Survey Analysis: Best Practices, Methods, and Examples

At their core, business decisions are an expression of what someone believes about their customers. 

Steve Jobs thought people wanted computers in their pockets — so he made the iPhone.

Larry Page thought people wanted a more human search engine — so he made Google. 

However, for every rousing success where someone saw a market need before others, there are multiple failures where the opposite is true. This is why survey analysis is one of the most critical practices a business can invest in.

With survey analysis, you can learn more about your customers and make business decisions that align with the needs of the market.

In this guide, we’ll explain survey analysis, how it works, and how you can get started today. This will help you make data-backed decisions that can put your company on the fast track to success.

What is Survey Analysis?

Survey analysis is the process of sifting through all the raw data you collect through a survey to learn something meaningful about your customer base. 

However, gaining these nuggets of wisdom can be a long, technical process that is as much an art as it is a science. It involves crafting the right survey, asking smart questions, cleaning the data, and applying appropriate survey analysis methods to find the answers you’re looking for. 

If all this goes right, you’ll gain a deeper understanding of your customers — what they like, what they don’t like, and how you can better serve them in the future.

Why Should You Care About Survey Analysis?

By investing your time and resources into doing better survey analysis, you’ll earn one of the most important competitive advantages out there — a better understanding of your customers. 

This is important because it allows you to: 

• Give customers what they want: Tailor your products, services, or strategies to meet the needs and expectations you unearth in your customer surveys.
• Identify trends and patterns: Pinpoint areas for improvement and spot emerging opportunities or potential issues.
• Make the right decisions: Measure the performance of your initiatives and experiments so you can make data-driven decisions to optimize your strategies.
• Compare your performance: Use industry benchmarks or competitor data to see how you fit into the larger landscape, with a focus on understanding your strengths, weaknesses, and areas where you can stand out in the market.
• Drive continuous improvement: Do regular survey analysis to create a feedback loop of continuous improvement.

With each survey you run and analyze, your understanding of your customers will grow, allowing you to serve them better and grow your presence in that market.

What are the Disadvantages of Traditional Survey Analysis?

While surveys are valuable, companies that solely rely on their survey responses as the source of truth for customer expectations will be at a clear disadvantage. 

Leading companies, especially those in the retail, restaurant, financial services, and multi-family property management industries, take a different approach by using AI to analyze surveys, reviews, and social media together in real time to understand their customers. 

Here are just a few reasons why they make the switch:

• Surveys can take weeks to analyze: The insights businesses get from traditional surveys may no longer be relevant, especially in quick-moving industries.
• Gives you the ‘why’ behind survey results: Adding qualitative data to your survey results helps businesses understand what customers are thinking and why when they give your business certain NPS scores or star ratings. 
• Surveys are only one channel of feedback. Many customers prefer to leave their feedback online, either through social media posts or online reviews. Without taking these into account and analyzing them quickly for real insights, businesses don’t know what their customers really want.
• Get feedback from more kinds of customers: Oftentimes, the people who leave a review are the happiest or angriest of your customers — not really representative of the majority. Using signals from social media and online reviews can help give you a better picture of what people are really thinking. 
• Go beyond number ratings: When you ask questions like “how satisfied or dissatisfied are you with the products you bought today,” and ask for a 1-10 rating, all you will get for the answer is the rating. You’ll miss the nuance that comes from full-sentence responses in open-text fields. 

What are Some Common Survey Analysis Methods?

When it comes to survey results, there are a ton of ways you can get actionable insights from your data. Here are a couple of the most popular ways you can try and the goals they are best suited for:

• Descriptive statistics: Involves summarizing your data by finding the median, mean, mode, standard deviation, and frequency distributions. Great for creating an overview of your survey responses.
• Cross-tabulation: Helps you establish the relationship between two or more variables in your data by creating a contingency table. Perfect for identifying underlying patterns and trends in the data based on different customer segments or demographics.
• Correlation analysis: Measures how strong of a relationship two variables have. Use this to determine if there is a positive, negative, or no correlation between two factors in your data, like between customer satisfaction and loyalty.
• Regression analysis: Helps you understand the relationship between a dependent variable (e.g., customer satisfaction) and one or more independent variables (e.g., product quality, service, price). It’s a good way to identify the underlying causes of key goals, like customer satisfaction.
• Text analysis: Analyzes open-ended survey responses by identifying common themes, sentiments, and keywords in people’s responses. Techniques like word clouds, topic modeling, and sentiment analysis are used to gain insights from unstructured text data.
• Net promoter score (NPS): Measures customer loyalty by asking customers how likely they are to suggest a company’s products or services to others. The responses are then categorized into promoters, passives, and detractors.
• Customer satisfaction score (CSAT): Measures customer satisfaction with a specific product, service, or interaction. It is often calculated by asking customers to rate their satisfaction on a scale (e.g., 1–5 or 1–10).
• Trend analysis: Involves analyzing survey data over time to identify patterns and changes in customer perceptions, satisfaction, or behavior. Use this to track the impact of business decisions and identify areas for improvement.
• Benchmarking: Compares a company’s survey results with industry benchmarks or competitors to assess performance and identify areas where the company is excelling or lagging.

From this lengthy list, we suggest you pick two or three methods to try out initially. This will not only help you get a feel for survey analysis but also help you get a feel for survey analysis before you find the method that best suits your company’s needs.

How to Conduct Survey Analysis in 3 Easy Ways

Now that you know a couple of ways to conduct survey analysis, let’s break down exactly how to do it with three common methods: descriptive statistics, cross-tabulation, and text analysis.

How to Perform Basic Descriptive Statistics

The Scenario

You have conducted a survey asking 100 customers to rate their satisfaction with your restaurant’s food, service, and ambiance on a scale of 1 to 5 (where 1 is very dissatisfied and 5 is very satisfied).

Step 1: Data Collection and Preparation

Conduct the survey and organize the data in a structured format, such as a spreadsheet in Google Sheets or Excel. 

In this case, you would have a spreadsheet with columns for food, service, and ambiance ratings, as well as rows for customers’ responses.

Step 2: Data Cleaning

To get the most out of your data, you’ll need to clean it up a bit. This means you’ll need to check for missing, incomplete, or inconsistent data and resolve these issues.

For instance, if a customer left the “service” rating blank, you might decide to exclude that customer’s data from the analysis or fill in the missing value with the average service rating.

When cleaning your data for the first time, you can follow the tips outlined in this guide.

Step 3: Calculate Frequencies

Count the number of responses for each rating category for each variable in your survey.

In the example, you would count how many customers gave a rating of 1, 2, 3, 4, and 5 for food, service, and ambiance separately.

Then, convert these frequency counts into percentages by dividing each count by the total number of responses and multiplying by 100.

Step 4: Calculate Mean, Median, and Mode

To get more information out of this data, you’ll want to run a couple of calculations. This includes:

• Calculating the mean (average) rating for each variable by adding up all the ratings and dividing by the number of responses
• Calculating the median (middle value) by arranging the ratings in order and selecting the middle number
• Calculating the mode (most frequent value) by identifying the rating that appears most often

Step 5: Visualize the Data

Take what you’ve learned and create charts or graphs to visually show off the data so others can easily see what you’ve learned.

For instance, your restaurant could create a bar chart showing the percentage of customers who gave each rating for food, service, and ambiance.

We could then see which areas are scoring well and which ones still need work. For example, if the food is highly rated but the service is lagging, you could try out some new customer service initiatives and redo the survey in three months to see if you’ve improved.

How to Perform Cross-Tabulation with Structured Data

The Scenario

You want to learn more about how well your clothing store is meeting the needs of both older and younger customers. To do this, you’ve designed a survey that asks customers about their age and their satisfaction with the store’s product selection, with responses being either “Satisfied” or “Dissatisfied.”

Step 1: Data Collection and Preparation

Ensure that the survey data is collected and stored in a structured format (e.g., spreadsheet or database).

In this case, you would have a spreadsheet with columns for age group (over 35 and under 35) and satisfaction level. Each row on our spreadsheet would represent a customer’s response.

Step 2: Data Cleaning

Check for missing, incomplete, or inconsistent data and correct them by either excluding them or filling them in with a survey-wide average.

Step 3: Create a Contingency Table

Set up a table with the independent variable (age) as rows and the dependent variable (satisfaction level) as columns.

Next, count the number of responses for each combination of age and satisfaction level. In our retail example, that might look something like this: 

• Over 35/Satisfied: 50
• Over 35/Dissatisfied: 20
• Under 35/Satisfied: 80
• Under 35/Dissatisfied: 30

Step 4: Calculate Row Percentages

For each row (age group), calculate the percentage of responses for each column (satisfaction level).

For people over 35 who took our survey, 50 out of 70 were satisfied, which is 71.4%. For people under 35, 80 out of 110 were satisfied, which is 72.7%.

Step 5: Calculate Column Percentages

For each column (satisfaction level), calculate the percentage of responses for each row (age group).

For example, for satisfied customers, 50 out of 130 (50 + 80) are over 35, which is 38.5%. For dissatisfied customers, 20 out of 50 (20 + 30) are over 35, which is 40%.

Step 6: Interpret the Results

Analyze the percentages to identify patterns or differences between the groups you surveyed.

For instance, you might conclude that there is little difference in satisfaction levels between people over and under 35, as 71.4% of people over 35 and 72.7% of people under 35 are satisfied with your product selection.

How to Perform Text Analysis

The Scenario 

Your hospital wants to learn more about how they can better serve their patients. You have decided that the best way to do this is to survey 1,000 patients, asking them open-ended questions about patients’ experiences during their stay. 

Step 1: Data Collection and Preparation 

Gather all text responses in a structured format, such as a spreadsheet. Each row should represent a patient’s response, with columns for the response text and relevant metadata (e.g., department, length of stay).

Step 2: Data Cleaning 

Review the text data to correct spelling errors, remove irrelevant content, and standardize formatting. This helps whichever software you use to create an accurate analysis of your responses.

Step 3: Choose Text Analysis Method 

Select an appropriate text analysis technique based on your goals. Here are a couple of common ones that companies frequently use: 

• Word frequency analysis: Identify the most commonly used words or phrases in the responses to better understand underlying trends. 
• Sentiment analysis: Determine the overall emotional tone of each response, categorizing them as positive, negative, or neutral. This helps you identify departments or aspects of care that are receiving particularly positive or negative feedback.
• Topic modeling: Use algorithms to uncover commonly discussed topics that might not be immediately apparent.
• Keyword extraction: Identify the most important or relevant terms in each response. Unlike simple word frequency, it considers the context and importance of words.

Step 4: Perform Your Analysis 

Begin by running your data through text analysis software, choosing the analysis method that makes the most sense for your needs. 

Review these initial results and look for any anomalies or unexpected patterns that might indicate issues with your analysis. For instance, if sentiment analysis shows overwhelmingly positive results in an area you know has problems, you may need to adjust your sentiment classification rules.

Next, refine your analysis by removing irrelevant terms, grouping similar concepts, and focusing on healthcare-specific language. In sentiment analysis, you might need to account for medical jargon that could be misclassified. For topic modeling, you may need to adjust the number of topics or refine stop words to get more meaningful clusters.

Don’t be afraid to go through the refinement process several times — it may take a couple of tries to get actionable, reliable results.

Finally, validate your results against a sample of responses that you manually review. This helps ensure that the overall analysis is truly in line with what your patients experience. By thoroughly refining your analysis, you’ll be better positioned to get valuable insights from your patient feedback data.

Step 5: Interpret Results and Visualize Findings

After refining your analysis, carefully examine the output to uncover key insights about patient satisfaction. Identify recurring themes and assess overall sentiment toward different aspects of care. Pay special attention to unexpected issues or patterns that emerge from the data.

To make these insights more accessible and impactful, transform your text analysis results into clear visual representations. Create word clouds to highlight frequent terms, sentiment distribution charts to show emotional trends, and topic network diagrams to illustrate relationships between key themes. These visualizations will help you connect findings to specific departments or services within the hospital.

Performing Survey Analysis With Chatmeter

Going in-depth into text analysis is not easy work. It requires a lot of time, effort, and knowledge to tease out the most important trends from the data you collect.

For this reason, there are a number of promising survey analysis tools that will do all of the heavy lifting for you. For instance, with Chatmeter, you can quickly create, share, and analyze surveys of all kinds. 

Plus, with Chatmeter, you aren’t stuck asking closed questions. Instead of restricting your customers to assigning a number grade or yes/no response, you can allow them to respond to questions in full sentences.

After filling in their responses to your questions, our systems will analyze that data, looking for important trends, keywords, and sentiments. Then, you can ask Chatmeter questions about the dataset, and our AI will answer those questions for you.

For instance, you could ask Chatmeter questions like:

• What do customers like best about our sales?
• What is the most common complaint we get about our product?
• Do people like our customer service?

All of this allows you to unlock real understandings of your customers which can give you that competitive advantage you’re looking for.

6 Survey Analysis Best Practices

Regardless of the survey analysis method you choose, there are some common issues beginners run into when starting to do survey analysis. However, follow these six best practices, and you should be able to get actionable insights from your efforts.

1. Clearly Define Your Objectives

Before you even draft a survey question, you should clearly know what you’re trying to find out from your customers. From this goal, you can then decide the best way to analyze that data (optimal survey analysis method) and the questions you need to ask.

For instance, if you run a chain of restaurants where one location hugely outperforms the others, you might want to learn from those customers what that branch is doing well.

With this goal in mind, you could use a combination of net promoter score (NPS) and text analysis to understand why that branch is doing well so you can try to replicate it elsewhere.

First, you’d use the NPS question: “On a scale of 0 to 10, how likely are you to recommend [restaurant branch] to a friend or colleague?”

This would allow you to identify customers who love that branch (those who score it a 9 or 10). With these customers, you could follow up on the NPS survey with the open-ended question: “What are the main reasons you would recommend this restaurant to others?”

Using a tool like Chatmeter, you could then analyze the responses and ask our AI what about this location people love. Or, if you prefer a more manual approach, you could use word cloud and keyword frequency analysis to see what words happy customers associate with that branch. 

Use what you learn to try out new initiatives at other branches, running trend analysis surveys to see how these changes perform. 

2. Ensure Data Quality

A survey means nothing if the data you input isn’t high quality. To make sure you’re getting the most accurate findings from your work, make sure to:

• Create survey questions that are clear, unambiguous, and relevant to your research objectives.
• Clean your data by checking for and resolving any missing, incomplete, or inconsistent responses.
• Remove outliers or extreme values that may skew your results, but do so cautiously and transparently.

3. Choose the Right Analysis Methods

Pick the right tool for the job. Your method of analysis should depend on your survey’s goal.

Here are a couple of suggestions when looking for the right analysis method for your objective:

• Choose a method for which you have the right tools. If you’re working off of a spreadsheet alone, trying to do text analysis might be biting off more than you can chew.
• Use descriptive statistics to summarize your data, providing an overview of the responses.
• Apply inferential statistics, such as cross-tabulation or regression analysis, to examine relationships between variables and draw conclusions about your population of interest.
• Try combining different methods for the best results. 

4. Interpret Results in Context

When interpreting your survey results, don’t forget to consider the context in which the data was collected. Survey data is not created in a vacuum, and you don’t want an outside variable to lead you to an inaccurate conclusion.

You can avoid this issue by:

• Taking into account factors such as the sample size, representativeness of the sample, and any potential biases or limitations of the survey design. 
• Being cautious about making broad generalizations based on a single survey.
• Using your industry expertise to provide meaningful insights and recommendations based on the analysis.

Make sure to filter your results to identify trends in your survey groups. If, for example, a clothing store in one region closed waiting rooms and the number of dissatisfied customers suddenly spiked in that region, analyzing those results by region would show the impact more clearly.

5. Communicate Findings Effectively

The best surveys won’t mean much if no one but you ever understand their results. To make sure everyone understands exactly what you have found, put the data in a clear, concise, and visually appealing form.

This can involve: 

• Using charts, graphs, and tables to summarize key findings and make the data easier for your audience to understand.
• Providing a narrative that explains the significance of the results and highlights the main insights and recommendations.
• Tailoring your communication style and level of detail to your target audience, whether it’s colleagues, clients, or stakeholders.
• Being transparent about the methods used, limitations of the analysis, and any assumptions made during the interpretation of results.

6. Find a Survey Analysis Tool That’s Right For You

Survey analysis can take a lot of time that many business owners just don’t have. For this reason, in most cases, using survey analysis software is the right choice.

These tools help in a number of ways, including:

• Creating surveys for you
• Cutting down on mistakes in calculations
• Saving you time
• Allowing you to analyze your data using more complicated data analysis methods

The big promise of survey analysis today is AI. 

Using AI, you can now easily analyze open-text fields, which can give you both the qualitative and quantitative data you need to understand what your customers expect from your business. 

This is why Chatmeter has invested heavily in AI. Using Chatmeter, you can analyze not just survey responses but also responses from online reviews and social media channels. With today’s customer experience being so multi-channel, your platforms for analyzing your customer feedback need to be as well. 

Learn More About Your Customers With Chatmeter

Chatmeter is an all-in-one solution for local and multi-location businesses that offers AI-powered survey analysis and survey creation software. Plus, it can help you maintain your local listings, boost your organic traffic on search engines like Google, respond to customer reviews online, and so much more.

Or, if you want to learn more about pricing and features, schedule a live demo with one of our team members. You’ll see how Chatmeter can give you the edge you need to grow your business online.

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