Increase Website Traffic Without Improving Search Rank: A Guide to CTR-Testing for Title Tags and Meta Descriptions

Last updated: 05-13-2018

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Increase Website Traffic Without Improving Search Rank: A Guide to CTR-Testing for Title Tags and Meta Descriptions

SEO has one fundamental objective: Drive organic traffic to your site. That is generally done by accomplishing one fundamental goal: Increase organic rankings in search engines.

However, what if there were a way to improve your traffic without having to worry about your rankings? What if you could improve traffic even if your rankings don't change?

You can! By applying creative optimization of your title tags and meta descriptions, you can improve your clickthrough rate, which can directly affect the amount of organic traffic you get from search engines without changing your rankings at all.

What does all that mean? We'll start with a bit of backstory.

The practice of conversion rate optimization (CRO) is a field of study in its own right, and I won't profess to be an expert. However, the concept of CRO will inform our CTR tests, so I will provide a brief outline.

Regarding Web traffic, the "conversion rate" is determined by the amount of visitors who take the action you want them to take, divided by the total amount of visitors to your webpage.

So if you have 1,000 visitors, and 50 of them take the desired action (form submission, phone call, product purchase, etc.), then your conversion rate would be as follows: 50 / 1,000 = .05 =5% conversion rate.

CRO works to improve that rate by modifying the design of a webpage. That is often done by a split-test, AKA "A/B-test": A certain portion of traffic is shown the original design (version A, or control), while the remainder is shown another design (version B, or variant).

By comparing the conversion rates of the two segments, you can determine whether version B resulted in a positive change, a negative change, or neither.

For you to derive any valid conclusions, the differences need to be statistically significant (i.e., not the result of random chance).

How does that translate to CTR experiments? Simple: treat the clickthrough rate exactly as you would a conversion rate.

Let's take the previous example: If you have 1,000 impressions on a Google search results page, and you get 50 clicks on your results, your CTR would be as follows: 50 / 1,000 = .05 =5% clickthrough rate.

When viewing CTR and conversion rate as similar formulas, you practice the principles of CRO directly on Google's search results page.

You do that by modifying what you have control over—the title tags and meta description of your pages:

By modifying those two elements, you can test how they affect CTR, just as you would test different design elements with CRO experiments.

Things to Keep in Mind

Before you hop into these tests, you need to grab some data. How do you do that? Google Search Console!

You can access the Google SERP data via the Google Search Console.

In the left-hand navigation, go to Search Traffic > Search Analytics. You'll see a screen similar to this one:

In Search Analytics, select the following options:

Find the "Download" button (at the top-right), and export a spreadsheet. You'll have a file that looks something like this:

The file will be a maximum of 1,000 pages (that's all that Google Webmaster Tools/Search Console provides).

If you have plenty of data to work with, you don't need to test every URL. If you are working with a smaller site, it's OK to use more or all of your URLs in a test; just make sure you have plenty of historical data to compare with.

I like to pick the top 100 for the test set. That offers two distinct advantages:

Copy and paste the top 100 pages (or whatever your test set is going to be) into the "Control Group Data" worksheet of your example spreadsheet.

At this point you've collected your historical data, which you'll compare against your test. Now would be a good time to talk about exactly what you are going to test.

There are several options...

Yes, this sounds counterintuitive, but you never know!

Google is now showing emojis in title tags, which means you can test callouts. Some popular emojis include a trophy, a thumbs up, and a finger pointed right.

You can visit to browse through the available Unicode emojis and copy/paste.

In your testing, think deeply about potential test variables. The best tests come down to gaining insight into your target audience (such as emotional trigger words). Other tests, such as emojis, are a little more gimmicky but can still be effective.

If you are struggling to come up with tests, just lock yourself in a room and don't let yourself out until you've thought of at least five unique tests. You'll have a breakthrough eventually—trust me.

Now that you've collected historical data and established a test variable, the next step is to actually run the experiment!

Here are some important steps:

Done all that? Now it's the waiting game. I like to give my tests at least one month of being live and indexed before analyzing any results.

Head back over to the Google Search Console and export the recent top 1,000 pages, making sure that your date range includes only data from after your pages were re-indexed by Google.

You'll want to dump the list into a spreadsheet so you can extract only your test set (top 100, etc.).

(This is relatively straightforward if you use Excel regularly, but if you don't, there are plenty of handy VLOOKUP tutorials online, so I will skip the step-by-step instructions.)

Take the new test set data and copy/paste it into the "Variant Group Data" worksheet of your example spreadsheet.

Now you have your control data and your variant group data ready to be analyzed. Through the magic of Excel formulas, all the analysis is done automatically in our "Statistical Significance" worksheet!

By inputting our example test data, we see the following output:

As you can see, we are statistically significant at all three confidence levels, which means less than 1% of our results happened purely by chance.

At this point, you want to rule out rank increases as a cause for an increased CTR. Obviously, if something ranks higher, it will get a much higher CTR regardless of title tag or meta description.

Whenever you deal with title tag changes, rank fluctuations are very possible. You should not only compare data after the fact (this is already pulled together in the spreadsheet) but also track your keyword rankings in real time to check for fluctuations.

In the above example, although our average ranking did drop, impressions improved 1%. That means that a few outlier ranking drops are likely the cause of the observed ranking decline, but it's not actually affecting SEO visibility.

Winning at SEO and digital marketing is all about how much you push to be ahead of the curve. Although studying industry research and following best-practices can help, the real gold is found through testing new approaches.

For large-scale sites that have thousands of pages, CTR experiments can provide ways to get "more juice" from your current organic rankings, as well as provide insight into the users' mindsets and/or pain points.

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