Survey Analytics: How to Collect and Interpret Survey Data?

Want to know how you can use survey analytics to leverage customer feedback data?

Survey analytics is the process of interpreting survey responses to get valuable insights. It helps you better understand your customers’ needs and pain points and continually improve your product.

So let’s see how you can collect customer feedback for analysis and understand user sentiment to improve the user experience.

TL;DR

• Survey analytics is the process of interpreting user feedback from surveys to gain actionable insights and identify improvement opportunities.
• There are five types of survey data: quantitative data, qualitative data, categorical data, ordinal data, and scalar data.
• To collect data for survey analytics, you can trigger surveys across the user journey, provide an option to submit feedback in-app, segment users to send in-app surveys and use passive feedback widgets.
• You can interpret survey responses using survey analytics dashboards to visualize data, response tagging to discover feedback trends, and cross-referencing results with behavioral data to better understand user interactions.
• Userpilot is an excellent analytics tool that lets you create in-app surveys, trigger them using advanced configuration, and analyze responses on an individual or segment-specific level with detailed dashboards.

What is survey analytics?

Survey analytics is the process of interpreting customer feedback from surveys to gain actionable insights and identify improvement opportunities.

You can continuously drive customer value once you successfully harness user feedback to build data-driven business strategies. The result is increased customer satisfaction and retention.

Types of survey data

Surveys allow you to collect multiple types of data when you ask specific questions. Here are the four major types of survey data.

Quantitative data

Quantitative data is also called numerical data. Numbers make it easy to analyze customer feedback and identify trends and patterns. You can get objective results and compare them across different time periods or user segments.

You can use Userpilot to collect quantitative data from close-ended questions like the one below. Based on the statistical data, you can easily capture data changes over time, which is, in this case, customer satisfaction with your product feature.

Although the multiple choice questions are given here in text format, a text analytics tool like Userpilot can easily analyze them to give the results in numbers.

Qualitative data

Despite its subjectiveness, using qualitative data is important because they include important details you shouldn’t ignore.

Qualitative data are non-numerical data, such as text, audio, etc. Since the data is unstructured, it’s harder to measure than quantitative data. However, you can still analyze the data to draw key insights that quantitative data miss out on.

You can collect qualitative feedback from open-ended questions. These could be follow-up questions in ranking/score-based surveys like the Net Promoter Score (NPS) surveys.

On the other hand, the entire survey can be on only one qualitative question, like the improvement request survey shown below.

Categorical data

Nominal scale questions provide categorical data, that is, data without any order or numerical value. For example, you can ask users to choose between different scenarios to identify their main use case. Depending on the categorizing purpose, your question can have as many options as required.

Therefore, you get discrete data that can be grouped or categorized into meaningful information, such as company sizes, user roles, most useful product features, etc.

Ordinal data

Also called ranking data, ordinal data are used to group variables into ordered categories. The answer options are ranked on a hierarchical scale, e.g., low to high.

It allows customers to indicate the relative importance of different options. For example, you can use ordinal scale questions to ask people to rate or rank five product features based on their perceived value.

Ordinal data are used to measure variables in a natural order so there are no constant intervals between the responses.

Scalar data

You can collect scalar data from either interval questions scales or ordinal scale questions. Scalar data are also called scale-rating data due to the use of rating options on a scale, e.g., from 1 to 7.

An interval scale survey question can be used to capture customers’ sentiments on a quantitative measurement scale. The scale has a consistent and measurable distance between the values. The rating question below, on a scale from extremely dissatisfied (1) to extremely satisfied (7), has an equal difference of 1 between the intervals.

How to collect data for survey analytics?

Now that you know the different types of data, let’s see how you can collect data for survey analytics.

Trigger surveys across the customer journey

You should trigger in-app microsurveys at different stages of the customer journey to stay updated on customers in different phases of their journey with your business.

You can include surveys in in-app flows or trigger them after specific user interactions/touchpoints. A customer journey map will help you identify these touchpoints, such as starting a free trial or seeking customer support.

There are numerous in-app flows in which you can trigger surveys. For instance, you can build a churn survey in the cancellation flow so that it’s automatically triggered when a user clicks the subscription cancellation button. This allows you to understand why customers churn and suggest alternative options like downgrading or pausing the account.

The image below shows a welcome survey included in the signup flow. Welcome surveys greet users who have just signed up and collect information on their role, company, jobs to be done, etc.

The data from welcome surveys is crucial for segmenting customers based on their jobs to be done. This helps to personalize their onboarding experience from the very beginning.

Provide an option to submit feedback in-app

You can give customers the option to give feedback at their leisure. For instance, you can include a feedback submission button in your in-app resource center.

Resource centers are used to provide self-service support. They include options to give feedback after a user encounters bugs. The reports allow teams to improve existing functionality to create a better customer experience.

Bug fix requests need immediate attention because if left unchecked, the bugs prevent users from completing their job and cause dissatisfaction. The bad user experience can even cause churn.

Moreover, resource centers can also let you send feature requests, such as asking for new features or suggesting improvements in existing features.

Segment customers to send in-app surveys

A survey might not be relevant to all your customers. Thus, you should trigger in-app surveys contextually to gather actionable insights.

Segmenting customers helps you collect segment-specific survey data. This lets you capture data on a more granular level.

For example, some new users may disengage and not reach the activation point. Thus, you can create a segment with disengaged users who signed up exactly 30 days ago but have not completed the onboarding checklist yet.

Then you can send an onboarding experience survey to only that segment. This will tell you why users are not getting value and what improvements they want to see in your product or onboarding flow.

Use a passive feedback widget

Passive feedback widgets allow customers to share their opinions without interrupting their user experience.

You can send targeted surveys to find specific issues you want to solve. However, you might miss out on other matters customers feel strongly about. This is where a passive feedback widget comes in.

For example, Miro keeps an ‘always-on’ widget for the feature ‘Feed’ that users can click on to prompt microsurveys and give feedback on the feature. You can also use a widget to collect spontaneous customer feedback on the product.

How to interpret survey responses?

Once you’re done collecting data, here’s how to interpret the responses received.

Use a survey analytics dashboard to visualize data

An analytics dashboard is used for reporting purposes as a visual representation of data. It gives you an overview of the survey results.

Thus, you can use a survey analytics dashboard to view details like survey results, dismiss rate, completion rate, etc. You can also view charts like the line graph below to observe data patterns.

The image below shows an NPS dashboard that displays the NPS survey data, NPS average score, NPS score charts, etc.

If you observe a reduction in NPS scores among a particular user segment, you can develop targeted re-engagement strategies to improve the user experience.

Tag survey responses to identify trends

Tagging survey responses are very effective for extracting insights from qualitative data. Survey response tags enable you to categorize the collected data and identify customer pain points.

The image below shows the insights from NPS response tags. You can add a qualitative follow-up question in NPS surveys to understand the exact reason behind each customer’s given score.

Tagging these responses lets you group feedback into actionable categories such as usability, customer support, performance, pricing, and more. You can understand feedback trends by looking at the NPS scores for each category.

When a category has a higher percentage of detractors than promoters, it indicates the presence of serious issues that you need to solve immediately to prevent customer churn.

Cross-reference survey results with user behavior data

You should cross-reference results from user behavioral data to get holistic insights into how users interact with and feel about your product or service. You can do this either on an individual level or a segment-based level.

If you’ve gotten negative feedback on, let’s say, product intuitiveness, you can cross-check this result with user navigation path analysis and perform click tracking. You may find a large number of rage/dead clicks that highlight the areas that cause frustration and need improvement.

Moreover, you can cross-reference survey insights with heatmaps to identify where customers face usability issues or other challenges.

You can even correlate dissatisfied feedback with various user frictions to discover specific stages of the journey or actions that lead to dissatisfaction or churn.

How to analyze survey results using Userpilot?

Userpilot is a product growth platform with extensive features allowing you to create, send, and analyze in-app surveys.

You can create surveys from scratch or the platform’s built-in templates without coding.

You can send surveys with advanced configurations such as localization, customization, audience segments, triggering conditions, etc.

Therefore, you can customize surveys to make them resonate with your brand. You can also trigger surveys for specific audience segments and make them appear contextually at the right time.

Userpilot also lets you translate survey questions to make them more inclusive and accessible. This can help increase customer satisfaction.

Userpilot enables you to analyze survey responses both on segment-specific and individual levels. This lets you understand all your customers in-depth and hyper-personalize customer experiences based on their feedback to improve user retention.

You can also visualize data in detailed dashboards to identify trends and patterns in feedback. It helps you make necessary improvements to your product and boost user satisfaction.

Conclusion

Survey Analytics offers rich insights into customer sentiment about your product and helps you develop innovative ways to increase user satisfaction. Userpilot allows you to create and send in-app surveys and analyze responses thoroughly to get product growth insights.

Want to get started with survey analytics? Get a Userpilot demo and see excellent results for your SaaS business.