A Bluffer's Guide to Text Analytics for Research

You can read the full version of this article over at Insight Platforms. It includes a rant about Amazon Alexa; an explanation of some of the key concepts and terminology in text analytics; and some suggestions for implementing it.

But if you can't bear to click out of LinkedIn, here's an extract:

10 things you can do with Text Analytics in Research

OK, enough with the theory. Let’s get practical. A brief, unordered list of handy things that text analytics can do for researchers, product managers and CX people.

1. Understand the Customer Experience better

Many organisations now have streams of both asked-for and unsolicited feedback: online reviews, customer service tickets, product feedback, experience tracking surveys, transcribed audio of sales calls … it goes on and on.

All this unstructured data can now be processed, tagged, understood and tracked with NLP based tools.

2. Prioritise product feature requests

Most product teams receive continuous feedback and suggestions for improvements or new features.

Tools exist to capture some of this in a structured way – but much of it comes in as unstructured ad hoc user input, complaints or reviews.

Text analytics can be used to make sense of all of this and pick out the most-requested features.

3. Turbocharge qualitative research

Generally, machine learning needs large volumes of data, so there’s not much benefit in applying text analytics to a single focus group transcript or a handful of interviews.

But ‘qualitative’ research can sometimes generate scale: many product and UX teams carry out continuous streams of user interviews. If these are transcribed, they can be analysed.

Online discussions in forums or communities can also generate large volumes of unstructured data; some startups are even building tools around the automated analysis of ‘qualitative at scale’.

4. Squeeze more value out of your video assets

Recordings of usability interviews, narrated user tests, focus groups and other one-to-one interviews can now be captured in video research platforms and automatically transcribed. This makes their content both searchable and analysable.

5. Measure brand sentiment

This is one of the more common applications for text analytics – social media listening. Increasingly, it is being applied to verbatim responses in brand tracking surveys.

6. Understand emotions

Apparently 97% of what I’ve written here is emotionally driven.

Something like that.

Or maybe I’ve read the Behavioural Economics stuff wrong.

Anyway, there are both generic NLP and specific EmotionAI tools for decoding the emotional content of language. And these are not just being used for research – they are screening job candidates, insurance claimants and mental health patients.

7. Create automatic summaries of content

This one is more of a stretch: going beyond just analysis and using NLP tools to generatecontent.

Platforms like Narrative Science and Wordsmith are used to read stock market indicators and create investment summary notes that are largely indistinguishable from content written by people.

Zappi does the same to produce summaries of its automated survey projects.

8. Monitor trends

This will be much more familiar: social media listening tools scan billions of items of user-generated content to work out if things are on the up or on the wane.

It might be pretty simple stuff – tracking mentions of a keyword – or much more sophisticated, looking for patterns of meaning in conversations around a topic.

9. Analyse competitors

This is another use for social listening tools. But text analytics can also be used for much more focused competitive intelligence.

Competitors who post news updates, content marketing and placed media provide a rich source of data. All this material can captured using RSS feeds or scraped with simple tools. It can then be analysed for specific keyword triggers, or monitored for changes over time.

Is the density of references to specific product features increasing? What can we deduce about their sales strategy? Do we need a campaign to counter what they’re saying?

10. Code verbatim responses from surveys

This one is a feature of some of the other applications here – but also worth drawing out separately, given its potential (still) to save time and cost in survey research over manual coding.

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