How to Analyze Survey Data & Get Insights? (9 Ways)

It can be hard to get honest customer feedback.

In order for the feedback you receive to be valuable, you have to start by asking the right survey questions…but it doesn’t stop there!

Nope, in addition to asking the right questions you also need a system that will distribute those questions at just the right time.

Unfortunately, the survey data that comes in doesn’t just do the analysing all by itself. You need a dedicated team to sort through it — or, a ton of time to do it yourself.

Regardless, today we’re talking about ways to analyse survey data so that you gain valuable customer insights without wasting time.

There are a few steps that you can take to ensure that you’re analyzing survey data correctly. Luckily, they aren’t hard to follow and won’t take you long to get the hang of.

Once you get good at analyzing survey responses and gaining actionable insights, you’re sure to see the success of your business sky rocket.

Let’s get started.

Table of contents
1. Get familiar with measurement levels
2. Come up with effective research questions
3. Determine an accurate sample size
4. Ask quantitative data questions first
5. Conduct a cross-tabulation evaluation
6. Learn why statistical significance matters
7. Understand theory of correlation vs. causation
8. Effectively compare your data
9. Avoid incomplete/inaccurate responses

1. Get familiar with measurement levels

Without having a true understanding of the four measurement levels, conducting an accurate survey analysis isn’t possible.

These levels dictate how individual survey questions should be measured and which analysis’ should be performed.

The four levels are: nominal, ordinal, interval, and ratio.

Nominal

Nominal scales are similar to categories. They are basic and don’t hold any qualitative value; the options given are not related to each other.

Questions like “which car brands have you used?” and “select the brand of car you own” are examples of nominal scales.

Using the nominal scale, you can keep track of which options are selected the most and how many respondents selected every option.

Ordinal

Ordinal scales are used to showcase the order of a group of values. In simpler terms, this measurement level ranks values by preference.

When in use, an example of ordinal data at work would be a question like “In order of preference, what are your favorite sports to play in the summer?”.

Interval

A double hat trick of a scale that acts as a two-for-one survey analysis tool, interval scales are a popular choice for market research.

They don’t just depict order, but also the difference between the values being ordered. They don’t have a true zero point.

Respondents are required to record their answers by using pre-ordained options. Price filters are an excellent example.

Questions like “how much would you pay for this product?” and “what is the maximum amount that you’d pay for this product?” use interval scales to gain a deeper understanding of their customers’ budget and willingness to splurge on their product.

IQ tests are also great examples of interval scales.

Ratio

Like interval scales, ratio scales also depict order and the difference between the values. However, ratio scales do have a true zero point where interval scales do not.

Racial scales holds a quantitative value despite holding no attributes. This is because even though ratio scales don’t have attributes, the absence of said attributes can still provide valuable information and data points for survey analysis.

Ratio scales always start at zero. For example, questions like “how many times per week do you go grocery shopping?” start at 0 and go up from there. Options for this research question include things like 0-2, 3-5, and 6+.

2. Come up with effective research questions

When you first determined that it was necessary to conduct a survey, there was an overarching question that you wanted an answer to, right?

Otherwise, you wouldn’t need to ask the question in the first place. Your question could be something like “how do respondents rate our customer service?”.

So, assuming that you know your base question and that you’ve familiarized yourself with the survey analysis methods above, it’s time to come up with a list of questions that will help you get the answer to the base question.

Questions to use could be things like “how likely are you to recommend our service team to your friends?”.

It’s good practice to include both open ended questions and close ended questions to garner better survey responses.

3. Determine an accurate sample size

Another key to ensuring that you are effectively analyzing survey data is to have a good understanding of the importance of sample size.

Sample size is the number of people you need to take your survey (and complete it!) in order for the survey data to be viable statistically.

In general, the larger the sample size, the more accurate your survey results will be.

If you have 100 customers but only five take your survey, the resulting sample size (5) isn’t big enough to accurately represent the majority of your customers; therefore, the data shouldn’t be used.

You can avoid the guesswork that comes with deciphering how large your sample needs to be by conducting a sample size calculation.

One popular calculation that you can use is Slovin’s formula. It’s a fairly complex calculation, but it gives accurate data.

The formula takes into consideration a margin of error and is a good way to generate an ideal sample size when you know next to nothing about the population.

On average, though, most industries aim for a sample size of 10%.

Regardless of how large your sample size is, Be sure to avoid surveying and factoring in every customer that fits your target audience; this is both impractical and expensive.

In addition, it can also skew your results because it is too large. Bigger is not always better, as the bigger the sample size gets, the higher the chances are that the data received won’t be honest feedback.

4. Ask quantitative data questions first

A good survey analysis should always start with the quantitative questions. It’s a good idea to start with the quantitative data questions first because they can help you understand the qualitative responses that you receive.

Since quantitative data comes from close ended questions, it is much simpler to work with than qualitative data, which is subjective.

These kinds of questions are easy to analyze because their responses are based on numbers and statistics, which makes them simple to analyze and come to conclusions with your findings.

If you discover that 70% of your customers are unhappy with your new app update, you would shift your attention towards the negative update reviews, putting the positive reviews on the back burner until the reported issues were ironed out.

5. Conduct a cross-tabulation evaluation

Analyzing all of your customers’ responses in one group isn’t the most effective way to gather useful, highly-accurate information. Using this tactic, it’s easy for customers who aren’t your target audience to sway your survey results on way or another.

Instead, segmenting responses using cross-tabulation, you can analyze how your audience responded to the customer surveys you asked them.

Cross tabulation analyses work by recording the existing relationship between variables. They effectively compares two different sets of data within one chart, revealing actionable insights that are based solely on how your participants responded.

Say you’re curious about customer advocacy among the customers you have in a certain city. You could use a cross tabulation analysis to determine how many are from the city and that said they would recommend your product or service.

Alternatively, you could use a cross-tabulation to determine how many are not from the city and would or would not recommend you.

Cross-tabulation analyses are incredibly diverse and can be used to figure out various different kinds of information.

6. Learn why statistical significance matters

Because all survey data is not created equal, it’s crucial to understand why the “significant” in statistical significance matters.

Unfortunately, it’s not as simple as just collecting survey data, conducting a survey analysis, and then running with it; often times, the data needs to be sifted through and vetted before it can be used to make big decisions.

The ‘significant’, when we’re talking statistical significance, refers to data accuracy.

Or, in other words, the likelihood that the data isn’t based on coincidence, but that it is actually representative of a sample audience.

If you find that your survey data is accurate, it means that it is statistically significant and that, in general, you can trust and use it to draw meaningful conclusions.

Example

As an example, the data collected shows that 44% of respondents answered that they would recommend your product.

80% of those respondents were under 30 years old, but your initial target audience was 40-60 years old.

Due to the fact that most of the respondents who would recommend you aren’t within your target audience, your data wouldn’t be considered statistically significant.

7. Understand theory of correlation vs. causation

The human brain can accomplish some amazing feats and do incredible things.

One of the many things that it is wired to do, is to find patterns and identify trends. We can find them in just about anything — in art, in nature, in behavior, and even in events.

In some areas of life, the ability to find patterns is incredibly useful. But in the business world, it can be just as harmful as it is useful — if you don’t know how to manage it.

Correlation versus causation is an important concept to know about because it is one of the easiest methods of analyzing survey results, but also because it can be used incorrectly so easily.

Example

For example, we observe a correlation between a frozen yogurt shop and arson in Lubbock, Texas.

Over the course of six months, traffic to the frozen yogurt shop increased and the instances of petty arson did, too.

At surface level, these survey results could suggest that there’s a link between the two variables. Using common sense, however, we can fairly safely determine that there isn’t a relationship here.

Just because the two are correlated -they have a mutual connection- doesn’t mean that one causes the other. There is usually a third, independent variable at play. These independent variables influence the first two.

In our situation above, the third variable is temperature. More people buy frozen yogurt as it gets hotter outside and, as it gets hotter outside, more people are also leaving their home, which leaves more room for crime to happen.

8. Effectively compare your data

Current survey data is ideal for keeping you updated but it’s not the only kind of data you need to have on hand.

It’s a good idea to keep survey data from previous surveys so that you can compare the old with the new for a full view into the backend operations of the business. Comparing survey data is one of the simplest ways that you can gain valuable insights.

If, according to your survey data analysis, 58% of survey respondents said that yes, they would recommend your product, is that number better than last year? Worse? How is it when compared to last quarter?

If you don’t have any previous data sets to analyze, then the first survey report that you run should become the benchmark for the next one.

Future research findings should be regularly compared against the benchmark report and be used to record any changes over whatever time interval that you choose.

You could record and track data changes over months, quarters, years etc. — whichever works best for you. In addition, you could even track changes within a specific subgroup in order to ensure that their customer satisfaction is improving.

Example

Here’s a quick example of data comparison in action.

Let’s assume that you send out yearly NPS surveys.

Your current data will keep you updated as to what’s happening this year, but taking the current data and comparing it to last year’s survey results will tell you if your performance has improved (or not!).

This is valuable information because no company with a goal of being successful wants to stay stagnant. Each year the goal shouldn’t be to stay the same but to improve instead.

If 40% of last year’s customers were deemed to be promoters, and this year 55% were promoters, you become aware of a general upward trend, which is exactly what you want.

Now, if 40% were promoters last year and only 35% were this year, then you’d be alerted to a trend of dissatisfaction. In this case, it’d be useful to go back and consider any new implementations that you had made over the last year.

How did they perform? Were they used as intended? Were they used by the intended groups? How can you improve them?

Or maybe you opt to remove a feature altogether. Once you have the data from your online surveys, you can move forward making changes that will better your business.

9. Avoid incomplete/inaccurate responses

There are many reasons why a respondent may leave your survey before they complete all the survey questions. Often times, respondents leave because they find the questions boring or repetitive, or because they feel that they are too personal.

Regardless of why they leave, this stands true: you need completed surveys to draw conclusions that make a difference. Without them, any conclusions that you come up with are nothing more than educated guesses.

Out-of-context and incorrect responses are responsible for inaccurate survey results more often than most people realize.

When trying to conduct statistically significant research, you may reach a point where you need to decide whether a respondent’s survey should be taken into consideration or scrapped completely because they have rushed through or only completed half of the questions.

If you find that an overwhelmingly large number of participants are skipping questions or speeding through, it’s likely a good idea to double check your survey. These instances could indicate that your survey requires editing.

Conclusion

Without a survey results analysis, you will be unable to gain the maximum amount of vital information for pushing your business forward.

For example, if you skip distributing a survey about how your customers feel about a new feature of your app, you’ll never know if the new feature went well or not.

As a result, you may see a decrease in app usage and a loss of your loyal customer base.

The statistical analysis of data is one part of the market survey process that can’t – and shouldn’t – be ignored. A good analysis of survey data can make or break your business in more ways than one.

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