data analysis Archives - Pollfish Resources

How to Analyze Survey Data Like a Pro

Survey data analysis may feel like a daunting task if you have not conducted many surveys, but the process is easier than you might think. The first few attempts at analyzing your data may feel clunky and cumbersome, but in time you will become adept at viewing and processing survey data.

Whether it is your first or 20th time conducting a survey, knowing how to analyze survey data is crucial to extracting maximum value from your survey results. This guide will walk you through four of the most common types of surveys and provide you with an approach for analyzing data for each type.

Analyzing Survey Data - An Overview

Survey data analysis refers to the process of organizing and examining survey data so you can draw conclusions. The process can be short or long, rudimentary or precise - it all depends on the purpose of the survey, type of data analyzed, consequences of the results, and the skill of the person analyzing the data.

Survey data analysis follows this general process:

Review the purpose of your survey and the most important questions. Before diving into the data, make sure your analysis stays focused on the survey’s purpose and answers your top questions.

Review, filter, and cross-tabulate. Sift through the results and think about the best way to organize the data to draw conclusions. Export your data in crosstabs to gain deeper insights.

Interrogate the data. In this step, you are looking for irregularities that may skew your interpretation of the data. You should consider the statistical significance of your results and identify any areas where sampling bias may lead to inaccurate interpretation.

Draw conclusions. Once you have gained a deeper understanding of the data, you can draw conclusions and utilize your findings to improve your business.

We have provided just a brief overview of how data analysis is performed before we jump into the specifics on data analysis for various survey types. If you want to learn more, we encourage you to read about how survey data analysis can help grow your business.

Customer Satisfaction Survey

Customer satisfaction surveys provide invaluable insight into your business and help you identify changes to improve the customer experience. When it comes to analyzing the data from a customer satisfaction survey, be prepared to carefully examine both quantitative and qualitative responses.

After segmenting and organizing your data into appropriate reports, such as crosstabs, the quantitative data from your survey will provide a rich view into the types of customers who were satisfied (or dissatisfied) with their experience.
Once you have a broad view of the types of customers who either enjoyed or struggled through their experience with your company, qualitative data can give you clues into how to improve the experience to improve satisfaction levels.
Review open-ended questions of the customers who reported a high satisfaction level. Look for specific examples of what they enjoyed. For example, respondents may mention an easy checkout experience or the helpfulness of your customer service agents.
Customers who were not happy with the experience are likely to provide even deeper insights. Carefully review their statements and consider what changes you need to make to address these pain points.

Market Research Survey

A market research survey can help you apply educated decisions on how to cater to your target market. For example, your research may help you understand the competitive landscape of a survey platform, but it may not give enough detail into a specific feature you want to develop.

A market research survey could provide more detail, especially when paired with secondary research sources.

Conduct secondary research, which involves reviewing published data such as consumer research reports, white papers, government statistics, etc.
Identify where you need additional or more specific information to answer any curiosities you may have about your target market.
Conduct primary research, which involves gathering self-conducted data, i.e., the data that has not already been obtained and made available.
Plan your market research survey, deploy, and gather results.
Analyze your results and draw conclusions. By combining data gathered in your survey with other aspects of your market research, you can leverage the survey data to make more confident decisions.

A survey alone will not suffice when it comes to market research, but when paired with secondary research and other forms of primary research, the data gathered in a survey can be illuminating.

Event Evaluation Survey

With the time and money dedicated to hosting an event, surveys are important tools for determining the success of the event, whether it was worth the investment and how to improve future events. If you host monthly or annual events, an event evaluation survey is absolutely critical for your success!

When it comes to analyzing your survey data, again you will need to pay attention to both quantitative and qualitative responses.
Quantitative data can give you a broad overview of the success of your event. Answers to the following types of questions can help you quickly understand the value of your event:
1. Will you attend this event again?
2. How likely are you to recommend this event to a friend?
3. Was the length of the event too long or too short?
4. Were the supplemental resources provided helpful?

Once you have sorted your data based on response and/or demographics, you can review the qualitative data in the form of responses to your open-ended questions. These personalized responses can help you understand what changes need to be made in order to improve satisfaction with the event.
If you will have more events in the future, the initial responses can provide you with a benchmark for future events. By deploying surveys after each event, you will be able to see how your changes influence overall satisfaction with the event.

Brand Awareness Survey

Brand awareness surveys are conducted to help a business understand how familiar the target audience is with their brand. Brand awareness has traditionally been very difficult to measure, but if you plan carefully, it will be easier to analyze your survey data.

First, be sure that you have selected the right audience for your survey via the demographics and screener sections. You need to be specific about your target audience.
Decide whether you want to do a random sampling to see how many people can recall your brand or whether you want to study brand awareness of existing customers.
When reviewing the data, carefully examine the characteristics of each segment. This will help you understand the demographics of the people who know the most or the least about your brand. You may discover, for instance, that only those aged 40+ demonstrate brand awareness.
Understanding the demographics of those who are and are not familiar with your brand can help guide your upcoming marketing decisions. You can decide if you want to strengthen marketing within a segment or try different marketing channels to expand your reach.

Better Analysis Starts with Better Planning

While data analysis is the last step in the survey process, you can make the experience more valuable by considering data analysis during the planning phase. Consider the type of survey you will conduct and think how different data sets will make it easier or more difficult to interpret results.

Data analysis is more fruitful when you have selected the appropriate target audience and have an adequate response rate for your survey. All of this is made easier when you use a professional survey platform to plan, deploy, and analyze the results of your survey.

Frequently asked questions

What is survey data analysis?

Survey data analysis the process of reviewing the data that was gathered during a survey and analyzing it in order to draw actionable conclusions.

What is a crosstab?

A crosstab, or crosstabulation, is a special type of table used in survey data analysis that shows the relationship between two or more variables.

What is statistical significance?

Statistical significance is a term that describes how likely or unlikely it is that a relationship between two or more variables is due to random chance.

When can sampling bias occur?

Sampling bias occurs when certain members of the sampled population are more likely to be selected than others, thus skewing the results. Sampling bias is more likely to happen when respondents are not chosen at random and/or the sample size is too small.

What is a quantitative survey response?

Quantitative responses are those that have a restricted number of options as the response. These responses can then be converted into numbers so they can be easily measured and charted.

by lorraine

Growing Your Business with Survey Data Analysis

Don’t let the term “survey data analysis” intimidate you – organizing and analyzing survey data so you can make actionable decisions to grow your business is easier than it sounds.

You already know that a well-executed survey can help identify areas for improvement in your business, but you may feel intimidated about the daunting task of analyzing your survey data.

After all, pages and pages of data will not help you upgrade your business unless you know how to meaningfully analyze the data and draw conclusions.

The good news is that you do not need a degree in data science to analyze your survey data like a pro!

This article explains how to execute survey data analysis, proving that the data you reap helps you draw conclusions about your target market and your industry. We broke down the process into four steps to make it easy to analyze survey data.

Step 1: Review your top questions and consider the responses

Start at the beginning by reviewing the goals you set for your market research survey. (If you are still in the planning phase, it will pay off later to carefully design your survey and set goals). Take the time to list out the top questions you want to answer during survey data analysis. This will keep you focused as you begin sifting through the data.

You also need to consider the types of responses your survey generated. Did you ask close-ended questions (yes/no or multiple-choice answers) or open-ended questions (fields with text entry allowing for a more elaborate response).

Close-ended questions let you generate empirical data that can be useful for drawing conclusions. Open-ended questions require careful review, but can reveal richer insights than empirical data can alone.

Let’s consider a sample scenario where a business owner wants to know how they can improve their ecommerce business. A top research question in this example might be: “Are customers happy with the checkout experience?”

It is easy to find the answer to this question since it was asked directly during the survey:

Were you satisfied with the checkout experience?	%	Number
Yes	74%	148
No	26%	52

In this scenario, the majority of users are happy with the checkout experience. But what about the 5 respondents who are not happy? How can we use data to understand how to improve the checkout experience? For that, we need to dig deeper.

Step 2: Review, filter, and cross-tabulate your data

If you are using a survey platform like Pollfish, you will have access to a powerful dashboard that allows you to view and filter your survey data. From the dashboard, you can filter and segment your survey results in real-time. For an advanced analysis, you should study it in a variety of formats, such as graphs, charts, and spreadsheets. You can use the latter to create crosstabs.

What are crosstabs?

Crosstab (short for cross-tabulation) is a special type of report that is used to explore the relationship between variables. It is essential in survey data analysis because it lets us segment survey data and examine responses for different segments. For example, we can examine satisfaction levels of the online shopping experience based on the subjects’ age, education level, payment type, etc. Pollfish provides crosstab functionality within the results dashboard, which streamlines the process for you.

Going back to our sample scenario, let’s see how we could determine which payment method is giving users the most problems. To do this, we need to crosstab the results to view payment types and satisfaction levels.

We are looking at two sets of data:

The type of payment method used by respondents
Whether they were satisfied during the checkout process

Payment Type	Satisfied (yes)	Unsatisfied (no)
Visa/Mastercard	86% (128)	6% (3)
Apple Pay	11% (17)	6% (3)
PayPal	2% (3)	88% (46)

The crosstab report reveals that customers who used PayPal overwhelmingly expressed dissatisfaction with the checkout process, providing insight that something in the PayPal process is falling short.

In the same way, you can apply crosstabs to examine the satisfaction levels voiced by other segments. Are people who shopped on their mobile devices happy with the checkout? What about an older segment of users compared to a younger one?

Step 3: Understand the statistical significance

Before drawing conclusions about your data and investing in changes to your business or website, you must crunch the data to understand if the results can be trusted. An important aspect of survey data analysis is assessing the statistical significance of your results. In the realm of data analysis, statistical significance is what helps us determine how accurate our data is.

To do this, you need to consider these factors:

Sample size refers to the number of respondents in your survey. The larger your sample size, the more confident you can be about the results.

Effect size describes the amount of difference between the data you are comparing. If you have a small effect size, you would need a larger sample size to understand if the difference is statistically significant (and worth acting on) or a result of chance.

In our online shopping example, the dissatisfaction voiced by PayPal users is significant and should be explored further. The percent of dissatisfied customers who used a credit card or PayPal is low enough that exploring this is unlikely to yield meaningful results, unless you can determine a third factor in this subset (for example, 100% of the dissatisfaction comes from mobile users).

Step 4: Draw conclusions and create a plan for improvement

Now for the fun part! After you have organized, reviewed, and understood your data, it is time to draw conclusions and determine how this information can be used to improve your business.

Go back to your original research questions. Sift through the data until you are able to answer each of these questions and draw conclusions.

In some cases, a course of action will be very obvious. In our sample scenario, it is clear that this business owner needs to uncover issues in the PayPal checkout experience.

It may be harder to understand why other segments feel unhappy with their shopping experience.

For example, you may understand that those aged 65+ stated dissatisfaction but cannot find a clear relationship that explains why. In these cases, your open-ended questions may reveal insights that may help you interpret the dissatisfaction voiced by this segment.

With your theories and conclusions in hand, create a plan for systematically improving each area of concern. In our example scenario, some changes to how the users move from the store to PayPal may improve the experience and overall satisfaction levels. Once you have made changes, you can understand their impact by running another survey and using your new data analysis skills to understand the change.

Pollfish makes it even easier

At Pollfish, we provide our clients with a dashboard that makes survey data analysis easier – you can review your responses in real-time and access visual data directly in the dashboard. You can view your data in classic mode or in a number of visual sources (thinks charts and graphs). You can also export your data for official reporting and set up cross-tabs. Ready to launch your survey?