Decoding Customer Satisfaction Analytics: Your 6-Step Guide

Customer satisfaction analytics are key to understanding what makes your customers happy and building a product that retains users.

But how can you leverage this user sentiment data to lead product development and grow your business?

In this guide, we’ll go over how you can use product analytics to conduct a customer satisfaction analysis in 6 steps, as well as provide tools and tips to do so.

TL;DR

• Customer satisfaction analytics refers to the process of collecting, analyzing, and interpreting data to evaluate how satisfied customers are with a product or service.
• Data analytics helps improve customer satisfaction by:

1. Personalizing experiences, as it provides customer data you can use to design a product experience that’s relevant to each user segment.
2. Predicting customer behavior, as it allows you to act proactively on improving the customer experience and prevent churn.
3. Increasing customer retention, since you can use customer feedback to listen to their pressing issues, solve them, and satisfy their needs.
4. Spotting opportunities for improvement by analyzing the customer journey and seeing if there are stages with particularly high friction and drop-offs.

• There’s no single KPI to measure customer satisfaction. However, the most relevant include:

1. Customer satisfaction score (CSAT): the percentage of users who report being satisfied with a product, service, or specific interaction.
2. Net Promoter Score (NPS): the number of promoters (users who are very likely to recommend your product) minus the number of detractors (users who wouldn’t recommend your product).
3. Customer effort score (CES): the percentage of users who find your product features easy to use.
4. Customer lifetime value (LTV): the total revenue your business receives from a customer during its lifetime.

• Running a successful customer satisfaction analysis involves:

1. Defining your goals and what type of data to measure.
2. Picking the right type of survey that aligns with the type of data you need to collect.
3. Asking good survey questions that are specific, clear, and diverse enough to get high-quality data.
4. Setting up when and where you’re going to trigger your surveys.
5. Analyzing the data to find insights and then coming up with data-driven strategies to improve customer satisfaction.
6. Repeating the process continuously to keep track of your satisfaction levels, see what works or stops working, and keep iterating unlimitedly.

• Tools suitable for customer satisfaction analysis in different contexts include:

1. Userpilot for in-app surveys, as it allows you to create, trigger, and analyze customer satisfaction surveys based on any type of condition.
2. Google Analytics for web analytics and understanding the demographic data of your site visitors.
3. Drip for email surveys, as it can automate data collection, build relationships with your email list, and help you understand your audience.

• Since you’ll need a reliable tool to perform this analysis. Why not book a Userpilot demo to see how you can improve customer satisfaction?

What is customer satisfaction analytics?

Customer satisfaction analytics refers to the process of collecting, analyzing, and interpreting data to evaluate how satisfied customers are with a product or service. This analysis can reveal valuable insights into customer preferences, needs, and pain points—enabling you to make informed decisions to enhance customer experience and increase customer loyalty.

It’s typically measured with KPIs such as customer satisfaction scores (CSAT), Net Promoter Scores (NPS), and customer effort scores (CES).

How does data analytics improve customer satisfaction?

It’s not enough to send surveys randomly and glance at the results to improve customer satisfaction.

You need to follow a methodical approach where you can ask targeted questions, collect statistically significant data from specific user segments, and filter the data in a way that can provide insights and help you make informed product decisions.

With this approach, there certainly are many ways in which data analytics can improve customer satisfaction.

1. Personalize experiences

Customer analytics is necessary to personalize the product experience.

This is because collecting customer satisfaction data helps you understand the needs, JTBDs, and goals of different customer segments. So, you can then design an onboarding process that fits their requirements and exceed their expectations.

That said, the more data you collect, the more targeted and effective the customer experience will be, resulting in improved customer satisfaction.

2. Predict customer behavior

You can significantly improve customer satisfaction with predictive customer analytics.

These analytics involve historical data, statistical algorithms, and predictive models that allow you to anticipate customer behavior. This way, you can provide proactive customer service to prevent problems, prioritize what’s working for you, and make data-driven decisions to improve customer satisfaction and loyalty.

3. Increase customer retention

Customer satisfaction analytics can be leveraged to sustain customer retention in two ways.

First, with behavioral data and comprehensive survey results. These customer experience analytics allow you to identify churn signals so you can act fast and prevent users from leaving (think of offering a discount or reaching out to see if there’s something wrong).

Second, by segmenting your user base and learning about individual customer profiles, you can deliver highly relevant experiences that keep them around for longer. This way, you will foster long-term relationships with customers who are more than satisfied with your product.

4. Spot opportunities for improvement

Proper product analytics can help you identify, understand, and fix any issues that your users might be encountering throughout the customer journey.

With tools like funnel analysis, for example, you can pinpoint exactly what stages of the customer journey are generating more friction and drop-offs. Then, combined with customer satisfaction feedback, you can expose potential weak points in your product you would’ve never noticed and improve those.

What is the KPI for customer satisfaction?

Although customer satisfaction is a simple concept, there isn’t just one KPI that will provide the full picture of user sentiments. Some important KPIs to measure customer satisfaction include:

• Customer satisfaction score (CSAT)
• Net Promoter Score (NPS)
• Customer effort score (CES)
• Lifetime value (LTV)

Let’s explore each of them.

Customer satisfaction score (CSAT)

Customer Satisfaction Score (CSAT) is a popular key performance indicator that measures the level of customer satisfaction with a product, service, or specific customer interactions.

It works by asking customers to rate their satisfaction on a scale, usually from 1 (very dissatisfied) to 5 (very satisfied). And in SaaS, this metric can offer insights into the customer experience, from the usability of your app to customer support performance.

Net Promoter Score (NPS)

The Net Promoter Score (NPS) measures customer loyalty by asking customers how likely they are to recommend your product to others on a scale of 0-10.

Processing this data is simple: customers are classified into Promoters (9-10), Passives (7-8), and Detractors (0-6), and your NPS is the percentage of Promoters minus the percentage of Detractors.

In SaaS, NPS provides a quantifiable measure of customer satisfaction and loyalty, while highlighting areas for improvement with follow-up questions. Plus, there are user feedback tools with multiple NPS survey templates (like Userpilot) that make it easy to actively collect and analyze this data on autopilot.

Customer effort score (CES)

Customer Effort Score (CES) measures the effort a customer has to invest to get their issue resolved, use a product, or obtain a service. It simply asks customers how much effort they had to put in on their side to perform a task with your app (like in the screenshot below), and the score is the percentage of users who provided a positive answer.

In SaaS, simplicity and ease of use correlate with higher customer satisfaction. Thus, CES can point out areas in your app that require attention so you can improve the customer experience.

Customer lifetime value (LTV)

Customer Lifetime Value (LTV) is a prediction of the total net profit attributed to the entire future relationship with a customer. It’s calculated by multiplying the average purchase value, average purchase frequency, and average customer lifespan.

Essentially, a higher LTV indicates more profit over the long term. That’s why this metric is essential to predict revenue and allocate the right budget to marketing campaigns to generate the most ROI.

How to run a successful customer satisfaction analysis

Now, let’s go over our six-step process to run a customer satisfaction analysis where you can learn, act, and iterate your way to higher product growth.

1. Define what to measure and collect

Your first step is defining your strategic North Star.

What are you aiming to achieve with customer satisfaction data? This could range from enhancing product quality to increasing customer retention or even identifying opportunities for improvement.

Once established, determine what type of customer data can feed these goals. For instance, behavioral data might reveal key consumer habits, psychographic insights might shed light on customer motivations, and demographic information could uncover crucial patterns.

This way, you can follow a better direction and get the most value from your analysis.

2. Roll out targeted surveys

Surveys are going to be your main channel for collecting customer satisfaction feedback.

That said, selecting the right type of survey and channel depends on your customer profile and specific objectives. For instance, you might want to use a CSAT survey to measure short-term satisfaction, an NPS survey to track long-term loyalty, or decide to send in-app or web-based surveys that could provide quick, real-time feedback.

For example, here’s a customer satisfaction survey sent by Amazon to assess the performance of their support reps. It includes two questions plus the option to add a note:

3. Ask the right questions

Asking good survey questions can make the difference between a successful or a lackluster analysis.

For this, avoid being generic. Instead of asking, “How satisfied are you with our product?” focus on being concise and stick to specifics such as their individual experiences, their pain points, and what improvements they’d like to see.

Then, use a mix of open-ended questions for qualitative insights and multiple-choice questions for quantitative data. For example, if a user responds to your CES survey with a negative score, trigger a follow-up question to ask them what made the experience or task difficult for them.

4. Plan your trigger

Good timing is everything if you want users to respond to your surveys.

For this, it’s ideal to initiate surveys when the customer’s interaction experience is fresh, so identify the moment or event in the customer journey where they are most likely to provide substantial responses. For instance, see below how HubSpot sends a check-in survey in the middle of their onboarding process.

This won’t only lead to higher response rates but also more honest and accurate answers (which equals high-quality customer data).

5. Analyze and act

Once you start to collect customer data, you can use product analytics tools to track survey responses and identify areas where product satisfaction is high or low and importantly, where improvement is needed.

For instance, if CSAT feedback indicates issues with your onboarding process, you can refine your onboarding sequence. It could involve introducing clearer tutorials or perhaps offering customer outreach via a live chat function during the first usage of your platform.

6. Rinse and repeat

Lastly, make sure to measure your customer satisfaction KPIs and goals consistently to keep track of your progress. If you see that there’s a tactic that’s working well, stick with it until it doesn’t.

And if something doesn’t work, then iterate and repeat the process.

Customer expectations and perceptions change fast, so always compare the results against your initial goals to evaluate the performance—and never stop improving.

Customer satisfaction analysis tools

No matter the channel you wish to send surveys on, you’ll need specific platforms to create them, send them, and retrieve the customer data.

So, let’s go over the best customer satisfaction analytics tools for each channel.

In-app: Userpilot

Userpilot is a customer success platform with the ability to create, design, and trigger in-app surveys for user research, either from scratch or by using any of the multiple templates available.

Userpilot brings more than the ability to trigger NPS surveys inside your app. You can also create onboarding flows, collect user behavior data, and give access to advanced product analytics—everything to nurture product growth.

Here’s how Userpilot can help you with your customer satisfaction analysis needs:

• Get a deep user experience understanding with analytics charts, such as funnels (to spot friction), trends (to understand what brings value across different plans), and paths (to draw the product journey of your users).
• Use feature-tagging and event-tracking to analyze user behavior and identify friction points.
• Analyze feature usage with feature heatmaps to understand product adoption and which areas of your product bring value to your users.
• Collect feedback for your research using a great variety of in-app surveys such as CSAT, CES, and NPS surveys.
• Use advanced survey analytics to tag and filter user responses based on recurrent themes and keywords and save time analyzing responses.

Web: Google Analytics

Google Analytics is a mainstream platform that allows you to track, analyze, and understand the behaviors of visitors on their website. It’s a useful tool for measuring customer satisfaction from your website and gathering insights into your users’ experiences, preferences, and engagement with your product. Here are some of its features:

• Behavior Flow Report: It enables you to track the path users take through your site and where they drop off, revealing points of friction.
• Audience Demographics Report: This can help you understand your users’ demographics.
• User Explorer: It provides aggregated demographic and interest data about individual users, informing you about user interaction patterns.
• Goal Flow: This allows you to visualize the steps your users are taking to complete a goal on your website, providing clarity on the effectiveness of your conversion funnel.

Email: Drip

Even as an email marketing platform, Drip provides robust features to set up email campaigns for gathering and analyzing customer satisfaction data. Here are some core features:

• Automated Email Campaigns: Drip allows easy automation of email campaigns based on customer behavior and preferences, allowing you to collect survey responses on your sleep.
• Advanced Segmentation: This feature helps systematically divide your customer base into different segments for targeted surveys, thus providing more high-quality customer data.
• Comprehensive Analytics: Drip offers detailed analytics and insights about customer behavior, their journey, and email performance, helping you design strategies to improve satisfaction.

Conclusion

In SaaS, customer satisfaction analytics can make the difference between achieving growth goals or not.

By analyzing user feedback, determining suitable KPIs, conducting meaningful surveys, and utilizing effective tools, you’ll be able to personalize experiences, predict customer behavior, increase retention, and uncover areas for friction.

Since you’ll need a reliable tool to perform this analysis, book a Userpilot demo today to see how you can improve customer satisfaction!