Customer Usage Event

No formal definition of customer usage events exists. Product interactions happen but nobody has defined what constitutes a 'usage event' worth tracking. The engineering team logs some server requests, but there's no business-meaningful event taxonomy. 'We know customers use the product but we can't tell you what they're actually doing.' Customer success has zero visibility into product engagement.

None — AI cannot analyze product engagement because no customer usage event definition or tracking exists. Without defined usage events, product interaction patterns are invisible to every system.

Some customer usage events are tracked inconsistently. The engineering team instruments a few key features — login and page views — but event definitions vary between services. One service logs 'report_generated,' another logs 'create_report,' and a third doesn't log report usage at all. 'We have some usage tracking but it's a mess — I can tell you login counts but not whether customers are actually using the product meaningfully.'

AI can report on the handful of consistently tracked customer usage events (logins, page views) but cannot build meaningful engagement profiles because event definitions are inconsistent across features and critical product interactions go untracked.

Customer usage events follow a standard schema with consistent naming conventions and required fields. An event catalog defines what's tracked — feature activations, workflow completions, configuration changes, and content interactions. Each event has a defined structure: user, account, action, feature, timestamp. But usage events are isolated facts with no connection to customer health models, account profiles, or product feature definitions.

AI can generate usage dashboards — feature adoption rates, active user counts, engagement trends over time. Cannot assess engagement health or predict churn because usage events aren't connected to customer context or success metric definitions.

Customer usage events are connected knowledge objects. Each event type links to the product feature it measures, the customer health score component it feeds, and the adoption milestone it contributes to. A CS analyst can query 'show me all customers whose usage of the analytics module has declined 30% over the past 60 days and whose health score is below 70' and get a contextualized, actionable answer.

AI can perform engagement-aware customer health analysis, predict churn risk from usage pattern changes, and recommend feature-specific interventions. Cannot dynamically discover new engagement patterns that aren't pre-defined in the usage event taxonomy.

Customer usage events are formally modeled entities in a product engagement ontology. Event types have typed relationships to feature hierarchies, user roles, workflow stages, and success metrics. An AI agent can reason: 'This user activated the advanced reporting feature but has not completed any report workflows — the feature exploration pattern without workflow completion correlates with disengagement within 30 days for this user persona.'

AI can autonomously interpret customer usage event patterns, generate personalized engagement interventions, predict feature-level churn risk, and recommend product changes based on usage analysis. Human judgment needed for novel engagement patterns outside the ontology.

The customer usage event model is self-evolving. New interaction patterns are automatically classified as meaningful usage events. Engagement correlation models continuously update from observed outcomes. When a new product feature launches, the system auto-generates usage event definitions and engagement benchmarks from early adoption patterns. The event taxonomy writes itself from observed product interactions.

Can autonomously define, track, and interpret customer usage events across the entire product surface. Event models evolve themselves from real-time behavioral patterns, requiring no manual taxonomy maintenance or engagement model tuning.

Ceiling of the CMC framework for this dimension.