Pamela SapioAttribution is dead.
What does that mean, exactly?
It means the way we have been doing things for the past decade or two no longer works. Specifically, we are talking about the way we have been attributing clicks to determine exactly which consumer actions resulted in conversions.
And it wasn’t just one thing that killed attribution. In fact, a number of things led to its demise, from the iOS 14 update that blocked ad tracking to the fact that it now takes up to 500 touchpoints—across multiple devices—before a customer makes a purchase.
Recently, Solutions 8 Google Ads Strategist Caden Thompson sat down with Nathan Perdriau, Co-Founder and Head of Paid Media at Blue Sense Digital, to talk about what they believe is the #1 way to solve the looming attribution problem. You can watch the video here, or read on for a recap of their discussion.
Table of Contents
What do we do now?
For business owners, ad agencies, and solo marketers, understanding what is actually driving revenue and driving customers is essential.
Without this information, it is nearly impossible to determine which of your paid ad campaigns are performing well (and where to optimize if they are not); whether or not your ads are reaching your target audience; and where to allocate ad spend to increase profitability.
Incrementality testing is a different way to look at data (vs. third-party attribution tools) that helps you analyze ad performance data and understand how it relates to actual sales and performance.
Why is this important? Because while in-platform metrics provide some triangulation data, they don’t tell the whole story. In other words, you really can’t get a clear picture of what’s working (or what isn’t) unless you have access to backend revenue data as a baseline.
Start with a budget increase.
When applying incrementality testing to a campaign or account, the first thing Nathan proposes to clients is a change in budget.
This typically involves a significant increase in budget to see if there is an uptick in revenue; we say “significant” because a slight variance in ad spend (going from, say, a $5K monthly budget to a $6K monthly budget) is not enough to show actual change.
As Nathan explains, “If you’re a $60,000 business and you have natural swings in top line each month, how do you tell that you added any revenue?” Because that $60K is likely to fluctuate from month to month, for example anywhere from $55K to $65K, and you can’t incrementality test like that because the incremental uplift on top line doesn’t become visible unless you wait three to six months.
Next, keep variables constant across the board.
In a nutshell, attribution allowed people to get lazy and not put in the effort. Now, it’s all about getting back to basics and establishing a benchmark to effectively determine what is really driving revenue.
When trying to grow any business, there will always be countless factors that impact success, either positively or negatively. Just as the stock market naturally ebbs and flows, companies will have good sale days and bad sale days, and things will happen that may be good or bad for the bottom line. The important thing is to make sure those things within your control remain as consistent as possible.
“The other thing that I think is really important with this is seasonality,” adds Caden, explaining that companies that have been tracking these numbers for years have a way better estimation on what their profit is going to be. The more years you have, the more accurate data you have to determine seasonality, and just being aware of those trends helps when you benchmark and allows you to know exactly what that incrementality test is going to do.
Geotargeting can also help you identify trends.
Geotargeting is a strategy that involves creating and delivering ads to a specific audience based on their geographical location; it is also another way to get a clearer picture of your top line performance. Running ads in a targeted area allows you to see what is actually driving revenue, creating a smaller test pool that you can more easily make waves in (versus using your full business model).
“Essentially, we’re just looking for the halo effect,” says Caden. In other words, we are looking to see the impact of the ad on the entire business. He goes on to say that there are multiple other ripple effects than can happen as well, such as an increase in organic traffic, all based on just how controlled your variables are (or aren’t).
Bottom line: You have to be aware of the full business picture—and digital marketing agencies that do the full top-of-funnel control actually have that insight. Why? Because they don’t have to worry about someone else making changes to another marketing channel that could prevent them from finding a key correlation or pattern in overall performance.
Don't rely on in-app metrics.
This is an important point that we try to drive home as often as possible, especially for those new to working with paid advertising platforms.
According to Nathan, if you only look in-platform to see what’s going on, it won’t necessarily make sense as to where you should allocate budget, because all the attribution goes to the last click. However, proper incrementality testing—where you are looking at top line revenue—gives you the ability and the confidence to be able to allocate budgets, even to a campaign that has a low return on ad spend (ROAS).
An important caveat, adds Nathan, is that you need to allow for enough time for potential customers to get all the way through the sales funnel. So, if it takes (on average) 14 days for someone to purchase from you, then you need to wait 14 days. But what if your client doesn’t have the capabilities of testing for that long? Caden explains that he will sometimes test “super aggressively” for a short period of time (e.g., two or three days), then trim it off to see if there is an uptick over that time lag.
“It’s not as accurate, but it’s a very good way to get a temperature check on what that actually is doing, so then it builds your confidence.” You can also decrease instead of increasing the budget to incrementality test, he adds.
Communication is key.
As Caden explains, a lot of these tests can be done to solve attribution issues. However, it all hinges upon how tolerant you are (or your client is) to testing. The more open you are to experimentation, the higher your confidence level will be—which is why communication is so important.
This is especially true when working with clients who have partnered with more than one agency for digital marketing. For example, if you have one agency running your Meta Ads and one running your Google Ads, they must communicate back and forth to provide a broader view of performance. Ideally, one agency would handle both, but this is not always the case.
Business owners as well should be aware of whatever tests are happening and provide as much information as possible to provide a holistic view. Something Caden does with all of his clients is to have a profit and loss (P&L) sheet with the test logs inside the P&L sheet so you can look back years later and see where revenue increased or decreased over time. You can then see how those dips and spikes related to whatever marketing strategies you were using at the time.
For example, if you noticed that using a specific set of creative assets or targeting a certain audience was successful in the past, you can now test those same elements again with an audience that has grown over time.
Incrementality testing is better than third-party apps.
In fact, Caden and Nathan agree that it is the #1 way to solve the attribution problem. Caden adds that when you are benchmarking, make sure you are benchmarking off of some form of attribution data. Even if it’s flawed attribution, if it’s still attribution, and that’s something.
Because, he says, if you look at a scenario where you trust an algorithm (for example, there are some third-party tools that will try to piece together the puzzle for you), it kind of muddies the water because you don’t necessarily know if the algorithm was set up this way during this date range or this way in this other date range.
Nathan agrees, saying that he was a big fan of third-party apps about six months ago until he realized that they are actually not providing any helpful information and just make things more confusing for everyone.
What does incrementality testing actually mean?
The term comes from the strategy of trying to measure incremental profit as a business scales. And in fact, it is a concept that is lost on many agencies—particularly junior marketers and startups, who tend to focus more on averages without homing in on whether or not additional spend is actually adding more revenue.
Incrementality testing as Caden and Nathan are recommending it involves really making sure that you are measuring the difference that occurs in the top line rather than looking at the blended average. Why is this important? Because a lot of people are overspending quite a bit, particularly at the bottom of the funnel, so incrementality testing is a really easy way to identify overspending and inefficiencies in your account.
One last thing: Pay attention to user engagement.
When using incrementality testing, it is important to pay attention to certain signals that give you confidence about whether or not you are on the right track and if people are flowing through the funnel at the right rate. For example, during the early stage of testing you should be looking at user engagement; if your ads are performing well and new users are clicking—and that holds when you scale—this is a positive signal.
Of course, there may be instances where you might be testing a new audience and you might see worse engagement for a period of time, but Caden says that just paying close attention to what is happening and being aware of your testing threshold is the key.
“And I think a lot of that just comes down to the client and the actual business model itself,” he concludes, reiterating that geotargeting or having a smaller test group may help to alleviate some of the stress of incrementality testing.
