SEO Forecasting — Whiteboard Friday


The author’s views are entirely their own (excluding the unlikely event of hypnosis) and may not always reflect the views of Moz.

Learn how to forecast and demonstrate the value of your SEO efforts in this week’s Whiteboard Friday with Tom Mansell. Quantify predicted SEO value, close performance gaps, and calculate ROI.

Click on the whiteboard image above to open a high resolution version in a new tab!

Video Transcription

Welcome to this week’s edition of Whiteboard Friday. I’m Tom, Director of Organic Performance at Croud. Today, we’re going to talk about SEO forecasting and more specifically how you can do it. With digital budgets being more scrutinized than ever, there’s a lot more pressure to demonstrate the value of your SEO activity back to your business or your client organizations.

So today, we’re going to spend some time talking about the process that I go through in order to do that forecasting and demonstrate that value. Now, I will say it’s not an exact science. We’re effectively trying to predict the future, which is quite difficult.

But there are processes that you can go through in order to begin to quantify that predicted SEO value.

What do you need for SEO forecasting?

The elements you need for SEO forecasting: Data, a target, keywords and a click-thru rate model.

So before we kick off, there are a few things that you’ll need in terms of data in order to build your forecast. Firstly, 12 months traffic data.

So this is effectively you can use your analytics platform or you can use Google Search Console to pull down 12 months’ worth of organic data by month. Secondly and most of the time you’re working within a business or you’re working with clients, you will be set a target of what that predicted future SEO performance needs to be.

So that’s incredibly important to bring into your forecast as well. Thirdly, the keywords that you’ll target across the course of your campaign, that’s really important to help try and demonstrate the incrementality that you’re going to be building for your business or your clients. Then, finally, the click-through rate model that’s somewhat linked to point number three here.

Click-through rate model allows you to start to reverse engineer by each keyword how much traffic they are driving and, by improving those keyword positions, how much additional traffic you’re going to be driving through to your website. So those are four things that you’ll need before you set out on this journey. What we’ll end up with, when you’ve gone through this process, is a graph like this.

So effectively, a very clear demonstration of what we’ve achieved over the last 12-month period from that traffic data that you’ve got up here and then some different scenarios. So firstly, your baseline. This is effectively what we think performance will be if we don’t do any value-added SEO activity.

Then we’ve got our forecast in the green here. So using all of that keyword data that you have, what we think the incremental value will be from your SEO activity improving the positions for those keywords. The black dot is the target that you need to work towards.

So very quickly what you can start to see is where you’ve got that gap from the keywords that you’re targeting against that initial target that you’ve been set by your business or your client organization, and that will start to tell you, actually, there are probably more keywords, more expansion that we need to do within our SEO strategy to close that gap.

Then, finally, as that year progresses and you progress through your SEO campaign, what the traffic is that you’re actually generating and how that tracks against the forecast that you’ve built. So this, ultimately, is what we’ll end up with, which you can show to your superiors, your client organizations to demonstrate the value of your SEO activity.

How to get your baseline

Image showing how to work out a basic and a better baseline

Now, breaking that out into a little bit more detail, in order to get this baseline, there are a couple of different methods that you can use in order to get that data. The first is what I call the basic method, so taking the last 12 months of your organic data that you’ve got.

Then you can use Google Trends and you can look at Google Trends to see the brand growth that has been delivered over the last 12 months, and you can use that to quantify what we think naturally will happen based on the strength of the brand as measured by Google Trends.

So that’s one way that you can build a basic baseline to build a model whereby you’ve got no SEO activity happening over the next 12 months. The second option, which is what I prefer to use, is using a time series model. Now, there are lots of different formulas that you can use in Google Sheets and Excel.

I’ve got one down here. It’s called the FORECAST function, and effectively what that does is it looks at the data over the last 12 months, and it starts to draw parallels from that data and begins to forecast what you think will happen or what it thinks will happen over the next 12-month period from all of that historic data that you fed it.

This is a really powerful way to get this accurate as possible baseline from your activity.

Understand the incremental data

Image showing how to understand incremental data, including must haves and optional elements

Now, we move on to incrementality. So how do we begin to influence this green line here? So there are two things that we must have in order to do this.

The keywords that we’re targeting as part of our campaign, we need to collect with those keywords the monthly search volume and also the current ranking position, where are those keywords currently ranking in search. Then we need to use Google Search Console to pull down the non-branded click-through rate, and that’s important because that will start to help us understand how much traffic we’re driving from those keywords and as we begin to improve those keyword positions, what the incremental traffic will be, which gives us this green line here.

An optional metric that we can put in is keyword difficulty. Now, that’s useful because if you can understand how difficult it is to target or drive improvements from each of those keywords, you know the rate of change that you need to put forward with regards to your increase factors. How quickly are we going to drive incremental ranking performance for those keywords?

If it’s super, super competitive, it’s probably going to take us longer to influence those increases. If there’s minimal competition on those keywords, we’ll be able to drive that incrementality a lot quicker.

So now that we’ve got this graph here, which models clicks in terms of how we’re driving positive performance over the next 12 months, there are further things that we can do to bring in revenue metrics.

Image showing how to take your incremental data further with conversion rate and average order value data, as well as calculating your return on investment

For example, which makes this a much more attractive proposition for finance teams and those in charge of your budgets.

So if you bring in conversion rate and average order value data, you can apply that on top of the incremental clicks to understand how much more revenue you’re going to be driving for your business or your client organization. Then, with that data, you can also begin to calculate the return on investment, so effectively looking at the incremental revenue that you’re driving divided by the cost of investment.

So how much money are you putting into your SEO activity in order to achieve those results? That calculation will give you an ROI number, which is really, really appealing when you’re putting this in front of finance teams and budget holders. So I hope that’s been useful. There is a link down here to a worksheet, which we’ve attached to this Whiteboard Friday.

It allows you to put in the raw data up here. Then, from that, you’ll get a model much like this. So I hope that’s useful. Hope this Whiteboard Friday has been useful and thanks for watching.

Video transcription by Speechpad.com



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