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The survey analysis report is the most important part of the survey data storytelling because it addresses the question: Show me the data or result.
If you’ve just collected your survey data and you’re kind of stuck. You really don’t know how to proceed. Or you want to craft a compelling data story using your survey analysis report, and you don’t know where to begin. Or, you’re confused about the next steps? Don’t be; keep reading.
There are a plethora of survey analysis report examples to guide you in your data storytelling. Besides, you’ll access additional tips shared and used by a community of thousands of data visualization experts. These tips do work, as you shall establish later. So whether you’ve just conducted your first survey or you’re a seasoned data visualization expert, we hope you’ll derive incredible value by knowing about ChartExpo data visualization library.
In this blog you will learn:
A survey analysis report provides the bigger picture as communicated by the data. So it’s just a report that conveys a story to the audience. You really need to give special attention to the survey analysis report because it can make or break the whole exercise.
Imagine you’re a teacher who wants to know why science scores are abysmal compared to other subjects. More so, you want funding to organize summer camps to help students overcome bad attitude toward science.
So you team up with other teachers and organize a pilot summer camp. And you also schedule a pre and post-survey about how students feel about science.
After everything, you’re now at the survey analysis report phase. Imagine all the effort put so far going to waste simply because you can’t develop a compelling data story to persuade the school admin to fund the noble exercise.
Remember data without relevant visualization cannot communicate insights. You need to find the best way to crystallize the survey data into insights that complement your story.
A report cannot be compelling if it’s not crafted as a well-organized story with visual images to augment clarity.
Let’s revisit the teacher and science example.
A group of us in the science department were brainstorming about resolving an ongoing issue with incoming fourth‐graders. It seems that when kids get to their first science class, they come in with this attitude that it’s going to be difficult and they aren’t going to like it.
It takes a good amount of time at the beginning of the school year to get beyond that.
So we thought, what if we try to give kids exposure to science sooner?
Can we influence their perception?
We piloted a learning program last summer aimed at doing just that. We invited elementary school students and ended up with a large group of second-and third‐graders.
Our goal was to give them earlier exposure to science in hopes of forming a positive perception. To test whether we were successful, we surveyed the students before and after the program.
We found that going into the program, the most significant segment of students 40% felt just “OK” about science. In contrast, after the program, most of these shifted into positive perceptions—nearly 70% of total students expressing some level of interest in science.
We feel that this demonstrates the program’s success and that we should continue offering it and expanding our reach with it going forward.
Recommendation: The pilot summer learning program was successful at improving students’ perceptions of science. Because of this success, we recommend continuing to offer it going forward.
Please approve our budget for this program.
Note that our example is missing a chart to complement our story. But we will show some survey charts examples later in this blog.
Sometimes, data and texts are enough. However, keep reading to know how to visualize your data for the best in-depth insights.
Now that you have a glimpse of survey analysis reports, including practical examples, let’s jump to why you should actually conduct one.
A survey is simply collecting voice of customer (VOC) data to personalize marketing within the business settings. So if you’re in marketing and you want to hit the chords that make people dance, a survey is your answer.
If you don’t conduct a survey to gain insights into changes in tastes and preferences, all you’re doing is blind archery. The competition is really intense. And besides, every marketer or brand is competing for attention.
How do you distinguish yourself from others?
Yes, by surveying to know what the market wants. Or what keeps them awake at night. Or maybe the itch they want to scratch.
Gathering data from the audience is just half of the journey. The other half–survey analysis reporting– is the most daunting and complex. So you need all hands on deck with this part to ensure the insights gathered complement your narrative.
So why opt for a survey as opposed to other data collection strategies?
Surveys are relatively inexpensive.
Online surveys and mobile surveys have a minimal cost per respondent. Even if incentives are given to respondents, the cost per response is often far less than the cost of administering a paper survey or phone survey.
Besides, the number of potential responses can be in the thousands.
Surveys are incredibly valuable in describing the characteristics of a large population. Besides, there’s no other research method that can provide this broad capability.
Surveys can be administered using a wider variety of modes, namely:
For remote or hard-to-reach respondents, using a mixed-mode of survey research may be necessary.
For instance, you can administer both online and paper surveys to collect responses and compile survey results, ready for analysis.
The anonymity of surveys allows respondents to answer with more candid and valid answers.
To get the most accurate data, you need respondents to be as open and honest as possible with their answers. Surveys conducted anonymously provide an avenue for more genuine and unambiguous responses than other methodologies.
And especially if it’s clearly stated that the answers will remain completely confidential.
Now that you know how game-changing survey data can be, let’s jump into the tools you need to employ to be the best out of the whole exercise.
Why?
Data is of no use to you if you don’t have tools and a strategy to analyze it.
Before analyzing your survey results, you need to read this section carefully to remind yourself about easy-to-overlook yet straightforward issues.
Keep reading.
These levels determine how you should use the survey questions to be measured. And, most importantly, the statistical analysis that should be performed. The four measurement levels are:
Nominal scales classify data without any quantitative value, similar to labels. An example of a nominal scale is, “Select your mobile phone’s brand from the list below.”
Due to the lack of numerical significance, you can only analyze mode from this type of scale. You can keep track of how many respondents chose each option and which option was selected the most.
Ordinal scales are used to depict the order of values. For this scale, there’s a quantitative value because one rank is higher than another.
An example of an ordinal scale is, “Rank your monthly needs.” Some needs, such as healthcare, might rank higher than others, such as entertainment.
You can analyze both mode and median from this type of scale.
Interval scales depict both the order and difference between values. These scales have quantitative value because data intervals remain equivalent along the scale. However, this scale lacks a true zero point.
And this means participants have to record an answer that falls somewhere along the scale.
An example of an interval scale is an IQ test. The difference between an IQ of 90 and 100 is the same as 100 and 110. Additionally, it’s impossible to score a zero on an IQ test as the minimum score is 40.
You can analyze mode, median, and mean from this type of scale.
Ratio scales as well depict the order and difference between values. But unlike interval scales, they have a true zero point. With ratio scales, there’s quantitative value because the absence of an attribute can still provide information.
For example, a ratio scale could be, “Select the average amount of money you spend online shopping.” Choices like $75-$100 rank higher than $50-$75. The difference between intervals remains the same.
But, there’s a true zero point since someone may spend $0 on online shopping. Even though this person’s answer is zero, that response still provides insight into your target audience.
Once you understand how survey questions are analyzed, you should highlight the overarching research question you’re trying to solve.
Assume it’s “How do respondents rate our brand?”
Then, look at survey questions that answer this research question, such as “How likely are you to recommend our brand to others?” Segment your survey questions to isolate data that are relevant to your goals.
Quantitative data is precious because it uses statistics to draw insights.
While qualitative data can bring more interesting insights about a topic, it’s subjective, making it harder to analyze. On the other hand, quantitative data comes from close-ended questions, which can be converted into a numeric value.
Once data is quantified, it’s much easier to compare results and identify trends in your survey data.
So it’s best to start with quantitative data when performing a survey analysis. This is because quantitative data can help you better understand your qualitative data. For example, if 60% of respondents say they’re unhappy with your product, focus your attention on negative reviews about user experience.
This can help you identify roadblocks in the customer journey and correct pain points causing higher churn rates.
Not all data is as reliable as you may hope. So it’s essential to be sure that your respondents are an accurate representation of your target audience.
Imagine your data states that 33% of respondents are likely to recommend your brand to others. And that 75% of them are over 40 years old, yet your target audience is 18 to 29 years old. Such data won’t be statistically significant to your business.
This is because the people who took your survey don’t represent your ideal audience.
Another critical aspect of survey analysis is the accuracy of your insights.
Consider survey data that shows a correlation between ice cream sales and car thefts in Boston. Over a month, as ice cream sales increased, so did reports of stolen cars. While this data may suggest a link between these variables, there’s — probably — no relationship.
Just because the two are correlated doesn’t mean one causes the other. In cases like these, there’s always a third variable – the independent variable – that influences the two dependent variables.
In the above case, it’s temperature.
As the temperature increases, more people buy ice cream. Additionally, more people leave their homes and go out, which leads to more opportunities for crime.
You really don’t want to draw inaccurate insights from your survey data. So knowing the context could spell the difference between a failure and a successful survey.
Analyze all sides of the story before concluding.
Now that you’ve gathered and analyzed all of your data, the next step is to share it with your target audience: coworkers, customers, and other stakeholders.
Well, presentation is crucial in helping others understand the insights you’re trying to explain.
The following section will explain how to present your survey results and share essential customer data with the rest of your organization.
Remember, learning how to analyze the survey results alone is not enough to throw you back into the battle. You need to be armed with best practices as well to create strong credibility for the whole process.
No matter what visualization type you use for your survey data storytelling, your results depend on the strength of the survey itself.
We’ve rounded up some of our best tips and practices to ensure your survey data achieves the credibility needed to make your story compelling. So check them out below:
Some question structures are likely to cause more harm than good to your survey.
For instance, the longer the survey questions, the less accurate the responses. So it’s imperative to consider how you order your questions. Also, you might want to put the most pressing items at the top of the list to capture the most accurate responses.
Open-ended questions invite the respondent to share their thoughts and feelings without limits. Besides, this makes their time and feedback valuable. So putting it at the top of the list is more like a warm invitation that will carry through the rest of the survey.
Also, aim to vary the types of questions to keep participants engaged throughout the survey.
When you approach your target respondents with surveys, consider the timing. Besides, survey participation should always be convenient to attract more respondents. You don’t want to ask for people’s feedback during a time that doesn’t suit them.
Also, the location or placement of the survey should be convenient and increase the participation rate.
It’s common for us to assume certain things, especially within business settings. For instance, it’s easy to attribute lower customer scores to a lack of enough support staff. In reality, the issue may be more complex than that.
So don’t fall into believing your assumptions. Instead, test them directly in your surveys.
One of the main reasons people participate in surveys is to voice their feedback and feel like they’re part of the solution.
So at the climax of a survey exercise, communicate the findings to the interested respondents. You have an ethical obligation to do so.
But surprisingly, this is often neglected during the process. Not only can this subtle gesture encourage increased participation in future surveys, but it can also increase the reach and accuracy as well.
It may not seem like it. But you’re asking a lot from your respondents, especially the information they may feel is private. Besides, time is increasingly becoming a scarce commodity these days.
This makes potential participants feel that there should be something in exchange for their valuable time. By incentivizing participants in some way, you’ll receive a more significant number of responses. The insights gathered will be more credible and accurate.
An incentive could be:
Rule of the thumb: always keep your survey short and simple. So don’t overload a single study with too many questions. Instead, focus on asking specific and immediate questions. This will attract higher participation.
Shorter surveys enjoy greater accuracy of responses.
Now that you’re armed with best practices, let’s head to the meaty section of the blog post: tools and charts for visualizing survey data. Let’s start with charts templates:
The most recommended charts for visualizing data, as recommended by seasoned data visualization experts, include:
A Likert scale is also known as a bipolar scale, which means that it consists of two opposing poles. You can use this chart to measure the intensity of feelings, opinions, and attitudes towards a subject matter.
A Likert Scale Chart visualizes how much a respondent agrees or disagrees with a particular statement. The scale assumes that the strength and intensity of the feelings are linear. More so, it goes from a complete disagreement to a complete agreement.
The questions in Likert scales range from general to even more specific topics. Besides, this chart is straightforward, so you can easily gain insights from just a glance.
The Likert Scale is one of the most used charts in visualizing survey data. Check out the second-most used visualization chart below.
NPS chart gives a complete picture of the user’s opinion from textual information. The whisker box represents the score.
You can deploy this chart to visualize your survey data with full confidence. Besides, this chart is straightforward to read and understand. So your audience won’t end up being confused when you incorporate it in your data story.
There are many other charts which are specifically used for survey data. You will learn about them in the coming section of this blog.
You may face technical problems when importing data from Google Forms to Google Sheets.
But how can you overcome this? Follow the steps highlighted below:
Now, let’s delve into the meaty part of the blog: recommended tools for survey data visualization.
One of the most used tools for visualization is Google Sheets. And this is because it’s affordable and has a user-friendly interface (UI).
However, this tool is more of a liability than an asset in analyzing your survey results. And this is why?
So what’s the solution in light of Google Sheets’ shortcomings in visualizing survey data?
The solution lies in strengthening your spreadsheet app (Google Sheets) with third-party tools. And one of the most trusted tools for visualizing survey data by thousands of data visualization experts is ChartExpo.
As the name suggests, this tool creates intuitive and simple-to-understand charts for your story.
ChartExpo comes as an add-on for Google Sheets. This tool transforms the aforementioned spreadsheet apps into a visualization juggernaut.
This chart comes highly recommended due to its incredibly friendly user interface (UI). Besides, it comes with an extensive library of 80-plus charts, including:
To access Likert or, Net Promoter Score (NPS), you need to install ChartExpo, a cloud-hosted add-on. Remember, you won’t be installing anything on your device.
You’ll be installing a cloud-hosted add-on on your Google Sheets.
There are two methods to installing the ChartExpo add-on for Google Sheets.
The first method is to visit the Google Workspace Marketplace and enter “ChartExpo” into the search bar.
Click on the ChartExpo tool and then press the blue Install bar on the resulting page.
This will begin the installation process. You may have to log in to your Google account and accept the plugin’s permissions. Once that is done, the add-on will install and be ready for use the next time you open Google Sheets.
Alternatively, you can download ChartExpo directly from the Google Sheets App. To get started, click on Extensions in the top toolbar.
In the small menu that appears, press the option to Get add-ons.
Search for ChartExpo in the bar and click the ChartExpo tool when it appears in the results.
Press the blue Install button. Again, you will have to accept some permissions and you may have to confirm your Google account.
Imagine you run a business that deals with electronics. You want to understand the aspects of your product benefits that attract your target audience.
You want to tailor-make your sales messages to align with what customers are looking for. So you administer a survey with a couple of questions and respective ratings.
The table below represents the sample survey responses.
| Question | Rating | Count |
| Overall I think the product was a good purchase? | 1 | 178 |
| Overall I think the product was a good purchase? | 2 | 76 |
| Overall I think the product was a good purchase? | 3 | 66 |
| Overall I think the product was a good purchase? | 4 | 35 |
| Overall I think the product was a good purchase? | 5 | 157 |
| The product does what it claims. | 1 | 75 |
| The product does what it claims. | 2 | 24 |
| The product does what it claims. | 3 | 89 |
| The product does what it claims. | 4 | 377 |
| The product does what it claims. | 5 | 348 |
| The product is affordable. | 1 | 240 |
| The product is affordable. | 2 | 26 |
| The product is affordable. | 3 | 144 |
| The product is affordable. | 4 | 152 |
| The product is affordable. | 5 | 281 |
| The product is easy to use. | 1 | 254 |
| The product is easy to use. | 2 | 164 |
| The product is easy to use. | 3 | 283 |
| The product is easy to use. | 4 | 26 |
| The product is easy to use. | 5 | 105 |
Let’s visualize the survey data above. Likert is the relevant chart to use in this scenario.
Note: The red color represents detractors (negative reviews) while the green color represents promoters (positive reviews).
Check out the ratings of the survey questions below:
Congratulations if you’ve committed to putting the tips you’ve learned so far into practice. Let’s jump to the next chart suited for visualizing survey data.
Imagine you run an e-commerce store, and you want to know the level of customer satisfaction among the existing customers. Let’s say you’ve commissioned an online survey to gather their sentiments.
The table below represents sample survey data. Let’s use this data for practical demonstration purposes.
| Question | Rating | Count |
| Would you recommend our ecommerce store? | 0 | 2207 |
| Would you recommend our ecommerce store? | 1 | 3223 |
| Would you recommend our ecommerce store? | 2 | 3874 |
| Would you recommend our ecommerce store? | 3 | 2947 |
| Would you recommend our ecommerce store? | 4 | 2638 |
| Would you recommend our ecommerce store? | 5 | 2222 |
| Would you recommend our ecommerce store? | 6 | 2779 |
| Would you recommend our ecommerce store? | 7 | 3419 |
| Would you recommend our ecommerce store? | 8 | 4683 |
| Would you recommend our ecommerce store? | 9 | 5621 |
| Would you recommend our ecommerce store? | 10 | 5410 |
Let’s power up our ChartExpo and unleash it on our survey data, as shown in the diagram below. And remember, the steps of doing the aforementioned are similar to that of Likert.
After visualizing the data, the resulting chart should look like the one below.
Insights
How easy was this?
An NPS chart is very simple and easy to use to derive insights. It gets better. There’s one more chart that’s also minimalistic in design and simple in appearance.
Let’s dive in without wasting time.
Imagine you want to know how your customers rate various aspects of your business, namely:
The aforementioned issues form the core of your sales funnel. And this means you really want to correct problems that may affect them to create a seamless user journey. Let’s use the data below as our sample exercise.
We’ll use a NPS to visualize the survey data.
You must know that rating of this chart should be considered as:
Negative = 0 to 6
Neutral = 7 to 8
Positive = 9 to 10
| Topic | Rating | Count |
| Product | 0 | 50 |
| Product | 1 | 50 |
| Product | 2 | 450 |
| Product | 3 | 90 |
| Product | 4 | 450 |
| Product | 5 | 650 |
| Product | 6 | 950 |
| Product | 7 | 1050 |
| Product | 8 | 350 |
| Product | 9 | 1750 |
| Product | 10 | 1650 |
| Services | 0 | 250 |
| Services | 1 | 55 |
| Services | 2 | 50 |
| Services | 3 | 90 |
| Services | 4 | 450 |
| Services | 5 | 650 |
| Services | 6 | 950 |
| Services | 7 | 1050 |
| Services | 8 | 350 |
| Services | 9 | 75 |
| Services | 10 | 550 |
| Delivery | 0 | 250 |
| Delivery | 1 | 550 |
| Delivery | 2 | 40 |
| Delivery | 3 | 950 |
| Delivery | 4 | 540 |
| Delivery | 5 | 650 |
| Delivery | 6 | 950 |
| Delivery | 7 | 100 |
| Delivery | 8 | 350 |
| Delivery | 9 | 1750 |
| Delivery | 10 | 550 |
| Customer care | 0 | 1250 |
| Customer care | 1 | 550 |
| Customer care | 2 | 450 |
| Customer care | 3 | 950 |
| Customer care | 4 | 450 |
| Customer care | 5 | 1650 |
| Customer care | 6 | 950 |
| Customer care | 7 | 1050 |
| Customer care | 8 | 350 |
| Customer care | 9 | 1750 |
| Customer care | 10 | 2550 |
| Packing | 0 | 1250 |
| Packing | 1 | 550 |
| Packing | 2 | 450 |
| Packing | 3 | 950 |
| Packing | 4 | 450 |
| Packing | 5 | 650 |
| Packing | 6 | 950 |
| Packing | 7 | 1050 |
| Packing | 8 | 350 |
| Packing | 9 | 1750 |
| Packing | 10 | 2550 |
The steps of visualizing our survey data with ChartExpo Add-on for Google Sheets remain the same, as shown by the screenshot below.
Check out our resulting Chart: Score Bar Chart.
Note: the points are not expressed as a percentage.
Note: the practical way to gauge your performance accurately is benchmarking using the industry standard. Some industries generally have bad review ratings or some have good.
Creating a survey analysis report example does not have to be time-consuming and mentally draining. And this is because it’s a critical component of survey data storytelling.
With ChartExpo, your surveys will never be a problem. Besides, you can sit, relax and craft a compelling data story without worrying about the charts.
ChartExpo produces beautiful and easy-to-read charts that complement your story seamlessly. This highly affordable tool helps you craft a compelling survey data story using its minimalistic and simple charts.
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