Web analytics transformed for the modern enterprise.

Web analytics proactively monitors, diagnoses, optimizes and quantifies website journeys for every team. As a sub-category of digital analytics, it is a method for empowering businesses with a clear, quantifiable vision for the digital customer experience.

The goal of web analytics is to empower fast and decisive action to enhance website marketing, product, design and technical strategies, so teams can improve the customer experience and optimize conversion rates, ultimately driving cost savings, loyalty and revenue growth.

As the need to design consistent user experiences across websites, native apps, kiosks and other digital sources has increased, so has the need to understand behavior across these various digital channels. Therefore web analytics has gradually been consumed under the broader umbrella of digital analytics. Web analytics specifically focuses on monitoring, diagnosing and optimizing the website experience (desktop and mobile web), whereas digital analytics has a broader scope, focused on monitoring, diagnosing and optimizing a variety of digital channels, including websites, native mobile apps, kiosks and other sources. Digital analytics encompasses multiple categories, including web and native app analytics, product analytics, experience analytics, and customer journey analytics.

As the need to better visualize and understand specific user interactions behavior increased, experience analytics emerged as a separate category from web analytics. While web analytics focuses on data to monitor, diagnose and optimize the website experience (desktop and mobile), experience analytics incorporates session replay and heat map technology into this mix. This enables product, UX and technical teams to visualize user interactions and better understand customer struggle tied to business impact. Therefore experience analytics integrates a whole new element of visualization and empathy into web analytics to make them more powerful together.

• UX/Design teams can get insights on how people navigate feature sets, identify popular features (and see what’s confusing) in the design, identify roadblocks, and pinpoint key moments of abandonment. Top use cases include: validate usability testing, analyze conversion funnels, identify UX issues (using heatmaps and click tracking), A/B testing for design iterations, mobile app design optimization, responsive design tracking, and more.
• Product teams can understand how customers interact with newly released features, pages, messaging, and determine why they’re dropping off. By visualizing the exact steps a customer takes to a specific experience, product managers can learn if there are too many steps, or if it’s confusing or conversion isn’t performing as expected. Top use cases include: user behavior analysis, feature prioritization, contact driver analysis, user onboarding and adoption, A/B testing and product iterations, user feedback analysis, and more.
• Customer service & support teams can diagnose issues in the customer journey in real-time to answer customer questions, resolve concerns more quickly, improve self-service and reduce call volumes. Top use cases include: triage support issues, diagnose customer feedback, diagnose slow response times, gauge customer satisfaction, identify support channel performance (live chat vs. email vs phone) and more.
• Analytics teams can extract valuable insights by collecting, measuring and interpreting customer behavior and the “why” behind it, sharing dashboards and insights with stakeholders across the org. Top use cases include: performance optimization, behavior analysis, goal and benchmark tracking, A/B testing analysis, audience segmentation, marketing attribution and more.
• Ecommerce teams gain valuable insights into user behavior, track key performance metrics, and make data-driven decisions to optimize their online stores and improve the overall shopping experience. Top use cases include: monitoring web traffic, product performance, conversion rate optimization, shopping cart abandonment analysis, marketing campaign analysis and UX optimization.
• CX and VOC teams can measure and validate optimal iterations in the customer journey across mobile app, web, contact center, physical stores and beyond, enabling more timely improvements and faster resolution to issue escalations. Top use cases include: customer journey mapping, diagnosing customer feedback, customer sentiment analysis, measuring customer engagement, triaging customer support issues, benchmark and KPI tracking, and more.
• Marketing teams can measure the effectiveness of their marketing efforts, understand attribution, and optimize their marketing strategies for better performance. Top use cases include: campaign performance measurement, attribution modeling, audience segmentation, landing page analysis, conversion tracking, content performance, A/B testing analysis, keyword and SEO analysis, competitive benchmarking, and more. 
• Technical teams can use it to optimize website development, performance optimization, troubleshooting, and infrastructure management. Top use cases include: website performance monitoring, error tracking and debugging, cross-browser device compatibility, code optimization, security monitoring, API performance, security compliance, and more.

Web analytics is only one component of the broader Quantum Metric platform, which is focused on solving the critical business needs of complex digital organizations. Unlike other web analytics solutions, Quantum Metric focuses on monitoring, diagnosing and optimizing all digital experiences (across websites, native apps and other channels) and quantifying their impact on business KPIs. Quantum Metric’s web analytics enables the following capabilities:

• 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 web analytics, there are several metrics and Key Performance Indicators (KPIs) it can help improve:

• Web analytics can help organizations directly measure and improve a wide variety of digital metrics and KPIs, including:
  • Traffic: Assess the volume, sources, and behavior of sessions and users on digital platforms, such as websites, native apps, or kiosks.
    • Examples include:
      • Sessions
      • Page views
      • Exits
  • Conversion: Calculate the percentage of web sessions who take a desired action. Robust digital analytics can tie events in the conversion funnel to session replay, helping visualize and diagnose reasons for abandonment.
    • Examples of conversion events include:
      • Making a purchase
      • Signing up for a new account
      • Filling out a form
  • Financial: Capture revenue or total income from digital sales
  • Technical Performance: Monitor client-side and server-side performance and understand how much slow web pages and errors are costing your business.
    • Examples include:
      • API errors
      • Median page load time
      • API response time and size
      • Long running spinners
      • App not responding
  • Engagement: Capture how website visitors interact with individual pages, links and content on digital.
    • Examples include:
      • Behavioral metrics, such as clicks, taps, scrolls and bounce rates
      • Frustration indicators, such as rage clicks, back buttons used and page reloads
• Optimizing web analytics can have an impact on broad organizational goals and KPIs beyond digital, including:
  • Cost Savings: Measure the impact of web self-service initiatives on the bottom line by reducing unnecessary contact center volumes or other more expensive channels.
    • KPI indicators of costs savings include:
      • First Call Resolution (FCR)
      • Average Handle Time
      • Call Deflection Rate
      • Customer Self-Service Rate
      • Cost per Conversion
  • Retention: Track the ability to retain customers and grow loyalty on web.
    • Examples include:
      • Retention rates, calculating how many website visitors return to your web or app within a certain timeframe.
      • Churn rates, or rate of subscription cancellations/downgrades by a paying user.
  • Customer Experience: Measure web experience impact on customer satisfaction.
    • Examples include:
      • Net Promoter Score (NPS)
      • Customer Satisfaction Score (CSAT)
      • Customer Effort Score (CES)
      • Mean time to identify and resolve issues (MTTI and MTTR)

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

• Are anomalies in web traffic or conversion impacting my business KPIs and what is the cost of this friction?
• What are the most common paths or journeys customers take on our website or app?
• Which marketing campaigns or channels are driving the highest engagement, conversion rates or bounce rates?
• 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?
• What is the average time spent on specific pages or sections of our digital platform?
• What are the click-through rates or conversion rates for key calls-to-action?
• 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 website 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?

Web 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.

Web analytics autocapture, also known as automatic event tracking or tagless data collection, is a method whereby user interactions and events are automatically captured without the need for manual tagging or implementation of custom tracking codes. In traditional web analytics, specific events and interactions (such as clicks, taps and form submissions) require manual tagging using custom JavaScript code for tracking and collecting data. Autocapture makes this process more efficient by automatically detecting and tracking customer signals and quantifying business impact. This can include hundreds of signals, such as rage clicks, taps, scrolls, errors, slow load times, back button usage, frustration events, session replay and heatmaps, without any additional effort from developers. The advantage of autocapture is that it saves time and effort for developers, website and app owners and helps ensure that important user interactions are not missed or overlooked, leading to more comprehensive data collection. Any custom site or app features not automatically tracked by autocapture can be easily customized and configured through a UI, without deploying code changes.