In this second part of How to Automate Revenue Optimization to Boost Organic Traffic ROI (read here the first part if you missed it), I will demonstrate how to build a comprehensive SEO revenue forecast, and how to bring your strategy to senior management to get your buy in, in a simple and clear presentation.
In terms of data segmentation and aggregation, I analyzed one category (out of the many categories across the site), and within that category, I focused on a group of 4 site’s categories.
I will break this post into 2 main sections. Now I know, you probably ask yourself why 2 and not 3? Our mind, somehow, works better when we break things into 3 pieces, nevertheless I have only 2, sorry about it 😊
- SEO Forecast Analysis – how to build your forecast?
- SEO Buy-In Plan – how to showcase your plan for a smooth buy-in process?
Take a deep breath, and here we go…
SEO Forecast Analysis
Providing an accurate forecast is impossible of course, since we don’t know what the future holds, nor sure if our optimization efforts will have a sufficient impact on rankings.
When you build your hypothesis around the impact of those optimization efforts, I suggest asking yourself the following questions, to help you balance your forecast and decide if you’re on the right track. These are the questions I asked myself during the process:
If you haven’t done that yet, think first where you want to go and how you get there.
Goal – Increase organic traffic for a specific category section of my website by 20% until the end of the year.
strategy – by increasing Avg. position for this category from 12.3 to 5 within X months left to the end of the year. Monthly incremental ranking improvements, until you reach your goal.
This is assuming that position increase and getting to first page – will boost revenues and other metrics.
Tactic – by implementing my optimization plan from part 1.
Or perhaps I should review again. i.e. go deeper around specific keywords analysis to provide more insights and sharpening my recommendations?
Ask yourself the hardest questions, don’t go easy on yourself. It’s always better to understand you made a mistake and fix it, and that’s exactly what will make you grow as a professional.
And it is ok to go back few steps and improve, but always consider to what extant you do that, because eventually it’s always better to start with something imperfect, than wait for the perfect one.
I tend to name it Agile SEO; build, launch, analyze, improve.
What data you need for your forecast?
Now, for the more technical part, in which we’re going to discuss what data we’re going to use, and how we’re going to forecast a potential traffic and revenue growth, if all goes according to plan (always have a plan B for any case).
The data I collected for the forecast was:
- Traffic – from any tool you’d like to choose. I use Tableau in this case.
- Revenue – same as above
- Position – from SEMrush. Feel free to use GSC or any other SEO ranking tool.
- Avg. Monthly Searches – from Google Keyword Planner / SEMrush or whatever you’re using for that. I used SEMrush since I’m already bringing the position data from there (same file)
- Average CTR – I’m using a model which correlates between a keyword position (1-20 in my case) to its potential CTR for a specific position. Figures can vary based on different models, so I suggest testing other models as well.
- I also used Avg. EPV (earning per visit), weighted Avg. EPV and CR (conversion rate). Talk about it in a sec…
What to do with the data above?
- Step 1: understand where you are, and where you’re going
- Step 2: Use a model of Avg. CTR by Avg. position
- Step 3: Build a solid business case
- Step 4: put it all together
Step 1: understand where you are, and where you’re going
I did it by segmenting and comparing the category’s metrics to all the other pages on the site (excluding the category pages), to get a first glance at how much this category currently generates.
What comes to my mind when I see the table above?
- Category A brings ~3.5% of total traffic, and ~10% of total revenue. Makes me wonder where I can take it… say 7% / 15% accordingly? perhaps…
- Though the Category A’s Avg. EPV is higher when compared to the non-category segment (please believe me, but I can’t show this data 😊), you might notice I’m also using a Weighted Avg. EPV here, and that it’s lower for Category A, so please let me explain why I used it as one of my metrics.
- Avg. EPV is nice as a ‘standalone’ factor, as I like to call, only by itself. But to be able to compare Avg. EPV more accurately between those 2 segments – we must normalize it against the Visits metrics.
- And now, the picture is already different, right? Again, thinking about the potential I might generate here, and take it to higher EPV as a first step.
- I am encouraged by the relatively high conversion rate for Category A. It makes me more confidence this could work well.
I’ve also built this cool Scatter graph, to compare Category A against other categories. It gives me rough idea, again, where the potential lays and where this is already might be a milking cow.
Step 2: Use a model of Avg. CTR by Avg. position
Here’s how it looks like on my Excel file:
I’m using a model of Avg. CTR by Avg. position, but you may use any other model and figures.
The Excel formula to use for Avg. CTR is =(VLOOKUP(C2,$I$1:$J$22,2,FALSE))
Step 3: Build a solid business case
Based on your data above, go ahead and think of 3 scenarios, based on estimated Avg. Position (this time breaks to 3 😊). Here’s what I did:
* Current – avg. position 12
- High impact scenario – avg. position 5, to be achieved by Q4, December 2020
- Medium impact scenario –avg. position 8, to be achieved by Q3, September 2020
- Low impact scenario – avg. position 10, to be achieved by Q3, July 2020
Next, measure the impact of each scenario (position) on results (estimated traffic / revenue), using your cool Excel file. This will give you a high-level view of what are the expected outcomes and whether you’re going the right way or not.
Additionally, that would be a great slide on your presentation, down the road.
Step 4: put it all together
Take your position and traffic estimations and place them on a table, such as the one below, where you’ll place your monthly / quarterly traffic data and sum it up.
The Total Visits row, going forward from May 2020 – is my forecast.
Based on my position estimation and the extra visits I’ll be able to drive out of it – I’m calculating the Revenue (which will be the result of multiply Visits by Avg. EPV or weighted Avg. EPV for Category A).
The visualization of this data gives us the graph below:
How to Showcase Your Plan for a Smooth Buy-In Process?
Well, this is the easy part, as the heavy lifting is already behind us. Now all we need to do is rap all this info and details into a simple-to-understand, clear presentation – and pitch the grand masters.
As for the PPC data, this is the time to clarify I used it as part of my buy-in justification, in which I show the business how much it will cost, paid-traffic-wise, to bring the exact same value (already talked about it in one of my previous post).
Here is a link to my full SlideShare presentation on SEO buy in. It also includes the first part of this mini-series, with all relevant data points from there. (I’m sorry for the bad quality, not sure why it’s pixelized)
When presenting, make sure you know what you talk about. And if you don’t know, that’s fine – ask question, learn, and come back later with solid answers.
Lead the presentation with confidence and keep the KIS rule – Keep It Simple.
That’s it for now ladies and gents, I will appreciate any feedback with regards to how much you think this piece will make you take an action vs. just a nice post, but not more than that 😊