How To Avoid Sparse Text Feedback In CX Survey?

Author: Frank Buckler, Ph.D.

Founder of CX-AI.com and CEO of Success Drivers
// Pioneering Causal AI for Insights since 2001 //
Author, Speaker, Father of two, a huge Metallica fan.

Author: Frank Buckler, Ph.D.
Published on: October 19, 2021 * 9 min read

Businesses need to make complete sense of the underlying meaning in user feedback surveys. AI can help detect common patterns in CX survey comments and indicate emerging trends in customer feedback. customer is trying to express.

At first, we are going to learn how we can deal with the sparse text feedback. There are several reasons for sparse text feedback that you CAN NOT control. It is a part of your target group.

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What Are The Reasons For Sparse Text Feedback?

The reasons for sparse text feedback are as:

Low brand loyalty – If you have low brand loyalty, people do not talk much. On the other hand, if you have a niche product with strong brand loyalty, they will talk more because they are much more involved. So, there’s a natural bias based on your brand.
Low involvement of a person – The same applies to the category. People talk more if it’s a high involvement category and talk less if it is a low involvement category.
Recency – The recency factor also takes effect. If something just happened, people are more involved in it as there’s been more stress recently.

Frequency of other requests – If there is someone who has lots to do, gets a request for feedback every day or every hour, whatever website he visits, he is prone to not participate so much and does not respond much.

Age and other reasons – There are some demographic factors like age, sex, etc. Some people don’t like to write as it is cumbersome for them to type on the phone or computer. All of these factors have an effect that you can hardly control because it’s part of the DNA of your customers.

There are some reasons that you CAN control, and they are as:

Change order – The first reason you can control is the order where you ask the open-end. If you ask the open-end at the end of the questionnaire with lots of close-ended items asking for feedback, people feel they have sent everything already. In short, the open-end comes AFTER other items as an add-on. If it does not, then you have to change the order.
Change the channel – It is a fact that people talk less and write less in online questionnaires. For instance, if there is a telephone channel where they feel more social pressure, it is impolite to say nothing. So, you may consider the channel change option if you have a problem with the sparse feedback.
Priming by none – If you ask for feedback in an open-ended field, the empty field makes people believe that any input is better than none. What you can do is you can give an example or even fill out an example with which you can prime.

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How To Leverage Audio And Video Feedback?

Audio and video feedback give richer feedback as compared to text feedback. There are many studies around that, but according to the rule of thumb,

“They give at least 2X (double) more feedback than text feedback.”

It depends on the context, and the field is very much evolving because customers get more and more used to those kinds of feedback, and they become more tech-savvy. Some years ago, a study showed that the feedback that was received by 15% of the customers doubled in the year after as more people participated.

The acceptance of giving feedback via mobile is rapidly increasing, and it will be a standard soon. It is because you can have everything with the push of a button, unlike desktops where you do not know whether they have a webcam and microphone on or muted. So, all these things are barriers, but typically they DON’T exist on a mobile phone. So, here we have a high likelihood to get feedback.

Do you know why the audio or the video feedback works? It works because it implies social pressure. So if you record something, you feel someone will listen to it. The process is as:

You ask for audio or video feedback, and you need to test it out to make it mandatory. There will be a lower response rate, but how much lower needs to be tested out.
The recording can go in real-time to auto-transcription service. You record the audio, and it goes in real-time to a service that gives you back the text.
The text can be automatically translated to the core language (by use of cloud services) in which you want to use that text.
The text can also be run in the cloud service that reads emotions and demographics like the customer’s age from the video.
You can use cloud services to read emotional components from the text.
You can use customized cloud services to categorize the text and the topics.

What Is Active Listening?

Active listening is evolving the tech and topic methodology you should use for your open-ends. But what is active listening? It is an adaptive real-time individual response to feedback to foster customer elaborating feedback. So, you get more of it as it is much more than a pre-formulated question.

But why does active listening work? It works because of the following reasons.

It is a positive affirmation so that the customer as a respondent is heard.
The customer feels as if there is someone who values his input. So, it primes his expectations.
There is a kind of social pressure if someone writes more and gives a response.

There are two different approaches or implementation examples of active listening we see in the market. They are as:

In-field probing – There is some probing within the open-end field.
Chatbot-type – This technique uses a chatbot where people answer, and it adaptively responds to them.

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Consider an example of in-field probing below:

This example shows when people write some short text; the active listening approach pops in what’s good. A meter (as shown below) also shows how detailed your text is and primes you how good you are.

So, this is one approach to active listening, and it depends a little bit on the complexity of your inputs. It can go wrong if you wrongly categorize what has been said.

There is another chatbot type of technique that is a little bit more foolproof. So, there is an open-end, and someone writes something in it and submits it. Then, the chatbot pops up and says:

“Hey, I’m a bot and I didn’t understand what you actually wrote.“ So, the person can write better as the bot has the option to be authentic and open. It can also ask whether it has understood the information correctly.

Even though the chatbot categorizes well what has been said, the respondents feel that it is not hundred percent true. They need to write more specifically about what they meant. So, this way, this feedback gives more power to probe for more feedback, and it is a dialogue conversation that doubles the number of topics mentioned.

In a Nutshell

So far, we discussed that open-ends often result in scar responses, so you have to do something against it. You need to make sure that you apply standard rules to get your customers talking. Further, you need to collect audio or video feedback that can be translated into text and categorized into topics.

Use active listening as it also applies to audio and video feedback. You should also know that better unstructured feedback is the most customer-centric way of collecting feedback, and that’s why it is really important to collect a lot of it.