7 Tips and Practices for Analysing Survey Responses

Surveys are a cornerstone of market research. Companies rely on them to gain feedback from customers, employees, and other stakeholders. In fact, online surveys (85%) are the most used method in the market research industry worldwide, followed by mobile surveys (47%). But conducting a survey is only half the battle. To get true value from your efforts, you need to thoughtfully analyse the resulting data.  

The process of survey analysis is both an art and a science. While software can crunch the numbers, human judgment is crucial for interpreting results. There are many techniques and best practices that can help you derive meaningful insights from survey responses. The goal here is to turn survey data into concrete takeaways that lead to business improvements. 

This article enumerates some practices and tips and will serve as your guide on survey response analysis, helping you get the most bang for your buck from your survey efforts and inform business decisions with hard data.  

1. Clean the data 

Before diving into serious analysis, it pays to take some time upfront to clean and prepare the survey data. Otherwise, having raw, disorganized data will make your analysis way harder. This process includes removing duplicate entries, deleting test responses, and checking for contradictory or nonsensical answers.  

You’ll also want to standardize any open-ended responses. For example, organize variations like “United States,” “U.S.,” and “USA” under the same code. This makes it easier to analyse and cross-tabulate.

Cleaning survey data may not be the glamorous part of survey analysis, but putting in this work will pay off in the long run with higher-quality insights. Remember that for survey analysis, clean data leads to clear insights.  

2. Run frequency distributions 

Once your survey data is all tidy, one of the easiest first steps is running frequency distributions, also called frequency tabs.

Frequency distributions provide a high-level view of the predominant trends and patterns in your survey data. At a glance, you can see which responses were most common and which were rare outliers. It’s a helpful way to get familiar with the overall shape of the results.  

For example, with a customer satisfaction question on a five-point scale, a frequency distribution would simply show how many people chose each rating option. You could instantly see if most customers clustered around the satisfied side, neutral middle, or dissatisfied side.  

While frequency distributions don’t provide deep insights, they give you a solid baseline understanding of the survey responses. From there, you can drill down into more nuanced crosstab analysis and start shaping the data into compelling stories. But it all starts with the humble frequency tab.  

3. Segment the data 

One mistake most people make when analysing survey results is looking at overall averages rather than segmenting the data. Overall results can mask really interesting insights from different groups.  

For example, your survey might show an average customer satisfaction rating of 4/5; it seems pretty good on the surface. But when you dig into the data, you may see that male customers gave an average rating of five out of five while female customers only gave three out of five. That’s a huge gap worth understanding! 

The power lies in slicing and segmenting the data – by gender, age, location, or whatever demographics makes sense. This allows you to spot variations across groups that you’d never see in the aggregate.  

In the provided example, if you only looked at the overall rating, you might miss the opportunity to improve satisfaction among female customers. So, resist the temptation to only analyse top-level survey results.

Dive deeper by applying filters and crosstabs. 

4. Use rating scales wisely 

Rating scales may sometimes seem straightforward. When you add up all the one to five ratings and divide by the number of respondents, you can easily get an average score. However, averages can be misleading with rating scale questions. Two products could both score a 3.5 average satisfaction rating but for very different reasons. 

One product might have most people feeling neutral and giving it a three rating. But the other product could have half the customers rating it a five and half a one, a polarized love/hate scenario. Those two situations suggest very different implications and actions, even though the averages are identical on the surface.  

So, go beyond basic averages with rating scales. Look at the distribution and spread of responses. Are they clustered around the mean or dispersed across the scale? How many outliers are there on each end?

You can plot the distribution in a histogram chart to really help bring issues to light. Pay attention to the shape of the data, not just the average. That holds important clues to understanding how people truly felt about rating scale questions. 

5. Look for correlations 

Uncovering correlations in survey data can surface some really intriguing insights. But it does require going beyond surface-level analysis.  

For example, your employee engagement survey might show a strong positive correlation between satisfaction with the work cafeteria and productivity scores. At first glance, that may seem surprising. But it could make sense if the cafeteria is an important part of employees feeling valued and enjoying their workplace culture. The cafeteria isn’t directly tied to productivity, but it drives engagement, which then improves work output.  

So, don’t skim for obvious correlations on survey questions that are closely related. Look for interesting hidden connections that span different topic areas. That said, remember that correlation doesn’t equal causation. If two survey metrics move together, be careful about assuming one drives the other without deeper analysis.  

6. Analyse open-ended questions 

Open-ended questions add rich context to survey data. However, making sense of all those free-form responses can be daunting. Rather than getting overwhelmed, use a mix of automated and manual techniques to handle open-ended survey analysis. Text analysis tools can create word clouds to visualize common words and themes.  

You can also use sentiment analysis to categorize responses as positive, negative, or neutral. This helps quantify the overall feeling toward a product or service. That said, don’t rely completely on automation.

Remember, no algorithm is perfect when interpreting human nuance and sarcasm.  

So, combine technology with human insight. Spot-check the automated analysis and read samples of actual responses to make sure the computer isn’t misinterpreting something. Open-ended questions require analytical versatility. Let smart tools do the heavy lifting of processing volumes of text. Then, lend your own human intuition to catch the subtleties.  

7. Make the data actionable 

The end goal of analysing all that survey data isn’t just to produce interesting reports. It’s to drive real business decisions and changes that will further boost the growth of your business. So, don’t stop at surface-level observations. Dig into the data to identify specific problems and opportunities. Then, translate those into tactical next steps and recommendations.  

For example, don’t just note that customer satisfaction declined this quarter. Figure out the likely reasons why and what you can improve. Maybe offer more educational resources or FAQs to reduce support calls.  

Share key takeaways across teams, especially with frontline employees who interact with customers. When everyone has access to those insights, it empowers them to take targeted actions.  

More importantly, follow through to track changes and continue gathering feedback. Adjust strategies over time based on the latest survey trends. Just remember, analytics is an ongoing cycle, not a one-and-done exercise. 

The bottom line 

While it’s important to lean on smart tools to crunch the numbers, you must also lend your human perspective. So, keep investigating beyond the surface for those hidden insights in your data. Apply these tips and practices to turn those survey responses into conversations that will guide your business forward.