Data Driven Strategy Via Ab Testing MRR Ebook

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What does the owner do? One option would be to change the text across all stores and to hope that the market research proves to be accurate.

The business owner did something right here, in that they at least conducted market research first, which helped to motivate a smart change. The worst case scenario is that the business owner would simply decide that they like a more ornate font and change it across all their stores without thinking about the potential implications. They could end up losing a lot of money and never knowing where the ‘leak’ was.

But the problem is that even with market research, you still can’t be certain that you’ve made the right decision. Market research can be wrong, and it can miss other factors (the visitors may prefer the more ornate font, but they might not see it as easily as the clear, bold font!).

A split test then would involve changing the font on half the buildings. It would then compare the profits of those two groups, in order to see which one performed better and whether or not the change should be employed across the entire chain. If the stores with the new font earned more money, then the same technique could be used globally. If not, it could simply be forgotten. And another new change could be tested.

This way, split testing allows you to be certain that something is the right move before you do it. And the same thing works just like that in SEO. Here, you might test changing keyword densities, or you might test using more headers. Maybe you test using more images, or perhaps you try using an image at the top of the page. All these things might end up impacting on the performance of your pages in the Search Engine Results Pages (SERPs), and thus help inform your practices in future. You might also go back through all older pages on the website and make the changes across the board.

How do you run a split test for SEO? One option is to take lots of similar web pages that are all performing equally well, and then to make changes to half of them. Head into your Google Analytics account and make a projection for how would normally expect them to perform, and then compare that projection to how the pages actually perform.

If all the pages with the changes start performing better than projected while the other pages remain the same, then you can take the changes and adopt them permanently on all versions of your site. If they start to fall in the rankings, then you can drop the change and move onto your next strategy.

Another option is to use a redirect – creating two versions of the same web page and sending half of your visitors to each. This will allow you to conduct a more scientific study, but it will also mean that you can’t measure your position in the SERPs (seeing as it’s just one page and Google will only rank it once). This can nevertheless be used to see how changes affect things like bounce rates, CTR, conversions etc., which all are likely to have a knock-on effect on SEO AND on your profits.

Not sure how to do this? Then you should check out the eBook: Split Testing in SEO for Data Driven Success, which will explain everything in more detail. This will also show you how to avoid letting split testing hurt your ranking, and how to be as scientific as possible about your results.

I know what you might be thinking at this point: do you really need to go to all the trouble of creating two different versions of the same pages? Or split your pages into groups that way?

Couldn’t you just introduce the changes to your site and then see how they perform?

Or perhaps just test the changes on one page?

Maybe just read a book??

Well the problem with the first option – introducing the changes to the site – are several. Firstly, this would take a long time. If you go to all the trouble of introducing changes to all of your pages, only for this to have no beneficial effect, then you’re going to have wasted a huge amount of time that could have been spent much better.

Not only that, but if you make all these changes to every page on your site and the changes turn out to be a mistake, then this can seriously hurt your business. Not only can this hurt your short-term profits as every page on your site begins to underperform, but it can also seriously hurt your reputation if those changes end up driving away your visitors.

Okay, so making changes across the board isn’t an option. But what about introducing changes to just one page and seeing how that works?

Well, the problem in that case is that you’ll be introducing what are known as ‘confounding variables’. A confounding variable is anything that could skew your results but that you haven’t accounted for. So in this case, if you include your new SEO strategy in just the very next blog post, you might see it thrive and assume that means it was a good move. You’d then make the changes to every post on your page.

But what if the only reason that page did so well, is that you happened to write a really good post that your audience loved? Or what if you accidentally stumbled upon some other SEO-secret sauce. Maybe your headers were all the perfect length and it had nothing to do with the resource box at the bottom of the page?

In this case, you could once again go to the effort of changing every single post on your site, only to find that the strategy doesn’t work and you just seriously damaged your website ranking and your reputation. Bad news!

Finally, why conduct these experiments at all? Why not just use the knowledge provided by the SEO community – or even other people who conducted those experiments? A great article on MOZ, shared by the creators of Optimization Delivery Network (Distilled), explains this very clearly.

Distilled provides SEO services for clients, and the company recently decided to try using split testing to make better choices (it developed Optimization Delivery Network for this very purpose).

The company then began to conduct hundreds of experiments on a huge number of blogs, to collect as much data as possible. So, what did they find and what can we learn? The first thing they found, was that using a more popular search term doesn’t always yield better performance in the SERPs.

Other Details

- 1 Ebook (PDF), 9 Pages
- 1 Squeeze Page (HTML)
- 4 Ecovers (JPG, PNG)
- Year Released/Circulated: 2020
- File Size: 7,517 KB

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