Customer success that runs on product reality, not spreadsheets
Turn product usage into live health, automation, and journeys so CS teams can focus on conversations that matter.
Skene reads your codebase and usage to infer what healthy accounts do, then lets AI agents maintain health scores, detect risk, and orchestrate interventions across the lifecycle.
Without a system, customer success becomes reactive heroics
When health is fuzzy and work is manual, CSMs burn out, leaders fly blind, and customers only hear from you when something is on fire.
Customer success teams operate from scattered notes, CRMs, and dashboards that never quite agree.
Health scores are tuned by hand and rarely match who actually renews or expands.
Most CS work is reactive: escalate when a crisis hits, rather than shaping the journey proactively.
Churn, NRR, and morale suffer when CS runs on anecdotes
You cannot consistently improve what you cannot see. Without a system, every quarter becomes a scramble to explain churn after the fact.
- •Retention and NRR depend on heroic individual CSMs instead of a repeatable system.
- •Leaders cannot see which accounts are truly healthy versus quietly drifting toward churn.
- •Headcount becomes the only way to "scale" because every action is manual.
Tooling that ignores the product cannot run customer success
CRMs were built for sales stages, not post-sale health and lifecycle journeys.
Generic "CS platforms" rely on manually configured rules that decay as the product and customers change.
Analytics tools show behavior but do not own follow-up actions or orchestrate interventions.
Vendor playbooks prescribe tasks but do not understand what success looks like in your actual product.
A CS system grounded in code and behavior, not manual rules
Anchor customer success in live product usage and lifecycle state instead of static attributes.
Read your codebase to understand what healthy accounts actually do across features and workflows.
Use AI agents to maintain health scores, detect risk, and trigger the right next action at scale.
Feed outcomes (renewals, expansions, churn) back into the model so the system improves over time.
Automated
- •Account and user-level health scoring tied to real usage patterns.
- •Detection of at-risk accounts based on leading indicators, not lagging ticket volume.
- •Play-style interventions such as nudges, education prompts, and product-led upsell paths.
- •Surfacing prioritized worklists so humans focus on high-leverage conversations.
Intentionally not automated
- •Replacing your CRM for pipeline management or forecasting.
- •Owning legal, procurement, or complex enterprise negotiation workflows.
- •Building bespoke workflows for each individual enterprise customer.
Signals in
- →Product usage events across onboarding, adoption, and key feature usage.
- →Account composition: seats, roles, and collaboration patterns.
- →Lifecycle state: trial vs paid, new vs established, expansion vs renewal windows.
- →Historical outcomes such as renewals, churns, and expansion events.
Outputs
- ←Continuously updated health scores with transparent contributing signals.
- ←Risk and opportunity flags surfaced at account and segment levels.
- ←Queues of recommended actions for CS teams and product-led automations.
- ←Views that connect onboarding, adoption, and retention into one journey.
A good fit for
- ✓PLG SaaS companies that want CS to be product-led instead of ticket-led.
- ✓Small CS teams supporting a large self-serve or hybrid customer base.
- ✓Leaders who want health to be grounded in behavior, not static account tiers.
Not a good fit for
- ×Purely high-touch enterprise motions where every account is bespoke.
- ×Organizations unwilling to wire even basic product usage events.
- ×Teams looking only for a CRM replacement rather than a usage-based CS system.