Welcome back to another edition of The Marketer's Playbook!
Today, I wanted to talk about something more conceptual. While recently auditing an ad account for performance, I realized that I have been using a few self-created concepts to help better understand the data. These concepts aren’t necessary by any means but rather assist in explaining the story that data can tell.
Here are the three marketing concepts that I’ve created to understand better what I’m looking at when managing ad accounts:
- The Waterfall Effect
- Volume Bias
- The Cliff
THE WATERFALL EFFECT
The waterfall effect fundamentally describes the hierarchy of metrics in your dashboard based on sales funnels and the relationship between the costs of one and another. To put it more simply, let’s use an example:
If your ad achieves 10 View Contents (product page views) at a cost of $2 each, and your average View Content to Add To Cart rate is 10%, then you should expect to pay $2 for an Add To Cart action.
It goes without saying that you should always keep benchmarks for each metric in mind. Typically, when optimizing an account, I always have a ‘Cost Per X’ column for every metric I look at: Cost Per View Content, Cost Per Add To Cart, etc. Using those, I can remove the ‘Volume Bias’ from metrics. Essentially, this describes how the amount of a metric in your dashboard can be misleading; where 100 purchases may look great, if they each cost $500, then it’s likely that performance has been poor despite a misleadingly high purchase number. Using a ‘Cost Per X’ column in your dashboard and therefore removing the volume bias, you can get a clearer picture of what performance actually looks like (let’s not touch on attribution loss here, we could go on forever).
THE WATERFALL EFFECT, CON'T
Once you have your ‘Cost Per X’ columns and a general idea of your goal benchmark for each, you can start to get a better idea of how you would visualize the waterfall effect. If a metric at the top of the waterfall is more expensive than usual, you can expect every metric below that to be more costly as they directly affect one another through conversion rates. Unless you’ve done some major recent renovations on your website, you can assume that your VC > ATC > P conversion rates will hold relatively stable. If that’s true, then the common denominator between the cost of each metric in your sales funnel is the metrics above it.
In my opinion, the waterfall effect truly begins at CPM. Several factors are considered while determining how much it will cost you to deliver an impression to your target audience. Being at the top of the waterfall, CPM has a direct impact on the costs you should expect to incur in all metrics below it in the funnel. To better understand this, check out the graphic below illustrating the waterfall of metrics in a standard sales funnel. Depending on their position in the funnel, you can begin to predict the expected size of that metric. As you work your way backwards through the waterfall, you should expect the metrics to start from smaller sized and increase over time:
- First, is there anything glaring that could be causing the poor performance? Ie. Broken links, typos in copy, targeting, etc. Fix that first.
- If nothing appears broken, start looking at your Cost Per X metric beginning at the bottom of the funnel, working up ‘the waterfall.’
- If the cost per purchase looks bad, check add to cart. If that looks bad, check view content. Etc.
- As you work your way up the waterfall, you might find that your metrics begin to look okay. Let’s use Cost Per Link Click as an example. Your Cost Per Landing Page View looks bad, and your Cost Per Link Click is bad, but CPM looks good. This tells me that there is a problem with something affecting CTR. It could be many things, so play around with this to find what's not working and fix it.
- Rinse, repeat.
The final concept might throw the above exercise off but should still be considered along the way.
A relatively simple concept, unaffectionately named, exists to describe the place in the funnel where attribution loss begins. This sits exactly between the metrics Link Clicks and Landing Page Views. Essentially, right where all metrics move from on-platform to off-platform. Meta can easily track all metrics from Impressions to Link Clicks as they exist within Meta’s owned ecosystem. However, once we hit Landing Page View, these metrics move outside of Meta’s owned ecosystem and can not be as efficiently tracked. This is especially true since iOS14.5+ and onward.
Please consult this excellent diagram I made to illustrate the cliff using my incredible design skills:
This chart was designed to illustrate only one thing: the difference between what you'd expect to see in a falloff of metrics through a funnel with and without full attribution. The reality is that we are experiencing a loss of attribution between Link Clicks and Landing Page Views that exceeds the rate of normal attribution loss, so it is at this point in the chart that you should expect to see a larger than normal drop-off.
It’s worth remembering that the cliff exists when evaluating performance and your ‘Cost Per X’ metrics, as this can (and likely will) affect your metrics below Link Clicks, so you should set your benchmarks accordingly.
With these three concepts in mind, you should be able to jump into any ad account and begin to truly understand the story that data can tell you about your funnel. With the waterfall method, you’ll be able to pinpoint at precisely what point of your funnel something isn’t working so that you can act on it. This method should help you build a more sustainable funnel overtime.
In this way, we can prove TLC wrong by saying that you should, in fact, chase waterfalls.
👋 UNTIL NEXT TIME
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