Survey Analysis How to Analyze Results

Survey Analysis: How to Analyze Results

Survey analysis is the backbone of data-driven decision-making in modern businesses. For insights professionals tasked with understanding customer sentiments and improving overall customer experiences, survey analysis is a key skill to master.

Beyond collecting data, how can you analyze survey results to ensure that you’re deriving meaningful insights that drive real improvements?

In this article, we’ll dive into best practices for survey analysis, so you can get back to focusing on the insights from your data.

Types of Data for Survey Analysis

Before you analyze survey data, one of the first things you’ll want to pinpoint is what type of data you’ll be dealing with.

Since survey analysis encompasses both quantitative data and qualitative data. Each type offers unique insights into customer behaviour, preferences, and sentiment. Understanding how to approach and analyze these types of data is crucial for getting to accurate insights. Recognizing the importance of survey data analysis methods is essential, as they help in turning raw data into actionable insights for business improvement, employing everything from simple cross-tabulation to advanced statistical methods to uncover valuable insights.

Quantitative Data

Quantitative survey data consists of measurable data points like ratings, rankings, scores, and counts, which can be analyzed statistically.

Quantitative survey questions are highly structured and often use closed-ended formats like multiple-choice questions, rating scales, and numerical scales. Analyzing quantitative data involves statistical techniques to identify patterns, trends, correlations, and statistical significance.

Qualitative Data

Qualitative survey data provides in-depth insights into customer opinions, attitudes, and perceptions.

It is non-numerical and often obtained through open-ended survey questions, comments, feedback, and observations. Qualitative data analysis focuses on identifying themes, sentiments, common phrases, and recurring patterns within the text. Techniques like text mining, sentiment analysis, and thematic analysis are often the best way to go to extract meaningful insights from qualitative data.

survey analysis methods

Approaching Data Analysis

Understanding the nature of your data type will help you pick the best analysis methods. Here are some tips to picking your method:

  1. Quantitative Analysis: Use statistical methods like descriptive statistics, correlation analysis, regression analysis, and significance testing to analyze numerical data and derive quantitative insights.

  2. Qualitative Analysis: Use techniques like text analytics, sentiment analysis, content analysis, and thematic coding to uncover qualitative insights from textual responses and comments.

  3. Integrated Analysis: You'll likely want to combine quantitative and qualitative data analysis to gain a more comprehensive understanding of customer feedback. Integration allows you to triangulate findings, and enhance the validity and reliability of your insights.

Survey analysis step by step guide

Steps to Analyze Survey Data

Analyzing survey data is a systematic process of organizing and interpreting meaningful insights from collected responses. Done effectively, your survey data analysis will help you identify trends and find statistically significant patterns that lead to actionable insights. Analyzing data is crucial for identifying trends and patterns that can inform strategic decisions and drive growth.

Here are the essential steps and methods for analyzing survey data effectively:

1. Define Analysis Objectives

Clearly define the research objectives and questions to guide your data analysis efforts:

  • Define success: Define what actions you want to be able to take once you have data to inform next steps

  • Select measurement: Identify the key metrics, variables, and dimensions relevant to achieving analysis goals.

2. Data Cleaning and Preparation

Start by cleaning the survey responses and dataset to remove incomplete, inaccurate, or irrelevant responses:

  • Organize the data: Arrange the survey responses in a tabular format with each row representing a respondent and each column a question or variable. This structure facilitates analysis and visualization.

  • Handle missing data: Identify and address any inconsistencies, missing values, or outliers in the data. Decide on a strategy for handling missing data, such as imputation or exclusion, based on the extent and pattern of what is missing.

  • Remove duplicates: Eliminate any duplicate or irrelevant survey responses to ensure the integrity of the analysis.

3. Pick Your Analysis Technique

Differentiate between quantitative data and qualitative data in your survey, and pick a method to help you do thematic analysis or coding.

Quantitative Analysis Techniques

  • Descriptive Statistics: Calculate and analyze descriptive statistics (mean, median, mode, standard deviation, etc.) to summarize and describe survey data.

  • Correlation Analysis: Explore the relationships between variables using correlation coefficients to identify patterns or associations to support your statistical analysis.

  • Regression Analysis: Use regression models to understand the impact of independent variables on dependent variables and predict outcomes.

  • Segmentation Analysis: Segment survey data based on demographic or behavioural variables to identify trends and patterns within different groups.

Qualitative Analysis Techniques:

  • Thematic Analysis: Identify recurring themes, topics, or patterns in open-ended responses using thematic coding and analysis.

  • Content Analysis: Analyze textual responses to categorize, code, and interpret qualitative data for deeper insights.

  • Sentiment Analysis: Utilize natural language processing (NLP) techniques to assess sentiment, emotions, or attitudes expressed in text-based responses.

4. Hypothesis Testing and Inferential Analysis

Once you've done a first pass at analyzing your survey results, formulate hypotheses about relationships or differences in your survey data, to further validate your findings.

  • Formulate hypotheses: Based on the research questions, develop hypotheses about relationships or differences in the survey data.

  • Conduct statistical tests: Choose and apply appropriate statistical analysis tests, such as t-tests, ANOVA, or regression, to determine the significance of the observed relationships and make inferences about the population.

5. Interpretation and Insights

Interpret the results of your analysis in the context of research objectives, hypotheses, and organizational goals.

  • Interpret results: Interpret the findings of the statistical tests in the context of your initial research questions and hypotheses.

  • Actionable insights: Translate the findings into actionable insights that can inform decision-making, improve products/services, and guide future research.

6. Data Visualization

Create visual representations such as charts, graphs, and dashboards to effectively present survey findings and trends.

  • Select appropriate visualizations to present survey data: Choose the most suitable visualization methods for your survey results, such as charts, graphs, or tables, based on the data type and analysis objectives. This step is crucial in ensuring that the survey findings are accessible and engaging to a wide audience.

  • Create compelling visuals: Design clear and informative visualizations that effectively communicate the key findings and insights from the survey analysis, into a survey report that can easily be shared across the organization.

7. Validation and Iteration

Validate analysis outcomes through peer review or validation checks across your team to improve accuracy and reliability of your conclusions.

  • Continuously iterate: Iterate analysis processes based on feedback, new data sources, or evolving research needs for continuous improvement in insights generation.

survey analysis challenges

Common Challenges in Survey Analysis

When doing survey analysis, it’s common to run into challenges that threaten to undermine your analysis efforts.

Being aware of these challenges, and putting strategies in place to address them from the beginning, is just as key as your ability to code data correctly.

Here are some common challenges in survey analysis: you’ll want to look out for:

Understanding the needs of survey respondents and accurately representing their data is crucial for the integrity of survey analysis, ensuring that the survey results are statistically significant and truly reflective of the survey population.

1. Low Response Rates

Obtaining a sufficient number of responses is becoming more difficult. But to achieve statistically significant results, it's essential you find ways to engage your customers so you can draw accurate conclusions from survey data. Low response rates can skew findings and reduce the reliability of insights.

Anticipating this, make sure you keep great survey design top of mind at the outset, so you don't end up making surveys too long, and a bad experience that no one wants to fill out.

Create personalized invitations that address respondent concerns, or offer incentives. And make sure to include a blend of solicited, and unsolicited feedback as raw data inputs into your process, to take the pressure off of formal surveys providing you insights.

2. Biased Responses

Respondent bias is something you should always keep in mind. Inaccurate responses can happen because of a range of biases. Like social desirability bias for example, or feeling the need to respond in a socially acceptable manner. Acquiescence bias is another one –– the tendency to agree with statements. Response fatigue, stemming from over surveying or repetitive questions often leads to careless responses that are unhelpful in your analysis.

Make sure you design surveys with neutral language, randomize question order to mitigate order bias, use balanced scales (positive and negative statements), include attention-check questions to detect inattentive respondents, and encourage honest feedback.

3. Data Quality Issues

Incomplete, inconsistent, or erroneous data entries can compromise the accuracy and reliability of survey results, leading to flawed analysis and incorrect conclusions.

To combat this, implement data validation checks at the survey design stage, and pre-test surveys with a small sample to identify and rectify data issues. You can use skip logic and validation rules in survey tools to ensure data integrity, and make sure to clean your datasets by removing duplicates or outliers before beginning any analysis.

4. Non-Response Bias

Non-response bias occurs when specific respondent groups are more likely to skip or avoid certain survey questions, leading to underrepresentation or overrepresentation of opinions, demographics, or behaviours.

Analyze response patterns across demographic segments to identify potential biases. You can use weighting techniques like demographic weighting, to adjust for non-response bias and make results more representative.

5. Overlooking Open-Ended Responses

Focusing solely on quantitative data and neglecting qualitative insights from open-ended responses can limit the depth and richness of analysis, causing you to miss out on valuable, nuanced feedback.

Incorporate qualitative analysis techniques such as thematic coding (identifying recurring themes), sentiment analysis (assessing emotional tone), and text analytics (extracting keywords or phrases) to derive meaningful insights from textual responses, complementing quantitative findings for a comprehensive analysis.

Tools for Survey Data Analysis

There are several powerful tools available to support your survey analysis process.

Whether you're after software to help you better visualize data, interpret it, code it, or clean it, here are some top tools and a description of their key features:


Best for: AI-driven sentiment analysis and thematic insights

Kapiche is an AI-powered text analytics platform that helps organizations quickly analyze and derive insights from open-ended survey responses, reviews, and other unstructured text data.

  • Top Features:

    • Automated sentiment extraction from unstructured text data

    • Theme clustering and visualization for identifying key topics

    • Actionable insights generation for data-driven decision-making

Survey analysis with SurveyMonkey


Best for: Easy-to-use survey creation and basic data analysis.

SurveyMonkey offers a range of analysis tools to help you get deeper insights from your survey data.

  • Top Features:

    • Drag-and-drop survey builder with ready-to-use templates

    • Basic data analysis tools including charts and trend analysis

    • Collaboration features for team-based survey projects

Survey analysis with SPSS Statistics

SPSS Statistics

Best for: Advanced statistical analysis and hypothesis testing.

SPSS Statistics is a powerful statistical analysis tool that makes advanced analysis accessible to non-experts.

  • Top Features:

    • Syntax editor for easier statistical modelling

    • Saves analysis steps for replication on future surveys

    • Generates detailed statistical reports

Survey analysis with RapidMiner


Best for: Data preprocessing, machine learning, and predictive analytics.

RapidMiner is a comprehensive data science platform that includes powerful feedback analysis capabilities alongside a wide range of other data analysis and machine learning tools.

  • Top Features:

    • Automated data cleansing and transformation workflows

    • Machine learning model building and evaluation

    • Integration with popular data sources and analytics tools

In summary

Now that you've gained insights into effective survey analysis practices and tools, it's time to put your knowledge into action.

Elevate your survey analysis game and unlock deeper customer insights with Kapiche. Our advanced AI-powered platform goes beyond basic data analysis, providing you with comprehensive sentiment analysis, theme extraction, and trend identification from your survey data.

Why Kapiche?

  1. AI-Powered Insights: Leverage cutting-edge artificial intelligence to extract meaningful insights from your survey responses automatically.

  2. Uncover Hidden Patterns: Identify emerging trends, sentiment shifts, and customer preferences with advanced thematic analysis.

  3. Actionable Recommendations: Receive actionable recommendations based on data-driven insights to enhance customer experiences and drive business growth.

  4. Scalable Solutions: Whether you're dealing with hundreds or millions of survey responses, Kapiche scales effortlessly to meet your needs.

Ready to Dive Deeper? Watch a free demo today and discover how Kapiche can elevate your survey analysis process. Take the next step towards actionable insights and data-driven decisions.

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