Lifecycle automation that matches how your product actually works
Run one system across onboarding, adoption, expansion, and renewal instead of stitching together four tools.
Skene models lifecycle stages from code and usage, then lets AI agents detect transitions and orchestrate interventions so customers experience one coherent journey.
Fragmented lifecycle logic makes journeys feel random
When every stage runs on a different tool and rule set, customers experience disjointed nudges instead of a clear progression from first value to renewal.
Onboarding, adoption, expansion, and renewal are each owned by different tools and teams.
Rules for lifecycle campaigns live in email tools, CRMs, and custom scripts with no single source of truth.
Changes to the product rarely make it into lifecycle logic, so messages and journeys fall out of sync.
You cannot fix retention if you cannot see the journey
Without a lifecycle system, teams optimize local metrics like open rates or feature usage without knowing how it adds up to renewal or churn.
- •Users receive messages that do not match what they see in the product.
- •Expansion and renewal campaigns trigger at the wrong time, or not at all.
- •No one can answer where a given account is in the journey without manual digging.
Lifecycle decisions cannot be a side-effect of campaign tools
Marketing automation tools were built for pre-sale campaigns, not post-sale lifecycle.
Lifecycle "rules engines" require hand-maintained conditions that decay as the product evolves.
Data pipelines introduce delay and inconsistency between what the product sees and what automations act on.
Journey builders treat stages as lists, not as behaviors expressed in the code and product itself.
One lifecycle model, many channels
Model lifecycle stages from the combination of code structure and product usage patterns.
Use AI agents to infer when accounts move between stages based on what they actually do.
Attach interventions (guidance, prompts, outreach) to stages rather than ad-hoc rules.
Continuously update stage definitions and actions as your product and customer base change.
Automated
- •Detection of lifecycle stages such as onboarding, activated, adopting, expanding, and renewing.
- •Stage-aware triggers that start, pause, or adjust campaigns when behavior changes.
- •Coordination of in-product prompts and CS touchpoints so customers experience one coherent journey.
- •Measurement of stage transition rates and dwell time so you can see where the journey slows down.
Intentionally not automated
- •Owning channel-specific creative or complex sales cadences end-to-end.
- •Replacing every marketing tool; Skene focuses on lifecycle decisions, not raw channel delivery.
- •Running arbitrary business workflows unrelated to product usage or customer journeys.
Signals in
- →Product usage events across key workflows and features.
- →Code-level understanding of capabilities, dependencies, and prerequisites.
- →Account lifecycle context such as plan, tenure, and contract dates.
- →Outcome data: upgrades, downgrades, renewals, and churn.
Outputs
- ←Lifecycle stage assignments for each account and user.
- ←Stage transition analytics and dwell time distributions.
- ←Stage-aware triggers that external tools can subscribe to.
- ←Recommendations for simplifying journeys that have excessive dwell or drop-off.
A good fit for
- ✓Teams that already see value from PLG but struggle to keep lifecycle campaigns coherent.
- ✓Product, growth, and CS leaders who want one source of truth for where customers are in the journey.
- ✓Companies that ship frequently and need lifecycle logic that can keep pace.
Not a good fit for
- ×Organizations without basic product usage instrumentation.
- ×Businesses whose lifecycle is dominated by long bespoke projects with little repeatable usage.
- ×Use cases where a single email campaign tool is sufficient and journeys are simple.