If you don't analyze survey data, you could be missing out on a lot of important data. We’ll show you how to get started with survey analysis in just three steps.
Surveys can be a powerful tool for any website. With just a few simple survey questions, you can learn a lot about your audience. However, if you're not analyzing those survey reports as a whole, you could be missing out on key insights.
Fortunately, there’s an easy way to conduct a survey data analysis. With the help of Formidable graphs and stats, you can immediately start drawing conclusions from your users' responses.
In this article, we'll cover a few reasons why you should analyze your survey data. Then, we'll show you how to do it in just three steps. Let's get started!
Why you might want to analyze survey results
You probably already know that forms and surveys can help you collect valuable data. However, you might not be making the most of that information if you're only looking at individual submissions.
Knowing how to analyze survey data can be crucial. By getting a big-picture view, you can more effectively shape your marketing strategy based on your users' feedback. This applies to almost every type of survey. For example, you might analyze:
- Experience reviews to get an idea of your overall performance
- Attendance numbers to understand which of your event dates are the most popular
- Information on how users learned about your brand to better guide your marketing plans
Fortunately, Formidable Forms users don't need to invest in a whole new tool to carry out their survey analysis. With the options to generate graphs and statistics, you're just a few shortcodes away from key audience insights.
How to analyze survey results
Before you get started, make sure you have the premium version of Formidable Forms installed and activated.
Step 1: Collect your survey data and check out default reports
First and foremost, you'll want to create your survey. Make sure to include all the fields you want to gather data from.
You may want to include a Net Promoter Score, multiple choice questions, and other close-ended questions to collect quantitative data. Open-ended questions can be included as well, but their qualitative data is much more difficult to analyze.
When the survey responses start coming in, you can do some basic analysis with the default reports. The higher number of people who respond will give you more statistically significant data. A sample size calculator template will tell you if your sample size is big enough.
How do I see survey results?
To view the reports, go to your form and click on the Reports tab at the top. You should see a few graphs:
As you can see, this data visualization is relatively simple. But even basic information like the distribution of responses over time can be helpful.
Additionally, these graphs display live data and update with every additional or edited entry. That way, you can always get a reliable overview of your results no matter how many people have responded.
While these default graphs can be quite informative, they may not offer the exact analysis that you're looking for. To learn more about creating custom parameters, move on to step two.
Step 2: Create custom graphs for survey results
Now that you've collected your data, you can start using it for customized analysis. This process mostly relies on writing and publishing shortcodes. Don't worry if you're not too familiar with coding – we've made the process simple.
First, you'll start with a base. You can choose between two different approaches. You'd use the first one if you want to graph specific fields, replacing "x,y,z" with their field IDs:
[frm-graph fields="x,y,z"]
If you'd prefer to analyze a form as a whole, you'd use this base, replacing "x" with the form ID:
[frm-graph form="x"]
If you don't change anything else, the shortcode will default to a column graph:
However, you can change this visualization. Simply add another bit of code to the end:
[frm-graph form="x" type="table"]You can change "table" to whatever type of graph you'd like. Fortunately, you have plenty of different style options to pick from:
However, customization can go beyond the visual aspects. For example, you might want to add title="x" to display a title on the graph. Or maybe y_title="x" and x_title="x" to do the same for your axis labels. You could also add show_key="1" to include a legend so you can better understand the graph data.
Cross tabulating and filtering
You can even get really specific, and filter survey data based on answers in other fields. Cross tabulating like this is super helpful for learning more about a specific segment of users.
For example, in a customer feedback survey for an online t-shirt store, you may want to compare the NPS between men and women. This could tell you that most men are much happier with your shirts than most women. Now, you can make changes to either cater only to your happiest segment, or add new options to make everyone happy.
Whether you want to change the way the graphs look or curate the data source, you have a lot of options available to you. We recommend checking out our knowledge base article on filtering parameters for more ways to customize the results.
Step 3: Analyze your entries using statistics
On the other hand, you might want to use a text-based statistical approach. The shortcode process for this is similar. You'll start with the following base, replacing "x" with the field ID and "count" with the type of stats you want to display:
[frm-stats id="x" type="count"]Some potential options for the "count" replacement include averages, totals, and min/max results. For example, we wanted to get an average on a star rating survey. So we published the following shortcode:
[frm-stats id=17 type=average]As a result, our front-end statistic looked like this:
It's a simple process, but it's still a powerful way to synthesize a huge amount of data. Furthermore, you can get just as specific with statistical analysis as you can with graphs. Some examples include:
- 25_less_than="4" to filter entries by quantity in another field, replacing "25" with the field ID and "4" with your threshold
- limit="x" to only include a certain number of entries, replacing "x" with your limit
- 25_contains="term" to limit it to entries containing a certain word or phrase in another field, replacing "25" with the field ID and "term" with your phrase of choice
As you can see, there's a lot of you can do with this data. Check out all of your options in our statistics knowledge base article.
Conclusion
Whether your audience is large or small, analyzing survey results could be the key to a more successful business. Fortunately, getting the most out of your data doesn't have to be difficult. By using Formidable Forms' built-in analysis tools, you can start using form entries to their full potential.
In this article, we showed you how to analyze survey results in three steps:
- Collect data through your forms and consult the default graphs.
- Create custom graphs for your survey results report.
- Examine those results using statistics.
Formidable Forms can help you both collect and interpret tons of user data. Check out what else you can do with the premium version today!
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super helpful! Thanks
This is awesome. I will have to investigate this further. It seems like a great way to understand the captured data.
Nice tutorial. 👍🏻 This help a lot.
Woo! Thank you, that is what I needed to manage better my data!!
Nice and very helpful.
I would like that this was easier to setup with some common measures etc. and also filtering options so I could filter data for a week, a month etc. I dont see how I can do this as it is now.
Hi Peter,
Improving how our graphs work is on our roadmap. In fact we are currently in the design stages for a way to add the various parameters and filtering options without clumsy shortcodes. We don't have an ETA on this release, but it is in the works.
For now, please check out our documentation and contact our support team if you are still unsure how to filter your graphs the way you would like.
Here is a link to a relevant doc: https://formidableforms.com/knowledgebase/graphs/#kb-filtering-parameters