If you don’t understand your customers, it’s almost impossible to improve your product or service for them.
Online surveys make it easy to get to know your users, but even a short, simple survey can leave you with a headache-inducing amount of user data to sort through.
So how do you make sense of it all? Read on to know how to conduct effective survey data analysis.
It can feel daunting not knowing how to analyze data from a survey you carefully crafted and posed to customers. There’s certainly an art to extracting actionable insights from customer feedback to help you improve your product or service for your users. Thankfully, that art can be learned.
This article looks at the different methods you can use to analyze and make sense of your survey results. We cover:
Hotjar has all the tools you need to run a survey, analyze survey data, and improve your customer experience within days.
Before we dive into how to analyze survey data, let's look at the big picture: while understanding traditional metrics and statistics can be helpful for analyzing customer survey results, the real key isn’t analysis—it’s planning.
To get meaningful user feedback, you need to ask the right survey questions. And to know what the right survey questions are, you should start at the end and establish clear goals.
With a clear understanding of the information you need, you can decide which questions to ask and determine how to conduct your survey.
Then, after you collect actionable survey data from relevant audience segments, your next step is to decide which kind of survey analysis to perform.
Here, it’s important to choose your analysis methods according to the type of questions you ask and the type of data you gather. For example:
1. Closed-ended questions give you quantitative data (i.e. numerical data) that you can analyze with graphs, charts, and comparison tables. Respondents have to answer in a specific way, like giving a 'yes' or 'no' answer, selecting a 1–5 rating, or choosing from predetermined responses.
Use closed-ended questions to measure and compare what your users think or feel about a specific issue. Closed-ended questionnaires are one of the most effective ways to carry out customer satisfaction surveys.
2. Open-ended questions give you qualitative data (i.e. descriptive, word-based data), so you can identify trends and organize responses into groups and categories. Respondents can answer in any way they choose, in their own words.
Use open-ended questions when you want to dig deeper and find out why users think, feel, or behave a certain way.
The benefits of quantitative vs. qualitative data analysis🔥 If you're using Hotjar
There are many online survey tools out there to turn to for your survey needs, and we’re pretty proud to recommend our own. Hotjar Surveys lets you create and run a survey in minutes, especially when you start building from one of our survey templates.
Want to make the process even faster? Watch as AI for Surveys generates a survey based on your research goal in seconds.
These five methods will help you get valuable insights from quantitative survey data:
If you ask a multiple-choice question in your survey, the answers will help you identify specific customer preferences (e.g. when you're testing a new product feature, service, or design).
To analyze comparative data, add the total number of responses for each multiple-choice option. Then create a comparison chart to organize the number of responses or percentages for each answer:
A data comparison chartThis kind of comparison is often easier to understand if you display it in a bar graph or pie chart:
A simple bar graph generated with HotjarIf you include survey questions that let you categorize respondents by demographic, you can see how different audience segments answer the same question—for example, how answers vary by age group or industry.
To analyze these responses, use a cross-tabulation chart to compare answers from each segment:
A cross-tabulation chart that breaks responses down by subgroupIf your survey asks users to answer by selecting from a scale, you can measure how strongly customers feel about specific topics, product features, or services you provide. This type of question is also a great way to understand the customer experience.
For example, you might ask survey respondents to choose from a likert scale involving numbers:
A Hotjar survey that asks users to mark their responses on a likert scaleOr you can use a more descriptive scale:
A Hotjar survey that asks users to rate their experience on a descriptive scaleIn either case, one of the easiest methods to analyze scale survey data is to create a bar chart showing response rates.
Using a spreadsheet editor like Excel or Google Sheets, enter the names of each response in one row, then add the frequency it was selected in the corresponding row above. You can then create a bar chart that looks something like this:
A bar chart showing how respondents answered a question using a five-point scale (Social Science Computing Cooperative)
If your scale involves numerical responses (e.g. a 1-7 rating), there are two other easy ways to analyze the data:
Mode represents the most common answer that appears in a set of data and can give you a quick snapshot of which rating on the scale respondents chose most often.
You can calculate mode in a spreadsheet with the MODE formula function. Select a blank cell, type =MODE into the formula bar, then select the cells containing the individual responses from survey data.
The mean is what we generally refer to when we say ‘average’. You can calculate it by adding up all the scores and then dividing the total by the number of responses.
Using the mean gives you a figure representing the typical response, which is helpful if you want to compare how customers’ responses to the same question change over time.
There's some debate about using averages to analyze scales, but we'll leave it up to you to decide whether this method will work for your survey analysis.
When you ask customers how likely they would recommend your product or service on a 0–10 scale, you can use the responses to calculate your Net Promoter Score® (NPS).
NPS is represented as a number between -100 and +100 and is considered a practical gauge of customer satisfaction and loyalty. However, it’s typically calculated using a different methodology:
You can also find your score using our handy (free) NPS calculator tool. For a more detailed breakdown, read our guide for calculating your NPS.
🔥 If you're using Hotjar
There's no need to go through the process of manually calculating your NPS: Hotjar automatically calculates your Net Promoter Score® and gives you a visual breakdown in your survey response tab.
You can also use our NPS survey template to get started now.
An example NPS survey
When you’re analyzing quantitative survey data for the first time, it can be hard to tell whether the numbers are as good or bad as they seem.
For example, you might learn that your landing page's conversion rate is 12%, which seems low. But if you then learn that the average conversion rate in your industry is 8%, suddenly you’re doing pretty well (yay!).
This is called 'benchmarking', and it’s a great way to interpret your data in a helpful context.
You can learn a lot more from your survey data by benchmarking it with past results.
For example, if you compare your current Net Promoter Score® against your score from six months ago, rather than labeling your score ‘good’ or ‘bad’, you can get a clear picture of progress over time.
🔥 If you’re using Hotjar
While viewing survey response data, filter the results by date to compare answers from different time periods.
How to compare survey data over time with Hotjar
