Usage Analytics

Overview#

The Usage Analytics module tracks user activity, feature engagement, and platform performance in real time, so product and customer success teams know which features are being used, which users are disengaging before they churn, and where onboarding is breaking down before it affects adoption numbers. Adoption metrics told after the fact are less useful than signals that arrive in time to act on them.

The module also serves as the platform's single authoritative view of tenant consumption. AI usage, storage, and platform services are metered on a schedule and rolled into one canonical, itemised bill per tenant, so administrators can see exactly which services drove cost. Privacy is built into the architecture. GDPR and CCPA compliance, PII anonymisation, and right-to-deletion support are not add-ons; they are part of how the module works.

Key Features#

User Activity Tracking: Capture and analyse user interactions across login patterns, feature usage, navigation paths, and performance events. Enrich activity data with user context (role, department, location), business context (licence type, account value), and product context (feature maturity, complexity) for multidimensional analysis.
Adoption Metrics and Cohort Analysis: Track product adoption from first login through feature mastery with activation rate monitoring, time-to-value measurement, and feature discovery tracking. Analyse behaviour by time-based, attribute-based, and behaviour-based cohorts to understand how different user segments engage with the platform.
Retention Analysis: Monitor day-N retention rates (D1, D7, D30, D90, D365) with cohort retention grids that visualise return rates across user segments. Identify retention patterns by feature, department, location, and device to understand what drives long-term engagement.
Churn Prediction: Machine learning models score users on churn probability based on declining login frequency, reduced feature usage, and other engagement indicators. Recommended interventions and win-back strategies help customer success teams act before users disengage.
Feature Analytics: Feature-level metrics including usage volume, duration, navigation patterns, and conversion funnels. Identify power user workflows, common friction points, feature gaps, and roadmap prioritisation signals based on actual behaviour.
Funnel Analysis: Track multi-step conversion funnels for onboarding, feature adoption, and business transactions. Drop-off analysis identifies abandonment points while optimisation recommendations estimate conversion lift.
Performance Metrics: Monitor response times, throughput, error rates, and user experience scores across all platform components. Automated bottleneck detection surfaces optimisation candidates.
Canonical Tenant Usage Metering: A single authoritative metering service measures everything a tenant consumes, spanning AI usage, storage, and platform services, and produces one canonical, itemised bill per tenant. Usage collection runs on a schedule and results are persisted, so consumption history remains available for inspection.
Platform-Wide Storage Measurement: Per-tenant storage footprints are measured across the entire platform, giving administrators an accurate picture of where capacity is consumed and a reliable basis for alerts on metered storage growth.
Itemised Bill Inspection and Credit Gating: Administrators inspect itemised usage and bills through a strongly typed API surface, while credit gating is integrated with metered consumption so entitlement enforcement is driven by the same figures that appear on the bill.
Customisable Dashboards: Build tailored views for executives, product teams, engineers, and customer success with a drag-and-drop widget library including time series charts, funnels, heatmaps, cohort grids, geographic maps, and real-time counters.
Privacy-First Architecture: GDPR and CCPA compliant tracking with PII anonymisation, consent management, configurable retention policies, right-to-deletion support, and role-based data access controls.

Use Cases#

Improving user adoption through data-driven onboarding optimisation, targeted in-app prompts, and personalised training recommendations based on individual usage patterns.
Reducing churn with early warning systems that identify at-risk users before disengagement, enabling proactive customer success interventions.
Product development prioritisation using feature-level usage data, power user analysis, and feature gap detection to inform roadmap decisions with evidence rather than assumptions.
Performance optimisation through automated bottleneck detection and A/B test performance impact analysis.
Licence optimisation by identifying underutilised seats, tracking feature adoption against entitlements, and discovering upsell opportunities based on usage patterns.
Cost accountability for administrators who review an itemised monthly bill per tenant showing exactly which AI, storage, and platform services drove cost.
Capacity oversight for operations teams who set alerts on metered storage growth per tenant before it becomes a budget or capacity problem.

Open Standards#

OAuth 2.0 Bearer Token (RFC 6750) / JSON Web Token (RFC 7519): every REST and typed integration channel resolves the caller's identity and organisation from an RS256-signed JWT presented as a Bearer token, enforcing role-based access control on all analytics data.
GDPR (EU Regulation 2016/679): the module is built around GDPR's requirements for lawful processing, including configurable data-retention periods, PII anonymisation, consent tracking, and right-to-erasure support for user activity records.
CCPA (California Consumer Privacy Act): the privacy service enforces CCPA alongside GDPR, applying the same anonymisation and deletion workflows for California residents accessing the platform.
ISO 8601 date and time format: all event timestamps are serialised as UTC ISO 8601 strings throughout the event-sourcing layer and passed as ISO 8601 query parameters on time-range filters.
k-anonymity: aggregate analytics reports enforce k-anonymity thresholds so that no individual user can be re-identified from cohort, funnel, or retention grid outputs.
OpenTelemetry (OTLP): platform performance metrics, including response times, throughput, and distributed trace spans, are collected and queried via an OpenTelemetry-compatible backend, feeding the module's performance analytics views.
OpenAPI 3.x: the REST export and alert-webhook endpoints are described by an auto-generated OpenAPI schema, enabling standard tooling for integration with downstream reporting consumers.

Getting Started#

Configure Tracking: Enable activity tracking for your platform components and define any custom events specific to your deployment.
Establish Baselines: Allow 30 days of data collection to establish normal usage patterns and performance baselines.
Build Dashboards: Create tailored dashboard views for your stakeholders using the widget library and pre-built templates.
Set Up Alerts: Configure anomaly detection, threshold alerts, and scheduled reports for ongoing visibility.
Activate Predictions: Enable churn prediction and optimisation recommendations once sufficient historical data is available.

Integration#

Analytics Platforms: Enriches data from Mixpanel, Amplitude, and Heap with business context
Alert Channels: Email, Slack, PagerDuty, and custom webhooks for real-time notifications
Export Formats: PDF, Excel, CSV, and programmatic access for reporting and downstream analysis

Last Reviewed: 2026-07-16 Last Updated: 2026-07-16