Open-ended questions are questions that can’t simply be answered with Yes/No, True/False, multiple choice, or rated on a number or star-rating scale. Instead of fixed response choices, they require customers to provide free-form responses, in their own voice and vernacular, otherwise known as voice of customer (VoC).
Whether on a customer survey, or in interviews and public opinion polls, the kinds of questions you ask will determine the kind of responses you get from your customers.
Try our voice of customer tools
Sometimes you just need close-ended, Yes/No responses – to simply calculate values on a spreadsheet.
But sometimes you need data that goes deeper, that delves into the opinions and thoughts of your customers.
Let’s take a look at the advantages of open-ended questions.
The advantages of open-ended questions are that they gather opinions and thoughts from respondents, offering much deeper, more thorough, often subjective information.
This has many advantages for businesses:
The possible responses to open-ended questions are endless, meaning there’s no limit to your data collection possibilities. Different respondents may approach the questions from vastly different angles, and conversational responses in the words of individual customers allows you to understand them more fully.
One of the biggest benefits of open-ended questions is the potential for wholly new information and customer insights. Because there’s no limit to possible responses, you’re likely to receive information and real opinions you hadn’t previously even considered.
Take these open-ended questions, for example:
These questions compel the respondent to contemplate their responses more thoroughly and drive them toward more discussion rather than the hasty conclusion of a close-ended question.
Maybe a customer has discovered a new use case for your product or recommended a new feature that you hadn’t thought of. The insights can be huge.
Open-ended questions allow respondents to go into as much detail as they care to. Open-ended responses offer more nuance, because they are written just as the respondents speak, so they can explain themselves more fluidly. Because they aren’t tied to a rated scale or multiple choice, open-ended questions lead to less ambiguous answers.
Close-ended questions offer quantitative data that’s expressed as numbers, percentages, or merely positive/negative. This data is easy to calculate, offering quick results, but it doesn’t go deeper than uncovering what has already happened.
Open-ended questions offer qualitative customer data that can help you find out why something has happened and inform decisions. Qualitative data helps read between the lines of customer patterns to understand them as individuals, rather than numbers.
Open-ended questions allow you to understand the ideas, feelings, emotions, and opinions of your customers – because they are explaining their personal POVs. To understand the sentiment of survey responses in super fine-grained detail, you’ll need to use AI tools, like this sentiment analyzer.
Use it to automatically analyze huge amounts of open-ended questions in a matter of minutes, and find out which aspects of your business perform particularly well and which you need to work on.
Use your data to go beyond mere NPS and customer satisfaction CSAT surveys to more thoroughly understand the customer experience (CX) and follow the whole customer journey from the perspective of your customers. When you’re constantly collecting qualitative customer data, you’ll need to establish a robust customer feedback loop for a regular, real-time understanding of customer satisfaction.
It’s time to weigh the advantages and disadvantages of open-ended questions. Although there are powerful advantages of open-ended questions, the disadvantages need to be considered, as well – with some tips below on how to lessen them.
With open-ended questions, respondents don’t have the option to simply select or click their choice from an online or in-app survey. They have to write out their answers, sometimes explaining in detail. This takes considerably longer, but the data is worth it, if you can convince them to participate.
Close-ended questions, on the other hand, have a limited pre-set amount of choices and are quick and easy to answer. Take the common Net Promoter Score (NPS) survey question, for example:
“On a scale of 0 to 10, how likely are you to recommend us to a friend or colleague?”
Or multiple choice questions like, “What is your favorite aspect of our product?”
A. Usability B. Features C. Security
While they may get a higher response rate, closed-ended questions won’t deliver any wholly new information.
Because they take longer to answer, you’re likely to get lower response rates than with close-ended questions. That means less data to analyze and fewer insights. Offering incentives to customers for completing surveys can sometimes increase your response rates.
Open-ended responses aren’t based on numbers or percentages, so they can’t be compared strictly mathematically. They are often objective, so they’re harder to compare with consistent data points and results.
Depending on how your survey is enacted, your responses may contain a lot of “noise” – things like emojis, URLs, non-word characters, etc. Furthermore, most people don’t write with perfect grammar – and spelling mistakes, misused words, etc., are common. Some respondents may even just ramble on, leaving you with information that is completely irrelevant or doesn’t make sense at all.
Open-ended questions are harder to analyze because they contain unstructured data. They can’t be easily computed as numbers, and contain subjective information, so the interpretation of the data may differ from person to person.
It used to be that you’d have to hand-annotate and manually analyze surveys with open-ended responses, wasting employee time on hours of tedious tasks, and producing results far below the desired accuracy.
However, with advances in natural language processing (NLP), machines can now do this work for us. Custom-trained (or pre-trained) text analysis tools can automatically analyze open-ended responses for topics, themes, opinions, keywords, and more – with accuracy levels above and beyond what humans could ever do. The aim is to quantify unstructured data, like open-ended responses, so that they can be easily interpreted in graphs and charts – in much the same way as numerical data.
MonkeyLearn is a text analysis platform with a suite of ready-to-use tools to ensure you get the most out of your open-ended survey data. They’re super easy to use and can integrate with tools you already use.
Take a look at these pre-trained text analysis tools to see what they can do with open-ended question responses:
MonkeyLearn’s tools are ready to go, right out of the box, with little setup necessary. Better yet, you can train these tools, and more, (usually in just a few steps) to the language, needs, and criteria of your business, so you never have to worry about accuracy.
It’s clear that open-ended questions can offer deeper and more powerful insights than close-ended, Yes/No or multiple choice questions. They may be a bit harder to analyze, but with the help of machine learning tools, like MonkeyLearn, the extra work is minimal. And you’ll save time and money, and get much more powerful results in the long run.
For best results, combine open-ended and close-ended questions for qualitative and quantitative data. When you have the tools in place, you can analyze open-ended questions (or customer feedback from all over the internet) constantly and in real time.
January 25th, 2021