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You have written great questions, asked the right target audience, and collected survey responses in time. But all these efforts will be useless if the your aren’t analyzing survey data properly.
Without data analysis, you cannot learn what needs improvement, e.g., If you are not conducting company research, you will not be able to pinpoint where the revenue opportunity lies and what’s bringing the sales. While survey data analysis may sound technical, it doesn’t have to be, thanks to online survey software.
We will focus on how to analyze survey data so that you can uncover actionable insights and find answers to what’s pushing your customers away.
Survey data analysis is the step that comes after data collection, where you convert all the collected data into insights using an online survey tool. It is an important step in the research to make a confident and effective business decision.
There are different ways to analyze survey data. You can use cross-tabs to find patterns and trends in your data. Or advanced statistical analysis to uncover insights you otherwise can’t. Or text and sentiment analysis to make sense of respondents’ feedback and understand why they answered the way they did.
All these methods lead to one conclusion – you learn how you can address the pain points and make the right changes.
But before we start survey data analysis, there are two things you need:
This information will tell you how reliable your data is.
Now let’s check the different types of data you collect during the survey before we start crunching the numbers.
Additional read: How to protect survey response rates?
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As we discuss the different types of survey data, let’s start with the two types of survey data you are most familiar with – quantitative and qualitative data.
The data you collect in numerical values which are quantifiable are called quantitative data. For example, in an NPS® , CSAT, or a star rating survey, whenever a customer leaves a review in these types of survey, the data you gather is quantitative.
Textual or verbal responses are called qualitative data. These data cannot be quantified and don’t hold any numerical value.
Any time you ask an open-ended question such as “Why did you give us a 5-star rating?” you gather qualitative data.
When we dig further into the types of survey data, we find the four scales. Four measurement levels determine how each survey data can be measured and which analysis method should be performed.
Let’s explore these in detail.
Categorical data, aka nominal data, exists in categories. There is no hierarchy within the data. For example, in the question below, none of the answer options is treated as better than the other.
“What did you find attractive about our product?”
It is well known because it is the simplest kind to analyze. However, due to the lack of numerical value, you need to track the total number of respondents who chose each option and which one was selected the most.
Ordinal data helps determine the order of the value. The answer options follow a rank that shows the relationship to quantity or quality, such as the following example.
“How much do you use our lawn mower?
To analyze survey data on an ordinal scale, you can analyze the mode and median. You can also use the cross-tabulation method for survey data analysis.
Additional read: Nominal Vs. Ordinal; Know the Difference!
Interval data determines the order of the value and the difference between them. The data interval between each value stays equivalent. However, there is an absence of true zero which means you cannot determine by how much the value.
The scale is mostly used to gather customer data on the likelihood of recommendations, satisfaction, agreement, etc.
The best way to analyze interval data is through correlation analyses, ANOVA, and t-tests. ANOVA can be used to determine the significance of the data. Correlation analyses and t-tests can help evaluate if the datasets have any relation.
Ratio data, similar to interval data, offers insight into order and differences in the values.
However, the point of difference is that ratio data has an “absolute zero.” This means it can determine how much the values differ.
In business, ratio data is used to calculate sales, the number of customers, the amount customers spend, etc.
You can analyze survey data on a ratio scale using ANOVA, t-test, and correlation analyses.
You can also analyze the mode, median, and mean. Now let’s explore the seven steps you need to take to analyze survey data for successful research.
Other than reaction rate, the next spot to examine an analysis is the genuine inquiries. Various sorts of inquiries accompany various contemplations that you ought to consider.
Most survey questions can be assembled into 4 kinds: straight out, ordinal, span, and proportion.
Straight-out data permits the survey taker to answer utilizing a rundown of explicit names or marks. For instance:
“What do you like most about our item?”
It is well known because it is the simplest kind to analyze. Gather, count, and gap. In any case, the categories to incorporate should be perceived before the review is assembled. In this way, for instance, on the off chance that you don’t realize which aspects are significant (for example, customer support, cost, and so forth), you can begin with an open-finished question, then, at that point, in a subsequent survey, use classifications that mirror the most famous responses.
Making more modest iterative reviews is generally better compared to an over-designed study that doesn’t pose the right inquiries.
Ordinal data is any sort of inquiry where the reactions just seem OK as a request. For instance:
“What amount do you utilize for our item?”
One significant thought while investigating ordinal information is being insightful that request matters. If it’s conceivable, arbitrarily flip the request for deals with each study taker.
Span data should be requested, and the distance between the qualities should be significant. For instance, a span data question would be, “What is your financial plan?” where the responses would be a foreordained arrangement of costs like “<5k, 10k, 15k”.
Span information helps section your members and serve them just inquiries that are pertinent to them. For instance, assuming that a customer chooses a “<5k” spending plan, you can ask them inquiries about your Small Business item.
It’s ideal to utilize similarly measured spans if conceivable because it makes it conceivable to utilize midpoints on the information and, all the more imaginative, the synopsis of the information. On the off chance that spans aren’t equivalent sizes, then it ought to be treated as absolute information.
Proportion data is the most extravagant type of study information yet asks the most from members. What is an exact estimation is proportion data. For instance, a proportionate data question would be “What is your precise financial plan?” and the information field would take into consideration any numeric reaction, for example, “$501”.
Proportion data is the most ideal decision If you have any desire to ascertain midpoints or proportions of change like standard deviation. This is because different sorts of information can’t be addressed as portions, and that implies that they, for the most part, can’t be found in the middle value of or transformed into fluctuations.
Now that you’ve made heads or tails of the sorts of information and what sort of analysis can be performed on each kind, now is the right time to go further into benchmarking, moving, and contrasting information.
Suppose that on your occasion feedback overview, you inquire, “How fulfilled would you say you were with the occasion, generally speaking?”. Your outcomes show that 80% of participants were happy with the occasion. That sounds very great all alone. In any case, imagine a scenario where last year’s fulfillment levels were at 90%. For sure, on the off chance that the normal fulfillment for occasions in your industry was 95%?
Assuming you had the option to pose this inquiry on last year’s occasion, you would have the option to make a pattern correlation. However, on the off chance that you don’t have information from last year, you could make this the year to begin gathering similar feedback after each occasion. This is known as a benchmark or longitudinal information investigation.
To reach determinations from a benchmark, recognizing significant changes from noise’s significance. That is the place where measurable importance becomes an integral factor.
Read how Voxco helped HRI conduct complex research & accelerate insight generation.
We have compiled six steps to analyze survey data and draw an insightful conclusion from your survey/research. Here’s a shorter version of it.
Let’s discuss these six steps in detail.
Your survey questions are designed to answer the survey goal/objective. Don’t confuse your survey goal with the survey questions. Your survey goal is the reason you are asking the questions.
You must review your survey goal to ensure that your collected data aligns with the ultimate goal. Only when you establish that the data can prove or affirm your survey goal is when you should start your survey data analysis.
It is important that you clean the collected data of any incomplete response. Often due to the number of questions or respondents’ mood, they skip certain questions or leave them blank.
While it may not cause much trouble, it is always better to analyze survey data that are clean and consistent. Incomplete data may skew the result, costing you more time to fix the mistakes.
Use online survey software that automatically cleans and preps the data for your survey analysis.
Quantitative data is easy to analyze and can be converted into numerical values. This also makes it easier to find trends and patterns and generate meaningful conclusions.
For example, let’s say you offer a music-streaming mobile app. You want to understand why users are not signing up for the paid service even after sharing a 5-star review after a free trial.
With this goal in mind, you survey your users on how useful they find the product and their satisfaction with the features and interface.
After survey data analysis, you find that 63% of free trial users loved the whole experience with the product. This helps you understand that the product, feature, and interface are not the problem.
In this example, you looked for trends among the different values and topics to find out what must be the issue. But you discovered that none of the topics you evaluated were the reason behind user behavior.
So you can now eliminate these topics and evaluate more on other categories, such as a collection of songs or price, to understand the root cause.
When conducting a survey, you should always remember that the smaller your sample size, the less likely your data will be statistically valid.
Respondents are likely to fill in wrong answers or leave them blank, which can create “noise” in your data. Having only a handful of responses and further separating the noise will leave you with insignificant data when you analyze survey data.
As we mentioned above, the total number of respondents and response rate are the two most critical factors that determine if the survey data is reliable.
Use an online survey tool that offers you a sample size calculator to see if your sample pool is big enough to represent the population and gather data that you can trust.
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Comparing your current survey result with the previously gathered on the same subject can help you discover what needs improvement and where you need to focus.
Let’s take the previous example. When you compare this year’s survey result with the previous year, you might notice that the users this year loved the app more than those last year.
However, there has been no drastic increase in sign-ups.
When you compare the results, you will be able to evaluate and determine that you need to focus on factors such as pricing, themes, or customer service instead of the feature and interface. Leverage a market research tool that enables you to perform internal and external benchmarking.
All the above steps in survey data analysis will help you tell a compelling story to your colleagues and stakeholder. With the percentages and the benchmarks, you can now bring attention to the key areas where you must make improvements.
Instead of long and boring reports, present only relevant information to the readers. Show them where the focus is needed and which areas are crucial. Use an online survey software that allows you to create visual storyboards with interesting graphs and charts that attract readers’ attention.
The online survey tool should also allow you to share the report via a secured survey portal to ensure that only authorized employees can see the report.
Now that we have discussed the six steps on how to analyze survey data let’s check out some best practices in data analysis.
Survey data is not always easy to analyze and evaluate by the human brain. The sheer volume is enough to cause errors if you try to explore it on your own.
Survey data analysis with the help of online survey software can make the data intuitive and comprehensive for you and everyone in your organization.
We have listed five best practices to analyze survey data and save you from making common mistakes. The tl;dr version of it is
We have explained these five best practices below.
For successful data analysis, it is important to ensure that you have a big sample size representing the target population. Your survey report can get skewed with a smaller sample size and lower response rate.
Don’t be haste to conclude the report as the data start to show up. Sort the data and find statistical significance. Use online survey software to ensure the sample size is correct and perform data analysis to generate the right insights.
Quantitative data is easy to analyze. It consists of numerical values making it easier to perform statistical analysis. The data comes from closed-ended questions, which you can convert into numerical values using an online survey tool.
Quantitative data helps you obtain the statistical likelihood of a scenario – how many customers liked their experience? how many are willing to recommend it? and how many are unhappy with the service?
The data comes from closed-ended questions and helps you identify customer wants behavior, and attitude trends. Analyzing quantitative data first will help you clearly understand the qualitative data. You can design your follow-up research to understand the root cause with statistical information on pain points and expectations.
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Cross tabs will help you segment your respondents and analyze the data based on the responses. If you go for a one-size-fits-all approach and analyze all the data together, it can leave you with errors and inconsistencies.
Use cross-tabulation to split up the data based on how the participants respond to different questions.
For example, say you are surveying California to find which horror movie among Saw, Conjuring, The Exorcist, or Annabelle.
You can create a cross tab of all the cities and the movies to record which movie is the respondent’s favorite and what city they live in.
If your respondents don’t represent the target population, then your survey result is useless.
Let’s say you are surveying an audience of 10,000 people who attended a music concert. If only 300 of them responded to the survey, the data you collect is not statistically significant.
Why? Simple, because 300 people cannot represent the voice of 10,000 audiences.
A survey pro tip is that the more responses you gather, the more accurate the data will be.
When you use an online survey tool to run survey data analysis, it will help you see if the data is statistically significant or not.
Researchers may, at times, commit a mistake by thinking that if there is a correlation between variables, one variable also affects the other.
This assumption is inaccurate unless validated. Correlation refers to when two variables move, increase, or decrease at the same time. Causation refers to when one variable is causing a change in the other.
For example, in winter, the sale of sweaters and hot chocolate will increase together.
But it does not mean you can assume that the sale of sweaters is why people buy hot chocolate. This just means there is a correlation between the two.
However, there is a causation between the season of winter and the sale of sweaters. The point is that only because you see a pattern in two variables does not mean that one variable is influencing the other.
This wraps up our tips on survey data analysis. If you are planning to analyze survey data, it’s important that you adopt the right tools to make the job easy and efficient. Look for an online survey software that helps you create your surveys, share them with the right people on every channel, and uncover insights without leaving the platform. Maybe something like Voxco.
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