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What are 'Themes' in Wordnerds?

This article explains what 'Themes' are in Wordnerds, the different types of 'Themes', and how they can be used to classify your data

What are 'Themes' in Wordnerds? 

Themes are a big part of using Wordnerds. Themes are used to create custom classifications so that you can group your data in the way you want.

Themes can be based on what is being discussed in the verbatim content itself (using Definition-Led Themes or Keyword Themes - more on those shortly) or based on metadata you already have attached to the verbatim content. (link to metadata article)

Once a 'Theme' is set up in your project, you can...

  • See the volume and sentiment of the Theme in your data 
  • Trend your Theme over time
  • Apply a Theme historically to get a retrospective view on a new issue 
  • Use your Theme as a filter and a way to slice your data during analysis
  • Group Themes into 'Theme Categories' to size and trend at a category level
  • Group Themes or 'Theme Categories' into 'Frameworks' that act as a lens through which to view and analyse your data 

Why can't I classify my data just using 'Topics'

Well, you can! But... we wouldn't if we were you. 

Unsupervised topics are great for..

  • Getting an immediate feel for what's in your data, no training/setup required
  • Finding surprises - the things you didn't know to listen for 

BUT they don't robustly size or classify issues.

'Topics' will surface the issues in your data at a given time, based on words and phrases commonly used together. However, the human job of deciding how these should be grouped and how your data should be classified has not been done. 

For example, "expensive", "poor value for money'", "price... too much" could all be 'Topics' that the platform surfaces for you, but if you want to know how many customers are talking about pricing (which these 'Topics' all are, in different ways), you would train a (replace) theme to do that. 

Definition-Led Themes

Definition-Led Themes let you create themes by describing what you're looking for in plain English. Instead of hunting through your feedback manually, you simply tell the platform what matters to you, and it builds the theme from your description.

Why we use them

Definition-Led Themes work particularly well for capturing nuanced or contextual feedback that would be hard to pin down with keywords alone. If you're looking for something that's more of a concept than a specific word or phrase like "a peaceful night's sleep" or "feeling rushed by staff", a definition lets you articulate that intent clearly. The platform then does the work of finding all the different ways customers express that idea across your feedback.

Why they're useful

They're intuitive and straightforward. You don't need to learn special syntax or think like a machine. You just describe what you're looking for the way you'd explain it to a colleague. The platform combines your description with AI to build a working definition, then refines it using Wordnerds technology to scale across your data. The result is a theme that captures what you actually meant, not just what you managed to tag as examples.

Do you need specialist skills?

Not at all. If you can describe what you're looking for in everyday language, you can create an effective Definition-Led Theme. Think of it as having a conversation with the platform about what matters to you.

Keyword Themes

Keyword themes allow you to build rules that include keywords and phrases, as well as specifying exclusions, so that you can classify your data based on specific language being used. But (there's a but!)... this relies on you being able to accurately predict how your customers speak. And humans aren't always that predictable. Therefore, while there are situations where keyword themes do make the most sense, for example if you want mentions of a specific location or product name, keyword themes can get really complicated for classifying 'issues', and are often not the best option (even if they are still pretty good).

Keyword themes are great for...

  • Identifying mentions of things (rather than issues) — like a product name, location, or service

  • Situations where what you're classifying is only described using specific, distinct words that don't typically occur in other contexts

Think of it this way: if you're after "Disney+" or "council tax", focused, unambiguous terms, keyword themes will do a brilliant job. If you're trying to capture a more ambigous problem like "slow service", keyword themes might struggle because customers describe that in endless different ways.

Upload Themes 

Upload themes are great for analysing your data based on the metadata already associated with your customer feedback - for example, the NPS scores or groups given alongside open-ended survey responses, complaint stage, demographic information, location or business unit - anything! While these are different to the (replace) and keyword themes that you create, you still choose what upload themes to use, and they can play an important part in your analysis.

Upload themes are great for...

  • Categorising data based on information you already have and don't need to find in the verbatim itself 
  • To add a metric you will use for analysis purposes - for example, the score given alongside an open-ended survey response 

Ready to dive in to creating Themes?

We have some in-depth articles for that! 

Definition-Led Themes (link)

Keyword Themes (link)

Screenshot 2024-11-27 at 11.54.50 ✍️ Article written by: Nat, Customer Success 

Still in need of some help? Give us an email on support@wordnerds.ai or reach out to your CSM directly.