Synthetic monitoring has rapidly become critical allowing technical teams to proactively identify digital experience issues before customers are impacted. This comprehensive guide provides an in-depth reference covering what synthetic monitoring entails and how it can be strategically leveraged.
What is Synthetic Monitoring: A Technical Deep Dive
Synthetic monitoring refers to the practice of continuously testing critical user journeys in applications and websites using automated scripted bots that simulate users. But how does it actually work under the hood?
Scripting Realistic User Flows
The starting point is identifying key user workflows like login, search, checkout etc. and scripting these flows to mimic real user interactions programmatically. Sophisticated solutions allow such scripts to be recorded directly from browsers to capture hard-to-script elements like drag-and-drop.
Strategically Setting Checkpoints
Instrumentation code is inserted at key steps within the scripts – known as checkpoints. This extra code enables capturing performance metrics when tests are executed and makes scripts configurable.
Emulating Browser Behavior
For authenticity, synthetic tests leverage real browser environments like Chrome, Firefox or custom browsers that are engineered to emulate users. Browser-based testing covers page load performance, JavaScript execution and visual verification.
Executing API Level Checks
For testing APIs and services without UI layer, synthetic checks directly invoke endpoints through HTTP requests and validate responses. Parameterization makes API checks configurable across environments.
Running Checks from Multiple Geographic Regions
To reflect real-world diversity, tests are orchestrated across nodes distributed globally across AWS, Azure and on-premise data centers. This provides region-specific web performance insights.
Logging Detailed Component Telemetry
Behind the scenes, synthetic checks log granular network & application layer telemetry for component-level diagnostics. Everything from DNS lookup to database query times can be captured.
Capturing Metrics for Analysis
At each checkpoint during test execution, metrics like response times, error rates and availability get captured and streamed back to monitoring platform for analysis, visualization and alerting.
Intelligent Failure Analysis
Sophisticated platforms can identify root causes when failures occur through contextual tracing across application topology and pinpoint problematic components causing service degradation.
This under-the-hood insight illustrates the technical depth behind synthetic testing solutions.
Emerging Capabilities and Innovations
Synthetic monitoring is a rapidly evolving technology category being reinvented through cutting-edge capabilities:
Intelligent Test Data Management
Solutions are emerging to externalize and parameterize test data enabling better test governance, reuse and consistency across environments. AI assists in managing this test data at scale.
Computer Vision Based Crawling
Traditional script creation requires manual user flows analysis. Computer vision crawlers now automatically traverse apps to identify test coverage gaps and contextually recommend user paths for synthetic testing.
Concept Tagging Through AI
ML algorithms can automatically tag monitored metrics with contextual concepts like user, product or environment. This allows faster analysis by dynamically grouping KPIs across dimensions instead of rigid naming conventions.
Integration with CI/CD Pipelines
Leading solutions can now automatically generate synthetic test scripts by tapping into CI/CD pipeline metadata of code changes. This enables shift-left testing earlier in delivery lifecycle.
Testing Microservice Architectures
Solutions are emerging to model microservices topologies and dynamically auto-discover tests spanning interconnected services as architectures change, avoiding complex manual script maintenance.
Third Party Synthetic Labs
Shared public cloud labs from vendors offer synthetic test environments with geographic distribution and connectivity integrations, avoiding overhead of private test infra setup and maintenance.
Synthetic Alert Noise Reduction Through ML
By dynamically profiling metric behavior to distinguish transient glitches vs real user impacting incidents, ML is being utilized to cut through false positives and alert fatigue.
These innovations showcase how synthetic monitoring is continuously being reinvented to provide richer insights through cutting edge technology.
Advanced Analytic Opportunities
While synthetic monitoring acts as an early warning system for digital experience issues, the wealth of timeseries data collected also unlocks powerful analytic opportunities.
Establishing Dynamic Performance Baselines
The continuous stream of synthetic performance data can be leveraged to establish dynamic baselines for metrics like page load times, transaction durations etc. automatically adjusting thresholds to application changes.
Anomaly Detection for Improving SLAs
By applying statistical algorithms to metrics history, anomalies signalling potential degradations can be reliably detected even before static thresholds are breached, improving SLA conformance.
Forecasting Outages for Proactive Planning
Time series forecasting methods like ARIMA modeling applied to synthetic KPIs provides capability to predict probable future outages and performance issues enabling proactive mitigation.
Capacity Planning Through Correlation Analysis
Correlating synthetic load time or error metrics with backend server metrics can reveal hidden utilization patterns to smartly provision capacity and avoid resource saturation.
Building Digital Experience Risk Models
By analyzing metric correlation and causal structures, probabilistic models can be built to quantify digital experience risk across business journeys enabling mitigation prioritization.
Ingesting into Data Lakes and BI Tools
Exporting synthetic monitoring data into enterprise data lakes and BI tools unlocks powerful multidimensional drill-down analysis and visualization capabilities augmenting monitoring dashboards.
These examples showcase the advanced analytic potential when leveraging synthetic monitoring data through techniques like big data, statistics and machine learning – especially valuable for digital analytics teams.
Adoption Challenges and Best Practices
However, multiple organizational and procedural challenges exist when adopting synthetic monitoring. Here are research-backed best practices for addressing these hurdles:
Start Small to Demonstrate Quick Wins
Beginning with a minimal set of critical flows as proofs of value builds confidence across stakeholders to expand monitoring reach gradually.
Integrate Synthetic Data with Other Data Sources
Ingesting synthetic metrics into platforms housing other digital data sources like product analytics and call center logs provides fuller context for decision making.
Foster Test Culture Through Digital Experience Ownership
Having feature teams own end-to-end digital experience integrity including synthetic testing promotes responsibility and advocacy for monitoring practices.
Develop Testing Guidelines Tailored to Organization
Documented best practices for aspects like script structuring, tagging, alert tuning tailored to institutional environment aids wider adoption.
Promote Collaboration Between Development and SRE Teams
Joint ownership of service quality goals between application developers and site reliability engineers enables effective leveraging of synthetic monitoring.
Assign Dedicated Staff to Manage Synthetic Solution
Having focused staff manage the underlying synthetic platform – authoring tests, analyzing alerts and reporting on data enables scale.
These measured adoption techniques can drive accelerated value realization from synthetic monitoring for Digital Experience Management (DEM) teams.
Comparing Synthetic Monitoring Solutions
With a variety of vendor solutions in the synthetic monitoring space, here is perspective on their technical capabilities:
Browser Testing Depth: Catchpoint, Smartbear and Micro Focus offer sophisticated root cause diagnosis for browser apps by logging detailed web vitals and user session metrics.
API Testing Features: Catchpoint, Runscope and Postman provide advanced API testing capabilities like OpenAPI specification based automated test generation and API mocking interfaces.
Geographic Test Distribution: ThousandEyes, Catchpoint and Dynatrace have wide global node placement across ISP networks for accurate location-specific web performance measurement.
Third Party Integration: Uptrends, SolarWinds and ManageEngine offer seamless integration with popular ITSM and collaboration tools for easier incident management workflows.
Actionable Insights: Dynatrace, NewRelic and Datadog provide advanced timeseries analytics, anomaly detection and service topology visualizations on monitored metric data for automated insights.
Evaluation criteria for selection should include capability depth, analytics offerings, extensibility of data and ease of acting on insights for CIO teams.
The Business Value of Synthetic Monitoring
Beyond the technical merits, synthetic monitoring unlocks significant business value, best quantified through financial impact.
Reduced Customer Churn Through Higher Digital Satisfaction
A 1 second delay in page loads can reduce customer satisfaction metrics by 15%. By proactively catching web performance issues before majority of users, synthetic monitoring mitigates customer frustration. Each percentage improvement in satisfaction metric translates to hundreds of thousands in savings through reduced churn for medium to large online businesses.
Lower IT Support Costs Through Proactive Alerting
Remediating digital experience issues flagged through synthetic monitoring before they widely impact customers significantly reduces influx of incident tickets. With modern support costing $150+ per ticket, the costs avoided are substantial.
Increased Revenue from Higher Conversion Rates
Slow performance is proven to negatively influence customer conversion rates. A 10% performance gain can raise conversion rates by over 15%, translating to multimillion dollar revenue upside at scale.
Lower Security Risk Through Faster Alerting
Web application vulnerabilities are often first exploited by automated bots. Synthetic scripts emulate this behavior allowing quicker detection of malicious breaches, mitigating digital risk. The average breach cost for mid-size companies is $4M+.
These economic benefits showcase how synthetic monitoring delivers outsized business value and is a strategic capability for CXOs.
The Road Ahead for Synthetic Monitoring
As consumer dependence on digital channels and low tolerance for errors accelerates, synthetic monitoring will likely see increased strategic adoption. We envision the solution space innovating across several dimensions:
Comprehensive Analysis Statements: Instead of just operational metrics, synthetic checks will provide interpretable analysis of business impacts – like probability of customer defection and revenue leakage projections – through observational AI.
Voice and Multi-Modal Experience Testing: With voice and conversational interfaces gaining dominance, testing solutions will evolve to support iterative multi-turn dialog simulation with natural language understanding.
Integrated Predictive Guidance: Synthetic monitoring platforms will offer precise remediation advice using counterfactual inference to quantify the exact experience improvements from taking certain actions.
Holistic CX Validation: Testing will expand beyond applications to emulate end-to-end journeys spanning apps, 3P services and physical environments providing system-wide customer experience integrity measurement.
As leaders responsibly progress digital transformation keeping experience quality paramount, synthetic monitoring will emerge as an indispensable aid and design partner improving how the world experiences technology.