Making your business a success is all about meeting customer needs. It’s as simple as this – would you give someone a knife when they want a spoon? Hence, you need to know what your customers want from you. Surveys are a great tool to understand their requirements and collect feedback on your brand experience. From designing a new product line to logistics and brand image, surveys play an important role in uncovering consumer insights.
Setting up your survey questions
Surveys should be designed to collect maximum information with minimal effort by the consumer. They typically contain close-ended and open-ended questions. Close-ended questions like multiple choice, ranking, checkbox or drop-down questions have anticipated results that are easy to quantify.
Let’s say you’re introducing new colors to a product and ask customers to pick between red and black. The results can be easily analyzed and you get a clear picture of your customer’s choices from the options presented to them.
But, what if you simply ask respondents to type their preferences? The results are not limited to 2 choices, you could also get variables like pink, green, blue, yellow, and so on as options. This is an example of an open-ended question.
Responses to open-ended questions may be a word, a phrase or even a paragraph. This type of data is categorized as qualitative data. Qualitative data analysis involves identifying themes, interpreting patterns and determining how these themes and patterns answer your questions.
With sentiment analysis, you can further transform qualitative results into quantitative results. For example, you could learn that 60% of your survey respondents prefer red while 20% prefer black and 10% prefer green. Let’s dive deeper into emotional sentiment analysis and survey data.
The importance of open-ended survey questions and qualitative data
Before delving into sentiment analysis for qualitative questions, it’s important to first assess the need for them. Having a survey with only quantitative results would definitely be easier to analyze and interpret. Give respondents the option to rank their preferences for products.
Create a choice between ‘Yes, I like it’ and ‘no, I don’t like it’. Let them rate their level of satisfaction on a scale of 1 to 10. The responses are statistical and can be easily analyzed to tell you what customers like and what they don’t like.
However, you might miss out on important details. A simple ranking system would tell you if customers like or dislike your products but it won’t tell you ‘why’ they feel this way. It is only by including questions that require qualitative responses and analyzing the sentiment behind these responses that you can get the answer to ‘why’.
You might also accidentally omit an option and hence get a skewed response. Take this survey question for example;
“How did you hear about us?
By limiting the variables to make this a closed-ended question, other communication channels like word of mouth, print and television advertisements, etc. have been omitted. This can be easily rectified by making it an open-ended question.
Open-ended questions also put you in a position to learn more from a small sample of respondents. For example, let’s say you introduced a new product and want to survey beta users on how they feel about it. Along with close-ended questions that tell you whether they like it or don’t, including open-ended questions can give them a way to tell you what specific features they think need improvement.
What is emotion analysis?
Emotion analysis or sentiment analysis is all about understanding the emotions behind a customer’s answers to a survey by using Artificial Intelligence (AI) types of software that do text analytics, Machine Learning (ML) and Natural Language Processing (NLP).
At its core, emotion analysis categorizes answers as positive, neutral and negative. It also analyzes the themes and subjects respondents are writing about. For each subject, it scores a response from –1 to 0 in the case of negative responses and numbers, 0 to 1, for positive responses. Neutral responses are scored as the number 0. These statistical responses make the interpreted results easier to visualize.
Sentiment analysis for surveys can help answer questions like:
- Which product has the most positive feedback?
- What do customers like and dislike about a product?
- Is the feedback trending in an overall positive or negative direction?
- Has there been a shift in the nature of responses since last year or last interval?
The variable of potential answers to these questions are vast. Learn more about sentiment analysis in our detailed article here.
Why is emotion analysis important for survey analysis?
Manually reading through every survey response, cross analyzing, and understanding the sentiment behind it is next to impossible. It’s tedious and there are a million better ways you can spend your time. Hence, it is best to automate the task with an emotion analysis tool. Let’s look at some of the benefits of doing so.
Accurate customer insights
Analyzing statements to understand the emotion behind them manually can quickly become subjective. Two people can look at the same statement and score it differently based on their own experiences. Hence, it isn’t easy to be consistent with manual emotion assessment. However, by automating the task, the same criteria are used to evaluate each statement. Hence, there are no biases.
Secondly, if you were to complete the task manually, you would probably pick a small sample from the survey answers to base your analysis. But, automating the task allows you to consider all the survey responses. As a result, the customer insights gained are more accurate and reliable.
Real-time survey analysis
A survey can have as little as 1 question. For example, you may want to include a survey at the end of every order confirmation to find out how the customer rated the experience and what they would like improved.
Using a sentiment analysis AI tool allows you to assess each survey response in real time. This helps you keep pace with customer expectations and spot issues and opportunities as they arise.
For example, if you see a spike in disgruntled clients, you know there is something wrong and can look into the sentiment expressed by them to understand where the problem lies.
Ability to tackle issues quicker
In continuation of the above point, emotion analysis helps you detect negative emotions at an early stage and give you the information required to tackle the issue before it grows bigger. For example, if you notice more than a few survey answers talking negatively about the quality of stitching, you can take a quick look at the product for yourself and make changes if required before you start losing customers.