One of the best ways to improve rankings and deliver a better online customer experience is to build user-suggested categories into your website structure. User-suggested categories are categories that you haven’t explicitly defined, but shoppers are searching for and for which you have product that enables you to satisfy the search and potentially the buyer. However, we find that most retailers like House of Fraser still define a hard category structure i.e. Home ? Women ? Jumpers for their site. So why is this?
Research shows that 59% don’t have enough time and resources to fully research trends and changes and a third struggle to get elements live in time for when demand changes. As a result, there is only a limited amount of sub-categorisation done and that’s what contributes to these other user-suggested categorisations being missed.
It’s only when you get into the data and start looking at what shoppers are searching for that you really understand the breadth and depth of taxonomy that every one of your categories and sub-categories needs. The rewards for retailers who define user-suggested categories is enormous – it’s possible to double your reach by growing your pages. So where do you go to find user-suggested category data?
Fortunately there is plenty of data available to help you do this. Here are my three favourite sources that will start of give you an indication of how shoppers are searching inside every one of these categories so you can better understand how to enrich, grow and augment every facet of your taxonomy.
1. On-site search logs
If you are using an on-site or product based search engine on your website it will probably generate a number of logs. Analysing these will help you to understand all the different keywords and phrases shoppers are keying into the search engine.
2. Google Analytics
Your analytics is hugely rich in data. If you look into your referring keyword report you’ll see all the individual keywords that are referring traffic to the website because your site is appearing in some shape or form in search engines.
3. Search Query report
Another great source for discovering the language shoppers are using is the Search Query report in AdWords. If you haven’t done an AdWords deep-dive before, the Search Query report is a report of all the keywords that are not just referring traffic, but are actually showing because another keyword is broader matched on your Google Adwords programme. For example, if you’re bidding on the keyword ‘red shoes’ and people are typing ‘red dkny shoes’ or potentially under match type ‘rouge shoes’ . those are the keywords that will appear in the Search Query Report.
Downloading and analysing these sets of data will help you to understand where there are opportunities to build out and deepen your taxonomy, improving your site’s rankings and delivering a better experience to your customers, ultimately leading to higher conversion rates and revenue growth.
As I previously mentioned, the time and resource required to do this is enormous and you will probably find yourself working with tens or hundreds of thousands of individual keywords. OneHydra makes this process easy, by analysing all of the keywords for you and grouping them together into clusters. What clustering does is identify all of the groups of product search or category searches that exist under every single category and by identifying those groups and those clusters you can start ot see where you have lots of searches for similar keywords where a new sub-category emerges.
For example, if you sell women’s clothing and you start to see searches ‘fluffy jumpers’, ‘pink fluffy jumpers’, ‘cropped black fluffy tops’, ‘v neck white fluffy wool jumper’ that come together as a cluster and we can start to see a new sub-category developing out of our Jumpers page. OneHydra then creates a new category page inside our taxonomy and as a result get a better ranking for those particular keywords.
I also discuss this topic in my video Hard category vs User-suggested category structures.