Use Cases

What users and systems are doing

Where users, customers, or employees encounter friction

Why a workflow, conversion, or technology problem occurs

How many users, transactions, and business processes are affected

Which improvements should be prioritized

Whether an implemented change resolved the problem

Increase application adoption

Measure application and feature usage, identify user struggles, analyze ineffective workflows, and understand why users avoid or abandon capabilities.

Improve employee productivity

Find waiting time, repeated work, unnecessary navigation, manual data entry, workflow overruns, and technology delays.

Increase eCommerce conversion

Analyze product discovery, search, campaigns, product pages, cart, checkout, payment, abandonment, errors, and customer journeys.

Improve customer retention

Identify frustration, unsuccessful conversations, cancellation intent, failed journeys, and customers who may require assistance.

Improve user and customer experience

Capture user behavior, feedback, errors, performance, crashes, and Session Replay evidence.

Reduce incident impact

Detect issues, determine affected users and workflows, identify reproduction scenarios, investigate root cause, and validate resolution.

Improve application reliability

Monitor availability, performance, code, services, APIs, integrations, databases, infrastructure, and user-facing impact.

Optimize IT operations

Reduce repetitive monitoring, diagnostic, notification, maintenance, and recovery work through configured automation.

Business-process monitoring

Completion, duration, delays, failures, abandonment, and progression through critical workflows

Workflow mining

Actual paths taken by employees or customers, including unexpected steps and process deviations

Lost-productivity analysis

Time lost to waiting, excessive navigation, repeated actions, manual work, errors, and slow technology

Process-overrun detection

Workflows taking longer than their configured or expected duration

Ineffective-operation detection

Failed jobs, incomplete transactions, stalled processes, and operational exceptions

Improvement validation

Whether a workflow, application, training, or automation change improved the expected outcome

Customer-friction detection

Identification of errors, failed journeys, repeated attempts, confusion, abandonment, and poor conversations

Contextual support

User, application, workflow, error, performance, and Session Replay context

Retention-risk identification

Detection of cancellation intent, unsuccessful service experiences, and incomplete customer journeys

Proactive assistance

Alerts or configured follow-up actions when a customer encounters significant friction

Faster ticket resolution

Searchable evidence showing what happened before, during, and after the reported problem

Resolution validation

Confirmation that the customer journey or application behavior improved after a fix

Product discovery

Searches, search results, filters, product views, unavailable products, and discovery failures

Conversion funnels

Progression and abandonment across product, cart, checkout, payment, and confirmation stages

Checkout optimization

Excessive steps, errors, performance delays, third-party checkout transitions, and payment failures

Campaign analysis

Campaign traffic, landing pages, referrers, engagement, behavior, drop-offs, and conversion

Customer-journey analysis

Paths taken before purchase, abandonment, feedback, or support contact

New versus returning users

Differences in engagement, behavior, friction, and conversion

Conversation conversion

Whether chatbot or human conversations help customers progress toward the intended outcome

User feedback

Customer comments, sentiment, issues, requests, and their associated session context

SEO and website quality

Website availability, page performance, structural issues, broken links, and supported audit measures

Product-led growth

Which capabilities drive adoption, engagement, retention, and conversion

Feature adoption

Which users and segments use or avoid specific features

Journey effectiveness

Whether users complete the intended workflow and where they deviate

Product friction

Errors, confusing interactions, dead clicks, rage clicks, repeated steps, and abandonment

Prioritization

Number of affected users, frequency, severity, lost time, and potential business impact

Release validation

Whether a new feature or product change improved usage and experience

User-interest analysis

Products, capabilities, content, and workflows attracting user attention

User behavior

Clicks, navigation, scrolling, searches, form activity, repeated actions, and inactivity

Session Replay

The sequence of interactions and application events experienced by an individual user

Heatmaps

Click, rage-click, dead-click, error-click, mouse-movement, and scroll behavior

UX friction

Confusion, ineffective controls, excessive orientation time, repeated attempts, and abandoned tasks

A/B testing

Differences in behavior, experience, and outcomes between versions or user populations

Form and workflow usability

Where users hesitate, make validation mistakes, repeat actions, or leave a process

UX-impact measurement

Users, sessions, workflows, conversions, and productivity affected by a design problem

Employee onboarding

Visibility into the steps and features that create difficulty for new users

Adoption acceleration

Usage, avoidance, workflow, and user-friction analysis

Support-ticket enrichment

Application, user, session, workflow, error, and performance context

Unreported-issue detection

Identification of problems affecting users who never create a support ticket

Training opportunities

Repeated behavioral patterns that may indicate confusion or missing knowledge

Reproduction assistance

Session Replay and event evidence showing the sequence that produced an issue

Application health monitoring

Track availability, errors, response time, capacity, throughput, and service health

Real-user impact analysis

Determine whether a technical issue affects real users, workflows, productivity, or conversion

End-to-end tracing

Follow execution from browser or application activity through services, integrations, code, and databases

Code profiling

Identify slow methods, functions, queries, external calls, exceptions, and resource-intensive execution

Regression detection

Compare application behavior before and after a release or configuration change

Issue reproduction

Use Session Replay, events, attributes, logs, and traces to understand the failing scenario

Root-cause investigation

Correlate user, application, code, database, integration, and infrastructure evidence

Release validation

Confirm that critical user journeys and technical transactions still operate after a deployment

Synthetic monitoring

Continuously test login, search, checkout, document access, and other essential workflows

Capacity planning

Analyze utilization, demand, transaction volume, response time, and resource constraints

Automated operations

Trigger alerts, incidents, scripts, API calls, diagnostics, maintenance, or approved recovery actions

Trend analysis

Identify improving, degrading, increasing, or decreasing KPIs

Outlier and anomaly detection

Find statistically unusual users, transactions, workflows, applications, or systems

Segmentation

Compare results by available dimensions such as application, page, user, product, workflow, region, version, or system

Correlation

Identify related events and transactions across users, applications, services, and technologies

Categorization

Group errors, comments, conversations, transactions, or other values into meaningful categories

AI and machine learning

Apply supported AI, ML, and NLP techniques to monitored or integrated data

Custom KPIs

Define measures, pivots, SLAs, rules, and analyses for organization-specific requirements

External analysis

Retrieve authorized data through supported APIs for additional processing or visualization

Customer interest

Products, services, content, features, and campaigns attracting engagement

Journey progression

How prospects move from discovery through conversation, registration, quotation, or purchase

Conversion blockers

Errors, poor performance, confusing workflows, failed conversations, and abandonment

High-intent activity

Relevant actions or journeys indicating stronger purchase interest

Conversation effectiveness

Whether chat or support interactions help prospects reach an intended outcome

Follow-up opportunities

Customers or prospects who may require assistance after an incomplete journey

Integrations

Collect user, application, code, service, database, infrastructure, conversation, and business-process data.

Analytics

Identify patterns, friction, impact, correlations, anomalies, root causes, and improvement opportunities.

Automation

Execute configured alerts, notifications, synthetic scenarios, integrations, scripts, and approved operational actions.

Dashboards and Reports

Communicate KPIs, trends, journeys, evidence, findings, and outcomes to relevant teams.

Management

Govern users, security, privacy, data, configurations, platform health, and deployment at scale.