Survey data provides actionable insights about customer behavior, preferences, and purchase habits \u2014 if companies can effectively analyze and apply this data.<\/p>\n
The challenge? Most small business owners, marketing professionals, HR managers, and customer experience teams don’t have degrees in advanced statistical modeling and analysis. With the right approach, however, this level of expertise isn’t required. Read on to discover simple tips for getting started with survey data analysis.<\/p>\n
The more you know about your customers, the better. According to recent data<\/a>, 81% of consumers now want personalized experiences, and 70% want this experience to include employees who are equipped with data about past purchases, buying habits, and service histories.<\/p>\n Customer surveys make this type of personalization possible, but only if your business can collect, curate, and analyze survey data at scale. When confronted with the scale and scope of this data, however, staff often feel stuck because they lack formal statistical training. They know there’s value locked away, but are convinced they don’t have the key.<\/p>\n The good news? You don’t need to be a data scientist to make sense of your data. In our getting started guide, we’ll explore simple methods to help turn raw responses into actionable insights.<\/p>\n Collecting survey responses is the first step in data analysis, but what follows matters more. Here’s a step-by-step guide to survey data prep:<\/p>\n Step-by-Step Setup<\/p>\n Step 1:<\/b> Export or download your survey responses, typically as a CSV file or into a spreadsheet.<\/p>\n Step 2: <\/b>Remove incomplete responses or irrelevant data.<\/p>\n Step 3: <\/b>Label your question, answer, and other columns clearly.<\/p>\n Step 4: <\/b>Separate qualitative (text) and quantitative (scale\/rating) data.<\/p>\n Step 5: <\/b>Use tools like Google Sheets or Excel for improved organization.<\/p>\n Pro tip: You can streamline these steps with our <\/i>SogoSurvey online survey maker<\/i><\/a> which handle many of these tasks automatically.<\/i><\/p>\n There are two basic types of survey data: Quantitative and qualitative.<\/p>\n Quantitative data deals with numbers. This data often takes the form of ratings or counts. For example, if you ask customers to rate your service on a scale of 1 to 10, their answer is quantitative.<\/p>\n Qualitative data refers to open-ended answers, which may include opinions or suggestions. Consider the example above. If you ask customers to provide a written opinion about your service, the answer is qualitative.<\/p>\n Four key metrics can help you better understand survey data:<\/p>\n Percentage<\/b><\/p>\n This is a measure of survey responses out of 100. But what if you don’t have 100 surveys? Multiply the number of answers received by 100, then divide by the total number of surveys.\u00a0 So, if you have 30 responses and 50 total surveys, you multiply 30 by 100 to get 3000, then divide by 50 to get 60%.<\/p>\n Average<\/b><\/p>\n To calculate the average, also called the mean, add all survey values and then divide by the total number of surveys. If you have values of 10, 12, 15, 17, and 18, you add them together to get 72. You then divide by 5 to get an average of 14.4.<\/p>\n Mode<\/b><\/p>\n The mode is the number that appears most frequently in your data set. Let’s say you ask customers to rate your service on a scale of 1 to 10 and you receive these answers: 6, 7, 9, 8, 8, 6, 9, 8, 7, 8. In this case, your mode is 8 because it appears four times. The other numbers only appear twice.<\/p>\n Pro tip: Don’t worry about standard deviation. While it’s useful for meta-analysis and other functions, you’re better served focusing on what’s clear, meaningful, and can be applied ASAP.<\/i><\/p>\n As noted by the Harvard Business Review<\/a>, “organizations are increasingly prioritizing data in their decision-making process.” The caveat? This process can easily go wrong. Reducing this risk means building better surveys<\/a> and then using simple and effective quantitative analysis techniques, such as:<\/p>\n Built-in analytics from tools can improve both the speed and accuracy of quantitative analysis. Check out how our online survey tool takes care of all your analytics needs<\/a>.<\/p>\n Open-ended survey data also plays a role in learning more about your customers, but it requires qualitative analysis techniques.<\/p>\nPreparing Your Survey Data for Analysis<\/h2>\n
Understanding Different Types of Survey Data<\/h2>\n
Depending on the type of data you have, different analysis techniques apply.\u00a0<\/i><\/h4>\n
Key Metrics to Know\u00a0<\/i><\/h4>\n
Simple Quantitative Analysis Techniques<\/h2>\n
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Analyzing Open-Ended Responses<\/h2>\n
Beginner-Friendly Techniques<\/i><\/h4>\n
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Data Visualization for Non-Statisticians<\/h3>\n
Easy Charts to Use<\/i><\/h4>\n