Why Ask for Open-Ended Feedback?

Receiving low response rates on your surveys? You are probably not the only one suffering from this problem. Have one question asking for open-ended feedback instead!

There can be several possible reasons for low survey response rates. A common reason is that the surveys are way too long. Most companies want to collect a lot of information because it can be valuable for them. It’s also good for the customer in the long-run since it can mean that products or services can be developed based on their feedback. It makes sense!

However, nobody wants to spend 20 minutes filling out a survey that often doesn’t seem like it adds value. Instead people will just skip it, and the survey that you thought would give you amazing insights about your customers actually gives you nothing!

Another problem with long surveys is that it can affect the quality and reliability of your data. According to SurveyMonkey, the more questions you ask, the less time your respondents spend, on average, answering each question. As you can see in the table below, the average second spent per question decreases as the number of questions increases.

Response time table surveys
Source: SurveyMonkey

Usually what happens in this scenario is that the respondent rushes through the questions, giving super short answers without any real thought or detail included. You are left without any actionable insights.

Depending on your business, level of resources and the ambition that you have with regards to surveying your customers, it could be smarter to just give them the chance to leave some open-ended feedback. Skip all the fuss, and just get straight to the point!

To learn more about best practices for collecting customer feedback, check out this article: How to collect customer feedback

Why ask for open-ended feedback and why some don’t like it

When you ask for open-ended feedback, what usually happens is that your respondents comment on what they experienced to be the most critical things in the interaction they just had with your company. According to Suvicate, open-ended feedback allows you to get more authentic responses where you get to know what your customers really think about you.

Think about it, if you encountered an exceptionally bad customer service representative, this would probably be the first thing you would comment on if you were given the chance to leave open-ended feedback. In some cases customers will write the equivalent of a short novel, sharing in great detail what was good and what was not so good. Responses like this yield really rich insights for companies who aspire to greatness!

Another important thing to consider when deciding between open-ended feedback and long surveys with lots of specific questions is bias.

If you have a survey with 200 questions (a bit overboard, but you get the point!), you practically give your customers a guided tour of the kind of answers you would like them to give you. Some companies add the chance to leave open-ended feedback after the 200 questions, at which point the customer has already forgotten about the most important thing they wanted to comment on in the first place. In cases like this it is not uncommon to get an angry comment saying; “survey far too long, don’t ask me for feedback again”.

The simplicity of asking just “tell us what you think” can be so much better. Then you really get to know what customers think and talk about, and what is most important to them.

What can be challenging with open-ended feedback, however, is processing the feedback in a scalable manner and ensuring that all valuable insights are realized. Open-ended feedback for many can be viewed as a great unstructured mess. It is often seen as way too difficult to manually structure the data, even with the promise of those golden nuggets of insight at the end!

How to manage open-ended feedback in a scalable and structured manner

There is a simple way to retrieve customer insights and structure the data you receive from open-ended feedback, and that is to analyze it with an AI-powered text analytics software made for customer experience management. The advantages of using text analytics are many- including scalability, real-time analytics, and consistent criteria.

Scalability means that it allows businesses to structure a large amount of feedback in seconds, rather than days (sometimes even weeks or months), and you can use it to structure text from different data sources, such as email, social media, chats and so on.

Text analytics enables you to get your results in real-time. Having a delay between a customer’s negative experience and you taking action to resolve the problem can often mean that you lose a customer and create a brand detractor.  Text analytics makes it possible to act on and resolve negative feedback as soon as it arises.

Another great about using AI-powered text analytics is that you can reduce the number of human errors. Manually analyzing a large amount of text is not fun, and it takes a lot of time.  Since the process itself is so monotonous, the risk of making errors is really high. As humans, we are also influenced by personal experiences and beliefs that can affect the manual analytics process and give biased results, leading to the wrong conclusions. AI-powered text analytics, trained to identify specific categories or topics for your industry, will definitely give you more consistent results.

You can harness the power of AI and let it process and analyze your customer responses, calling out the positive and negative things that people are saying about your brand, product or service. AI can categorize your feedback and help you to answer a key question- “What are my customers talking about?”

Wrapping it all together

It makes sense to avoid making surveying too complex- especially if you don’t have the resources to manage all the responses to your 100+ questions! And be honest, are all these questions really that important? Probably not. The only result you’ll get is a frustrated customer and a pile of responses that you cannot structure or retrieve any insights from. Keep it simple- ask for open-ended feedback and use an AI-powered text analytics service.