Deciphering Survey Data Analysis For eCommerce

by Bryan Teo

Deciphering Survey Data Analysis For eCommerce

In the realm of data-driven decision making, survey data analysis stands as a crucial process for extracting meaningful insights from survey results.

By effectively analyzing survey data, businesses and researchers can uncover trends, understand consumer behavior, and make informed decisions. This essay delves into the intricacies of survey data analysis: essential methods, data analysis techniques for surveys, analysis of survey data, tools, and more. We will also emphasize the significance of survey data in driving strategic actions.

What is Data Analysis in Surveying?

Survey data analysis involves the systematic examination of responses collected through surveys to identify patterns, draw conclusions, and support decision-making processes. The goal of survey data analysis is to transform raw data into actionable insights. These insights can guide business strategies, product development, customer service enhancements, and a whole lot more.

By employing various techniques such as data cleaning, statistical analysis, and visualization, analysts can derive valuable information from survey results.

The importance of data analysis in surveys cannot be overstated. It helps in understanding the effectiveness of products, services, and campaigns. Moreover, it identifies areas for improvement and innovation.

For example, companies may use survey data analysis to gauge customer satisfaction and loyalty. This can be done by asking Net Promoter Score (NPS) survey questions. By finding out their NPS scores, companies can get a better understanding of how likely their customers would promote their brand. NPS scores can easily be analyzed using tools such as NPS Analytics from Fairing.

Types of Survey Data

Survey data can be broadly categorized into several types. Each type requires different analysis methods:

Quantitative Data

Quantitative data, also known as numerical data, involves numerical values and quantities. For instance, the number of times a customer visits a store, city temperatures, or NPS survey scores are all examples of quantitative data.

When performing survey data analysis, quantitative data allows for straightforward statistical analysis. This enables researchers to uncover patterns and trends through various survey data analysis methods.

Qualitative Data

Qualitative data encompasses non-numerical information, such as verbal responses, images, audio, or video recordings. This type of data provides descriptive and subjective insights, highlighting the "why" behind the "what."

Understanding how to analyze survey data with qualitative responses involves using qualitative research data analysis tools. These help in uncovering deeper insights into respondents' thoughts and feelings, and offer a different perspective from quantitative data.

Close-ended Questions

Close-ended questions offer a limited range of responses, like 'yes' or 'no,' multiple-choice, ranking, or drop-down menu items.

This type of data is structured, making it easy to sort, code, and quantify, facilitating efficient data analysis in surveys. However, close-ended questions may miss some nuanced information that open-ended responses can provide.

Open-ended Questions/Natural Language Data

Open-ended questions, allowing respondents to answer in their own words, produce unstructured data rich in detail. This type of data requires advanced survey analysis techniques like Natural Language Processing (NLP) and sentiment analysis to extract valuable insights.

Knowing how to do analysis of survey data with open-ended responses is crucial for gaining a comprehensive understanding of respondent perspectives.

Categorical (Nominal) Data

Categorical data represents distinct categories without any inherent order, such as gender, location, or brand preferences.

This type of data is useful in identifying patterns and preferences among different groups, aiding in targeted data analysis for survey research. Examples include primary colors, gender distinctions, or brand names.

Ordinal Data

Ordinal data involves ordered categories, such as satisfaction levels from "very satisfied" to "very dissatisfied." Unlike categorical data, ordinal data has an intrinsic ranking, making it suitable for statistical analysis of survey data that measures preferences or degrees of agreement. Likert scales and ranking scales often generate ordinal data.

Scalar Data

Scalar data employs scales like 1-10 rating systems to measure responses. It deals with quantity and quality on a relative basis and uses established scales, such as age, test scores, or time. Scalar data is typically collected through drop-down or sliding scale question formats, providing a robust dataset for detailed survey data analysis.

Understanding the type of data collected is essential for effective survey data analysis. Different types of survey data require specific survey data analysis techniques to yield meaningful insights. By employing the right survey data analysis tools and methods, researchers can accurately interpret data analysis of survey results, ensuring reliable and actionable outcomes.

The type of data you receive affects the kind of survey results analysis you’ll be doing, so it’s very important to consider the type of survey data you will end up with when you’re writing your survey questions and designing survey flows.

How to Analyze Survey Data

Asking how to analyze survey data is a fundamental question for anyone involved in survey research. It is vital to have an understanding of the general process to take when doing so to prevent any gaps where mistakes can take place.

The process typically involves several key steps:

  1. Data Collection: The foundation of any survey data analysis process is the collection of high-quality data. This involves designing effective surveys, choosing the right sample size, and ensuring diverse and representative responses. The quality of the survey questions directly impacts the reliability of the data collected.
  2. Data Cleaning and Preparation: Raw survey data often contains errors, missing values, or inconsistencies. Data cleaning involves rectifying these issues by removing duplicates, handling missing data, and standardizing responses to ensure accuracy and reliability. This step is crucial as clean data forms the basis for accurate analysis.
  3. Choosing the Right Survey Data Analysis Methods: Depending on the nature of the survey and the research questions, different survey data analysis methods can be employed. These include qualitative methods for open-ended responses and quantitative methods for numerical data. Choosing the right method ensures that the analysis is relevant and insightful.
  4. Performing the Analysis: Once the data is clean and the method is chosen, the next step is to perform the actual analysis. This could involve statistical analysis of survey data, text analysis, or a combination of techniques. The goal is to uncover patterns and trends that address the research questions.
  5. Interpreting the Results: The final step is to interpret the results of the analysis. This involves making sense of the data, drawing conclusions, and identifying actionable insights. Effective interpretation can help in making informed decisions and developing strategies based on the survey data.
  6. Common Challenges: Address issues such as data bias and sample size limitations to ensure reliable results.

What Statistical Analysis Should I Use for Survey Data?

Statistical analysis of survey data is a critical aspect of the overall analysis process. This is because it transforms raw data into meaningful insights. By applying statistical techniques, researchers can identify trends, patterns, and relationships within the survey data that are not immediately apparent.

This process allows for a rigorous and objective interpretation of data, ensuring that conclusions drawn are supported by evidence rather than anecdotally. Statistical analysis helps in validating the reliability and validity of the survey results, thus providing a solid foundation for decision-making.

Furthermore, it enables the quantification of responses, facilitating comparisons across different respondent groups and time periods. Overall, statistical analysis is essential for deriving actionable insights and making informed decisions based on survey data.

The type of statistical analysis used depends on the nature of the data and the research questions. Common statistical methods for analyzing survey data include:

  • Descriptive Statistics: This involves summarizing the data using measures such as mean, median, mode, and standard deviation. Descriptive statistics provide an overview of the data, highlighting central tendencies and dispersion.

People use descriptive statistics to simplify complex quantitative insights across a large data set into bite-sized descriptions. A prime example of this is a student's grade point average (GPA). It takes data points from a wide range of exams, classes, and grades, and averages them together to provide a general understanding of a student's overall academic performance. A student's personal GPA in essence reflects their mean academic performance.

  • Inferential Statistics: These methods allow analysts to make predictions or generalizations about a population based on sample data.

Inferential statistics are often used to compare the differences between treatment groups. Inferential statistics use measurements from the sample of subjects in the experiment to compare the treatment groups and make generalizations about the larger population of subjects.

There are many types of inferential statistics and each is appropriate for a specific research design and sample characteristics. Techniques such as hypothesis testing, confidence intervals, and regression analysis are commonly used.

  • Regression Analysis: This method is used to understand the relationship between dependent and independent variables. It helps in identifying factors that influence certain outcomes.

Suppose you’re a sales manager trying to predict next month’s numbers. You know that dozens, perhaps even hundreds of factors—from the weather to a competitor’s promotion to the rumor of a new and improved model—can impact the numbers. Perhaps people in your organization even have a theory about what will have the biggest effect on sales. “Trust me. The more rain we have, the more we sell.” “Six weeks after the competitor’s promotion, sales jump.”

Regression analysis is a way of mathematically sorting out which of those variables does indeed have an impact. It answers the questions: Which factors matter most? Which can we ignore? How do those factors interact with one another? And, perhaps most important, how certain are we about all these factors?

  • Factor Analysis: This data analysis technique for surveys is used to identify underlying relationships between variables. It reduces the data to a smaller set of factors, making it easier to understand complex relationships.

Factor analysis is valuable for extracting the maximum common variance from all variables, transforming them into a single score for further analysis. As a part of the general linear model (GLM), factor analysis is predicated on certain key assumptions such as linearity, absence of multicollinearity, inclusion of relevant variables, and a true correlation between variables and factors.

Choosing the right statistical analysis for survey data is crucial for deriving meaningful insights. It is essential to consider the nature of the data and the research objectives while selecting the appropriate statistical methods.

What is the Best Tool to Analyze Survey Data?

The selection of the right tool is essential for effective survey data analysis. Various tools and software are available, each offering different and unique features and capabilities. Some popular options include:

  • SPSS: A powerful statistical analysis software, SPSS is favored by researchers for its comprehensive range of analytical techniques and data management features. It is ideal for quantitative research data analysis.
  • R and Python: These programming languages are highly flexible and versatile, allowing for advanced statistical analysis and custom data manipulation through libraries and packages. They are powerful tools for performing data analysis of survey results.
  • NVivo: Specialized in qualitative research data analysis. Qualitative researchers are able to use the tool to describe, evaluate and interpret social phenomena. They analyze data from interviews, surveys, field notes, web pages and journal articles and work in sectors from social science and education to healthcare and business.

NVivo allows them to organize, analyze and visualize their data, finding the patterns it contains.

  • Tableau: Known for its exceptional data visualization capabilities, Tableau is able to help transform complex data into interactive dashboards and reports. This significantly enhances data comprehension. It is a valuable tool for data analysis on survey results.

In a similar vein, there are also survey providers that are capable of helping you collect and analyze data within their platforms. These include:

  • Fairing: A popular post-purchase survey tool, brands use Fairing to gain zero-party data insights into customer preferences and identify their best marketing channels. On top of its survey capabilities, Fairing is armed with a suite of analytics tools that make the process simple and efficient. These tools include: LTV Analytics, Time Series Analytics, Last Click Insights, and more.
  • Typeform: Known for its robust survey design and analysis capabilities, Typeform makes collecting and sharing information comfortable and conversational. It's a web-based platform you can use to create anything from surveys to apps, without needing to write a single line of code. It is a comprehensive tool for survey and data analysis.
  • SurveyMonkey: Widely used for its user-friendly interface, SurveyMonkey offers tools for data collection, analysis, and visualization, making it accessible for both novice and experienced analysts. It simplifies the process of how to do analysis of survey data.

Selecting the best survey data analysis software depends on the specific needs and expertise of the user. It is essential to consider factors such as ease of use, analytical capabilities, and integration with other tools while choosing the right software.

Which Method is Used to Analyze Survey Results?

Different methods are used to analyze survey results, each suitable for different types of data and research objectives. Some common survey data analysis methods include:

  • Cross-Tabulation: This method involves comparing the relationships between different variables. It is useful for identifying patterns and trends in categorical data.
  • Trend Analysis: This technique is used to analyze changes in survey responses over time. It helps in understanding how opinions or behaviors evolve.
  • Correlation Analysis: This method measures the strength and direction of the relationship between two variables. It is useful for identifying associations and dependencies.
  • Text Analysis: For open-ended responses, text analysis is used to identify common themes, sentiments, and patterns. This involves techniques such as coding, content analysis, and sentiment analysis.

Different types of surveys may require different data analysis techniques for surveys. For instance, customer satisfaction surveys may benefit from correlation analysis, while employee engagement surveys might require trend analysis.

Survey Data Analysis Plan

Having a well-defined survey data analysis plan is essential for conducting effective and organized analysis. The plan should outline the objectives, methodologies, and steps involved in the analysis process. Key components of a survey data analysis plan include:

  1. Defining Your Research Objectives: Clearly defining the goals and objectives of the survey analysis is crucial. This includes identifying the key questions the analysis aims to answer.
  2. Identifying Key Metrics and KPIs: Determine the key performance indicators (KPIs) and metrics that will be used to measure success. This helps in focusing the analysis on relevant data.
  3. Selecting Appropriate Analysis Techniques: Choose the right survey data analysis techniques based on the nature of the data and research objectives. This ensures that the analysis is relevant and insightful.
  4. Planning Data Collection and Storage: Ensure that data collection methods are robust and that data is stored securely. This includes planning for data cleaning and preparation.
  5. Outlining Analysis of Complex Survey Data: For complex surveys, outline the steps involved in analyzing multifaceted data. This may include advanced statistical methods and segmentation.
  6. Presenting Data and Findings: Prepare to present your findings clearly and concisely.
  7. Common Mistakes: Avoid pitfalls like data misinterpretation and improper sample sizes.

A well-structured survey data analysis plan helps in conducting thorough and systematic analysis, leading to more accurate and actionable insights.


Survey data analysis is a vital process that empowers organizations to make informed decisions, understand their audience, and improve their offerings. By following a systematic approach, utilizing the right tools, and adhering to best practices, analysts can unlock the full potential of survey data. The insights gained through meticulous analysis not only drive strategic actions but also foster a deeper understanding of market dynamics and consumer preferences. In an increasingly data-driven world, mastering survey data analysis is essential for staying competitive and achieving sustainable growth.

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