It’s no secret that customer feedback is the lifeblood of all major brands and corporations. Talk to any marketer, and they will tell you at least 4 or 5 different ways to collect customer feedback.
What they will also tell you is how hard it is to get actionable customer feedback.
Receiving customer feedback is not just about surveys and data collection. It is much more than that. It also involves selecting the right questions and incorporating the right distribution channels.
Sure, an online survey maker helps you create a survey and access the right distribution channels, but what next?
When the survey data comes back in, how do you use it for the benefit of different teams – sales, marketing, product, etc.?
Effectively analyzing this treasure trove of data is crucial to give your employees valuable insights into customer behavior and identify the major trends prevalent in the market.
In this post, we’ll be looking at how to analyze survey data after you have collected and organized them, and the different ways of presenting them.
5 Effective Ways to Analyze Survey Data
1. Understand the Measurement Scales
Survey analysis is not possible without really understanding the scales. As we know, survey data can be divided into qualitative and quantitative data. While qualitative data encompasses open-ended questions and does not require a numerical scale, quantitative questions require numerical scales. So let’s take a look at the measurement scales used in surveys.
- Nominal scales: These are used to classify qualitative data. These are similar to labels as the choices available are not directly related to each other. These are apt for questions like, “What brand phones do you use?”
Nominal scales don’t have numerical values and are mainly used to analyze the preferences of people. It helps you keep track of the number of people who picked a similar option.
- Ordinal scales: This scale is used to rank options based on the order of preferences. Something as simple as “In order of preference, who do you think is most likely to win the current NBA season?” Combining the data with cross-tabulation analysis can help you analyze two sets of correlated data. We’ll take a better look at cross-tabulation in the next part of the article.
- Interval scales: When participants are required to record an answer that falls somewhere along with a pre-ordained range, that’s where interval scale helps. The best example of interval scales would be price filters.
“How much would you pay for this product?”
Interval scales have no starting point or absolute zero.
- Ratio Scales: Ratios scales are similar to interval scales in function and purpose. The only difference between the two being that ratio scales start at zero. An example of this would be:
How often do you work out in a week?
2. Start With the Quantitative Questions
Start analyzing quantitative questions first. Since these questions are based on statistics, you can easily analyze and draw conclusions from this data.
Take your NPS results, for example. They provide different customer ratings on brand loyalty. You can use survey analytics to evaluate the ratings provided. Doing this gives you an exact score of customer loyalty.
You can then refer to the previous results to know whether your customer loyalty score has increased or decreased. Now that you have a general idea of your results, you can then form a plan of action to improve your Net Promoter Score.
3. Consider Using Cross-Tabulation Analysis
Cross tabulation enables you to understand the relationship between independent variables. Frequently your survey responses will include responses from people who do not ideally fit your target audience. This could result in your survey results being skewed towards one opinion or the other, or just being generally irrelevant.
Say you’re planning on opening a gym, and you place a survey on your website asking people how often they work out.
Now you would prefer the data mainly for people in New York, aged between 18-35, as they are your primary target audience. But since your website and the survey will be visible to everyone, you will be receiving responses from people all around the country.
Cross-tabulation analysis can help filter out the data for people aged between 18-35 currently living in New York.
4. Understand Correlation vs. Causation
The human mind is adept at finding patterns between events. This often leads to linking two independent events as mutually inclusive, even though they may exist independently of each other.
An article published by Harvard business review shows how easy it is to misrepresent data by correlating two independent variables. The article presents a graph showing how iPhone sales and deaths caused by falling from the stairs had increased since 2010. Connecting these two can lead you to believe that – More iPhones mean more people are falling from the stairs.
That is certainly not the case. There exist other factors that determine these results. If you just go by the correlation between two variables, chances are you will arrive at an inaccurate result.
You must analyze all sides of the story and identify all the variables before drawing your conclusions.
5. Compare against Past Results
One of the best ways to analyze survey data is to study them against past results. Say you run quarterly NPS surveys.
While the current results keep you updated, comparing it against last quarter’s results shows whether your performance has improved or not. If you had 30% promoters the previous quarter and now you have 40% promoters, it shows an overall trend of increasing customer loyalty.
Now that brings us to the end of survey data analysis methods. What next? Presenting the survey data and results in a manner that is acceptable and pleasing to the other stakeholders is equally important.
Explore: What Is a Good Net Promoter Score
So let’s take a look at the different ways of presenting survey data.
How to Present Survey Data
- Graphs: Graphs are one of the most visually appealing survey data analysis methods. Not only are they easy on the eye, but they also help simplify complex data and make it easy to understand for the viewer. Depending on the type of data collected, you can choose to present your data as:
- Pie charts
- Venn diagrams
- Scatter plots
Make sure that you pick the graph that best depicts the data to all readers.
- Data Tables: Data tables are an excellent way to share numerical data. You can use Excel and other similar software to display data.
- Presentations: Presentations allow you to present your findings in a mix of textual and graphical data. This helps you present the earlier stages of your survey, including the research questions, the hypothesis, and the methods used for survey results analysis.
- Infographics: If you want to present your data in an easy to consume manner for your customers, infographics are your best option. This helps you present the survey results as statistics and adds to your website’s visual UI.
- Reports: When it comes to investor or shareholder meetings, presentations, and data tables may not always be the best option as the relevant personnel would like an in-depth study in the whole process. You could hand the report out while you present your data in the manner you prefer. While you may not directly quote from the report, anyone with doubts or questions regarding the data can refer to the report for clarification.
Survey Analysis – Crucial For Business Success
Collecting customer feedback has always been an important part of the success of every brand. What companies ignore is analyzing it to derive meaningful results that can facilitate smart decision making.
Hope you are now aware of how to analyze survey data and capture useful insights. Follow the above-mentioned steps to collect survey data, decode and evaluate it, and finally arrive at meaningful conclusions.
Don’t forget to add a survey creation tool like ProProfs Survey Maker to your arsenal. It has got all you need to create surveys, analyze them, and draw actionable insights to identify trends and understand customer behavior.
In case you have any questions, feel free to ask us in the comments below.
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