The digital analytics platform to quantify the "why" behind every experience.

Digital analytics is a method of generating insights using behavioral data from websites, native mobile apps, kiosks and other sources. It helps organizations and teams across the org monitor, diagnose, and optimize customer behavior across digital experiences and micro-journeys. It does this by helping teams understand and visualize the digital experience and quantify its impact on business KPIs. 

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

Quantum Metric, Adobe Analytics, and Google Analytics (GA4) are all web analytics tools, but they have some key differences in their features, focus, and capabilities. While Adobe and GA can identify high level trends and what’s happening to conversion rates and other digital metrics, Quantum Metric differs in its ability to more quickly identify “why” customers behave the way they do, and quantify the business impact of any micro-journey. 

• Quantum Metric specializes in monitoring, diagnosing, and optimizing the digital experience in order to quantify its impact on business KPIs. This includes identifying friction points, uncovering user struggles, and surfacing opportunities to optimize the overall customer journey across web, native app and other digital sources. When a KPI drops below baseline, teams are alerted in real-time and can easily diagnose issues with out of the box dashboards that steer you step by step to answer questions faster. Quantum Metric’s autocapture includes hundreds of metrics and KPIs out of the box, including session replay, heatmaps and click tracking with no code changes. 
• While Adobe Analytics includes some user experience analysis features, it is more focused on traditional web analytics, marketing attribution, and segmentation rather than in-depth user experience optimization. Adobe requires extensive expertise and coding of javascript tags to collect digital data based on custom data variables and events to track user interactions. It does not include session replay or robust heatmap capabilities to diagnose issues in the user experience, and quantifying the impact of any given behavioral event requires additional configuration and expertise.
• Google Analytics (GA4) is a basic digital reporting tool designed with simplicity and cross-platform tracking in mind. With both free and premium offerings, it’s ideal for supporting basic web and native app reporting needs of small and medium size businesses. It is not designed to support the complex analytical needs of large enterprises.

Digital 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 to track and collect digital analytics 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.

Digital analytics can help organizations directly measure and improve a wide variety of digital metrics and KPIs, including:

• Traffic: Assess the volume, sources, and user 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 and app 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 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 users interact with 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 digital analytics can have an impact on broad organizational goals and KPIs beyond digital, including:

• Cost Savings: Measure the impact of digital 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 digital.
  • Examples include:
    • Retention rates, calculating how many users 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 digital 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)

• 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.
• Technical teams can use it to optimize website and application development, performance optimization, troubleshooting, and infrastructure management. Top use cases include: website and app performance monitoring, error tracking and debugging, cross-browser device compatibility, code optimization, security monitoring, API performance, security compliance, search engine optimization, 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: performance measurement on digital marketing campaigns, attribution modeling, audience segmentation, landing page analysis, conversion tracking, content performance, A/B testing analysis, keyword and search engine optimiztion analysis, competitive benchmarking, and more. 
• 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.
• 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.
• CX and VOC teams can measure and validate optimal iterations in the customer journey across mobile apps, 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 and pain points, customer sentiment analysis, measuring customer engagement, triaging customer support issues, benchmark and KPI tracking, and more.
• Customer service & help desk 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.

Digital analytics helps businesses clearly understand what’s working (and what’s not) in digital to increase conversion, revenue, customer satisfaction, retention and loyalty to ultimately drive market share and competitive advantage. It can help them identify customer behavior trends so they can adjust their digital strategies and stay ahead of the competition. It can also help them realize economies of scale and achieve comparative cost efficiencies against their peers.