The popularity of online surveys has skyrocketed in recent years. Low cost, incredible ease of distribution, and access to broad audiences make digital surveys an attractive data collection tool.
But simply gathering survey responses is not enough. To derive true value, organizations need to rigorously analyze the data. Without proper analysis, raw survey data does not tell much and is hard to interpret – and can even be misleading.
As a data analytics leader with over a decade of experience in statistical analysis and data mining, I cannot emphasize enough the importance of in-depth survey analysis.
In this comprehensive guide, I‘ll share the 5 essential steps that I always follow to extract impactful insights from online survey data through rigorous analytical techniques.
Overview of the Online Survey Analysis Process
Here is a quick overview of the key phases involved in analyzing survey data:
Now let‘s explore each of these steps in detail:
Step 1: Understand the Data Types and Levels of Measurement
The first step is to thoroughly review the survey questions and understand the nature of data that has been collected.
Qualitative vs. Quantitative Data
Broadly, survey data can be categorized into two main types:
-
Qualitative data: Open-ended survey questions gathering descriptive data in the respondent‘s own words e.g. "What do you like most about our product?"
-
Quantitative data: Closed-ended questions with response options that can be measured numerically e.g. rating scales, multiple choice questions etc.
This table summarizes the key differences:
| Qualitative Data | Quantitative Data |
|---|---|
| Descriptive, textual | Numerical, quantifiable |
| Unstructured | Structured |
| Subjective | Objective |
| Nuanced insights | Statistical analysis |
Levels of Quantitative Data
For closed-ended quantitative survey questions, it is crucial to identify the level of measurement as it determines the type of applicable statistical analysis:
- Nominal data: Simple categorical labels without an intrinsic order e.g. gender, region, department.
- Ordinal data: Categories with a clear order e.g. rating scales.
- Interval data: Numerical values with equal intervals between points e.g. temperature in Celsius.
- Ratio data: Numerical with a true zero point e.g. age, income.
This table summarizes the characteristics and examples of each measurement level:
Correctly identifying the data types and measurement levels allows for the appropriate analytical techniques to be applied in the next steps.
Step 2: Start with Basic Descriptive Analysis
For most surveys, I recommend starting the analysis by taking a broad descriptive look at the quantitative survey data using spreadsheet tools like Excel or dedicated survey software platforms.
Important metrics to examine through basic descriptive statistical analysis include:
- Response frequency: What were the most common responses for each closed-ended question?
- Response distribution: How were the total responses distributed across the options for each question?
- Central tendency: What was the average, median or mode for questions gathering numerical data?
- Variability: What was the range or variance in the responses?
For example, this frequency table shows the overall response distribution to a survey question "On a scale of 1-5, how satisfied are you with our product?"
Simple data visualizations like bar charts, pie charts and histograms can be created to help visually analyze the broad response patterns.
This initial descriptive overview provides a high-level sense of the quantitative survey results before diving into more complex analysis.
Step 3: Conduct In-Depth Quantitative Analysis
After getting an overall picture, more rigorous quantitative analysis techniques can help derive deeper insights and trends from survey data:
Statistical Testing
Statistical hypothesis testing determines if patterns in survey data are statistically significant or simply occurred by chance:
- t-tests: Compare means between two groups like customers vs. non-customers.
- ANOVA: Compare means across multiple groups.
- Correlation analysis: Measure strength and direction of relationship between variables.
- Regression analysis: Model and quantify relationships between variables.
- Factor analysis: Reduce data into key underlying factors.
For example, this ANOVA output tests differences in customer satisfaction across age groups:
Segmentation and Group Comparison
- Analyze response differences across segments like demographics, behaviors, needs etc.
- Identify patterns between groups like high vs. low satisfaction customers.
For instance, in the sample below, satisfaction levels are compared across demographic cohorts:
Statistical software like SPSS provide powerful capabilities for thorough quantitative analysis. Many online survey tools also have built-in analytic options.
Step 4: Establish a Framework for Qualitative Analysis
While quantitative data analysis is relatively straightforward, analyzing open-ended survey responses requires a systematic framework to convert qualitative data into meaningful insights. Here are 5 key steps I follow:
1. Data Preparation
Prepare unstructured qualitative data for analysis by:
- Correcting any spelling errors
- Removing irrelevant content
- Structuring and formatting responses into a consistent, analysis-ready format.
2. Develop a Coding Scheme
Design a coding scheme to categorize open-ended survey responses based on key themes, topics, sentiments, attributes etc. This creates a classification system for rigorous qualitative analysis.
For example, this is a simple coding scheme for the question "What food/beverages would you like us to add to our cafe menu?"
3. Manual Coding
Methodically go through the qualitative data to code each open-ended response using the developed coding scheme. Dedicated QDA tools like NVivo can support the manual coding process.
4. Analyze Coded Data
With responses coded into clear systematic categories, the frequency and distribution of key themes, topics, and sentiments can be analyzed to uncover insights.
For example, this bar chart shows frequency analysis of coded cafe menu suggestions:
Group coded data by attributes like customer segments for deeper analysis.
5. Context Analysis
Beyond just coding frequency, contextual analysis of themes, relationships, behaviors, emotions etc. provides a nuanced understanding of the qualitative data.
For instance, while coding suggests coffee is the most preferred new menu item, contextual analysis reveals customers also want more premium specialty coffee options.
Step 5: Optimize Analysis Using AI
Sophisticated AI technologies can optimize both quantitative and qualitative survey analysis:
- NLP for data preparation: Auto text cleaning, classification and formatting.
- Unsupervised ML models: Automatically detect themes/topics in open-ended data.
- Sentiment analysis: AI classifies text sentiment as positive, negative or neutral.
- Auto coding: NLP replicates human coding rapidly without manual work.
- Predictive analytics: Machine learning models identify insights from survey data.
This tableau shows some key AI techniques that can enhance survey analysis:
AI empowers deeper and faster analysis, especially for surveys with huge volumes of unstructured text data.
Step 6: Strategic Data Visualization
Impactful data visualization is pivotal for communicating key survey insights internally across the organization.
Tailor Visualizations to Audience and Medium
- Interactive dashboards to allow self-service exploration.
- Executive summaries concisely conveying key takeaways.
- Presentations, emails, reports etc.
Maintain Focus and Clarity
- Avoid cluttered, overly complex visuals
- Showcase critical insights and metrics
- Enable understanding at a glance
For instance, this is an executive dashboard highlighting key customer satisfaction metrics:
Key Takeaways
The true value of online surveys comes from rigorous analysis of the collected data. By methodically following the techniques outlined in this guide, organizations can unlock deep insights into customer preferences, perceptions, behaviors, needs and market trends.
While proper analysis requires meticulous work, the payoff is game-changing insights that drive strategic decisions and planning. Combining statistical testing, segmentation, AI-enablement and strategic visualization provides a robust framework to maximize survey ROI.
As online surveys continue seeing exponential growth in adoption, organizations must invest in analytical capabilities to get the most from their survey data. With the right expertise and approach, they can translate raw survey inputs into data-driven intelligence that fuels growth and success.
