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Text Analytics

4 Things to Look for in a Text Analytics Solution

by William Braün, on Jan 24, 2018

When you really need to get inside the mind of your visitors, there is very little that can match the power of unstructured feedback.  

Commonly referred to as 'open-ended feedback', this type of feedback is where your customers can use their own words to share their thoughts and opinions, as opposed to needing to select from a pre-determined list of answers (close-ended / structured feedback).

The insights you gain from unstructured feedback can be eye-opening, as after all, who better to suggest what you can do to meet customer expectations than your customers themselves?

Technology has made it possible to collect a potentially endless stream of customer feedback from a vast number of places. On the other side of that coin, this has opened the floodgates where it can make it difficult for you to monitor and easily fish out key insights you can use to boost the customer experience.

Text Analytics solutions can make it easy to sift through and analyze open-ended feedback, but not all text analytics solutions are created equal!

How do you know which one is right for you?

In this post, I outline 4 things you should look for in a text solution to make sure you get the most out of your customer feedback.


1. It easily bridges the gap between your unstructured and structured feedback

Knowing the key items that are appearing in your unstructured feedback is great, but in today’s data-driven world, it’s often not enough. For you to get the most out of your unstructured feedback, you need to go deeper. You need some context about your users – their needs, wants and expectations – to better understand the mindset of your users, why they may have provided that unstructured feedback, and what you should do about it.  

Structured feedback ("close-ended feedback") from Voice of the Customer (VoC) surveys can be a great source for this additional context. For example, what user groups or segments were significantly more likely than other groups to experience issues with your menu navigation? 

A good solution helps you sift through your unstructured feedback, but a great one helps you enhance it. They do so by providing you with a clearer picture of exactly who your users are and how they viewed their customer experience, and makes it easier for you to find those key insights. 

What you should look for in a Text Analytics solution:  

  • The ability to easily combine your unstructured and structured feedback at the user level and automatically identify key profile characteristics of users most likely to mention certain keywords and phrases in their feedback.  


2. You shouldn't need a PhD to set it up and use it 

Setting up, managing and using traditional text solutions can be tricky if you don’t have much text analytics experience. In this day and age, ‘plug-and-play’ has almost become an expectation when setting up a new solution – no one has the time anymore, and you don’t want to feel like you need a PhD to properly get it up-and-running.  

On top of that, you don’t want to have to deal with an overly complex interface when you want to analyze your customers’ feedback, regardless of whether you’re just looking to do some high-level analysis, or to do more of a deep dive.  

Plus, if you want to share a piece of insight from within the solution to a colleague, you shouldn’t have to worry about whether they can find their way around the tool, or be able to do their own analysis.  

Simply put, a text analytics solution should be easy and intuitive to use from setup to analysis, and not require you to seek help from different resources at any stage or worry too much about training time.  

What you should look for:  

  • No work needed to set it up (plug-and-play), or at least the ability for anyone in your organization to easily get it up and running, and a user interface (UI) that is intuitive for anyone on your team to perform their own analysis, if required. 


3. It lets you blend feedback from all touchpoints into a holistic view 

You can collect unstructured feedback in many ways. From surveys to social media to your call center – these are all mediums through which your customers can share their opinions about their customer experience. Each source of feedback is typically accompanied by their own respective sets of analysis tools, which can lead to you and your colleagues bouncing between different tools to analyze this feedback. Plus, these analysis tools can often be managed by different departments, which can make it difficult for everyone at your organization to sync together and share key insights.

Overall, it’s crucial to keep this in mind:

All the unstructured feedback you collect ties back to the same thing – your Customer Experience

The Customer Experience is not limited to any single point in time; it encompasses every single interaction your users have with your brand. As such, looking at unstructured feedback from your different mediums separately can lead to a fragmented view of your Customer Experience.

The right solution takes care of this issue and provides the ability to centralize all your unstructured feedback in one location, allowing you to get a better understanding of your overall Customer Experience, as opposed to examining it piecemeal.

What you should look for:

  • The ability to compile unstructured feedback from all your different user touchpoints (customer surveys, social media, call center transcripts, etc.) in one place.

4. AI or bust – It should not just use keyword dictionaries 

Artificial intelligence (AI) and machine learning is now everywhere. You should look to leverage these technologies if you can, especially if it means saving you hours upon hours of manual work.

Traditional text solutions ask you to leverage keyword dictionaries to ‘teach’ it and to make sure it remains accurate over time. This requires a lot of manual upkeep and can hinder your experience with the text analytics tool itself.

However, the value of a text solution comes from the insights you get from it – not the amount of time you spend getting it up-and-running.

AI and machine learning technology helps take care of this issue, as it helps your Text Analytics solution get ‘smarter’ over time as more feedback is uploaded to it. This means that your feedback gets analyzed and categorized more accurately as time goes on, without any manual work on your end.

This adds up to become a massive time-saver, taking the intensive manual work that can come with analyzing customer feedback using traditional solutions. 

What you should look for:  

  • Leverages AI and machine learning to ‘teach’ itself as you collect unstructured feedback, and makes it easier to identify emerging trends in your feedback to prevent crucial insights from slipping through the cracks. 


Pick a Text Analytics solution that’s right for you (and does the hard work for you) 

With the constant source of feedback that’s now available to your company, you need to invest in a Text Analytics solution that’s right for your needs. Ideally, one that does the heavy lifting for you, allowing you to focus on what you really should be doing with your customer feedback – finding key insights you can use to optimize your customer experience


Banner image source: Unsplash

William Braün
William Braün

William is a seasoned analytics expert with over 12 years of experience in the areas of analytics and project management. As VP, Insights, William works closely with all facets of iperceptions’ analytics services offering and account strategy to help clients derive the most value from their digital Voice of the Customer data.

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