The 5 Step Guide to Actionable Online Survey Analytics in 2023

Online surveys have become an indispensable tool for gathering critical insights from customers, employees, students, or any group an organization wants feedback from. But without proper analysis, the data collected can seem overwhelming and fail to provide true value.

This comprehensive guide will walk you through the essential, step-by-step process to transform your survey data into impactful, data-driven business insights.

Step 1: Categorize Your Data Types and Levels of Measurement

The first crucial step is to categorize the types of data you collected through your survey. Each data type requires different analytical techniques to derive meaning.

Quantitative Data

This numerical data provides measurable objective facts and stats.

Levels of quantitative data:

  • Nominal: Simple categories without inherent order (gender, department, etc). Best analyzed using frequency distributions.
  • Ordinal: Categories with a clear order (rating scales, rankings). Best analyzed using median and mode.
  • Interval: Numerical data with equal intervals between points (temperatures, dates). Best analyzed using mean and standard deviations.
  • Ratio: Interval data with a true zero point (income, weight). Best analyzed using mean, standard deviation, and statistical modeling.

According to survey analytics expert Nancy Drayton, "Understanding the level of measurement for your survey data is crucial for identifying the appropriate analytical techniques to apply."

Qualitative Data

This subjective data comes from open-ended survey questions. It provides valuable insights into experiences, opinions, and motivations that quantitative data cannot capture. Qualitative data is usually analyzed by categorizing responses into common themes and topics.

Step 2: Start With Quantitative Question Analysis

For most surveys, it is advisable to begin your analysis with the closed-ended quantitative questions with structured response options.

According to a survey report from QuestionPro, 60% of questions in the average survey are quantitative. Quantitative data also has established analysis techniques making it far easier to analyze than qualitative responses.

You can calculate important metrics like means, frequencies, cross-tabulations, and statistical testing on varying levels of quantitative data using standard analytics functions in survey software platforms. Online survey tools have baked-in support to generate these statistics quickly without any manual number-crunching needed.

Key quantitative analysis to perform:

  • Frequencies – calculate percentages for selection of each answer choice
  • Means – find the central tendency of numeric data
  • Cross-tabulations – understand how responses vary between respondent segments
  • Correlations – identify relationships between variables
  • Statistical testing – discover significant differences and outliers

Starting with the quantitative survey questions provides some initial insights to guide your further analysis.

Survey data analysis

Step 3: Develop a Coding Framework for Qualitative Data

The abundant insights provided by qualitative survey responses can get lost without a proper framework to categorize and structure the data.

Follow this process to develop an effective coding framework:

  1. Read through a representative sample of the qualitative responses to identify common topics, feelings, and themes.
  2. Organize these topics into a hierarchical coding scheme with main categories and sub-categories.
  3. Code each open-ended response by assigning it the relevant categories from your coding scheme. Responses can have multiple codes.
  4. Quantify the coded data by calculating the frequency of each code.

For example, an e-commerce company surveyed customers for feedback on its website. Respondents consistently mentioned themes around ease of use, product selection, and delivery. The team developed a coding framework as follows:

Qualitative coding framework

This framework allowed them to quantify the qualitative data – for instance, 50% of responses mentioned delivery concerns, 30% complained about ease of use, etc. This coded data revealed key focus areas.

Hiring crowdsourcing teams is an efficient way to code qualitative data. Human intelligence still significantly outperforms AI in capturing subtle nuances within open-ended text.

Step 4: Apply AI to Scale Qualitative Analysis

Manually coding and analyzing open-ended survey responses is incredibly time-intensive. For surveys with hundreds or thousands of respondents, it is essential to use technology to scale the qualitative analysis process.

AI tools like natural language processing (NLP) can help categorize open-ended survey data to match a coding scheme like the one above. The machine learning algorithms can classify unstructured text much faster than humans.

According to an MIT study, NLP techniques can code qualitative data up to 85% faster than manual work. This dramatically increases speed and reduces costs.

Within NLP, there are a variety of techniques that can provide additional insights:

  • Sentiment analysis – detect emotion and feelings within text
  • Topic modeling – discover themes and relationships between words
  • Content analysis – make inferences about attitudes, perceptions and author traits

Leading tech giants like IBM and Microsoft provide enterprise-grade NLP services. There are also startups like MonkeyLearn focused specifically on NLP for market research and survey analysis.

Step 5: Visualize Key Data Insights

"Data has power only when it‘s visualized." – Dave McCandless

While numbers provide the hard metrics, visuals help uncover key takeaways and allow wider audience comprehension. Modern data visualization software makes it simple for anyone to create engaging, informative charts and graphs from survey data.

Follow best practices for visualizing different data types:

  • Bar charts for comparing nominal data like age, gender, etc.
  • Column or bar charts to display Likert scale survey results
  • Scatter plots and heat maps to visualize large quantitative datasets
  • Word clouds to highlight common words and themes from open-ended questions
  • Pie charts to show proportional responses for metric questions
    -plotting relationships between two variables

data visualization

Interactive data visualizations allow users to slice-and-dice data on the fly to uncover key insights. Look for survey software with strong built-in visualization capabilities to simplify the analysis process.

Bonus: Leverage The Crowd For Added Perspectives

Getting additional perspectives on your survey data can lead to more comprehensive insights. Crowdsourcing provides on-demand access to a diverse global workforce that can assist with:

  • Data processing like cleaning and structuring
  • Coding and categorizing open-ended responses
  • Sentiment analysis of feedback
  • Statistical analysis and hypothesis testing
  • Identifying trends, patterns and outliers

This scalable human intelligence amplified by technology can help uncover subtle insights that may be missed by solely automated analysis. It provides a cost-efficient way to enhance survey analysis versus hiring additional in-house staff.

Leading crowdsourcing platforms like Appen and Amazon Mechanical Turk offer enterprises survey analysis solutions handling millions of data points.

Turning Survey Data into Insights – A Case Study

Let‘s walk through how a consumer goods company put these steps into action to analyze their latest customer satisfaction survey.

First, they categorized their 20 survey questions into quantitative and qualitative types based on the response format.

They began analysis with the closed-ended product rating and demographic questions to get frequency counts and see how ratings varied by customer segment. This revealed that new customers gave 30% lower satisfaction scores.

For open-ended feedback on delivery times, they developed a sentiment coding framework categorizing responses as positive, negative or neutral. By bucketing the sentiment of responses, they identified that customers in certain ZIP codes were much more unhappy with delivery times.

The company used the MonkeyLearn NLP platform to automatically categorize the remaining open-ended questions using the sentiment coding framework. This amplified their analysis capabilities without needing to manually code thousands of responses.

Finally, they visualized key data insights around ratings, sentiment, and demographics in an interactive Tableau dashboard. The dashboards made it easy to filter data by different dimensions to uncover trends.

By rigorously following the 5 steps for survey analysis, the company extracted valuable insights to improve customer satisfaction including:

  • New customer onboarding needed improvement
  • Delivery times in certain regions was unacceptable
  • Feature requests to guide product roadmap priorities

The survey analytics led to a focused action plan that increased repeat purchase rate by 15% in 6 months.

Go From Data to Decisions

Proper survey analysis takes work – but pays untold dividends through data-driven decision making when done right. Follow the techniques outlined in this guide to extract genuine value from your survey data investment.

With sound analytics, your survey data morphs from overwhelming numbers into a strategic asset providing the trusted insights to guide your business forward. Just be sure to enlist the power of technology via AI and crowdsourcing to enhance and scale the demanding analysis process.

The possibilities for innovation and improvement uncovered in your survey data are endless. But you only unlock these precious insights by analyzing your data the right way. With the exponential growth of online surveys, mastering survey analytics is an essential skill for businesses today.

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