Regularly analyzing the user experience (UX) throughout the entire journey is the only way to bridge this gap and create a product that fulfills user needs and desires. Digital experience analytics allow you to quantify user happiness and turn those UX insights into tangible business outcomes.
Read on to learn how to set up customer experience analytics that gauge user happiness and give you the necessary tools to optimize your product for maximum customer satisfaction.
Here’s everything we’ll cover in this guide:
- What is digital experience analytics?
- The correlation between user happiness and business impact
- User happiness metrics
- How to set up digital experience analytics to understand user happiness
- Using data analytics to improve customer experience
What is digital experience analytics?
Digital experience analytics is the practice of monitoring and interpreting customer behavior to drive informed decisions regarding the further direction of product development and customer experience strategies.
To paint a complete picture of the UX, product teams collect and analyze both quantitative and qualitative data on user behavior within a website or app. You can turn to any means of data collection to obtain the necessary information — from running customer surveys to monitoring user activity with a product analytics solution.
This in-depth analysis helps to identify user pain points, friction points in the product experience, and opportunities for enhancing user satisfaction and engagement.
It could be a bad website experience, email, or customer service interaction. Whatever it is, it’s our obligation to listen, respond, and optimize.
[…] Not just for leads. Not just for sales. But for the relationship.
If we focus on defining and seeking such a relationship, we will have more than a portfolio of satisfied customers.”
The correlation between user happiness and business impact
In the realm of digital experience analytics, user happiness isn’t just another aspect of product performance. It has a profound impact on user engagement and, therefore, the overall success of your product.
Engaged users are more likely to:
- Convert and upsell: satisfied users willingly convert from free users to paying customers. They are also receptive to upsell offers because they trust the value your product provides
- Stay longer: users who are happy and find value in your product are more inclined to stick to it (and spend more with your business)
- Advocate and refer: happy users are your best brand advocates. They share their positive experiences with others, contributing to word-of-mouth marketing and driving organic growth
The longer your users stay with you, the higher your ROI. Just a 5% increase in customer retention can lead to a 25% growth in profit.
So how do you create happy customers?
The first step toward happier users is assessing the current levels of satisfaction with your product to create a baseline for improvement. This involves building a digital experience analytics system to capture valuable insights into how users perceive your product and what they like and dislike about it. By doing so, you can craft a strategic approach to enhance user happiness and drive tangible business outcomes.
Let’s begin with the user happiness metrics you need to monitor.
User happiness metrics
These quantitative metrics serve as the initial touchstone, helping you gauge user satisfaction and setting the stage for comprehensive root cause analysis and subsequent UX improvements.
You’ll also be using them to track the results of your product optimization efforts. By monitoring changes in quantitative data over time, you’ll be able to assess your progress and gauge the impact of your initiatives.
Customer Satisfaction Score (CSAT)
CSAT measures the satisfaction level of customers based on a specific interaction or experience.
To calculate CSAT, you need to run a CSAT survey asking users to rate their satisfaction with your product — it’s best to focus on a particular aspect of the product experience, such as the onboarding process or a recent feature update. When you have the answers, you’ll divide the number of satisfied customers by the number of total respondents.
Net Promoter Score (NPS)
NPS gauges customer loyalty and the likelihood of your users recommending your product to others.
To measure it, ask your users a simple question: “On a scale from 0 to 10, how likely are you to recommend our product to a friend or colleague?”
NPS categorizes respondents into Promoters (9-10), Passives (7-8), and Detractors (0-6), providing a snapshot of overall customer sentiment.
Customer Effort Score (CES)
CES measures the ease with which customers can complete specific tasks or interactions within your product. The goal is to assess the level of effort customers feel they need to make to achieve their goals.
To gather CES data, ask a question like: “How easy was it for you to [complete a specific action]?” Respondents typically provide answers on a scale such as “Very easy,” “Somewhat easy,” “Neutral,” “Somewhat difficult,” and “Very difficult.”
CES is particularly important for ensuring long-term user happiness. Products that consistently require low effort will keep users engaged and loyal over time. High-effort interactions, on the other hand, can lead to user frustration, decreased engagement, and, eventually, churn.
User Retention Rate
User retention rate is the percentage of customers who continue to use your product over a specific period. This metric reflects how well your product meets ongoing user needs.
To calculate the user retention rate, use the formula:
User Retention Rate = ((Number of Users at the End of a Period – New Users Acquired During the Period) / Number of Users at the Start of the Period) * 100
Customer Churn Rate
The churn rate represents the percentage of customers who stop using your product over a specific period. It’s the inverse of the user retention rate and provides insights into how many users are leaving your product.
Here’s how you calculate the churn rate:
Churn Rate = (Number of Customers Lost During a Period / Number of Customers at the Start of the Period) * 100
How to set up digital experience analytics to understand user happiness
Pursuing user happiness goes beyond measuring satisfaction metrics. You need to establish a consistent digital experience analytics system that not only provides quantitative UX data but also pinpoints the user emotions tied to those metrics and uncovers the “why” behind the numbers.
Choose the right analytics methods
You need to select analytics methods that align with your objective. And since your objective is to enhance UX by quantifying and interpreting user happiness, your chosen methods should focus specifically on monitoring user frustrations and sentiments.
Get ready to use a mix of quantitative and qualitative data:
Quantitative data collection
- User surveys: design surveys that capture specific aspects of the user experience, such as CSAT, NPS, and CES. These surveys can be distributed through various digital channels, including email, in-app prompts, or after specific interactions
- User behavior tracking: use analytics tools to collect quantitative use behavior data like page views, clicks, navigation paths, and feature usage
Qualitative data collection
To understand what user experiences affect those happiness metrics you’ve just collected, incorporate qualitative data collection methods:
- Feedback forms: encourage users to share their suggestions and challenges directly within your app. Place feedback forms at various touchpoints to gather customer insights at relevant moments
- User interviews: conduct one-on-one interviews with selected users to delve deeper into their experiences. These interviews allow you to explore user perspectives and pain points in greater detail
- Session recordings and heatmaps: Visualize real user experiences through session recordings and heatmaps. These tools offer visual insights into how users navigate, interact, and engage with different elements of your product
Only by combining both quantitative and qualitative methods will you gain a holistic understanding of user happiness.
Map out user journeys
To help you fully understand your customers’ digital experience, your analytics tools should capture data at every touchpoint users encounter during their journey.
Start by creating visual maps that outline the steps users take along these journeys. Document all the touchpoints, interactions, and decision points users face.
When you’ve outlined user journey maps, define milestones that will indicate users have achieved success in a particular stage. For instance, in the early stages of the customer journey, a milestone could be completing the onboarding flow and reaching the “aha!” moment.
Configure your analytics tools to gather user happiness metrics data the moment a user completes a milestone. This could involve displaying a survey or feedback prompt immediately after the milestone is achieved.
As users progress through their journey and reach different milestones, you’ll see how user happiness metrics vary. Look for trends, patterns, and fluctuations in satisfaction scores. Do certain milestones consistently correlate with higher or lower satisfaction scores? These are the areas you should focus on for now.
Watch users interact with your app to spot points of frustration
You can’t collect real-time feedback on each and every interaction between your users and your app. Well, technically, you can, but it will create unnecessary friction.
Session recordings capture real-time user interactions, allowing you to replay sessions and see exactly how users navigate, click, scroll, and interact. When watching session recordings, look for:
- Repetitive actions: users attempting the same action multiple times could indicate difficulty understanding a feature
- Abandoned interactions: abruptly abandoned actions might reveal points where users encounter challenges or lose interest
- UI confusion: detect instances where users seem uncertain about the purpose or functionality of certain elements
To make it easier to analyze session recordings, Smartlook allows you to highlight events in your session replays that can then be filtered, segmented, and quantized.
Heatmaps visually represent aggregated user interaction data. They showcase which areas of your app receive the most engagement (hotspots) and which areas receive less attention (cold spots).
There are three types of heatmaps:
- Click heatmaps point out non-clickable elements that users are trying to click
- Scroll heatmaps show how many people make it to certain points on a page, making it easier to decide where to place critical elements
- Mouse movement heatmaps identify areas that distract or confuse visitors
Smartlook collects heatmap data and presents it in your preferred format — all you need is to know what you’re looking for.
Identify recurring patterns of frustration and focus on them. These patterns will guide your efforts to improve specific aspects of your product that are causing user discomfort.
Capitalize on positive user experiences
While identifying and addressing points of friction is essential, it’s equally important to learn from positive user interactions and experiences. Positive data can provide insight into what’s working well and help you leverage those aspects to enhance product growth.
Look for instances where users achieve their goals seamlessly and exhibit positive emotions. This could be when they complete a task, navigate smoothly, or express satisfaction. These moments are valuable because they signify that your product is delivering value as intended.
Examine the features or touchpoints that consistently lead to positive user experiences:
- What elements are contributing to their happiness?
- What aspects of your product are resonating with them?
- Are there specific user segments or personas that tend to exhibit high satisfaction?
- How could you replicate those experiences in other areas of your product?
Use these insights to incorporate elements contributing to positive user experiences throughout the entire user journey.
Interpret data to drive actionable insights
No matter how deep the data you collect — if you don’t act on it, it’s useless. To connect your user happiness insights to business outcomes, you need to start interpreting your data.
Look for connections between quantitative and qualitative findings. For instance, if users are consistently giving low scores for a particular aspect of your product (quantitative insight), delve into qualitative data to see if there are recurring issues or pain points related to that aspect (qualitative insight).
Next, develop hypotheses about the potential solutions to the identified issues. These hypotheses will guide your experimentation and optimization efforts. Whether it’s tweaking a feature, improving onboarding, or refining a specific interaction, use data-driven hypotheses to guide your changes.
Using data analytics to improve customer experience
There are no limits to what you can achieve with the help of digital experience analytics. These are the most common use cases illustrating how data helps to enhance user journeys.
Addressing pain points based on rage clicks
Rage clicks occur when users repeatedly click on the same element of an app out of frustration, usually because they expect it to perform a specific action that it doesn’t. These issues usually lead to customer frustration and low satisfaction levels. So if you spot your CSAT dropping abnormally, follow this process:
- Set up tracking for rage clicks in Smartlook
- Use heatmap and session replay tools to identify instances of rage clicks
- Dig deeper into the elements that are causing rage clicks. Is the design misleading? Are there broken links? Are users trying to access information that’s hard to find?
- Based on your findings, take action to optimize the user experience and eliminate rage clicks. This could involve making clickable elements more prominent, fixing broken links, or enhancing error messages to provide clear guidance and assistance
Analyzing rage clicks can provide valuable insight into areas of your digital product that need immediate improvement.
Driving personalization with cohort analysis
Different user segments may require a tailored approach to satisfy their unique needs. This simple strategy helps gauge the happiness of separate customer cohorts and craft experiences that resonate with them most:
- Group users based on attributes like sign-up date, behavior, or demographics
- Monitor how each cohort engages with your product or service, including how their behavior evolves
- Implement in-app surveys targeting specific cohorts to learn about the pains of particular user groups
- Use insights to create personalized user flows, offers, or features for different cohorts
Tip: Use Smartlook to run a cohort analysis. You can group users based on common characteristics or events and track engagement statistics for different cohorts in Retention Tables. When you spot engagement anomalies, set up targeted in-app surveys with Survicate — the platform integrates with Smartlook enabling you to connect survey responses to user sessions later.
Understanding user emotions with sentiment analysis
Say you receive hundreds of reviews across various online channels — how do you make sense of the piles of customer feedback? Sentiment analysis is the answer.
Sentiment analysis involves analyzing text data, such as customer reviews or social media posts, to determine user sentiment (positive, negative, neutral). It helps to identify trends in user feedback and address issues more effectively.
- Collect user-generated content like reviews, comments, and support tickets
- Use natural language processing (NLP) tools to analyze and categorize text data by sentiment
- Identify recurring themes, positive and negative sentiments, and areas of concern
Using positive user experiences to develop new features
Another powerful strategy is using positive experience data to develop new features or enhance existing ones. This approach involves using the data you’ve collected from users who have had positive interactions with your product to shape the direction of your development efforts.
Here’s how you can do it:
- Analyze user feedback, surveys, session recordings, and other qualitative and quantitative data to identify instances where users have had positive experiences
- Identify the elements that resonate well with users and contribute to their happiness
- Think of how these enhancements can further amplify the positive experiences for a broader user base
- Focus on the ones that address pain points or provide value to multiple user segments
Proactively addressing churn with predictive analytics
Churn is a direct indicator of user happiness. When users choose to discontinue using your product, it’s a sign that they’re not finding the value they expected. But the problem isn’t always in the product itself — it could as well be caused by poor personalization or lack of user guidance.
If the churn issue isn’t related to product functionality, you can address it by:
- Collecting relevant data on user behavior, interactions, and engagement with your product
- Setting up algorithms to identify common characteristics, behaviors, or actions associated with users who have churned in the past. No need to be a data analytics pro. Just use a predictive analytics tool like Churn360 — it will do all the heavy lifting for you
- Keep track of customer health scores and spot early signs of customer churn in your predictive analytics platform
- For users in the high-risk segment, proactively implement personalized interventions aimed at improving their experience and addressing potential pain points
Customer experience analytics tools
You don’t need a massive analytics kit to measure and enhance user happiness. In fact, these three digital experience analytics tools will help you gain a comprehensive view of your user experience, enough to build smooth product journeys and achieve customer loyalty.
Smartlook is a digital experience analytics platform that connects quantitative (revenue insights, UX metrics, etc.) and qualitative (session recordings) data that’s easy to interpret and act on. Its funnels, session recordings, heatmaps, and other features help product managers understand how users interact with their products so they can make informed decisions and enhance product experiences.
With Smartlook, you can uncover user behavior patterns or dive into individual user experiences — whatever you need to build stronger relationships with your customers.
Survicate is a survey and feedback platform that enables you to gather qualitative insights directly from users.
It integrates seamlessly with other analytics tools like Smartlook, allowing you to connect survey responses to specific user sessions. This synergy between quantitative and qualitative data helps you better understand user emotions and create more satisfying user interactions.
Lexalytics is a leading sentiment analysis tool that focuses on understanding user emotions from qualitative data sources like customer feedback, reviews, and social media conversations.
Aside from sentiment analysis, Lexalytics can help you automatically categorize outstanding tickets and reviews, allowing you to focus on the most pressing topics.
Gauge product experience and maximize user happiness with Smartlook
Digital experience analytics help you understand user emotions, experiences, and frustration behind your product performance data. Only by uncovering and acting on these insights can you go beyond your hypotheses and create a product that meets the needs of the target audience.
User happiness is an asset you can convert into tangible business outcomes with the right tools in your tech stack.
Smartlook empowers you to gain deep insight into user behavior by capturing and visualizing their interactions in real time. Through session recordings and heatmaps, you can see how users navigate, click, scroll, and interact with your product. This level of visibility allows you to identify pain points, areas of frustration, and moments of joy within the user journey.