AI in UX Analytics: The Data You Need for User-Centric Decisions

Designers live to create impactful customer experiences. But how do they determine that impact?

By measuring and analyzing a user’s experience while they are immersed in a product. UX analytics demonstrate how humans actually use a product or service.

And now… we have access to AI-powered UX analytics, which means designers can make more informed and user-driven decisions, faster.

By integrating AI-driven automations into the research and analysis stage, designers can understand a user group’s attitudes, behaviors, motivations and frustrations at scale. In turn, they can create more intuitive and responsive digital products.

Let’s dive into how designers can leverage AI to collect and analyze usable user data.

How AI Augments the UX Analytics Process

It’s not rocket science: A positive experience leads to users staying on a platform. These users return and eventually become loyal customers. A negative experience causes them to abandon the product altogether.

Encountering issues in navigation and usability causes friction. Reducing this friction in the user flow is a designer’s forte. It shortens the path to value, creating better product experiences.

AI-powered UX analytics help designers find these problematic areas. With AI, they can better identify and understand performance issues. Insights help them prioritize issues and take necessary measures to optimize the product.

UX Analytics help answer the following questions about the user journey:

User Expectations. What do users expect from your website or application? How will they interact with it?
Activity. What’s the most used feature? How are users engaging with it? How easy is it to switch between features? What behavior correlates with power users?
Roadblocks. Where are the friction points in the user flow? Any bugs deteriorating the user journey?

AI allows you to catch issues and fix them before users jump ship. It creates a seamless experience that ultimately boosts sales.

What AI Brings to UX Analytics

Traditional UX analysis is a manual and tedious process. By enlisting the help of AI, researchers bring efficiency to their workflow.

Automating tedious tasks frees up a designer’s time. Let AI handle the data collection and processing. Designers can shift focus and delve deeper into analysis.

AI has the ability to analyze datasets with vast data volumes. In less time, AI conducts a more in-depth analysis than traditional methodology. This helps unearth valuable insights that reveal user behavior, preferences and pain points.

We’ve listed some benefits that AI brings to UX analytics:

Data-Driven Design. AI analyzes collected data from various sources. With it, designers can create more responsive and user-centric designs.
Efficient Automation. Automating repetitive tasks makes design more efficient. Designers can focus on creative aspects and complex strategic initiatives.
Increased Scalability & Efficiency. AI can handle large swathes of data. Swift data processing speeds up the research phase. Designers are able to iterate more.
Accessibility. If programmed correctly, AI can analyze data objectively. It can uncover diverse user insights, ensuring inclusivity in design.

AI has also made data analytics more accessible to startups and small businesses. Without highly advanced technical expertise, they leverage AI to improve the user experience.

AI-driven user behavior analysis improves operational efficiency and helps drive strategic growth. It combines design intuition with rapid data processing.

Let’s further explore how various AI technologies are revolutionizing the design process.

For a quick AI terminology refresher, head over to our glossary.

Natural Language Processing in UX Analytics

Natural language processing (NLP) enables machines to comprehend human language. This opens many doors for further analysis with AI:

Understanding Language. AI can make sense of unstructured data. This includes text from customer reviews, social media & forum posts, survey responses and support tickets.
Automate Customer Feedback Cycle. AI automatically collects and analyzes data from a variety of sources. Researchers can glean insights from this data. They get a comprehensive overview of satisfaction levels and areas that need improvement.
Demystify Customer Sentiment. AI’s data processing sheds light on large unstructured text. This provides deep insight into customer preferences and attitudes. Further, AI can unearth patterns overlooked by traditional analysis.
Real Time Sentiment Analysis. AI detects consumer emotions and moods in real time. This allows for quick and agile adjustments of the product. It also makes for a highly responsive customer service experience.

NLP helps UX professionals monitor user sentiment. With ever-improving data interpretation, companies gain valuable insights from unstructured information.

AI automates the qualitative analytics process. It increases product agility and helps improve customer experiences.

Machine Learning & Predictive Analytics in UX Analytics

Future planning is critical to a business’ success. No one can predict the future with 100% certainty.

Predictive analytics give researchers an accurate basis from which to work. Businesses identify risks and opportunities as they try to understand their evolving marketplace.

Predictive analytics leverages Machine Learning(ML). Machines perform statistical analysis on past and current data to make future forecasts. ML algorithms are capable of forecasting future outcomes, trends and behaviors. Machines learn from historical data autonomously with no human intervention. This enhances the precision of predictive abilities over time.

There are plenty of applications for predictive analytics in businesses today. It helps businesses optimize operations and make informed decisions.

Examples of predictive analytics

Inventory and Supply Chain Management. Companies use historical sales data to forecast demand. AI systems can predict shortages before they occur, enabling companies to deploy backup plans. This enables them to manage inventory in warehouses more efficiently. Shelves are always stocked, and waste reduced.

Enhanced Risk Assessment. In the finance industry, predictive analytics uses people’s transaction history to detect and prevent fraud. Machine Learning (ML) models offer an accurate assessment of loan default risks. This leads to smarter lending decisions.

So how can UX professionals leverage this promising technology?

Designers can use ML algorithms to analyze historical and current data on user behavior. They can forecast a user’s future behavior and preferences.

Machine learning algorithms are highly adept at identifying complex patterns and trends. They provide deep insights into customer behavior and market dynamics.

Predictive analytics helps in forecasting user behavior patterns. By identifying potential UI roadblocks, it highlights navigation weaknesses in the user flow. It helps designers adapt the interface for more user-centric design. Designers make anticipatory changes and feature improvements to create a smoother UI.

Predictive analytics is far more reliable than relying on gut feeling. It allows designers to go beyond a reactive strategy. Digital interfaces can now grow faster than customer expectations.

Use AI to Personalize the User Experience

Users want a product that feels natural to use. One that feels tailor-made for them. Where they get what they want out of it. And then some.

AI ushers in a new era of customized experiences.

Today, companies offer omnichannel experiences. We begin on our phones, and expect to seamlessly jump into our computers, without skipping a beat. Personalized products increase a user’s attentiveness and engagement with a product:

Netflix uses predictive AI analysis to provide personalized recommendations. Their algorithm analyzes viewing history, user preferences and ratings. It then suggests content that matches user tastes. This improves the viewing experience and keeps people glued to their platform.
Amazon also uses your purchase history and browsing data. They personalize product recommendations for each individual. They push forward product suggestions, reviews and special offers. By anticipating user needs, Amazon encourages repeat purchases (subscriptions) and therefore, revenue.
With autocomplete, Google finishes your sentences for you. Ever notice when you misspell a search item, you get what you were after anyway? This makes search so much more efficient. A smooth user experience.

AI analytics help companies understand users’ needs, behaviors and goals. Algorithms identify an individual user’s inclinations. They can automatically adjust interfaces based on personal requirements. It creates dynamic user profiles that evolve based on ongoing user interactions.

The algorithms adjust information based on an individual’s preferences. It only pushes relevant recommendations, features and content to your devices. This increases user engagement and (therefore) chances of conversion.

Personalization is a cornerstone of modern UX design. It’s all about creating something that feels tailor-made to each and every user. AI analytics makes this increasingly possible.

Metrics used in UX Analytics

Every click you take,
Every (cursor) move you make,
Every single day,
Companies will be watching you.

A click, hover, scroll or time spent in an app. These are all quantifiable UX metrics. UX metrics connect user needs with business objectives. Tracking UX metrics helps designers draw a line in the sand. It establishes a point of reference:

Currently, we’re here. How do we get over there?

Types of UX Metrics

UX analytics goes beyond evaluating quantitative data though. It combines qualitative and quantitative data from surveys, interviews and analytical tools. UX metrics fall into three distinct categories:

Descriptive Metrics provide a basic overview of how users behave when using a product.
Behavioral Metrics observe how users interact and engage with a product.
Attitudinal Metrics are tougher to measure than behavioral metrics. They describe a user’s perception when using a product.

Quantitative metrics help evaluate the user experience with numbers. This data helps pinpoint the most important problems and bottlenecks. It also informs you of the severity of each problem. Is it good, bad or neutral?

Quantitative data doesn’t tell you the ‘why’. The underlying reason behind the numbers.

That’s when you need qualitative data — gathered in lab testing or remote environments via interviews, observation or surveys. Qualitative data is more abstract and includes the voice of the customer. It reveals why users aren’t enjoying a particular aspect of your product.

A great user insight is a combination of qualitative and quantitative data. It provides a holistic understanding of user needs.

What’s happening? Why is it happening?

Here are some common forms of data collection:

Quantitative	Qualitative
A/B Testing	Focus Groups
Click Testing	Interviews
Cohort Analytics	Heatmaps
Form Analytics	Session Replay
Web Analytics	Usability Testing

Check out this article for tips on when to use qualitative and quantitative research methods.

UX Metrics to Track

There’s no list of must-track metrics, but we think this is a pretty good start. Remember, the most useful UX metrics have a:

Timeframe
Benchmark
Reason for tracking
Link to a customer action

Let’s dive into some different metrics, organized by what they measure.

Usability Metrics

*Usability describes a product’s ease of use. What tasks do users have to complete?*
Efficiency	time on task & time based efficiency	Measures how well a user completes a given task within a product. How well does our new feature work?
Task completion %	average time taken to complete task / total # of participants	Compare task completion time between experienced and new users. Shows you how easy it is to pick up for newbies.
Success rate	# of completed tasks / # of attempts	The higher the number, the better usability.
Number of errors	# of errors / # of total possible errors	Expect errors. The percentage of what went wrong compared to all possibilities of going wrong.

Effectiveness Metrics

Can users fully comprehend and complete their designated goal? Measure effectiveness before and after a feature or product update to establish a baseline for comparison.
Engagement	page views session duration user feedback	User engagement analytics – as a rule of thumb, the higher the following engagement statistics, the better. Likert scales aside, qualitative feedback common sections open ended questions.
CSAT – Customer Satisfaction	# of satisfied customers / # of survey responses	Customer Satisfaction Scores measure exactly that. Based on a 1 to 5 likert scale. Only scores of 4 & 5 count as satisfied users.
SEQ – Single Ease Questions	average scores (higher = better)	Typically asks users to rate how difficult a task was. It assumes that the task completion metric (above) may be inaccurate. Typically a likert scale of 1(very difficult) to 7(very easy).
NPS – Net Promoter Score	% of promoters – % of detractors promoters = 9-10 detractors = 1-6 passive = 7-8	One question – an indicator of customer loyalty. On a scale of 1 to 10 users rate “how likely are you to recommend this product to a friend or colleague?”
CES – Customer Effort Score	sum of customer effort ratings /total # of survey responses	How much effort did customers put in while interacting with your product?Uses a likert scale or emoticon rating.
CCR – Customer Churn Rate	(# of customers @ t₁ – # of customers @ t₀) / # of customers @ t₀	Measure of customer retention over a period of time. A higher churn rate indicates that a high proportion of customers are leaving your website or product. Eg. our churn rate was 12% for the quarter ended March 31st.

Business Impact Metrics

Measures impact of design on business goals such as revenue to calculate design’s ROI
Revenue or Sales	(sales @ t₁ – sales @ t₀) / sales @ t₀	How do product changes move the needle?
Customer Lifetime Value (CLV)	(avg. purchase value x avg. purchase frequency) x avg. customer lifespan	It’s easier to retain existing customers than get new ones. This value reflects how much a customer is worth to your company and how much they spend during the course of the relationship.
Return on Investment (ROI)	[(net gain from investment – cost of investment) /cost of investment] x 100	Welcome to the Shark Tank. Every business decision must tie back to an ROI. Considering the time, money and effort expended on the user experience.
Conversion rates	free users converted / total free users	If you offer a freebie version and an upgraded paid plan. It’s helpful to see how many people have switched over to paying customers.

To Be Continued…

Learn how to establish your UX analytics strategy with AI in part 2 of article.

Check out some of our other resources about incorporating AI in your UX workflows:

^{Hero photo by Hunter Harritt on Unsplash}

AI in UX Analytics: The Data You Need to Make User-Centric Choices