You’ve just received the results of your survey. You’ve spent time carefully crafting questions, distributing the survey, and now you have a treasure trove of responses. But what’s next? How do you turn this raw data into actionable insights that drive your business forward?
In this article, we’ll guide you through the process of analyzing and reporting survey findings step by step. By the end, you’ll be equipped to transform your survey results into meaningful, data-driven decisions.
Get ready to uncover the untapped potential of your survey data and take your business to the next level.
What is Survey Analysis?
Survey analysis involves examining the responses from your survey to extract meaningful insights. Doing this is crucial for understanding trends, preferences, and behaviors within your target audience. Effective survey analysis allows you to identify areas for improvement, measure satisfaction levels, and make informed decisions based on real data to improve your overall business strategy.
Types of Data in Survey Analysis
When analyzing survey results, it's essential to keep in mind the two main types of data you’ll encounter, and how to treat them differently in your analysis: quantitative and qualitative.
Quantitative Data: This type of data is numerical and can be easily measured. Examples include ratings on a scale, percentages, and counts. Quantitative data allows for statistical analysis and is useful for identifying patterns and trends.
Qualitative Data: This data is descriptive and non-numerical. It includes open-ended responses, comments, and feedback. Qualitative data provides depth and context to your findings, helping you understand the reasons behind certain trends.
7 Steps to Analyze Survey Results
To analyze your survey data effectively, follow these seven essential steps. You'll want to segment results and pinpoint differences between groups, and prioritize areas of improvement based on the most critical issues you identify.
1. Clean and Prepare Your Data
Before diving into the analysis of your survey results, you'll want to clean and prepare your data. Think of this step as tidying up a cluttered workspace before starting a new project. By ensuring your data is accurate and well-organized, you set the stage for reliable and insightful analysis.
Review for Errors
Start by reviewing your data for any inconsistencies or errors. This might include checking for duplicate responses, incomplete answers, or any data points that seem out of place. For instance, if your survey includes a question about age and you find an entry stating “200 years old,” you’ll need to address anomalies like this. Tools like Excel or Google Sheets can help you sort, filter, and identify these issues quickly.
Handle Missing Data
Next, handle missing data. Decide how you’ll treat responses with missing information. Depending on the extent and importance of the missing data, you might choose to exclude these responses from your analysis or use statistical methods to fill in the gaps. For example, if only a small percentage of your data is missing, you might opt to remove those entries. Alternatively, if missing data is a significant portion, you could use techniques like mean imputation or regression to estimate the missing values.
Create Standards
Standardize your data for consistency. This involves ensuring that all responses are in the same format. For instance, if you asked participants to rate their satisfaction on a scale of 1 to 10, make sure all responses fit within this range. If some participants used different scales or provided textual answers like “very satisfied,” convert these into a numerical format that aligns with your intended scale.
Work with your team to establish what constitutes "clean" data for your specific survey, and be aware to not 'over-clean' your data which can bias outcomes.
Remove Incomplete Responses: Ensure that your dataset only includes complete responses to maintain accuracy.
Standardize Data Formats: Convert all data into a consistent format to simplify analysis.
Example: If your survey includes a mix of date formats, standardize all dates to a single format (e.g., YYYY-MM-DD).
2. Organize and Categorize Responses
Once you’ve cleaned and prepared your survey responses, the next step is to organize and categorize them. This process sets the foundation for meaningful analysis by transforming raw data into structured insights. If you've ever gathered feedback from hundreds of customers, you know that sorting through these responses can be overwhelming. But by following a methodical approach, you can make things more manageable.
Data Types
Start by organizing the responses based on the type of data: quantitative or qualitative. Quantitative data, such as ratings and numerical scores, can be easily organized in spreadsheets or databases. Qualitative data, such as open-ended responses, need to be categorized based on themes or patterns that emerge from the text.
Create Categories
Develop a set of categories that accurately reflect the themes in your survey data. As you go through feedback, you may want to create sub-categories to capture more detailed nuances. After an initial categorization, review and refine your categories to ensure they are distinct and non-overlapping. This helps in identifying commonalities and trends, making your analysis more robust.
As you create your categories, make sure to keep your coding flexible to accommodate new themes that may emerge as you analyze survey data. This flexibility allows your analysis to adapt to different contexts and provides more comprehensive insights.
Group Similar Responses: For open-ended questions, group similar responses into categories.
Create Categories for Open-Ended Responses: Develop themes or codes to categorize qualitative data.
Example: If respondents mention “customer service” in various ways, group these mentions under a single category.
3. Analyze Quantitative Data
Quantitative data, derived from closed-ended survey questions, numerical responses, and measurable metrics, provides a foundation for statistical analysis and objective insights. This type of data allows you to identify trends, compare variables, and draw data-driven conclusions.
This process can be time consuming, and labour-itensive. To help make things a bit easier, Kapiche’s analytics platform helps you quickly slice data in different views. So you can get tailored insights and accurate quantitative conclusions from across your entire feedback ecosystem.
Descriptive Statistics
Start your analysis with descriptive statistics, which provide a summary of your data's main characteristics. Descriptive statistics help you understand the general trends and distributions within your dataset.
Compare Results Across Different Segments
To gain deeper insights, analyze the survey results by different demographic factors, such as age, gender, job role, or location. This segmentation allows you to identify any significant differences in responses between various groups.
Compare subgroup results
Compare the results of each subgroup to identify any notable differences. Are customers in a particular demographic more satisfied than others? Do newer customers have different concerns than those who have been around longer? Understanding these differences can help you tailor your action plans to address the specific needs of each group.
Measures of Central Tendency: Calculate the mean (average), median (middle value), and mode (most frequent value) to understand the central points of your data.
Measures of Dispersion: Determine the range, variance, and standard deviation to see how spread out your data is. These measures indicate the variability in your responses.
Frequency Distributions: Create frequency distributions to show how often each response occurs. This can be especially useful for categorical data.
Example: Calculate the average satisfaction rating from a Likert scale question.
4. Analyze Qualitative Data
Qualitative data, often derived from open-ended survey questions, emails, or product reviews, provides rich, detailed insights that go beyond numbers and statistics. This data captures the voice of the customer, revealing their thoughts, feelings, and experiences in their own words. Analyzing qualitative data involves identifying themes, patterns, and sentiments to understand the underlying messages and derive meaningful conclusions.
Organize Your Data
To begin your qualitative coding process, start by organizing your qualitative data. Collect all open-ended responses in one place, such as a spreadsheet or a qualitative data analysis tool. Ensure that each response is clearly labeled with relevant metadata, such as respondent ID, date, and any applicable demographic information. This organization helps you keep track of your data and makes the analysis process more manageable.
Read Through Responses
Begin by reading through all the responses to get an overall sense of the data. This initial review helps you familiarize yourself with the content and identify any immediate patterns or standout comments. As you read, take notes on recurring themes, interesting insights, or surprising responses that could be significant in your analysis.
Identify Themes and Categories
The next step is to identify themes and categories within the responses. This process, known as coding, involves tagging segments of text with labels that represent key concepts or topics. For example, if respondents frequently mention "customer service," "product quality," and "pricing," these can become your initial themes.
Manual Coding: Manually read through each response and assign codes to different sections of text. This method allows for a deep understanding of the data but can be time-consuming, especially with large datasets.
Automated Coding: Use qualitative data analysis software like Kapiche to assist with coding. These tools can quickly scan through text and identify common themes, saving time and providing consistency in coding.
Use Text Analysis Tools: Use software tools for text analysis or manually code responses. If you go the manual route, this may involve assigning labels or categories to different pieces of text based on their content.
Identify Common Themes: Look for recurring themes and patterns in open-ended responses. Group similar codes together to form broader themes that capture the essence of the feedback.
Example: Use word clouds to visualize frequently mentioned terms in feedback.
5. Visualize Your Data
After cleaning and organizing your data, the next key step is to visualize your data. Visualizing data transforms raw numbers into meaningful insights that are easy to understand and interpret. It helps you and your stakeholders quickly grasp complex information, identify patterns, and make data-driven decisions.
Choose the Right Visualization Tools
Start by selecting the appropriate tools for data visualization. Tools like Excel, Tableau, Google Data Studio, and Kapiche offer a variety of options to create compelling visual representations of your survey data. With Kapiche, you can create a cx survey results dashboard, to quickly get to insights, and communicate with stakeholders on your team. Choose a tool that aligns with your technical skills and the complexity of your data.
Select Suitable Chart Types
Different types of data require different types of visualizations. Here are some common options:
Bar Charts: Ideal for comparing the frequency or magnitude of different categories. For example, a bar chart can show the distribution of responses to a satisfaction survey, highlighting which aspects of your service are rated highest and lowest.
Pie Charts: Best for showing proportions and percentages. Use a pie chart to illustrate how your respondents are divided among different categories, such as age groups or customer segments.
Line Graphs: Perfect for displaying trends over time. A line graph can track changes in customer satisfaction scores across multiple survey periods, revealing whether your efforts to improve have been effective.
Heat Maps: Useful for showing the intensity of responses across a matrix. For instance, a heat map can display customer satisfaction levels across various regions or departments, identifying areas that need attention.
Word Clouds: Effective for visualizing open-ended responses. Word clouds highlight the most frequently mentioned words in qualitative feedback, making it easy to spot common themes and concerns.
Use Charts and Graphs: Create visual representations of your data using pie charts, bar graphs, and word clouds.
Tools for Data Visualization: Use tools like Excel, Google Sheets, or specialized software like Kapiche.
Example: Create a pie chart to show the distribution of responses to a multiple-choice question.
6. Interpret the Results
Now that you have visualized your data, it’s time to interpret the results. This step involves understanding what your data is telling you and drawing meaningful conclusions from it. Interpreting survey results is both an art and a science. It requires a keen eye for detail and a holistic view of your survey’s objectives.
Draw Meaningful Conclusions
Start by revisiting the goals of your survey. What were you hoping to learn? Align your findings with these goals to ensure your interpretation is relevant and actionable.
Look at the big picture. Identify key trends and patterns that emerge from your data. For example, if you conducted a customer satisfaction survey, you might notice that a majority of respondents are satisfied with your product’s quality but are unhappy with your customer service. This indicates a clear area for improvement. Leverage helpful feedback analytics software so you can delegate the heavy lifting, and focus on asking the right questions.
Draw Meaningful Conclusions: Analyze the data to derive insights and conclusions.
Identify Actionable Insights: Determine what actions can be taken based on the findings.
Contextualize the Results: Consider the broader context in which the survey was conducted, like industry trends, market conditions, or organizational changes. This can provide valuable perspective and help you interpret the results more accurately.
Example: If a significant number of respondents express dissatisfaction with a particular product feature, consider revising or improving it.
7. Report Your Findings
After completing your analysis, the final step is to report your findings in a way that is clear, concise, and impactful. Think of this as telling a story with your data, where the goal is to communicate your insights effectively to stakeholders who may not be as familiar with the data as you are. You want to make it as easy as possible for your audience to get to actionable insights, when you present survey results.
Summarize Takeaways
Start by summarizing the key takeaways from your analysis. Highlight the most important findings that address the core objectives of your survey. For example, if your survey aimed to measure customer satisfaction, begin with the overall satisfaction score, verbatim from real customers, followed by any notable trends or patterns you discovered.
Contextualize Your Findings
Contextualize your findings by explaining what they mean for your organization. Don’t just present the data; interpret it. For instance, if you found that a significant number of customers are dissatisfied with your product’s pricing, discuss the potential implications for your pricing strategy and create actionable recommendations. This approach demonstrates the practical relevance of your insights and guides decision-making.
Prioritize Areas for Improvement
Identify the themes that have the greatest impact on your organization's goals or the respondents' experience. These may be the issues that were mentioned most frequently or those that have the potential to cause significant harm if left unaddressed.
For each priority area, develop a clear action plan that outlines the steps needed to address the issue. Assign responsibilities, set timelines, and establish metrics like NPS to measure progress. Regularly review and update these action plans to ensure they remain relevant and effective. Providing a roadmap for action demonstrates the value of your analysis and helps ensure that your insights lead to actual tangible improvements.
Create a Comprehensive Survey Report: Summarize your findings in a clear, concise report.
Present Results Clearly: Use visuals and straightforward language to convey your findings.
Example: Develop a survey report with sections for methodology, key findings, visualizations, and recommendations.
Types of Survey Data Analysis
Now that we've seen the step by step breakdown of how to analyze survey results, let's dive a bit deeper on two main analysis methods: descriptive and inferential.
Descriptive Analysis
Descriptive analysis focuses on summarizing and describing the basic features of the survey data. This type of analysis often involves using charts, graphs, and tables to visualize the data and make it easier to understand.
For example, if you conducted a survey asking customers to rate their satisfaction on a scale of 1 to 5, a descriptive analysis might involve calculating the average rating, identifying the most common response, or creating a pie chart to show the distribution of responses.
While descriptive analysis provides a useful overview of the data, it doesn't allow you to draw conclusions beyond the specific sample surveyed.
Inferential Analysis
Inferential analysis, on the other hand, uses statistical techniques to draw conclusions about a larger population based on a sample. This type of analysis involves testing hypotheses and identifying relationships between variables.
For instance, if you wanted to know whether there was a significant difference in customer satisfaction levels between two different store locations, you could use inferential analysis to test this hypothesis. By comparing the survey results from each location and using statistical methods to account for sampling error, you could determine whether the observed differences are likely to reflect real differences in the overall population.
Inferential analysis allows you to make broader generalizations and predictions based on their survey data, but it requires a more advanced understanding of statistical concepts and methods.
Examples of Survey Data Analysis in Action
Let's take a closer look at some common examples of survey data analysis and how they can benefit businesses:
Customer Satisfaction Surveys
Customer satisfaction surveys are designed to gauge how happy customers are with a company's products, services, or overall experience. By analyzing the results, you can:
Identify specific areas where customers are dissatisfied
Uncover trends or patterns in customer feedback
Prioritize improvements based on customer preferences
Track changes in satisfaction levels over time
For example, a software company might use a customer satisfaction survey to evaluate the usability of their product. If the analysis reveals that many users struggle with a particular feature, the company can focus their efforts on simplifying or redesigning that feature to improve the user experience.
Employee Engagement Surveys
Employee engagement surveys measure how connected and motivated employees feel at work. By analyzing the results, companies can:
Identify factors that contribute to job satisfaction or dissatisfaction
Uncover potential issues that may lead to turnover
Develop targeted strategies to improve employee engagement
Monitor progress and track the effectiveness of initiatives
Imagine a manufacturing company that conducts an employee engagement survey and discovers that many workers feel unsafe on the job. By taking action to improve safety protocols and training, the company can create a safer work environment, boost morale, and reduce turnover.
Market Research Surveys
Market research surveys help businesses understand consumer preferences, behaviors, and opinions. By analyzing the results, you can:
Identify target audiences and customer segments
Uncover unmet needs or pain points in the market
Evaluate the potential demand for new products or services
Gather competitive intelligence and benchmark against rivals
For instance, a beauty brand might use a market research survey to explore consumer preferences for natural and organic ingredients. If the analysis shows a strong demand for these types of products, the company can develop new formulations and marketing strategies to capitalize on this trend.
In summary
Analyzing survey results can be a complex process, but the insights gained are invaluable for driving your business forward. By following these steps, you can transform raw survey data into meaningful, actionable insights.
Kapiche is here to help. Our platform streamlines the survey analysis process, allowing you to gain insights faster and more accurately. If you're ready to take your survey analysis to the next level, watch an on-demand demo of Kapiche today to see how to get to customer insights fast.
