Exceed expectations with experience analytics.

Digital experience analytics encapsulates a rapidly growing category with an evolving definition. Gartner defines digital experience analytics as “diagnostic insights into visitor activity and the CX…enabled by advanced analytics and machine learning, session replay and heat map technology.” Meanwhile, Forrester focuses on the capabilities that allow “experience design pros to log and analyze users’ interactions with interfaces, which may be high fidelity prototypes or in production.”

Digital experience analytics has evolved from the surveys of the past. Today, digital experience analytics involves capturing data from various touchpoints, such as websites, mobile apps, customer support, and kiosks. The data can include quantitative metrics like click-through rates, conversion rates, and time spent on a page, as well as qualitative data such as user feedback, sentiment analysis, and behavioral patterns. The collected data points are then analyzed to gain insights into customer behavior, preferences, pain points, and overall satisfaction. These insights help organizations identify areas for improvement, make data-driven decisions, and enhance the customer experience.

Digital experience analytics tools track specific interactions within and across sessions using heat maps or session replay, and aims to understand any struggles during an interaction, or what prevents a user from converting.

Product analytics is used to understand how users engage with a digital product or specific feature across multiple touchpoints or sessions to inform decisions about how to improve the product experience and increase product engagement and retention. Simply put, product analytics tends to focus on tracking the behavior of unique users across sessions, whereas experience analytics – while not always the case – tends to focus on activity within the session.

• UX/Design teams can leverage heatmaps and session replay to evaluate user interactions and identify usability issues to improve the overall user experience. They analyze data on user behavior, navigation patterns, and feedback to make informed design decisions and enhance the usability and user satisfaction of digital products or platforms.
• Product teams can gather insights into how customers use their products, measure feature adoption rates, and identify areas for improvement to help prioritize enhancements and align product strategy with customer needs.
• Customer service & support teams can use experience analytics data points to gain insights into common customer issues and track customer satisfaction levels with the goal of improving response times and providing more personalized support experiences.
• E-commerce teams can analyze shopping behavior and increase sales on websites and mobile stores by measuring conversion rates, monitoring shopping cart abandonment, and identifying opportunities to optimize the sales funnel.
• CX teams can utilize experience analytics data to measure customer satisfaction tied to customer behavior, track customer journeys, and identify areas for improvement.
• Marketing teams can understand how customers interact with marketing campaigns, website content, and digital advertising, identify friction points that cause abandonment or lack of engagement, and ultimately optimize campaign performance, and improve the ROI of marketing and ad spend.

Experience analytics, which includes capabilities like friction detection and session replay, is a core component of the Quantum Metric platform, and we’ve built our platform to focus on critical needs for complex digital businesses:

• Ease of deployment via tagless capture
• Time to value with out of box B2C industry approach
• Real time monitoring and friction detection
• Ease of quantifying business impact
• Insights that serve business, product, and technical teams
• High fidelity session replays with minimal impact to site performance, even at 100% capture

When it comes to experience analytics, there are several metrics and Key Performance Indicators (KPIs) it can help improve:

• Conversion rate (micro and macro-conversion)
• Customer satisfaction score
• Customer lifetime value
• Mean time to resolution
• User engagement metrics (i.e. open rate, bounce rate, pages per session, cart abandonment rate)
• Click-through rate
• Churn rate
• Net promoter score
• Customer effort score
• Return on investment
• Click through rates

Experience analytics can help answer a wide range of questions related to customer behavior, preferences, and overall experience. Here are just a few examples:

• What are the most common paths or journeys customers take on our website or app?
• Where specifically do customers encounter obstacles or drop off during their interactions, and what does that experience look like (scrolls, taps, clicks, etc)?
• What features or content are most popular among our customers?
• How much time do customers spend on specific pages or sections of our digital platform?
• What are the click-through rates or conversion rates for key calls-to-action?
• Which marketing campaigns or channels are driving the highest engagement and conversion rates?
• What is the overall sentiment or satisfaction level of customers based on their feedback or reviews? How can we tie that to user behavior?
• How do different user segments or personas engage with our digital platform?
• Are there specific user interface elements or design elements that need improvement?
• Which customer touchpoints or channels contribute the most to customer satisfaction and loyalty?

Experience analytics plays a crucial role in improving customer journey design by providing insights into customer behavior, preferences, and pain points. Here are a few sample use cases:

• Identify pain points and obstacles where customers face difficulties, such as confusing navigation, lengthy forms, or slow-loading pages. 
• Optimize touchpoints and interactions to improve website navigation, streamline funnels, and enhance the user experience at critical touchpoints.
• Personalize the customer journey by delivering tailored recommendations, customizing content and offers, or rescuing customers in real time based on behaviors or friction.
• A/B testing and experimentation by testing variations of design elements and identifying which variations perform better to make data-driven decisions and optimize the experience.
• Continuously improve using an iterative approach to allow for rapid, continuous optimization and refinement of the experience over time.
• Tie customer feedback to behavior by measuring how customer satisfaction levels (or specific survey verbatims) impact the overall user experience and business outcomes, helping quantify and prioritize improvement efforts.