consumer actions based on that amount. For example, you might set a target CPA of $20 per new customer. Machine learning will then automatically optimize bids towards gaining as many conversions as possible at or near that target cost.
Target CPA is often used in lead generation campaigns and is best suited for teams who want to acquire customers at a predetermined cost. Target ROAS, on the other hand, is best for those who want to generate the most revenue possible for a specific amount of ad spend, and it can provide more flexibility amidst market changes. For example, an e-commerce brand might benefit from using a target ROAS strategy to maximize their ROAS leading up to the holidays. A B2B startup, on the other hand, might benefit from using a target CPA strategy, to ensure they’re acquiring customers within their set budget.
Both target ROAS and target CPA strategies rely on conversion data, but they use it in different ways. With target CPA, machine learning optimizes towards a specific conversion value. With target ROAS, on the other hand, machine learning uses historical conversion values to try to achieve your target ROAS. For example, if your online store historically saw $6 worth of sales for every $1 of ad spend, you might use that information to set a target ROAS of 600% ($6 in sales ÷ $1 in ad spend x 100%) to continue to make the same return on ad investment.
Advertisers should use a target ROAS strategy when they have a specific ROI (return on investment) goal for their advertising efforts. It’s also important they have a clear understanding of the value associated with different conversions, so that the bidding strategy can use those values to optimize for desired returns.
Beyond having sufficient data to inform this strategy, there are specific scenarios when advertisers might benefit from using target ROAS:
To set up a target ROAS strategy, advertisers must first define their conversion values based on historical data. In other words: They need to have a clear understanding of how much revenue can be generated by different customer actions. Once those values are defined, advertisers can select a target ROAS bidding strategy based on existing data and set a target ROAS value based on their unique objectives.
This target ROAS value then guides the bidding system as it makes optimizations. But just like any strategy, it’s important to monitor and adjust based on performance metrics (more on this shortly!).
Often, teams will set an ROAS target based on a known value of doing business, likely due to an understanding of what margin of revenue is required on ad spend to ensure profit.
But remember that the goal of this bidding strategy is to maximize revenue—and simply setting a target ROAS and forgetting about it is not guaranteed to result in the best returns. In fact, with a bit of testing and optimization, teams can make adjustments that result in a greater ROAS.
So, how can advertising teams go about determining what ROAS target will result in the most profit? Even if a target ROAS metric is being met, that’s not a guarantee that you’re capturing the maximum amount of profit available. A higher ROAS target may result in similar revenue, with lower spend. A lower ROAS target may conversely result in increased revenue, with similar spend.
To determine if performance is short of what it could be, here are a few things to consider:
By observing the interactions between these components over time, teams can figure out whether their target ROAS is too low, too high, or just right. Let’s dig into a few scenarios to see what this looks like in action.
In this scenario, your target ROAS is likely too low. Though you’re getting closer to your target ROAS, you’re losing out on profits because you’re spending more on ads even though your revenue is the same.
Let’s dig into an example of what this might look like:
Imagine a team’s target ROAS is set to 200% (i.e., make $2 in revenue for every $1 of ad spend). In the past, they were spending $25 on ads and bringing in a revenue of $100, with an ROAS of 400%. But, since their target ROAS is 200%, their ad spend is being shifted in a way that spends more on ads without an increase in revenue. If they’re now generating a revenue of $100 at a cost of $40 in ad spend, they are technically getting closer to their target ROAS with their new ROAS of 250%. But, they’re spending more to do so! In other words, they’re getting less bang for their buck.
That’s why the recommendation here is to increase your ROAS target based on the higher historical ROAS your account had. In other words, if you were spending $25 on ads and generating $100 in revenue, set your target ROAS to 400% and monitor.
In this instance, your target ROAS is too high. The optimization strategy is reducing spend in an attempt to hit a potentially unattainable ROAS target at an “acceptable” revenue volume.
Here’s what this might look like and how a team could adjust their target ROAS in such a scenario:
Let’s say a team sets their target ROAS to 500%. In the past, they generated $100 in revenue at a cost of $50 in ad spending, meaning their past ROAS value was 200%. Now that their target is set to 500%, they are currently generating $75 in revenue at a cost of $25, putting their current ROAS at 300%.
Like scenario 1, this team’s ROAS is getting closer to the target ROAS, but they are missing out on revenue—only, this time, it’s because their target ROAS is too high, which means the platform is lowering spending in an attempt to hit a higher ROAS. In this scenario, the best thing the team could do would be to lower the target ROAS and monitor campaign performance.
This is a great situation to be in! In this instance, you’re spending a bit more, but making a bit more at the same time. Here’s an example of what this might look like:
Imagine your target ROAS is set to 200%. In the past, you generated $100 in revenue at an ad spend of $50, meaning you were hitting your target at an ROAS of 200%. Now, you’re generating $200 in revenue at a cost of $100 in ad spend. Your ROAS is the same—200%—but you’re generating more revenue than you were in the past.
Teams have a few options here, depending on what their business goals are:
This is another beneficial situation to be in! Your team is consistently hitting its target ROAS, but they might be able to increase profits and/or revenue—either by spending less for the same revenue, or by spending a bit more in advertising to generate additional revenue.
Here’s what this might look like:
Similar to the last example, let’s say the target ROAS is 200%. In the past, your team generated $100 in revenue at an ad spend of $50, making your ROAS 200%. Now, your team is still generating $100 in revenue at the same spend of $50, and your ROAS has stayed consistent at 200%. Since you’re hitting your target, this is good—but, it’s possible you could make slight adjustments to your target ROAS that would make your campaign even more profitable.
Teams in this situation have similar options to scenario 3, but with a slight twist: It’s difficult to know if you are hitting the limits of available revenue, or if there is more out there to obtain. As such, here are a few options to explore:
Fine-tuning your target ROAS is both an art and a science: It takes time and experimentation to figure out what the ideal ROAS target should be in any given scenario. In complementing that experimentation with the insights and advice outlined above, advertisers will be well on their way to making the most of target ROAS bidding strategies.
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