I read an interesting post on Dave Naylor’s blog on Wednesday by David Whitehouse on using Google Analytics to segment short and long tail keywords using regular expressions. This post was then to be discovered as something initially developed by Ben Gott over at search engine land.
Well I’m really bad with regular expressions, but i am good with Excel, I also love data so I thought about all the post’s I read about Search journeys and how the common conception is that the longer the search query the smaller the search volume but larger the conversion rate.
The image below shows the common conception of keyword type by search volume.
Anyway I want to know, “Does a longer keyword actually give a better Conversion rate?”
It’s a big question so I thought how can Google Analytics and some real data help me.
Setting up the test
OK well the good thing about working for a large Internet marketing company is that I get access to hundreds of Analytics accounts, so I took 13 Analytics accounts who have either e-commerce tracking or goals set up and set out about exporting the data.
Creating a custom report in Analytics
The next part of the post is just a quick overview of how to create a custom Google Analytics report which can pull out the number of conversions per keyword.
Ecommerce Conversion Rate
- Custom Reporting
- Create a new custom report
- Metrics –> Site Usage Visits
- Metrics –> Ecommerce Transactions
- Metrics –> Total Goal Completions
- Dimensions –> Traffic Sources –> Source
- Dimensions –> Traffic Sources –> Keyword
- View Report Google –> Organic
- Take 1 years worth of data
- Add &limit=50000 to top URL
- Advanced Filter –> keyword Excluding –> Company name
- Export –> Data as a CSV
Overview of Data
So we now have the data all pasted into an Excel sheet detailing:
- Transactions / Goals Complete
We then put a little Excel trickery into the mix adding a column with the code of:
This adds a column with Query Length i.e. how many word made up the search term i.e. Identifying what are short and long tail keywords.
Finally I created another column dividing the No. of Transactions / Goals Complete by the number of visits, hence giving us a Conversion rate.
Finalizing the Data
Now I have all the information in an Excel table, the simple thing is to create a pivot table of the information.
For my first example I have chosen to pivot Query Length as the row and then No. of Visits and Conversion rate as columns. I have chosen to display the average results in columns to give the overall picture.
The graph below shows the average conversion rate versus the average number of visitors by query length.
Now I used 13 clients data over a total of 166,699 keywords.
We can see a clear picture that from a 1 phrase visit up to a 5 phrase visit the conversion rate is over double.
It’s not as uniform from 5 phrase visits to 10 phrase visits but I think this may shows that people using 5 phrases and above are still unsure about finding the right product or service.
Although the overall trend does show that conversion rate does increase as the search query increases.
Extending the results
The beauty of having data in excel is that it can be manipulated in any way, so I took the data above and filtered the results by removing keywords which had “0″ / “Zero” conversions, just to see what the affect was.
Apart from the obvious of average Conversion rate in the data increasing massively and the same for Average number of visits this does correlate with the overall data but shows a more uniform conversion rate by query length.
From the data presented it does show clear support that the long tail theory in SEO still exists and it is still right to assume niche keywords will drive a higher conversion although they have a lower search volume.
If you have any questions on the data please leave a comment.