​Observability began in a world where software was more predictable. You could usually see what went wrong and where. AI changes that. Now a system can be technically healthy and still produce a bad answer, break a policy, or quietly burn money at scale.​

That shift is forcing observability to evolve quickly. It must account for behavior, judgment, cost, and risk, not just uptime. That’s why we’re proud to share that Dynatrace has been named a Leader in the 2026 Gartner® Magic Quadrant™ for Observability Platforms, marking the 16th time Dynatrace has been recognized as a Leader in this report. We think Dynatrace continues to deliver consistent business value for our customers at scale as technology evolves.​

We believe this recognition reflects a simple reality: Teams need a unified view across applications, infrastructure, cloud environments, and AI systems in one place, with the context to get answers—not guesses—about what is happening, why it matters, and what to do next.

The AI era demands end-to-end visibility​

Observability was already under pressure. Cloud-native architectures, distributed systems, and constantly changing environments made it harder to follow cause and effect across the stack. AI raises the stakes.​

AI systems do not fail like traditional software. They can look healthy at the system level while producing low-quality outputs, violating policy, exposing sensitive data, or quietly consuming far more resources than expected. Those problems often sit outside the view of conventional monitoring tools. And when observability is fragmented across separate products, teams lose the context they need to understand how AI behavior connects to infrastructure, applications, and business outcomes.​

Organizations need a complete, connected view from GPU to business outcome. That is what makes it possible to adopt AI safely, operate it confidently, and avoid trading speed for risk.

The Dynatrace difference: A unified platform, powered by AI and built for AI​

Dynatrace approaches observability with a unified platform, not a collection of fragmented tools. Built on Grail®, Smartscape®, and Dynatrace Intelligence — with integrations into the tools, clouds, and AI agents your teams already rely on — the platform brings together a unified data foundation, real-time contextual understanding, and AI-powered intelligence to help teams understand complex systems and act with confidence.​

That matters for two reasons:​

• Dynatrace is powered by AI. Dynatrace applies deterministic AI to deliver precise, trustworthy answers across applications, infrastructure, and cloud environments. Dynatrace agentic AI can then act on those answers, helping teams move faster, resolve issues sooner, and execute at scale with confidence. ​
• Dynatrace is built for AI. AI is now becoming part of the software stack itself, and it introduces new failure modes that traditional observability cannot fully explain. Dynatrace gives teams visibility into what their AI is actually doing — not just how the system is performing — with insight into performance, cost, quality, and compliance, all on the same platform and with no additional tooling required.​

Together, these capabilities help organizations move beyond isolated dashboards and alerts. They make it possible to observe, analyze, and automate across modern environments with the context required to keep AI systems reliable, governed, and aligned to business goals.​

We believe this recognition reflects where the market is going

The observability market is changing quickly as organizations invest in AI-powered applications, modernize technology stacks, and look for ways to reduce operational complexity. In this environment, platform depth, unified data, context, and AI matter more than ever.​

​We feel Dynatrace’s continued recognition as a Leader reflects the strength of this approach: A platform that helps customers unify and contextualize data across complex environments, transform it into actionable answers, and support intelligent automation at scale. We think sixteen times as a Leader also speaks to consistent delivery through wave after wave of technology change.​

Read the full Gartner® report​

We’re proud of this recognition, and grateful to the customers, partners, and teams that continue to push observability forward with us.​

Read the 2026 Gartner® Magic Quadrant™ for Observability Platforms to learn more about why Dynatrace was recognized as a Leader and how we think the category continues to evolve in the AI era.

FAQ​

What does it mean that Dynatrace was named a Leader in the Gartner® Magic Quadrant™ for Observability Platforms?​

The Gartner Magic Quadrant evaluates vendors based on Completeness of Vision and Ability to Execute. We believe Dynatrace’s position as a Leader reflects the strength of our unified observability platform and our ability to help customers manage modern complexity at scale in the AI-era.​

Why does observability need to change in the AI era?​

AI introduces new kinds of operational risk. A system can appear healthy while still producing poor outputs, violating guardrails, or increasing cost. Teams need observability that can connect AI behavior to the rest of the environment and provide context across performance, cost, quality, and compliance. ​

What makes the Dynatrace approach different?​

Dynatrace combines a unified data foundation, real-time topology and context, and AI-powered intelligence in one platform. That lets teams move from fragmented signals to precise answers and intelligent action, without relying on separate tools to understand AI systems. Dynatrace offers a combination of:
​
– A unified platform with Grail® lakehouse, exabyte-scale data foundation​
– Business-aware insights with Smartscape® real-time topology and contextual understanding
– Answers, not guesses and governed agentic automation with Dynatrace Intelligence

Gartner Disclaimer

Gartner, Magic Quadrant for Observability Platforms, Padraig Byrne, Martin Caren, D.B. Cummings, Neil Young, 13 July 2026

Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s Research & Advisory organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

Gartner and Magic Quadrant are trademarks of Gartner, Inc. and/or its affiliates

Dynatrace was recognized as Compuware from 2010-2014.

The post Dynatrace named a Leader in the 2026 Gartner® Magic Quadrant™ for Observability Platforms for the 16th consecutive time appeared first on Dynatrace news.

Every organization runs on data. But for most leaders, the challenge is clarity. Traditional dashboards and reports often arrive too late, and system-level metrics don’t explain the business consequences of technical events.

When a payment API slows by 200 milliseconds, what does that mean for daily revenue? When user sessions drop, is it a performance issue or a recent pricing change? These are the questions business observability is designed to answer.

Business observability connects technical telemetry to business outcomes, creating a shared understanding of how digital performance drives, or hinders, organizational results. It’s less about adding more data, and more about connecting the right data in real time to the decisions that matter.

Importantly, business observability is broader than observing individual business processes. While end‑to‑end workflows like “order to fulfillment” or “claim to payout” are one expression of business observability, the discipline also encompasses digital experience, customer behavior, revenue impact, operational efficiency, and risk. Business observability focuses on understanding how technology performance influences business outcomes across the organization and not just how a single process executes.

Moving beyond traditional monitoring

Traditional monitoring focuses on uptime, latency, and error rates. These are essential for engineers but often disconnected from business context. Business observability closes that gap by linking system health directly to business performance.

That connection – the ability to translate technical telemetry into business insight – is what distinguishes business observability from conventional monitoring.

What makes business observability different

Business observability builds on traditional monitoring by expanding its scope from system health to business outcomes. It introduces three essential capabilities that operate across customer experience, revenue‑generating transactions, and end‑to‑end processes:

• Business context integration: IT metrics are linked with business KPIs, translating system behavior into measurable impact.
• End-to-end business process visibility: Processes like “loan approval to disbursement,” “claim to payout,” or “application to onboarding” are monitored across applications, infrastructure, and external systems.
• Real-time decision support: Business observability surfaces business implications in real time, allowing teams to respond to issues before they escalate.

Together, these principles give organizations a live understanding of how every digital interaction affects outcomes, from conversions and revenue to efficiency and satisfaction.

Where business observability gets its data

Business observability relies on unifying multiple data streams into a single, contextual view of performance:

• Business events: Structured, contextualized data that represents key business outcomes, such as completed checkouts, submitted claims, bookings, or failed transactions.
• Logs: Time-stamped records of system activity that show what happened and when, often containing critical technical and business data.
• Real user monitoring (RUM): Continuous insight into how users actually experience applications and digital services across devices, channels, and geographies.
• External business tools: Data from tools such as ERP, CRM, billing, and payment platforms that provide commercial, operational, and customer context.
• Instrumentation and agents: Technologies that collect telemetry and business-relevant data from applications and services, ranging from manual instrumentation to automated approaches that observe activity as it flows through systems with minimal configuration effort.
• OpenTelemetry: A standardized instrumentation framework that enables consistent collection of metrics, logs, and traces across hybrid and cloud-native environments.

When correlated, these data sources bridge the gap between technical operations and business results, providing a comprehensive view of how technology supports, or disrupts, performance.

How organizations put business observability to work

Modern organizations apply business observability in three key ways. Organizations may pursue these use cases through a variety of approaches, ranging from manually instrumented metrics and custom reporting to more automated, integrated platforms that reduce effort and time to insight.

1. Drive real-time decisions with IT context: When issues arise, teams and business leaders can see the business impact immediately. A sudden dip in conversions, for example, can be traced to a misconfigured API or third-party service outage, enabling immediate, targeted response.
2. Track and optimize business processes: Complex workflows—like “order to fulfillment” or “quote to claim”—are mapped from end to end. Teams can pinpoint where time, cost, or customer satisfaction are being lost and address bottlenecks before they affect outcomes.
3. Accelerate sustainability and reduce costs: By correlating business events with resource usage, organizations can identify inefficiencies in automation, cloud consumption, or scheduling that inflate cost and carbon impact.

Business observability, in action

Each of these examples shows how business observability does more than surface anomalies. It provides the context to act on them. By connecting business events, telemetry, and user experience data, organizations move from simply detecting issues to understanding their impact, cause, and resolution path in real time.

In practice, achieving this level of insight often depends on how business and technical data are captured and correlated. While some organizations rely on custom instrumentation, manual analysis, or post‑incident reporting, others use more automated approaches that make it possible to detect impact, trace root cause, and act in real time.

Financial services

A large financial institution monitored loan application volume as a business event rather than relying solely on system health metrics. When completed applications began declining, traditional monitoring showed no infrastructure failures. Business observability revealed that timeouts from a third-party credit scoring service were affecting only new applicants following a recent integration change. By correlating business events with external service performance, the bank isolated the issue quickly and rolled back the configuration—preventing lost loan volume and downstream compliance exposure.

Payments

A global payment services provider processes billions of transactions annually and needs to understand not just whether systems are available, but how performance impacts transaction success and revenue. By connecting transaction latency and failure rates directly to payment outcomes, the organization can quantify the financial impact of technical issues in real time. This shared visibility allows both internal teams and customers to see how payment flows are performing and proactively optimize transaction speed and reliability.

Travel and hospitality

A large travel platform aggregates booking data across partners, regions, and channels. Business observability enables teams to track quotes, bookings, and completed reservations as business events, segmented by partner and geography. When booking volume dips, teams can immediately determine whether the cause is a partner integration issue, a regional performance problem, or a downstream service slowdown—allowing rapid response to protect revenue across the ecosystem.

Retail and consumer services

A national restaurant chain observed high abandonment rates during online reservation flows. Rather than treating this as a generic user experience issue, business observability correlated real user behavior with backend availability and booking outcomes. This insight enabled automated recovery workflows that re-engaged customers who abandoned reservations due to technical or availability issues—recovering lost bookings without manual intervention.

Aviation and transportation

An international airline struggled with fragmented visibility across booking, pricing, and fulfillment systems. By modeling bookings as end-to-end business processes, business observability provided real-time insight into how technical issues affected customer bookings and operational teams. This shared context improved collaboration between IT, call centers, and operations, reducing response times and improving passenger experience during disruptions.

Insurance and regulated industries

An insurer tracked claims submissions as business events and noticed rising exception rates on mobile channels. Business observability revealed that document uploads from newer devices exceeded a backend file-size limit introduced during a recent update. Because business events, logs, and user session data were correlated in real time, teams deployed a same-day fix and notified affected customers—preventing claim backlogs and demonstrating operational transparency to regulators.

Manufacturing and public sector

Organizations running complex, multi-step production or licensing processes use business observability to monitor each step as it moves across applications, infrastructure, and external systems. In manufacturing and government services alike, correlating process steps with technical events allows teams to identify bottlenecks that delay outcomes—such as throttled payment validation or downstream capacity limits—and resolve them without disrupting customer- or citizen-facing services.

Enabling data-driven executive insight

For executives, business observability shifts technology from a cost center to a source of executive intelligence, providing leaders with real‑time visibility into revenue, customer experience, operational performance, and risk. It enables:

• Real-time business health monitoring: Live dashboards show key performance indicators such as order volume, claim processing times, or fulfillment success rates.
• Anomaly detection: Advanced analytics identify deviations in both technical and business metrics before they escalate.
• Impact analysis: Teams can immediately quantify how issues affect revenue, engagement, or satisfaction, and prioritize based on real business value.
• Trend analysis: Historical and real-time data combine to forecast outcomes and guide strategic decisions.

Improving collaboration between business and IT

One of the most transformative aspect of business observability is the way it unifies language and priorities across teams.

• IT teams can prioritize work by business impact instead of technical urgency.
• Business stakeholders gain visibility into technical dependencies that influence performance.
• Cross-functional teams align around shared outcomes rather than isolated metrics.

This shared context strengthens trust and speeds response, particularly during digital transformation, where both technology performance and customer experience are constantly evolving.

Laying the groundwork for business observability

While business observability may be expressed through dashboards, process views, or executive metrics, its success depends less on how data is visualized and more on how consistently business outcomes are connected to technical signals across teams. Adopting business observability effectively requires thoughtful preparation:

• Data integration: Pull from diverse sources – applications, infrastructure, and business systems – to form a cohesive view.
• Shared metrics: Define how business KPIs map to technical signals.
• Organizational alignment: Ensure teams are trained and incentivized to act on shared insights.
• Platform scalability: Choose an observability solution that supports hybrid, cloud, and partner ecosystems as data volume grows.

The road ahead

Business observability represents a shift from reactive monitoring to outcome-driven intelligence. Rather than focusing solely on system health or individual process performance, it enables organizations to understand in real time how technology influences revenue, customer experience, operational efficiency, and strategic decision‑making. For executives, it means real-time visibility into how technology influences outcomes. For IT teams, it means prioritizing based on business value. For organizations, it means a unified, data-driven way to make decisions with confidence.

In practice, many organizations attempt to reach this level of insight through manually instrumented metrics, custom dashboards, and offline analysis – approaches that require ongoing effort and often delay understanding when it matters most. Dynatrace Business Observability takes a different approach, capturing business events from multiple sources and automatically correlating them in real time with full‑stack telemetry. This delivers the context leaders need to act decisively, without the manual overhead of traditional approaches.

FAQs: Business observability

What is business observability in simple terms?

Business observability is the ability to understand how technical performance affects business outcomes in real time. It connects IT telemetry, such as logs, traces, and metrics, with business data such as transactions, claims, or orders, so teams can see both the cause and the consequence of an issue in one view.

How is business observability different from traditional monitoring?

Traditional monitoring reports on system health (uptime, latency, or resource use) without showing the business impact. Business observability goes further by linking these technical metrics with key performance indicators (KPIs), providing the context to understand how technical changes influence revenue, customer experience, and operational efficiency.

What types of data does business observability rely on?

Business observability combines multiple data sources, including:

• Business data from transactions or processes
• Logs and traces from applications and infrastructure
• Real user monitoring (RUM) for end-user experience
• Data from external business tools like CRM, ERP, or payment systems
• Instrumentation frameworks such as OneAgent and OpenTelemetry for consistent telemetry across environments

Who benefits most from business observability?

Executives gain real-time visibility into how technology affects performance and outcomes. IT and engineering teams gain business context to prioritize fixes based on impact. Together, these perspectives drive faster decision-making and closer alignment between technology operations and business goals.

What are common use cases for business observability?

Typical use cases include:

• Detecting and resolving process slowdowns in finance, healthcare, or logistics workflows
• Tracking and optimizing customer journeys and digital transactions
• Measuring the business impact of new releases or integrations
• Identifying inefficiencies that increase costs or carbon footprint

How does business observability support sustainability and cost efficiency?

By correlating business events with resource consumption, business observability helps identify where automation, compute, or storage are overused. This enables teams to optimize cloud spend, reduce energy consumption, and track sustainability metrics alongside operational performance.

What challenges do organizations face when implementing business observability?

Key challenges include integrating data from multiple systems, defining the right shared KPIs between business and IT, and ensuring teams are trained to act on insights collaboratively. Success depends on cross-functional alignment as much as on technology.

Is business observability only relevant for large enterprises?

No. Any organization that relies on digital processes – from mid-sized financial firms to healthcare networks or government agencies – can benefit. The ability to link technical performance to business results is valuable at any scale

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Digital experiences define how organizations compete and grow. Even the smallest performance issue can disrupt journeys, stall transactions, or slow innovation. As ecosystems expand across multicloud environments, microservices, and serverless functions, understanding what’s happening beneath the surface and how it affects your end users becomes exponentially harder.

That’s why we’re proud to announce that Dynatrace has been named a Leader in the 2025 Gartner Magic Quadrant for Digital Experience Monitoring for the second year running, and positioned furthest for Completeness of Vision in 2025.

Real-time intelligence beats reactive dashboards

Modern applications don’t fail gracefully; they fail in cascades. A backend service slowdown ripples through APIs, affecting frontend performance, which impacts user sessions and ultimately drives customers away. Traditional monitoring tools show you the symptoms after the damage is done. Today, businesses need real-time intelligence that connects every dot across your entire digital ecosystem.

Digital experience monitoring (DEM) brings the entire user journey into focus, revealing how every click, transaction, and session shapes experience and drives results. When done right, DEM transforms reactive firefighting into proactive optimization.

The Dynatrace difference: AI-powered observability, at scale

Dynatrace delivers comprehensive digital experience monitoring (real user monitoring, session replay, and synthetic monitoring) through our AI-powered observability platform that automatically discovers, maps, and monitors every component of your digital ecosystem, enabling the following key capabilities:

• Real-time user session analysis. Track every user interaction across web, mobile, and API endpoints with real user monitoring, session replay, and AI-powered anomaly detection. Understand not just what happened, but why it happened and what to do about it.
• Automatic root cause analysis. Our Davis® AI engine analyzes billions of dependencies in real time, automatically identifying the precise root cause of performance issues before they escalate into business-impacting outages.
• Proactive experience optimization. Move beyond reactive monitoring to predictive insights. Dynatrace identifies experience degradation patterns, monitors around the clock with synthetics, and recommends optimizations before users feel the effects.
• Unified data model. Break down silos between infrastructure, applications, and user experience data. Our unified approach means faster resolution times and clearer business impact visibility.

Turning digital experience into business impact

Organizations using Dynatrace for digital experience monitoring report seeing:

• 90% reduction in mean time to resolution
• 60% fewer customer irritants
• 65% reduction in customer churn

Flawless digital experiences create momentum that frees teams to innovate, keeps customers loyal, and turns performance into measurable growth.

Read the full Gartner® report

In our opinion, being named a Leader in the 2025 Gartner® Magic Quadrant™ for Digital Experience Monitoring is more than recognition. We believe it’s a celebration of the teams who deliver flawless digital experiences with Dynatrace every day.

We’re proud of this recognition, but we’re even more excited about what’s ahead. With Davis AI at the core of our platform and unified observability powering every insight, we’re helping organizations transform complexity into their greatest competitive advantage.

Huge thanks to our amazing customers, partners, and teams. We believe you’re the real driving force behind this position as a Leader.

Frequently asked questions

What does being a Leader in the Gartner® Magic Quadrant™ mean?

The Gartner Magic Quadrant evaluates vendors based on their Completeness of Vision and Ability to Execute. In our opinion, being positioned as a Leader indicates that Gartner recognizes Dynatrace as having both a strong strategic vision for digital experience monitoring and the proven capability to deliver that vision for customers.

​Where can I access the full Gartner® Magic Quadrant™ report?

You can access the complete 2025 Gartner Magic Quadrant for Digital Experience Monitoring report here. Gartner Magic Quadrant gives enterprise technology shoppers an unbiased assessment of how well competing providers are performing against the Gartner market view and is supplemented by validated user reviews.

What is digital experience monitoring (DEM)?

Digital experience monitoring is a comprehensive approach to tracking, analyzing, and optimizing how users interact with digital applications and services. Unlike traditional monitoring that focuses on individual system components, DEM provides end-to-end visibility into the complete user journey, helping organizations understand and improve customer experiences.

How is DEM different from traditional application monitoring?

Traditional monitoring typically focuses on infrastructure metrics and application performance in isolation. DEM takes a user-centric approach, correlating technical performance with actual user journey and business outcomes. It provides context about how technical issues impact real user experiences and business results.

What makes the Dynatrace approach to DEM unique?

Dynatrace combines full-stack observability with AI-powered analysis through our Davis AI engine. This means automatic discovery of all dependencies, real-time root cause analysis, and proactive identification of issues before they impact users. Our unified data model breaks down silos between teams and provides complete visibility across the entire digital ecosystem.

Can Dynatrace DEM work with cloud-native and microservices architectures?

Yes. Dynatrace is built for cloud-native architectures and automatically adapts to dynamic environments, including Kubernetes, serverless functions, and multi-cloud deployments. Our technology scales automatically as your environment changes, maintaining complete visibility regardless of architectural complexity.

How does Dynatrace help with compliance and security?

In addition to DEM, Dynatrace also provides comprehensive audit trails, data privacy controls, and security monitoring. Our platform helps organizations maintain compliance requirements while ensuring complete visibility into user experiences and system performance.

^{Dynatrace, Davis, and the Dynatrace logo are trademarks of the Dynatrace, Inc. group of companies.}

_________________________

^{Gartner, Magic Quadrant for Digital Experience Monitoring, Padraig Byrne, Pankaj Prasad, DB Cummings, Martin Caren, and Tanmay Bisht, 27 October 2025.}

^{GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, MAGIC QUADRANT is a registered trademark of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved.}

^{Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.}

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When users land on your website, the digital experience is everything. A slow-loading page, unexpected layout shifts, or unresponsive interactions can frustrate potential customers—causing higher bounce rates, abandoned carts, and low search engine rankings. To combat these issues, Google introduced Core Web Vitals (CWVs): a set of metrics designed to measure and improve the user experience of websites.

Whether you’re a developer, product owner, or IT operations leader, understanding Core Web Vitals is no longer optional—it’s an essential part of delivering flawless and immersive digital experiences. But despite their importance, understanding and mastering these metrics can be just one more set of signals your team has to manage. To unpack—and demystify—Core Web Vitals, we’ll explore what they are, why they matter, and how you can seamlessly integrate them into your operations for enhanced insights and optimization using Dynatrace.

What are Core Web Vitals?

Core Web Vitals are three specific metrics that Google uses to measure the health and quality of a web page’s user experience (UX):

• Largest Contentful Paint (LCP). LCP measures loading performance. It records how long it takes for the largest visible element on a page—like an image or a block of text—to load. A “good” LCP score is under 2.5 seconds.
• Cumulative Layout Shift (CLS). CLS quantifies visual stability by measuring how often elements on a page unexpectedly shift while it’s loading—e.g., an image loading late and pushing text out of position. A “good” CLS score is 0.1 or less.
• Interaction to Next Paint (INP). Replacing First Input Delay (FID) in 2024, INP measures the overall responsiveness of a webpage, capturing how quickly it reacts to user interactions like clicks or keystrokes. A “good” INP score is under 200 milliseconds.

These metrics provide a foundation for website optimization aimed at creating faster, more stable, and more interactive web experiences.

Why are Core Web Vitals important?

Core Web Vitals aren’t just technical jargon—they directly influence a website’s user experience, business outcomes, and search engine rankings.

• Enhanced user experience. Page load speed is a crucial factor in how users experience a site, with over 50% of users saying they’ll abandon a page that takes more than 3 seconds to load. Core Web Vitals address this by ensuring pages are faster, more stable, and easier to use, contributing to lower bounce rates and better engagement.
• Improved SEO. Since 2021, Google has incorporated Core Web Vitals into its ranking algorithm, rewarding websites that meet these performance benchmarks. A high CWV score improves a website’s position in search results, which can boost organic visibility.
• Business impact. Poor site performance can significantly impact revenue. For example, an improvement of just 0.1 seconds in site speed has been shown to increase conversion rates by up to 8%. Core Web Vitals serve as actionable metrics to help businesses achieve these performance gains.

Why Core Web Vitals should be part of your observability strategy

Core Web Vitals help measure and monitor user experience and front-end performance. They are important metrics, but only one part of the picture on their own. When CWV are integrated into observability—which provides deep insights into the performance, health, and user experience of applications, infrastructure, and the business itself—organizations can understand why their Core Web Vitals measure as they do and how to improve them. Core Web Vitals help organizations improve:

• End-to-end observability. Monitoring Core Web Vitals via an observability platform offers visibility into user experiences across devices, browsers, and locations. Incorporating these metrics into your observability stack ensures no blind spots in performance data from front end to back end.
• Proactive optimization. Instead of reacting to complaints, monitoring CWVs helps teams identify and address issues proactively. For example, if LCP deteriorates due to slow server response, teams can take corrective action before it impacts users.
• Bridging technical and business metrics. Integrating Core Web Vitals into observability dashboards connects technical metrics to business outcomes, such as revenue or customer retention, making them easier to communicate to stakeholders.

Monitoring Core Web Vitals with Dynatrace today

Dynatrace goes beneath the Google Search Console by offering unparalleled insights into Core Web Vitals across both the front and back ends of applications to help you understand the drivers of CWV metrics. Here’s how it stands out:

• Full metrics integration. With zero setup required, Dynatrace incorporates LCP, CLS, and INP into its Real User Monitoring (RUM) and Synthetic Monitoring tools, delivering real-time and actionable insights.
• Anomaly detection. The Dynatrace Davis AI engine identifies the patterns or behavior that deviate from the norm impacting CWVs—whether it’s a slow CDN, inefficient JavaScript, or back-end latency—so teams can resolve them efficiently.
• Custom alerts and dashboards. Teams can define CWVs as key performance indicators (KPIs) and configure automated alerts, ensuring they’re notified of potential performance regressions instantly.

Enhancing support for INP

As mentioned above, Google replaced FID with INP recently. While FID was used to measure the responsiveness of the initial interaction on a page, INP observes all interactions on a page and reports the worst value.

In Dynatrace, customers can dashboard the built-in INP metric across various dimensions, such as front-end applications, browsers, and locations. In addition, a user can assess INP values via the front-end application overview screen, as well as page analysis screens, and filter for pages with the worst INP values to initiate improvements.

Maximizing the value of Core Web Vitals with Dynatrace

Optimizing Core Web Vitals isn’t a one-and-done effort; it’s an ongoing process for maintaining a high-quality user experience. Dynatrace will continue to strengthen support for Core Web Vitals to help teams maximize their value and deliver exceptional digital experiences.

With the latest Dynatrace advancements, the platform will provide more granularity of data captured with OneAgent as well as the analytics power of Grail. These capabilities will enable customers to go even deeper in monitoring, analyzing, and optimizing Google’s Core Web Vitals. This functionality is currently available on a trial basis in the preview program. Here’s a look at what the preview includes.

Seamless integration

Core Web Vitals are integrated into the new Dynatrace DEM apps, allowing users to understand the health across multiple KPIs at a glance and across monitored front-end applications. From the health overview, users can drill down into details about pages and views within their front-end applications to discover and optimize vitals across various dimensions.

Deeper analysis

With the power of DQL and ready-made Dashboards and Notebooks, users can drill deeper into the root cause of an issue related to web vitals. The following examples illustrate such an analysis with a ready-made Notebook for Core Web Vital analysis.

LCP analysis

For LCP, users can not only discover the pages with the slowest LCP but also identify the exact element that triggered the worst LCP values, simplifying the process for a developer or performance engineer to optimize page performance and user experience.

INP analysis

The ready-made Notebook also enables INP analysis. In this case, Dynatrace automatically captures the interaction that led to bad INP values and makes it easy to query across the monitored landscape with DQL. Developers and performance engineers can swiftly discover interactions that lack latency and require optimizations to reach better levels of user experience. With the context of the interaction type, tag name, and latency duration, users have all the context they need to make the right choices.

Take your digital experience to the next level

Core Web Vitals are no longer “nice-to-haves.” They’re essential metrics that define the digital experience of every user who visits your site. By understanding their importance and leveraging tools like Dynatrace, organizations can deliver faster, more stable, and more responsive websites—building trust and loyalty with their users.

Are you ready to take control of your Core Web Vitals? Start optimizing today with Dynatrace and experience the impact on your digital performance firsthand.

…

This blog may contain forward-looking statements about our product plans, upcoming features, and anticipated improvements. These statements are for informational purposes only and are not promises or guarantees. The development, release, and timing of any features or functionality described remain at the sole discretion of Dynatrace LLC and may be modified, delayed, or canceled without notice. We encourage readers to make decisions based on the product’s current capabilities and features.

© 2025 Dynatrace LLC

Dynatrace, Davis, Grail, OneAgent, and the Dynatrace logo are trademarks of the Dynatrace, Inc. group of companies. All other trademarks are the property of their respective owners.

The post Why Core Web Vitals are crucial for optimizing digital experience appeared first on Dynatrace news.

We’re excited to announce that Dynatrace has been named a Leader in the inaugural 2024 Gartner® Magic Quadrant™ for Digital Experience Monitoring. We believe this recognition underscores our dedication to empowering organizations to achieve outstanding digital performance.

Digital experience monitoring enables frontend-to-backend observability

As digital transformation accelerates, the complexities of hybrid and multicloud environments have introduced observability challenges that can make it harder to deliver the exceptional experiences customers expect. Dynatrace digital experience monitoring (DEM) monitors and analyzes the quality of digital experiences for users across digital channels by collecting data from multiple sources. With real user monitoring, Session Replay, and synthetic monitoring natively built into the Dynatrace unified observability platform, organizations gain visibility into the challenges users experience when interacting with your website and mobile applications. Dynatrace helps to quickly understand the impact of these challenges and identify their root cause for faster remediation. This enables teams to optimize digital experience, improve customer satisfaction, and drive business results.

Observability for digital transformation

We believe this acknowledgment highlights our dedication to enabling organizations to achieve seamless digital transformation. We equip our customers with the insights necessary to enhance user experiences and improve operational efficiency. Unlike traditional monitoring services, Dynatrace offers end-to-end observability that delivers precise, real-time data on application performance issues, complete with the context needed for rapid problem resolution and prioritization by business impact. By harnessing the power of three AI techniques—causal, predictive, and generative AI—Dynatrace analyzes billions of data points, providing customers with a unified perspective to drive their digital transformation efforts.

Additional Gartner recognition

For us, being recognized a Leader in Digital Experience Monitoring signifies more than just meeting industry standards; we see it as highlighting our capability to partner with customers to deliver results against their digital strategies, enabling them to proactively manage their digital environments and drive business growth.

This recognition comes in addition to the company’s acknowledgment as a Leader positioned furthest for Vision and highest in Execution in the 2024 Gartner Magic Quadrant for Observability Platforms and its ranking as #1 in the Application Health and Performance Monitoring (4.27/5), Hybrid Infrastructure/Platform Operations (4.25/5), and Business Insights (4.22/5) Use Cases in the 2024 Gartner Critical Capabilities for Observability Platforms Report.

Dynatrace named a Gartner Peer Insights™ Customers’ Choice for Digital Experience Monitoring

In the 2024 Gartner Peer Insights™ Voice of the Customer for Digital Experience Monitoring report, Dynatrace was the only vendor named a Customers’ Choice based on 132 reviews as of February 2024.

Here is what customers say about Dynatrace:

• “Using Dynatrace, we are able to monitor the user’s experience like we have never done before.”
• “Dynatrace is an easy-to-deploy, powerful solution to see your customers’ journey.”
• “Dynatrace DEM provided us with extended monitoring beyond APM.”

At Dynatrace, we’re honored to be recognized as a Customers’ Choice in digital experience monitoring by Gartner. We believe this acknowledgement reflects our continuous effort to innovate and improve, always with the goal of helping our customers deliver better digital experiences. We’re proud of the progress we’ve made together and remain committed to helping organizations navigate an ever-changing landscape.

Download the 2024 Gartner® Magic Quadrant™ for Digital Experience Monitoring for more information. 

__________________________________________________________________________

^{Gartner Disclaimers} 

^{Gartner, Magic Quadrant for Digital Experience Monitoring, Padraig Byrne, Matt Crossley, DB Cummings, Martin Caren, Pankaj Prasad, 21 October 2024}   

^{Gartner, Magic Quadrant for Observability Platforms, Gregg Siegfried, Padraig Byrne, Mrudula Bangera, Matt Crossley, 12 August 2024} 

^{Gartner, Critical Capabilities for Observability Platforms, Gregg Siegfried, Padraig Byrne, Mrudula Bangera, Matt Crossley, 12 August 2024} 

^{Gartner, Voice of the Customer for Digital Experience Monitoring, Peer Contributors, 24 April 2024} 

^{Gartner® Peer Insights™ content consists of the opinions of individual end users based on their own experiences, and should not be construed as statements of fact, nor do they represent the views of Gartner or its affiliates. Gartner does not endorse any vendor, product or service depicted in this content nor makes any warranties, expressed or implied, with respect to this content, about its accuracy or completeness, including any warranties of merchantability or fitness for a particular purpose.} 

^{Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.}  

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Customer and employee expectations for seamless, high-quality digital experiences are continually rising. Digital experience monitoring (DEM) is crucial for organizations to meet this demand and succeed in today’s competitive digital economy. By proactively implementing digital experience monitoring best practices and optimizing user experiences, organizations can increase long-term customer satisfaction and loyalty, drive business value, and accelerate innovation.

DEM solutions monitor and analyze the quality of digital experiences for users across digital channels. They collect data from multiple sources through real user monitoring, synthetic monitoring, network monitoring, and application performance monitoring systems. This data provides organizations with end-to-end visibility of the entire user journey across the tech stack. It also enables ITOps to identify performance issues in real time for fast mean time to detect/repair and to continuously optimize performance to improve the overall user experience.

How to improve digital experience monitoring

Implementing a successful DEM strategy can come with challenges. Depending on where an organization is on its journey, these challenges may include the following:

• Digital ecosystems complexity
• Data overload
• Existing IT systems and cloud frameworks integration
• User experience variability
• Business objectives alignment
• Data privacy and security
• Scalability

Addressing these challenges requires a combination of IT process improvements, clearly defined objectives, and organizational alignment.

Here are 10 digital experience monitoring best practices that can improve a DEM solution’s effectiveness and enhance the quality of your organization’s digital offerings.

1. Identify and review key user journeys

Understanding users’ paths when interacting with your applications provides a roadmap of how a customer or employee interacts with your services. To start, define different user journeys within your digital application and create a visual map from start to finish. Was the user able to accomplish their goal?

The map should illustrate every step a user takes as they interact with different pages, forms, and features. It can help understand the flow of user interactions, identify areas for improvement, and drive a user experience strategy that better engages customers to meet their needs. Review and refine key user journeys at set intervals to ensure maps are up to date and relate to clear business goals.

2. Define monitoring goals and user experience metrics

Next, define what aspects of a digital experience you want to monitor and improve — such as website performance, application responsiveness, or user engagement — and prioritize what to measure for each application. This allows ITOps to measure each user journey’s effectiveness and efficiency.

Align business and development teams’ input on what user experience metrics to measure to understand users’ most critical digital experience aspects. Prioritize monitoring efforts to ensure the performance metrics align with your organization’s goals and user expectations.

Common user action metrics (or performance testing metrics) measured and monitored in DEM include the following:

• User action duration. The time taken to complete the page load.
• Time to first byte. The time from browser request to the first byte of information from the server.
• Time to render. The time it takes for a page to load enough that a user can interact.
• Visually complete. The time to fully render content in viewpoint.
• HTML downloaded. The time it takes the user to receive the last byte or transport connection closes, whichever comes first.
• Speed index. How quickly visible parts of the page are rendered.
• Load event start. The time it takes to begin the page’s load event.
• Load event end. The time it takes to complete the page’s load event.

3. Establish baseline performance metrics

Establishing a baseline for key performance indicators (for the metrics listed above and others) enables ITOps to continuously monitor and compare application performance to identify deviations, anomalies, and recurring issues that may impact customer experience (CX). When analyzing the data, consider factors such as time of day, device types, geographic locations, and user demographics.

Document these metrics, including the benchmark values and any insights gained from analysis, to use as a reference for tracking progress and evaluating the effectiveness of optimization efforts over time.

4. Monitor end-to-end transaction paths

Monitor end-to-end transaction paths leveraging distributed tracing to gain visibility into the entire user journey. This includes monitoring components such as web servers, databases, application performance interfaces (APIs), content delivery networks, and third-party integrations. It should also extend from the user experience to the backend for AI-driven root cause analysis with real-time alerts that pinpoint where an issue is occurring and why.

A unified DEM platform with advanced AIOps observability can provide precise, automated insights and the context of each user’s digital journey in real time.

5. Leverage synthetic monitoring

Synthetic monitoring involves simulating user interactions and transactions to proactively monitor your digital services’ performance and availability. Use synthetic monitoring to conduct regular tests and identify potential issues before they impact real users. Synthetic tests and monitoring can be used across development and production environments, as well as for public and private locations, to provide a comprehensive view into performance and availability.

6. Implement real-time monitoring

Implementing real-time monitoring of all transactions out to the end user allows ITOps to detect and respond to performance issues in real time and near real time before they impact the user experience or affect service-level agreements (SLAs). It also tracks performance metrics in real time so ITOps can take corrective and proactive measures to comply with SLAs.

7. Enhance visibility with video-like recordings

Session replays (also called session recordings) show you exactly what the user saw when interacting with your application. Use these recordings to get a more qualitative view of the user experience and better understand negative and positive interactions. Session replays can also help align teams by simplifying communication and increasing collaboration with clear-cut video evidence that both technical and non-technical stakeholders can understand.

8. Extend your team with dedicated expertise

Digital experience monitoring requires specific skills that some organizations may not have in-house. Depending on the resources you have, you can enhance your team with experts who work full time across different businesses. This will ensure you have the right skills, experience, and analytic power to implement the best digital experience monitoring strategy for your organization and goals.

9. Adopt proactive performance optimization

Fostering collaboration between different teams involved in digital experience monitoring requires a product mindset approach that encourages knowledge sharing. Hold regular meetings and troubleshooting sessions to ensure a holistic approach to DEM that considers all aspects of your infrastructure and CX.

10. Establish cross-functional collaboration

Continuously optimizing your digital services’ performance with cross-functional collaboration of all company stakeholders, not just IT, will boost the end user’s experience. Regularly analyze monitoring data, identify performance bottlenecks, and take necessary actions to improve the speed, responsiveness, and overall performance of your applications and services.

Digital experience monitoring with Dynatrace

DEM encompasses various practices to optimize the performance, usability, and reliability of digital services and applications from the end user’s perspective. By following best practices with advanced AIOps and observability, organizations can effectively monitor and optimize digital experiences to meet users’ expectations and drive business success.

DEM is a core solution within the Dynatrace unified observability and security platform, providing AI-driven, automated, frontend-to-backend context into end-user experience. With real user monitoring, synthetic monitoring, and Session Replay natively built within Dynatrace, it empowers development, operations, and business teams with a single source of truth to deliver flawless user experiences and drive business results.

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To give your customers a top-quality digital experience, it’s important to make sure your applications are always working properly. Synthetic monitoring, also known as synthetic testing, can help to confirm your applications are performing as intended, and if they’re not, help you quickly figure out what’s going on. Although synthetic monitoring tools have become a crucial part of application performance monitoring, not all solutions cover all types or yield the best results.

As modern cloud architectures are evolving, so too are the ways users can interact with your applications. We’ll get into how the different types of monitoring work, but first, some background.

What is synthetic monitoring?

Synthetic monitoring is an application performance monitoring practice that emulates the paths users might take when engaging with an application. It uses scripts to generate simulated user behavior for different scenarios, geographic locations, device types, and other variables.

After collecting and analyzing this valuable performance data, a synthetic monitoring solution can:

• Give you crucial insight into how well your application is performing
• Automatically keep tabs on application uptime and tell you how your application responds to typical user behavior
• Zero in on specific business transactions — for example, by alerting you to issues users might experience while attempting to complete a purchase or fill out a web form

How does synthetic monitoring work?

In synthetic monitoring, a robot client application that has been installed on a browser, device, or computer transmits a series of automated transactions to your application. These server calls and testing scripts simulate an end user’s clickstream as they navigate through key areas of your application. Typically, they run every 15 minutes, but you can configure them for different frequencies or to run immediately based on a specific action.

Once the robot client receives a response from your application, it reports the results back to the synthetic monitoring system. If the client detects an error during one of its regularly scheduled synthetic tests, the monitoring system will ask it to run that test again. If the follow-up test also results in an error, then the monitoring system will consider the error confirmed and escalate it within the organization as appropriate.

Teams can configure synthetic monitoring tools in various ways according to a company’s requirements. For example, you can set up a robot client on a machine that’s located behind your firewall to confirm that the internal environment is running as you expected, or you can deploy a robot client to a computer outside the firewall to get a sense of how well an application is performing. If you want a more comprehensive view of application availability and performance, you can configure several robot clients on browsers in multiple locations.

Synthetic monitoring vs. real user monitoring

Synthetic monitoring is often compared with another application performance technique known as real user monitoring (RUM). As the name suggests, RUM tracks actions taken by actual users instead of emulating them. Organizations often implement RUM by injecting JavaScript code into a webpage and then collecting performance data in the background as actual users interact with that page.

So, what is synthetic monitoring typically used for, and when might a business decide to use RUM instead? Synthetic monitoring is often helpful to identify short-term performance issues that may impact the user experience while an application is still under development. Early detection helps businesses nip potential performance issues in the bud. This approach is handy for regression testing and production site monitoring, for example. Real user monitoring, by contrast, can help a business understand long-term trends in an application’s performance after it has been deployed.

Why use synthetic monitoring?

If your application doesn’t perform well when your customers try to use it, they will quickly leave in pursuit of a better customer experience. This could play out in a variety of ways. For example:

• High bounce rates. Your website might take too long to load, resulting in a high bounce rate. Or, you might be lagging behind your competitors when it comes to application performance without even knowing it. This will hurt your ability to acquire new customers and grow your market share.
• Difficulty troubleshooting. Even when your organization is aware something is amiss with an application, it may not know where to begin troubleshooting. When it comes to application performance, IT teams can’t always get to the bottom of what’s going on quickly — especially when they’re overextended and juggling several priorities. And while your people are in the dark and searching for answers, your organization could feel an immediate and significant impact on its bottom line.

Emulating user behavior paths in a test environment helps you avoid these issues so you can:

• Monitor system health. Synthetic monitoring can tell you if your website is available, how fast it’s running, if key transactions are functioning as expected, and where a potential slowdown or failure might lie.
• Improve performance. Over time, synthetic monitoring can give you performance benchmarks, highlighting areas for improvement and optimization.
• Prevent issues early. You can also use synthetic monitoring to find and fix potential errors before they affect your users, raising the bar on the user experience. This is particularly useful in continuous integration and continuous deployment (CI/CD) environments.
• Increase resiliency. Synthetic monitoring can also help you prepare for peak traffic periods or anticipate performance requirements in a new region or market.

Synthetic monitoring tools are also useful for making sure you’re honoring service level agreements (SLAs) with your end-users. If an issue comes up involving third-party providers, you will be better equipped to hold them accountable, as well.

Types of synthetic monitoring

Synthetic monitoring usually includes three types: availability monitoring, web performance monitoring, and transaction monitoring.

• Availability monitoring enables an organization to confirm that a site or application is available and responding to requests. Availability monitoring can also use a more granular approach — for example, by checking to make sure specific content is available or that a specific type of API call is successful.
• Web performance monitoring typically looks at specific web metrics such as page load speed and the performance of specific elements on a webpage. It checks web content, errors, and sluggish response times.
• Transaction monitoring attempts to complete specific transactions such as logging in, completing a form, and checkout.

Within the realm of synthetic monitoring, there are also two main categories of synthetic tests:

• Browser tests – a robot client simulates a transaction a user might attempt (such as making a purchase)
• API tests – an organization monitors specific endpoints across each layer of the network and application infrastructure

Within API tests, there are different types of tests, including HTTP, SSL, and DNS. For example, API tests often use HTTP tests to monitor application uptime and responsiveness. Meanwhile, SSL tests confirm if users can securely complete transactions on a site using valid SSL certificates, and DNS tests make sure the site’s DNS resolution and lookup times are within expected parameters. A company might use multiple API tests to monitor whether a specific workflow is working properly from end to end — this type of API monitoring is called a multistep API test.

Challenges of synthetic monitoring

Modern applications are inherently complex. Because users access them from a variety of locations and contexts, synthetic monitoring is often not comprehensive enough to account for all the potential errors or situations that might arise. DevOps teams are accounting for this problem by placing a higher priority on introducing application testing earlier in the software development life cycle. However, synthetic monitoring is still often difficult to properly set up without specialized technical knowledge, and it is time-consuming even for the team members that have the required skill set.

Synthetic tests aren’t very resilient, and they can easily fail when small UI changes are introduced, generating unnecessary alert noise. This means that whenever a minor application element such as a button is changed, the corresponding test must be, too. And lastly, many synthetic monitoring tools lack the context needed to explain why a specific failure happened or what the business implications might be, lengthening time to resolution and making it unnecessarily difficult to prioritize application performance issues.

Benefits of synthetic monitoring

Proactive Monitoring: Synthetic monitoring allows you to proactively spot performance issues and availability problems. By imitating user interactions, it identifies irregularities before they impact real users. This early detection helps prevent potential outages and ensures a smoother user experience.

Global Testing: You can assess your application’s performance in various locations. Synthetic tests run from multiple regions, providing insights into regional variations in performance.

Browser and Device-Specific Testing: Synthetic monitoring lets you evaluate your application’s behavior across different browsers and devices. This helps identify compatibility issues and ensures consistent performance for all users.

Works Inside and Outside the Firewall: Unlike Real User Monitoring (RUM), which relies on actual users, synthetic monitoring can be completely automated and run inside and outside your organization’s network. This flexibility ensures comprehensive coverage.

Automated with Regular Frequency: Synthetic monitoring can run at regular intervals, such as every minute or hour, 24/7. This continuous monitoring ensures timely detection of any performance degradation.

Synthetic monitoring tools

A good synthetic monitoring solution should give your organization complete, 24/7 visibility into your applications. To accomplish this, it should include the following kinds of synthetic monitors:

• Single-URL browser monitors. A single-URL browser monitor simulates the experience a user would have while visiting your application using an up-to-date web browser. When run frequently from public and private locations, a browser monitor can alert you when your application becomes inaccessible or when baseline performance degrades significantly.
• Browser click paths. Browser click paths also simulate a user’s visit, but they monitor specific workflows in your application. An advanced synthetic monitoring solution can let you record the exact sequence of clicks and user actions you want to monitor, then set the browser click path to automatically run at regular intervals.
• HTTP monitors. HTTP monitors are useful for monitoring whether specific API endpoints are available, and they can also perform straightforward HTTP checks to confirm single-resource availability. HTTP monitoring tools should allow you to set up performance thresholds for HTTP monitors, too.

Synthetic monitoring use cases

Financial Services

Online Banking Systems: Synthetic monitoring ensures that online banking platforms remain available and responsive for users.

Payment Gateways: Monitoring payment gateways helps prevent transaction failures and delays.

Trading Platforms: Synthetic tests verify the performance of trading systems, which are critical for financial institutions.

Healthcare Technology

Electronic Health Record (EHR) Systems: Synthetic monitoring ensures EHR systems are accessible and responsive for healthcare professionals.

Patient Portals: Monitoring patient portals helps maintain seamless communication between patients and healthcare providers.

Telehealth Platforms: Synthetic tests validate the reliability of telehealth services, especially during high-demand periods.

General Web Applications

Availability Testing: Proactively detect failures even when there is no user traffic (for example, off business hours).

API Endpoints: Monitor the availability and performance of third-party API endpoints.

How Dynatrace can power your synthetic monitoring

If you’re thinking about better understanding how your applications are performing, you might be wondering what synthetic monitoring tools you need to get started. You’ll want to pick a solution that simulates business-critical journeys through your most important applications across your mobile and web channels. This can give you immediate answers to questions about application availability and the impact it’s having on the user experience. Your synthetic monitoring solution should also be able to help you quickly identify the root cause of any application performance issue so you can resolve it as soon as possible.

Dynatrace Synthetic Monitoring provides all the information you need to know the moment an application’s performance falters. By using all major desktop and mobile browsers to simulate user activity, Dynatrace helps ensure that web, mobile, cloud, and streaming transactions go smoothly for customers around the globe.

This is key for evaluating whether applications meet your SLA requirements, and it can determine whether business outcomes have been impacted. It can also eliminate troubleshooting through AI-driven automation, as well as rank problems in order of importance to the business — significantly reducing the time required for your IT team to identify and address root causes.

To catch those longer-term trends, Dynatrace RUM uniquely captures the full visibility of the customer experience to eliminate user experience blind spots, and Session Replay provides indisputable video evidence of the complete digital experience, so business, development and operations stakeholders can collaborate and agree where to make improvements.

These advanced digital experience monitoring capabilities help you proactively identify and address application performance issues from anywhere in the world. With the right synthetic monitoring solutions in place, your business can go a long way toward ensuring a consistent, satisfying customer experience.

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As the world becomes increasingly interconnected with the proliferation of IoT devices and a surge in applications, digital transactions, and data creation, mobile monitoring — monitoring mobile applications — grows ever more critical. To navigate this complex landscape, organizations require a comprehensive approach to observability that enables a holistic view of applications. Such a view is essential for ensuring a seamless user experience and enabling organizations to achieve their business objectives.

With the recent retirement announcement of Microsoft Visual Studio App Center, users seeking alternative options can turn to the Dynatrace unified observability and security platform. Dynatrace observability, security, and data analytics capabilities empower users to derive greater insights and benefits from their monitoring data, ensuring they stay ahead in their mobile monitoring environments while offering similar feature parity to Visual Studio App Center.

Benefits of an integrated observability platform for mobile monitoring

For Microsoft Visual Studio App Center users, migrating to Dynatrace for a mobile monitoring solution provides an all-in-one, integrated approach, delivering the context, visibility, and answers needed to solve complex problems across all the technologies involved in mobile app development while ensuring a seamless user experience. Mobile monitoring with the Dynatrace observability and security platform provides the following key benefits.

• Enhances business insight and efficiency with real-time data
  Organizations can focus on what is important with deep insights on user sessions, top devices, OS versions, and behavioral analytics for iOS, Android, and hybrid apps. Dynatrace enhances operational efficiency and helps drive business results, ensuring the precise information mobile app developers need to deliver effective user experiences.
• Reduces blind spots with complete visibility from front to back end
  With an observability platform, organizations are not limited to only the backend performance or the front-end user experience data because the entire picture is delivered in one place. With this holistic view, mobile developers can not only see what users are doing by looking at individual user actions, full user sessions, and errors, but also see how the experience flows to the back end through web requests, called services, and more. As a result, developers avoid the blind spots that may arise from disconnected data or tools, and most importantly can quickly learn why something happened.
• Helps fix issues faster with answer-driven root cause analysis to release higher quality applications with confidence
  The end-to-end mobile app observability from Dynatrace automatically provides developers with the root cause of problems impacting their mobile users so they can quickly address them, rather than waste time diagnosing what needs to be done.
• Prevents negative customer experiences with robust crash analytics
  Mobile app crashes are one of the biggest pain points for mobile users and developers, with crashes known to cause 71% of app uninstalls. Dynatrace mobile RUM provides advanced crash analytics that show how many real users are affected by a crash across dimensions such as app version, OS version, device, jail-broken status, and more. These analytics help mobile developers quickly diagnose and fix mobile app crashes. In addition, Session Replay enables developers to immediately reproduce crashes and view exactly what actions a user took.
• Enables teams to collaborate effectively with a single source of truth
  When mobile monitoring is an integrated part of a larger unified observability and security platform strategy, it fosters more effective collaboration because everyone has access to the same data. With Dynatrace, mobile developers, mobile application owners, and mobile DevOps practitioners can work together better and more efficiently to deliver enhanced mobile app experiences.

End-to-end mobile observability with Dynatrace

Dynatrace Mobile RUM is built into the Dynatrace platform to provide complete mobile monitoring for mobile developers and app teams to optimize applications and improve customer engagement and retention. With industry-leading AI at the core of the platform to drive advanced crash analytics, continuous topology mapping, root cause analysis, and quantifiable business impact, Dynatrace simplifies mobile monitoring so mobile app teams can deliver the high-quality performance and user experiences mobile users expect today.

Check out the three-part mobile monitoring video series to learn more about Dynatrace mobile app monitoring.

• Part 1: Instrumentation for the Developer
• Part 2: Value for Mobile Application Owners
• Part 3: Value for Mobile DevOps Practitioners

For existing Microsoft Visual Studio App Center customers that need support migrating to the Dynatrace mobile monitoring solution, click here for more information.

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A business process is a collection of related, usually structured tasks or steps, performed in sequence, that achieve a defined business goal. Tasks may be manual or automatic, and many business processes will include a combination of both. Business processes are important because they improve the efficiency, consistency, and quality of the business outcome.

Monitoring business processes is one thing organizations can do to help improve the key business processes that enable them to provide great customer experiences. Business process monitoring refers to continuously tracking and analyzing key performance indicators (KPIs) from relevant process milestones. The goal of business process monitoring is to provide real-time visibility into the performance of a business process, including end-to-end delays so that stakeholders can quickly identify and respond to issues as they arise, and drive business outcomes that will deliver a better customer experience.

Benefits of business process monitoring

Business process monitoring enables organizations to understand how an end-to-end process is performing and pinpoint where in the process they can make improvements. Business process monitoring helps organizations:

• Increase efficiency by identifying and addressing bottlenecks or inefficiencies that may slow down a business process.
• Improve quality control by tracking KPIs and identifying areas where quality may be lacking to enable corrective action.
• Improve customer satisfaction by improving processes to provide customers with faster and higher quality service.
• Make better decisions by providing managers with real-time data about the business.
• Reduce costs. Organizations can reduce their operating costs and increase their bottom line with more efficient and better processes.

Digital transformation increases the importance – and challenges – of business process monitoring

Most organizations rely on many business processes across different departments to meet their goals and deliver outcomes to their stakeholders, whether customers, partners, or employees. As organizations continue to undergo digital transformations, more and more of their business processes are dependent on increasingly complex digital technologies. In fact, most business processes still rely on multiple systems, long ago outgrowing the capabilities of built-in platform-centric monitoring. This leaves business leaders with incomplete visibility into these critical business assets.

Approaches to achieve end-to-end visibility have met limited success, in part due to these common challenges:

• Business data is scattered across multiple systems, with inconsistent formats and different means of access. Code changes are often required to expose important business data, delaying implementation. Fragile data pipelines introduce ongoing maintenance overhead.
• Data at rest, the source for many pipelines, can be stale, often by days or longer, preventing real-time business agility.
• Business data lacks connection to the supporting IT context, limiting effective collaboration between business and IT teams. Consequences include delayed anomaly detection and missed optimization opportunities.

The results can include delayed, incomplete, and disconnected views of business process health, impacting a range of stakeholders:

• Business leaders lack real-time visibility into some of their most important assets.
• IT teams lack insight into how the systems they manage impact business outcomes.
• Customers experience product and communication delays.
• Opportunistic competitors encroach on market share.

Traditional approaches to business process monitoring must evolve to provide the end-to-end visibility, IT context, and timeliness that organizations need.

Business process monitoring examples: A retail use case

It can be helpful to put business processes in the context of a specific type of business or industry. For example, consider three common retail industry business processes that must work together efficiently to meet customer expectations:

• Inventory management to anticipate and meet dynamic customer demand
• Order processing workflow triggered by customer orders
• Order fulfillment to deliver orders to customers

Anomalies or delays anywhere in these business processes can impact customer satisfaction and revenue – yet we know these anomalies will occur.

• Without timely inventory data, business owners may be unable to respond in real time to supply chain issues or sudden shifts in customer behavior.
• Without IT context, business owners may not be able to identify the root cause of order processing failures.
• Without viewing the business process as an asset, IT teams will have little understanding of business process health, instead relying on isolated system health.

While the examples above illustrate one industry, it’s easy to see how similar processes or flows would extrapolate to other industries, such as a financial transaction workflow, applying for a loan, or booking and checking in for a trip.

How Dynatrace helps improve business process monitoring

Business Observability helps organizations across all industries gain visibility into their business processes, overcoming many of the challenges outlined above. With Dynatrace, organizations benefit from one AI-powered data platform for unified observability, security, and Business Observability, providing real-time observability for data-driven business decisions. The ability to capture business data from anywhere, whether that’s OneAgent, real user monitoring sessions, log files or external tools and data sources, simplifies access to the real-time, precise business data organizations need for better visibility into their business processes.

With access to topology metadata to automatically enrich business events, Dynatrace enables a straightforward approach to business process monitoring that goes beyond traditional methods, putting individual business process steps in context with business and IT data. Organizations can visualize an entire business process through a purpose-built Business Flow application or a custom dashboard, leveraging Davis AI for root cause analysis and Smartscape to drill down into host and process details.

*^{Salesforce customer engagement research}

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Mobile applications (apps) are an increasingly important channel for reaching customers, but the distributed nature of mobile app platforms and delivery networks can cause performance problems that leave users frustrated, or worse, turning to competitors.

As a result, organizations need to monitor mobile app performance metrics that are meaningful and actionable by gaining adequate observability of mobile app performance.

From the customer perspective, mobile devices have become the singular touchpoint between businesses and users, for example, the new storefront, office, and customer support line. As a mission-critical part of a business, organizations and their teams need to understand how mobile applications are performing both from a technical and customer experience point of view.

What is mobile app performance?

Mobile app performance is a measure of how well a mobile application is meeting technical expectations. This includes how quickly the application loads, how much load it is putting on the device, how much storage is being used, and how frequently it crashes. There are many common mobile app performance metrics that are used to measure key performance indicators (KPIs) related to user experience and satisfaction. For example, an app that does not crash often but is frequently slow from a user’s perspective is providing a poor user experience. That should be apparent from the business-side KPIs.

Closely monitoring mobile app performance will help ensure customer interactions via mobile apps are meeting the expectations of the customers.

Mobile app performance metrics

There are several metrics that can be tracked to evaluate the performance of a mobile app and go beyond just assessing the technical performance. Some of the most important KPIs are listed below.

• User acquisition measures the number of new users downloading and installing an app. This can help teams understand how effectively the app is being promoted to reach new users.
• User engagement measures the level of interaction that users have with an app. Metrics such as time spent in the app, number of sessions per user, and retention rate help to understand if users find value from the app.
• App store ratings and reviews provide valuable feedback on the quality and usability of a mobile app. Monitoring these metrics can help to identify areas for improvement and improve app store search rankings.
• App crashes and errors help identify issues that may be impacting the user experience, so they can be remediated.
• In-app purchases can help to measure the overall effectiveness of your business strategy.
• User demographics, such as app version, operating system, location, and device type, can help tailor an app to better meet users’ needs and preferences.

By tracking these KPIs and similar, organizations can gain valuable insights into the performance of their mobile apps and make data-driven decisions to improve the user experience and drive growth.

Why observability matters for mobile app performance monitoring

Observability data is becoming increasingly important to mobile app performance monitoring because it provides mobile developers with deeper insight into their applications. Examples of observability data include metrics, logs, and traces which provide visibility into the app’s behavior and performance at different levels of the stack, including the application code, infrastructure, and network.

Here are some ways observability data is important to mobile app performance monitoring.

Issue remediation. Observability data can identify and diagnose mobile app issues, including performance bottlenecks and crashes. By analyzing log data and tracing user interactions, developers can pinpoint the source of the problem and enact targeted fixes.

Proactive monitoring. Observability data can be used to proactively monitor mobile apps for potential issues before they arise. By monitoring metrics such as error rates, response times, and network latency, developers can identify trends and potential issues, so they don’t become critical.

Performance optimization. Observability data can be used to optimize mobile app performance by identifying areas of the app that are slow or resource intensive. By analyzing performance metrics, developers can improve app responsiveness and reduce resource consumption.

Capacity planning. By analyzing trends in resource consumption and performance metrics, developers can predict future needs and plan for capacity upgrades or infrastructure changes.

Focused crash and error reporting. With observability data, mobile developers can see crash and error reports in context to help them prioritize where to focus. For example, if crashes only occur on low-resource devices running a version of the operating system that is several years old and there is only a small number of users experiencing crashes, then fixing the error may be a lower priority.

Load time and network latency metrics. If apps are experiencing load time and network latency, which can be detected through observability data, it should warrant further investigation. For example, distributed traces can highlight backend services that might be a bottleneck in an app task. This kind of information will help app developers isolate the cause of performance issues so they can modify their code and improve performance.

Observability data is essential to mobile app performance monitoring because it provides developers with a more complete view of their app’s behavior and performance. By analyzing this data, mobile developers can identify issues, optimize performance, and plan ahead.

Mobile app performance best practices

Best practices for monitoring app performance start with app instrumentation so teams can get the full visibility needed to improve app performance. The following includes best practices for optimizing mobile app performance.

• Prioritize user experience. Ultimately, the success of your mobile app depends on user experience. Prioritizing user experience by optimizing performance, reducing load times, and providing a seamless experience can lead to improved app retention and engagement.
• Minimize network requests. Minimizing the number of network requests that your app makes can improve performance by reducing latency and improving load times. This can be achieved by reducing the size of files or images, using caching, and compressing data.
• Optimize images and videos. Images and videos can be resource-intensive and can slow down your app’s performance. Optimizing images and videos by reducing their size, using compression, or using lazy loading techniques can improve app performance.
• Optimize battery life. Mobile app performance is not just about speed and responsiveness but also about battery life. Optimizing your app to use less battery can improve user experience and reduce negative reviews.
• Continuous monitoring. Monitoring an app’s performance constantly with observability can help identify issues and optimize apps for improved performance.

Optimize mobile app performance with end-to-end observability

Mobile app monitoring is natively built into the Dynatrace observability and security platform. This provides complete mobile performance monitoring for mobile developers and application teams so they can optimize applications and drive customer engagement and retention. With industry-leading AI at the core of the platform to drive advanced crash analytics, continuous topology mapping, root cause analysis, and quantifiable business impact, Dynatrace simplifies mobile monitoring so mobile app teams can deliver the high-quality performance and user experiences mobile users expect today.

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Synthetic testing simulates real-user behaviors within an application or service to pinpoint potential problems. Here’s a look at why this testing matters, how it works, and what companies need to get the most from this approach.

What is synthetic testing?

Synthetic testing is an IT process that uses software to discover and diagnose performance issues with user journeys by simulating real-user activity. Also called continuous monitoring or synthetic monitoring, synthetic testing mimics actual users’ behaviors to help companies identify and remediate potential availability and performance issues.

For example, teams can program synthetic test tools to send large volumes of simultaneous resource requests to a new application and evaluate how well it responds. Are all requests met in a reasonable amount of time? If not, what was the median increase over baseline response times? Did these requests impact users’ overall application experience? How?

By understanding how an app or service may respond in real-world conditions, teams can better pinpoint potential problems and fix these issues before live deployment or before users are affected. Along with real user monitoring (RUM), synthetic testing provides a comprehensive view into the user experience to ensure software meets user requirements.

Synthetic testing vs. real user monitoring

Both synthetic testing and real user monitoring (RUM) play a role in application development. The biggest difference between synthetic testing and RUM is synthetic simulates real-world scenarios, whereas RUM measures actual real-user behavior.

Another difference is where they happen in the development life cycle. Because synthetic tests don’t require a live environment, teams can conduct them in production or test environments to assess overall performance or the performance of a specific application component. In addition, these tests’ simulated nature requires minimal resource overhead, meaning teams can run them continuously.

RUM, meanwhile, requires actual users. As a result, it can only be conducted once applications or services are live. While this makes RUM more limited in scope, it doesn’t negate its value. Synthetic tests attempt to mimic both common and uncommon user behaviors, but even the best simulations are no substitute for the real thing. Users often interact with technologies in unpredictable ways that could reveal previously unknown issues.

Put another way, users may act out of frustration in response to a set of circumstances — such as “rage clicking” if an application doesn’t respond fast enough. Synthetic tools, meanwhile, can only simulate frustration. While teams can program them to rage click after a certain point or even randomly, they can never truly replicate the real-user experience.

As a result, organizations benefit from a mix of RUM and synthetic tests.

Types of synthetic testing

There are three broad types of synthetic testing: availability, web performance, and transaction. Each offers its own strengths and abilities for different needs.

Availability testing

Availability testing helps organizations confirm that a site or application is responding to user requests. It can also check for the availability of specific content, or if specific API calls are successful.

Web-performance testing

Web-performance testing evaluates metrics including page loading speed, the performance of specific page elements, and the occurrence rate of site errors.

Transaction testing

Transaction testing sees robot clients attempting to complete specific tasks, such as logging into accounts, filling in an on-site form, or completing the checkout process.

How synthetic testing works

Synthetic testing works by using a robotic client application installed on a browser, mobile device, or desktop computer. These applications send a series of automated test calls to a service or application, which simulate a user’s “clickstream,” or the actions they take while on the site.

Consider a synthetic test designed to evaluate an e-commerce shopping application. First is a test of the home screen. Does it open quickly and consistently with no visual artifacts? Next, the synthetic test links to product pages. It then moves on to shopping carts, shipping rates, and, finally, a simulated purchase. The robotic client application reports if any of these transactions fail or perform too slowly, allowing companies to make changes before services go live.

When designing synthetic tests, it’s worth using a combination of browser-based, mobile, and desktop tests to assess application performance. This is because each of these traffic vectors comes with unique challenges. For example, even if response times are consistent across all testing environments, mobile users could still experience issues with automatic screen size scaling or application design choices that favor desktop users.

Requirements for synthetic testing

To create a reliable testing environment, several components are critical.

Well-defined goals

Effective synthetic testing depends on well-defined goals. Organizations need to identify what they’re trying to measure before they write and deploy test scripts. Then, it’s ensured the data captured is relevant to these goals.

Customizable robot clients

Robot clients are the foundation of synthetic testing. But in the same way that testing needs to evolve over time, organizations need client components that can be configured to keep pace with change as their on-premises, cloud, or hybrid environments evolve.

Comprehensive synthetic testing tools

To effectively evaluate and optimize digital experiences, organizations need synthetic testing tools that cover the major user touchpoints with their environments. At a minimum, a synthetic monitoring program should include the following tools:

• Single-URL browser monitors. Single-URL browser monitors simulate a user’s visit to a site using an up-to-date web browser. By consistently running single-URL browser testing from both public and private sources, organizations can ensure that sites maintain an established baseline performance.
• Browser clickpaths. Browser click-path testing monitors specific click sequences through critical application workflows. Running browser click paths regularly can ensure that specific function sequences remain available and high-performing.
• HTTP monitors. HTTP monitors use simple HTTP requests to ensure that specific application programming interface (API) endpoints and website resources are available.

In addition to running these tests regularly, organizations should ensure their synthetic testing tools follow basic security best practices. For example, a synthetic testing tool should not send requests to local hosts or default IP addresses.

Synthetic testing best practices

A few best practices can help organizations achieve reliable, actionable data from synthetic testing tools. The following are three strategies worth considering.

1. Reduce setup complexity

The level of application integration required for synthetic solutions to collect key data can lead to complexity. To help mitigate this risk, clearly define your goals before setting up tests to streamline configuration and leverage tools designed to simplify the process with visual or script-based setups.

2. Prioritize continual monitoring

Continual monitoring is critical for organizations to understand how applications and users interact over time. It also plays a key role in addressing the issue of test fragility. Even small changes to the user interface — such as removing or replacing a button — can cause tests to fail. Continual monitoring allows organizations to detect and fix these failures immediately.

3. Keep context front and center

Testing doesn’t drive action without context. While many synthetic tools provide data about what’s happening when users interact with applications, they don’t answer the question of why. Keeping context front and center with synthetic testing integrated into an end-to-end observability platform provides complete visibility without adding additional tools.

Synthetic testing as part of a unified observability strategy

To make synthetic testing easy to develop and maintain, it should be part of a wider observability strategy. By taking a unified platform approach to observability, organizations can integrate synthetic testing into their overall application performance efforts.

Dynatrace synthetic monitoring provides continuous and on-demand answers to questions about application performance, reliability, and the overall user experience. By combining this approach with Dynatrace RUM, it’s possible to capture the full range of customer behavior, from common clickstreams to unexpected actions that may suddenly tax resources or lead to strange application behavior.

Dynatrace helps organizations proactively evaluate the performance of applications no matter where they are in the development cycle. The result is enhanced application performance and improved user experience that keeps customers coming back.

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What is customer experience analytics: Fostering data-driven decision making

In today’s customer-centric business landscape, understanding customer behavior and preferences is crucial for success. Customer experience analytics is the systematic collection, integration, and analysis of data related to customer interactions and behavior with an organization and/or its products and services. The analysis of this data offers valuable insight into the overall customer experience, enabling businesses to optimize their strategies and deliver exceptional experiences.

Customer experience analytics best practices

As organizations establish or advance their customer experience analytics strategy and tools, the following five best practices can help maximize the benefits of these analytics.

1. Define clear objectives

Establish clear objectives and identify specific insights you want to gain from the data. For example, are you looking to understand customer preferences, improve satisfaction, or identify pain points in the customer journey? Defining clear objectives will guide your analysis efforts and help maintain focus on extracting the most relevant and actionable information. It will also help to gain alignment among the necessary stakeholders across executive leadership, digital, product, development, or analytics teams.

2. Capture and consolidate data from multiple sources

To get meaningful insights, it’s crucial to collect comprehensive and relevant data by capturing data from various touchpoints and channels that customers interact with. This may include digital experience monitoring, such as mobile or web real user monitoring, product analytics, website analytics, customer relationship management data, customer feedback, Net Promoter Score (NPS), and more. The data should cover both quantitative metrics (e.g., purchase history, and clickthrough rates) and qualitative feedback (e.g., surveys and reviews). By gathering a range of data, organizations can develop a holistic view of customer journeys and uncover meaningful patterns and trends.

3. Use advanced analytics techniques

Customer experience analytics goes beyond basic reporting. Embrace advanced analytics techniques to unlock deeper insights. Employ segmentation to group customers based on shared characteristics, which allows you to tailor experiences and strategies to specific segments. Implement predictive modeling to forecast customer behavior and identify opportunities for personalized engagements. Embrace sentiment analysis to understand customer emotions and gauge satisfaction levels. With advanced analytics techniques, organizations can extract greater value from data and ultimately make better data-driven decisions.

4. Integrate data sources for a unified view

Customer experience analytics often involves analyzing data from multiple sources. To ensure a unified view of the customer journey, it’s important to integrate these disparate data sources. This integration allows you to connect the dots and gain a comprehensive understanding of customer behavior across touchpoints. Consider how easy it is to integrate different tools and data sources. For example, you may benefit from enriching digital experience monitoring data with insights from web analytics or tracking every step in a business process regardless of the data source – to understand the end-to-end customer experience.

5. Foster a culture of data-driven decision making

To make the most of customer analytics, it’s crucial to foster a culture of data-driven decision making within your organization. Encourage cross-functional collaboration and ensure that decision makers have access to relevant insights. Train employees how to interpret and use customer analytics effectively. Regularly share success stories and case studies that demonstrate the impact of data-driven decision making on customer experience. By instilling a data-driven mindset, organizations empower teams to make informed decisions that drive improvements in customer experience.

Driving decisions with data

Customer experience analytics has the potential to transform how organizations understand and optimize customer interactions. By following these best practices — by defining clear objectives, collecting comprehensive data, using advanced analytics techniques, integrating data sources, and fostering a culture of data-driven decision making, you can extract the greatest value from customer experience analytics. Embrace the power of data to gain actionable insights, enhance customer satisfaction, and drive business growth in today’s competitive landscape.

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In today’s competitive business environment, customers demand seamless user experiences. As businesses compete for customer loyalty, it’s critical for organizations to harness tools that help them see how users interact with their services. By understanding the difference between two such tools, synthetic monitoring vs. real user monitoring, teams can use them to develop high-performing applications and services that deliver loyalty-building user experiences.

Although both of these development and testing practices examine user behavior, they have distinct—and complementary—goals. Here, we’ll explore synthetic monitoring and real user monitoring, and how both help to deliver user experiences that win—and keep—customers.

What is real user monitoring?

Real user monitoring (RUM) is a performance monitoring process that collects detailed data about users’ interactions with an application. RUM gathers information on a variety of performance metrics. If you collect data on page load events, for example, it can include navigation start (when performance measuring begins), request start (when the user initiates a server request), and speed index metrics (measure page load speed).

A user session, also known as a click path or user journey, is a user’s sequence of actions while working with an application. User sessions can vary significantly, even within a single application. RUM collects data on each user action within a session, including the time required to complete the action. As a result, IT pros can identify patterns and where to make improvements in the user experience.

Ideally, a RUM tool would record all user actions to capture the complete picture of a user’s experience. In reality, only highly scalable RUM solutions can collect data on all user actions, while less scalable tools must sample user actions and make inferences from partial data.

Benefits and challenges of RUM

Real user monitoring is great for providing real metrics from real users navigating a site or application. The benefits of RUM include the following:

• Access to data from real end users across various applications, services, and environments.
• The ability to collect a diverse set of data points for every user accessing the application or services.
• Customizable variables to track, collect, and evaluate application-specific data points using JavaScript.
• Real-time monitoring of user application and service interactions.
• Enhanced issue remediation with the option to watch visual session replays of users interacting with web services or applications.

RUM, however, has some limitations, including the following:

• RUM requires traffic to be useful. If teams use RUM in a pre-production environment, it’s challenging to get useful information. Because teams use pre-production environments for testing before releasing an application to end users, they have no access to real-user data.
• RUM works best only when people actively visit the application, website, or services.
• In some cases, you will lack benchmarking capabilities. Because RUM relies on user-generated traffic, it’s hard to indicate persistent issues across the board.
• RUM generates a lot of data. RUM’s attention to detail results in a more accurate diagnosis of end-user issues and experiences. However, the volume of data it generates can make responding to specific issues cumbersome and difficult to prioritize.

What is synthetic monitoring?

Synthetic monitoring, also known as synthetic testing, is a performance monitoring practice that emulates users’ paths when engaging with an application. It uses scripts to generate simulated user behavior for different scenarios, geographic locations, device types, and other variables.

After collecting and analyzing this valuable performance data, a synthetic monitoring solution keeps tabs on application updates and how an application responds to typical user behavior. For example, synthetic monitoring can zero in on specific business transactions, such as completing a purchase or filling in a web form. This gives teams crucial insight into how an application is performing.

Benefits and challenges of synthetic monitoring

Synthetic monitoring is good at catching regressions during development lifecycles, especially with network throttling. The benefits of synthetic monitoring include the following:

• Simulation of entire user journeys in a controlled application environment.
• The ability to identify application performance issues and potential issues by running interval tests.
• Customized tests based on specific business processes and transactions — for example, a user leveraging services when accessing an application.
• Complex transaction and process monitoring that might have deeper dependencies. For example, in e-commerce, you can validate and test checking out a shopping cart.
• Application or service lifecycle testing at every stage. This includes development, user acceptance testing, beta testing, and general availability.
• Geofencing and geographic reachability testing for areas that are more challenging to access. For example, the ability to test against a wireless provider in a remote area.
• Performance testing based on variable metrics (i.e., connectivity, access, user count, latency) of geographic regions.

Much like RUM, however, synthetic monitoring has its limitations. Here are some drawbacks:

• Synthetic monitoring can be too predictable. In synthetic monitoring, tests and results are generated in a controlled and predictable testing environment. Because synthetic monitoring doesn’t track real users, you’ll have challenges gauging what an end user might experience in the event of an unpredictable variable.
• Tools may be limited. Depending on the vendor or technology you work with, you may not be able to integrate existing tools with scripts for your tests.
• The range of costs and feature sets can vary widely for synthetic monitoring tools. It’s recommended to work with partners that can deliver RUM and synthetic user monitoring to deliver the most value.

Synthetic monitoring vs. real user monitoring: Which do you need?

The real answer may not be one or the other. Instead, when working with websites, applications, or services, you may need both. While synthetic monitoring allows you to create a consistent testing environment by eliminating the variables, both RUM and synthetic monitoring provide feedback about site, application, or service performance. The strength of both solutions comes when you combine them.

By using synthetic monitoring and RUM together, you can thoroughly investigate specific user issues, and discover and resolve shortcomings. Furthermore, both tools provide full visibility into user and service performance. Using both, you can gauge how fast a site or service needs to be to ensure user satisfaction and to deliver optimal performance.

Together, RUM and synthetic monitoring can accomplish the following:

• Help correlate business requirements with performance levels by providing the performance data you need to analyze the end-user experience.
• Pinpoint challenges before active users access your website or services by identifying problems, such as configuration issues, so you can fix them before users log on.
• Use data from one engine to facilitate testing for the other. For example, you can use RUM data to provide real use cases you can use for simulation in synthetic monitoring for detailed testing in a staging environment.

Finally, combining RUM and synthetic monitoring data can make troubleshooting faster and easier. For example, real-user monitoring metrics might reveal a user performance issue that you can then apply to synthetic testing to replicate the issue by exercising the same transaction across several different variables.

Synthetic monitoring and RUM—better together

The bottom line? Both RUM and synthetic monitoring tools provide insight into every step within the application delivery chain and life cycle.

Working with both RUM and synthetic monitoring creates a healthy long-term solution to support the best possible user experience. While using one or the other tool will undoubtedly help to analyze performance in different ways, the true power comes when you use them as a complementary toolset. The result is a more comprehensive and robust monitoring strategy that will have a longer-lasting impact on user performance and experience.

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