Open-Ended Customer Feedback Questions: Get Ideas For New Products and Services
There are lots of ways to gather customer feedback to improve our products and services, including surveys and focus groups. But one of the most effective methods is asking customers for open-ended responses, often in the form of a customer review.
According to an Aberdeen Group study , 90% of customer experiences are shared voluntarily. This means that even if you don't ask customers for feedback, whether positive or negative, they'll still share it with others. The challenge is making sure you get the right kind of reviews so you can learn valuable lessons from customers and improve your business.
Truly open-ended questions allow customers to express themselves however they want, which means more relevant responses and more accurate analysis of the data. However, that doesn't mean open-ended questions are easy to analyze — there are a few technical challenges for businesses using them. In this post, I'll explain how a simple text analytics tool called Brieferr can help businesses interpret customer reviews and extract key insights from the responses.
Forms are one of the most effective ways to know what the customers think. One should be aware of its essential points. It can be time-consuming to read all the answers and extract needed insights. Also, visualization can be crucial to understanding the data. However, it would be best to get profound answers to make sense of feedback. This is where open-ended questions come into play.
There were some barriers to choosing open-ended questions in surveys. Opinion scales are easier to analyze and quantify even they give a very superficial idea of satisfaction. Also, unstructured sentences could make the one-by-one review only option.
However, things have changed; with NLP, structured natural language is ready to be quantified, and visualization techniques give interactive ways to understand the results. No other input methods can be used to predict actual customer behavior (and ultimately revenue) better than the free-form text response to the right open-ended question. Text comments enable customers to tell you exactly what they feel you need to hear.
There are many techniques to analyze customer reviews, but none of them is as honest and unbiased as open-ended questions. The stats could be boring otherwise. It is not so hard to understand why: customers were asked to express their opinions about products. So, if the review is too restrictive, or if there are even needed artificial categories at all, it just takes time away from really valuable insights and analytical data.
So, how do you make the most of your open-ended questions?
The first step is to provide an adequate number of options. The fewer the options, the more the answers will be alike. This takes away a lot of the value that open-ended questions can offer.
The second one is to avoid leading or biased questions. It's tempting to think of asking about people's opinions on specific features or issues that matter for you, but these questions don't allow for unbiased answers. It's better to give people a broad topic and let them formulate their own opinions instead of influencing their answers.
Leading with "What do you like best about our app?" doesn't give the respondent much room to say anything else. You can get more information by asking "What's your opinion on..." instead.
In order to make sure that your respondents won't be limited in what they say, you should offer them a lot of alternatives. The more, the better! Remember that it's not just about getting more data; it's also about getting more actionable insights into customer satisfaction and behavior.
After analyzing your results, you might want to change some things in your product or marketing strategy on the basis of those insights. If so, it would be worth it.
One way or another, open-ended questions are going to be asked, so companies should prepare for it and harness the power of NLP to extract real insights from such data. So, in brief, the ultimate purpose of a form is to collect information and get honest feedback.
However, choosing open-ended questions as your validation technique should be accompanied by thoughtful attention, proper text representation, and quite some statistical analysis. The key benefit of getting this kind of primary data is that it provides a step-by-step understanding (and not just a global one) of customer
The era of summarizing each and every field individually is gone. Brieferr uses NLP to get detailed information – no need to scroll down the page. Just glance at the picture below, and that's what you need. I hope everyone who wants to work with a text will try it!
Brieferr helps you to convert open-ended answers into data points. Using NLP, we analyze overall satisfaction, complaint detection, and summarization. Results are visualized in a one-page dashboard to review. By overcoming the complexity, we make open-ended surveys scalable and insightful.