Keyword Research Feature – Tube Stations
This Keyword Research Feature is dedicated to the stations of the London Underground.
With over 300 stations serving millions of people in London, there’s plenty of people searching for them on teh interwebs.
We examined all the identifiable search terms on Google relating to the names of all the Tube stations on the London Underground network. We aggregated all the search terms for each station to get an overall figure of search activity for each station and then each Tube line.
Results
The most searched for station
And the winner is Victoria! With Angel a very close second.
The most searched for Underground Line by station searches
The District lines wins this competition due to it’s large number of stations. The Northern line comes in respectable second place. Waterloo and City has the lowest overall number of searches with just 2 stations.
The most searched for Underground Line relative to size
The chart above now shows the number of searches per station and per km of track for each tube line.
Now we get a completely different story. The Waterloo and City line scores the highest as it has the greatest search volume relative to it’s number of stations, 2, the smallest number possible to qualify as a transport link.
The District line can’t really compete in this arena as it’s crippled by it’s high number of stations and long tracks.
Methodology
I started off getting a list of tube stations from Wikipedia. This also had the tube lines so I grabbed that as well and pasted into excel, and cleaned up. 5minutes.
I then create created lower case versions of the tube stations in another column and added as many variants or cut down the keywords to ensure good matches. e.g. apostrophes removed with s removed variation and with s joined, e.g. st paul, st pauls instead st paul’s. 5 minutes.
Then I concatenated the tube station names with a few key phrases, tube, underground, under ground. I used the output as my seed keyword list. 10 minutes.
Chunks of seed list are pasted into Google AdWords Keyword Tool ensuring that returned results were less than 150, after which keywords would be omitted. Selected ‘Exact Match’ and saved CSV for Excel files. It took 40 searches and saved files. Google kicked me off one account so I had to log into another. 45 minutes.
All 40 excel files were manually joined into a single file. The braces on the search term [ ] were removed. The Global Monthly data column was hidden and the search term, Advertiser Competiton and estimated monthly search volumes copy and pasted into the Bulk Import completing the keyword generation. 20 minutes
Taking the full list of tube stations from Excel with ‘:’ separator and the keyword matches, a filter was created and the All Stations filter was created. Remaining keywords were sorted through and additional keyword matches added where necessary. 15 minutes
Irreleant keywords were removed. In this instance, a number of ‘briana banks tube’ terms which had been included which were probably people looking for youtube videos. I might do a bit more research into that later on
10 minutes
From the excel document, the list of stations was sorted by tube lines. The station matches for each tube line were created and put into keyword tool as new dimensions. As stations can exist on more than one tube line, the tube lines could not be set up in a single filter as keywords are exclusive unlike at dimension level. This stage was quite painful as the excel sheet required . This is the 3rd piece of research that has used structured information to build interesting filters but it requires manual manipulation and repetition. At some point, we should look at the uploading of a 2 dimensional array which can automatically create multiple views.
The research project started off by getting a list of tube stations from Wikipedia. This data also had the tube lines for each station so the the number of stations and total length of track per Tube line was extracted. The data was placed into 2 charts in Excel.
A seed keyword list generated by concatenating the station names a number of tube related phrase modifiers, ‘tube’, ‘underground’ and ‘under ground’.
The seed list is used with the Google AdWords Keyword Tool to generate estimate monthly search volumes for a range of relevant keywords.
Irreleant keywords were removed. In this instance, a number of ‘briana banks tube’ terms which had been included which were probably people looking for youtube videos. Another Lady called ‘Alison Angel’ was also surprisingly popular.
The search terms were then filtered for each station. The number of searches for each station was aggregated for each tube line using a more complex set of dimensions as each station can be counted within multiple Tube lines.
