July 15, 2021
Forecasting marketing performance is always difficult.
This is particularly true in the noisy world of online marketing, where a million different variables can affect your campaigns — how much you bid, what your ad copy looks like, whether it’s raining…
How are you supposed to know what effect all of these different variables will have on performance?
The truth is that it’s impossible to predict future performance — but try telling that to to your CEO when they ask you to justify a budget increase or predict how much you’ll spend by the end of the quarter, and at what return.
Not a fun prospect, is it?
That’s why, despite it being nearly impossible, you do need to find a way to properly forecast the performance of your marketing efforts.
There are a few different ways you can approach forecasting. Some work, while others fall far short of the mark. This post will cover the most common way people currently forecast marketing performance, as well as a guide on how to improve that process and actually get high-quality forecast data for your reports.
First, let’s start with the most common way marketers forecast performance today:
Having to send their CEO some numbers but not knowing enough statistics to work out anything advanced, most people turn to the simplest method: extrapolation
Here’s how that method works:
Example data for a typical Six-figure campaign.
Extrapolation is rather simple: first find out your average daily spend and your average daily conversions. Then multiply them by how many days left in the month. That gives you an estimate of how much you’re due to spend over the remaining days of the month, as well as how many conversions that will buy you.
You also know your CPA (cost per acquisition/conversion) so if you know how much a conversion is worth to you, you can work out return on investment for your campaign.
Seem too simple? It is. Take a further look at the data:
Certain days had a very high Cost per Conversion, skewing performance.
The days highlighted in blue had a really high comparative cost per conversion, which was typically sitting around the 3 cents mark.
Is the 13 cents CPA really representative of the performance you’ll see by month end?
Additionally spend was very low all the way up until row 16, when it looks like we drastically increased our run rate, and therefore saw worse performance .
How do we take that dynamic into account? This data looks so random. Is our CPA even predictable?
And this is where the extrapolation method falls flat. Judging overall performance based on prior performance can only get you so far before outliers, changes in spend, and strategic shifts start to mess with the model.
At the end of the day, the biggest problem you’ll face when extrapolating your marketing forecast is the ability to adapt your model for changes, both deliberate and unforeseen.
For that, you need a different approach at determining how your marketing campaigns will perform.
That method is…
The good news is that there is a better way to predict future campaign performance. I call it the “Efficient Frontier” method after the incredibly smart company I learned it from (who later became part of Adobe).
They borrowed the term from the world of finance where it is used to predict the value of a portfolio of investments. Though the way they implement this method is more accurate (and expensive!), involving advanced algorithms, we can actually make our own basic version (for free!) in Google Spreadsheets or Excel. It’s as simple as taking the same data set we used above and running a scatter plot diagram, to get the following:
Scatterplot diagram using the same data as above.
As you can see, we’ve plotted the Conversions on our Y axis, and Spend on the X axis to get an interesting pattern… but what causes this shape?
Any economist will recognize this shape as a representation of ‘diminishing marginal returns’ — what this means is that for every additional dollar (on the margin) we spend, we’re getting a decreasing (or diminishing) number of conversions in return.
So to take the above example: when we spend around $400, we’re getting 4,000 conversions, but when we double that spend to $800, we’re only getting 5,000 conversions.
That’s just 1,000 more than when we were spending $400.
To think of this another way, we paid $400 for those additional 1,000 conversions, or 40 cents per conversion.
This is an incredibly important concept in marketing because if you weren’t aware of this (and many aren’t), you might make the mistake of thinking you’d be able to double your conversions when you double your spend.
This shape will appear in almost every marketing campaign that you will run. It’s pretty much a law of nature.
It occurs in campaigns that are well-optimized for a simple reason — you spend on the best performing placements first, in the least competitive areas (sometimes called the low hanging fruit). As the volume of the campaign expands you start getting into less profitable and more competitive territory, so your performance (CPA) starts to suffer.
Note: If this pattern doesn’t emerge, either you need more data, your campaign might not be fully optimized, or your campaign is being affected by something big (a sudden increase in inventory, change in ad format, or general improvement in account structure).
Let’s take another look at what that chart tells us. Looking at it says a lot about what we can expect from future spend. This is true both for considering higher and lower spend, allowing us to judge a real sweet spot for getting the biggest bang (conversions) for our buck (spend).
The chart tells us quite a bit about what to expect from future spend.
We can pick almost any spend point on this chart and guess at roughly how many clicks we’ll drive, and therefore our CPA. For example if our CEO asked us to spend $1,600 per day on this campaign, and expected us to drive much more than 6,000 conversions we can show them this probably won’t be possible.
We can now run a much more plausible forecast to see what our CPA will be by the end of the month by figuring out how much we want to spend per day, finding the point on the graph, and extrapolating how many conversions we’ll get. Or we could set an acceptable CPA, then forecast how much we need to spend per day to get to that level.
Day-to-day things will always fluctuate, but this is our way to use data to make an educated guess.
Pro tip: Plot CPC vs. Spend to forecast how much you should be bidding to hit your desired daily spend level.
Say we only wanted to spend around $100 a day — we can see that we’ll pretty reliably get close to 3,000 conversions per day. In fact, this chart indicates that even if we paused this activity we’d continue to get about 2,000 conversions a day — an indication of cannibalization. This means that we’re attributing conversions to our marketing activity that we would have gotten anyway . If that’s the case, you might want to down-weight your clicks by this amount in order to get a better idea of real marketing performance.
At this point you’re probably thinking “But how can I predict performance on a specific day? This chart won’t help me if its raining and I sell ice cream, if my site goes down, or if my intern screws up and hurts performance by 20%…”
And you’re right — this method can’t possibly account for all of the possible things that could affect performance on any given day, just as I can’t possibly predict if you will get hit by a bus tomorrow.
No forecasting solution is perfect, and you’ll never be able to paint a perfect picture of the future of your marketing campaigns.
What I can actually tell you with more accuracy is how many people will get hit by busses tomorrow… and that’s the magic of aggregate data. What is impossible to predict with a sample size of one, is more accurately predictable once you have enough data.
Though the Efficient Frontier method will be completely wrong with its prediction on certain days, over the course of a month it should actually be pretty spot on.
So that’s it — a simple scatterplot diagram can give you a decent idea of what to expect and you don’t have to learn any advanced statistics.
Advanced technologies like Adobe Media Optimizer can do this at scale, modelling this dynamic for every keyword and placement, and adding a bunch of other advanced maths into the mix — so if you can afford a solution like that I’d say go for it.
If you’re a small startup, with less than a six-figure marketing budget, you can’t go too far wrong with this method.
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