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Nearly every advertising report ever produced includes a line for return on ad spend (ROAS). Often, it’s used as the top-line indicator of ad effectiveness. The unfortunate truth is that ROAS is a commonly misused or incomplete metric.
It’s my sense that many advertisers already understand these flaws yet will not admit it. Why? Because they began believing a different story when their incentives were misaligned. If not properly framed, ROAS is set up to pour increasingly more money into the pockets of advertisers, even if your ads aren’t actually producing a true return.
So, what can be done, and how did we get here in the first place?
Related: How to Improve the Efficiency of Your Marketing Spend By 40 Percent
Where ROAS went sideways
Imagine you run a business, and your leading candidate for CFO told you that they’d only take the job if their compensation was a percentage of how much money your company spent in a year. Not a percentage of company revenue or EBITDA, just spending. Would you hire that candidate?
No, of course not. Nobody would accept a candidate that has strong incentives to deprioritize the health of the company for the size of their paycheck. And yet, brand leaders are willing to enter into a similar agreement with their advertising partners.
Ad agencies and online commerce platforms are typically paid a percentage of the budget you allocate for ad spend. As your spending increases, so too does their payout. They’re incentivized to get you to spend more on ads, independent of their effectiveness. Here are the five problematic layers of ROAS below:
1. Imprecise impressions
In the advertising world, “total impressions” is often used to calculate ROAS. The problem is that not all impressions are equal. You might think your ad is being shown to two million undecided people per month when, in fact, you might only be getting a fraction of that.
Say someone searches for your product by name on Amazon. This already shows strong intent to buy your product. They don’t need to be “impressed.” However, since your ad team is boosting this product, it now comes up first in search results.
Online marketplaces would count this as an impression attributable to your ad campaign, which is technically true. But you don’t care about impressing customers who are already planning to buy from you. You want to get in front of customers who have marginal-to-no awareness of your product.
What’s worse, algorithms may count every time your product appears on a page of search results as an impression even if it appears at the bottom of the page and the shopper never scrolls down far enough to see it.
Related: Ecommerce Analytics: 4 Metrics That Are Always Overlooked
2. Miscounting clicks
Let’s build the same scenario as before. Say someone searches for your product by name on Amazon, sees your product appear at the top of the results page (as they should), and then clicks on that product listing.
Was that shopper converted? No, but the ROAS formula on that marketplace may not discriminate. These clicks would be counted as attributable sales alongside clicks that actually come from consumers discovering your product for the first time, thanks to your ad program.
What’s more, ROAS often indexes too heavily on last-touch attribution. Last-touch (or last-click) is the final step in the customer journey to your listing, but it may not accurately represent how much each touchpoint in your ad strategy impacted that sale.
3. Wasted keyword spend
Say you sell probiotics on Amazon. Let’s also say you rank very high for the search term “probiotic for women over 50”, and your ad team has you bidding on that keyword. Once your product ranks high for this keyword phrase, your ads may not be doing much because the product already appears high in organic search results.
Brands are sometimes surprised when they switch off their ads and still get nearly equivalent results. Daily revenue from attributable sales may have been $10k when ads were on but only dropped by $1,000 once ads were shut off. So, was all of that $10k of revenue actually attributable to your advertising campaign?
Once you rank high for a term, organically, it may be time to adjust your ad spend. Knowing when to do that — and how to calculate true ROAS after that point — requires incredible data science.
Related: Good Decision Making Requires Good Data
4. Blocking and tackling
There may be times when you need to leverage ad spend to lock down the top row of a search or to knock out competitors who would have stolen sales when consumers searched for you. Knowing when and how to do that, again, requires some brilliant data science. And no matter how you tackle it, these moves will severely impact your ROAS.
5. Accounting for cannibalization
What are you willing to pay to continue advertising on a page that you’ve already won organically? And how do you measure that ad spend when much of it is cannibalizing organic sales? Accounting for cannibalization is fundamentally different than saying we spent X to get Y. Stellar data science is needed to help you account for dynamic cannibalization factors that are dependent on keywords, ad placement and organic placement that will tear standard ROAS calculations apart.
Related: The 5 Steps to Selecting the Best Advertising Agency for Your Business
A fresh perspective
ROAS is in desperate need of new parameters — ones that are rooted in modern data science and map to better incentives. This is the future, and many companies (like ecommerce accelerators) are already using new frameworks to quantify the true impact of an ad strategy.
In an ever-shifting economy, ad money is precious. If you don’t want that money to go to waste, you need a partner whose incentives are closely aligned with yours. Be mindful of potential partners that will make more money when you spend more on advertising. Instead, look for a partner whose ROAS methodology is grounded in the latest data science and that wins only when you actually do. They will be able to zero in on the true impact of your ad spend, helping provide meaningful insights that will propel your business forward.
