Author: Charles Meaden Tips and Tricks To Really Understand Charles Meaden Digital Nation

Author: Charles Meaden No More Than 30 Seconds On Me • Started Digital Nation 26 years ago • Specialise in search (paid and organic) and analytics • Reside in the strangely named The Mumbles (Swansea, Wales) • If you’re looking to move out from the city, you get views like thse

Author: Charles Meaden What’s The User Intent? • The phrases users type into search boxes are a goldmine of useful data • The combination of words lets us know precisely what the user was looking for • Also, where they are on the journey – Best lightweight hiking boots • Informational query – Roclite 345 price • Transactional query

Author: Charles Meaden Quickly Establish What People Are Looking For • The most searched term on UK councils sites is… • Recycling – People need to know what weeks and what recycling to put out

Author: Charles Meaden Top Level Search Intent • The most common are – Navigational – Informational – Commercial – Transactional • Google has a slightly different model – Know” queries: – “Do” queries: – “Website” queries: – “Visit-in-person” queries • My challenge with them is that they are way too broad

Author: Charles Meaden I hate word clouds… • Single words that is

Author: Charles Meaden 2 Or 3 Words Provide Context • In this case adding – Gender – Brand – Colour • Tells us far more about what someone is looking for

Author: Charles Meaden There Is A Lot Of Data To Be Crunched • For two major UK retailers we’ve extracted over 750,000 unique search terms via the Google Search Console API • For our GoSimpleTax client with a lower search volume per month, you’re still looking at – 82,000 unique search terms – 7,800 different words – 26,000 question phrases • What, where, how

Author: Charles Meaden Google Search Console to Big Query Export • Google slipped this out in May 2023 • Does a daily export to Big Query • Tested it across 3 different sized clients • You can do some really clever stuff like generate ngrams • If you are at involved with search you should enable this

Author: Charles Meaden Tip 1: Every Data Set Is Slightly Different • We’ve worked on search query intent projects for a wide range of customers • The phrases people use to find them and enter in their internal search engines are slightly different • Take the time to build an adaptable model which you can use time and time again

Author: Charles Meaden Tip 2: Eyeball The Data • Before running any automated process over the top, use your eyes • You’ll get a feel for the data • You spot patterns and anomalies in the data straight away

Author: Charles Meaden Tip 3: Find The Most Used Words • This will allow you to spot quickly any potential issues • In this example we’ve got – Plurals – Apostrophes – Synonyms • You need to decide for each project which ones to change • The list was generated using the Hermetic Word (and Phrase) Frequency Counter Advanced Version

Author: Charles Meaden Tip 4: Clean Up Your Data • Assume your original data isn’t perfect • Lowercase all your search terms – Thank you GA4 for not lowercasing search terms • Remove unnecessary spaces • Remove apostrophes and any unnecessary special characters • Correct common spelling mistakes • My favourite tool for doing this is Analytics Edge – Excel plugin for Windows – Mac and standalone versions in Beta • Allows me to create macros that automate common text cleaning tasks

Author: Charles Meaden Tip 5: Depluarlise Your Words – Part 1 • What we are looking for is the intent • Tracksuit and tracksuit is the same word • Women, womens and women’s is the same word • This regular expression will do the trick • \b(\w+)(s)\b • However there is a gotcha…

Author: Charles Meaden Tip 5: Depulararise Your Words – Part 2 • Taking off the s works for most words • But not if they are a name – Brands – Citys • Eyeballing the data will help you spot these – Reusable queries are even better • Have a routine that adds the S back to these

Author: Charles Meaden Tip 6 – Make A Decision On Synonyms • Some phrases are treated the same by search engines – Does your internal search do the same? • Kids and Childrens • Ladies, Ladys and women • There is no hard and fast rule here, apart from common sense

Author: Charles Meaden Tip 7: Two Word Combinations • Some words were meant to go together • Splitting these up makes analysing them harder – HMS Daring – Ralph Lauren – San Francisco – Trailfly 270 • Add a hyphen between these words – hms-daring – ralph-lauren – san-Francisco • These will now be treated a single words

Author: Charles Meaden Tip 8: Word Order Doesn’t Always Matter – Part 1 • What’s the difference in intent between these queries – womens french-connection jeans – french-connection womens jeans – jeans womens french-connection • None what so ever - they’re all looking for the same thing • Sorting the phrases into alphabetical order and deduping can massively reduce the number of phrases you need to work with • On a recent project, we cut the number of phrases we needed to analyse by 50% • This is how we do it – We do it in Analytics Edge using a macro, but the process could be easily adapted

Author: Charles Meaden Tip 8: Word Order Doesn’t Always Matter – Part 2 1. Load your queries into a table 2. Take each query in turn such as ‘womens french- connection jeans’ 3. Split this into separate words each on a separate row 4. Sort alphabetically 5. Merge the rows into one row and separate by a space 6. You’ll now have a query that looks like this ‘french- connection jeans womens’ 7. Repeat across all queries 8. Deduplicate the data and total any numerical columns

Author: Charles Meaden Tip 9: Look For Common Word Combinations (Ngrams) • It’s easy to spot patterns if it’s just 100 phrases • Harder at 1000, • Impossible at 10,000 or more • Couple of handy tools • Hermetic Word Frequency Counter – Best £30 you’ll spend I promise • Analytics Edge • Big Query ML.Ngrams function – Calculate 2,3 and 4 word combinations over millions of rows in seconds

Author: Charles Meaden Tip 10: Build Up Libraries of Common Phrases • Every project will be similar, but different • We build up libraries of common terms – A general level – Client and Industry specific • Some examples – Question words such as what, why, how – Colours – Industry specific terms

Author: Charles Meaden What Next – A Couple of Ideas • Clustering – Look for common patterns using machine learning – https://www.keywordinsights.ai/ – Follow Lee Foot on Twitter @LeeFoot as he has done some amazing stuff using Python • Missing Content on Your Sites – Combine the data with data from website crawls and SERP scraping tools such as ValueSERP to find missing content gaps • Product Discovery – What products and services are people looking for that you don’t have

Author: Charles Meaden Thank You • Find me on Twitter and Linkedin • https://twitter.com/charlesmeaden • https://www.linkedin.com/in/charlesmeaden/