Composed AI gives CS teams a continuous signal layer — detecting engagement drops, adoption gaps, and support surges early to prevent renewals friction.
Churn is predictable. But only if you have a system that monitors the right signals continuously. Most CS teams don't.
NPS scores and renewal dates are trailing signals. By the time they show risk, the decision to churn has already been made internally by the customer.
Usage data lives in the product. Support tickets live in Zendesk. Onboarding notes live in a spreadsheet. CSMs piece together account health manually before every call.
Preparing for a quarterly business review means pulling data from five different systems. CSMs spend more time in spreadsheets than in conversations with customers.
CSMs manage too many accounts to monitor every signal. The accounts that churn are often the quietest — no tickets, no complaints, just silence.
Health scores based on login frequency and NPS miss the real signals. Accounts churn for reasons that don't show up in any single metric.
A green health score based on logins ignores that the user is logging in to find a feature they can't locate. Usage without context is noise.
Every QBR prep, every check-in, every renewal conversation requires CSMs to manually pull account history. There is no shared intelligence layer.
The most dangerous churn signal is the absence of engagement. Standard tools surface active problems, not the quiet disengagement that precedes cancellation.
Composed AI connects your product usage data, support tickets, onboarding notes, and account history into a shared knowledge layer — then monitors it continuously for signals your CS team should act on.
Composed AI detects patterns that precede churn: declining engagement combined with unresolved support tickets, onboarding drop-off followed by silence, key feature avoidance despite training.
Before every QBR, CSMs open Ask Composed and ask: 'What's the account health for Acme Corp this quarter?' and receive a sourced summary across every connected data point.
When a churn signal fires, the CSM receives it with enough lead time to intervene: schedule a call, escalate to a technical resource, or activate a feature adoption campaign.
No CS-specific tool. The same platform your support and product teams use — applied to customer success operations.
Composed AI monitors your connected account data continuously: product usage logs, support ticket frequency, onboarding completion rates, and billing events. When a combination of signals indicates risk — declining usage + open tickets + approaching renewal — a Signal is created with the account name, risk indicators, and a recommended action.
Before every customer call, open Ask Composed: 'What is the current health of Acme Corp?' 'What support issues are unresolved for this account?' 'What features have they adopted this quarter?' Every answer is cited and sourced from your connected data.
Deploy a customer-facing chatbot grounded in onboarding guides, success playbooks, and knowledge documents. The chatbot resolves routine setup queries for customers natively, while capturing engagement signals and monitoring user adoption trends for your CS team.
Composed AI detects that an Enterprise account has had three unresolved support tickets in 14 days, combined with a 40% drop in weekly active users. A churn risk Signal is created with evidence and the assigned CSM notified.
The CSM opens Ask Composed and asks: 'What is happening with this account?' The answer surfaces the three unresolved tickets, the usage drop pattern, and the fact that the account's primary admin hasn't logged in for 11 days.
Armed with evidence, the CSM schedules a call and knows exactly what to address: the unresolved tickets, the usage drop, and the missing admin. The conversation is proactive, not reactive.
Two of the three tickets require a technical resolution. The CSM escalates via the Customer AI live support handoff, passing the full conversation context and evidence to the technical team.
The tickets are resolved. Usage recovers. The investigation is saved as a persistent thread. The CS team has a documented intervention playbook for similar future patterns.
Surface churn signals weeks before renewal — not days before. Early signals mean early intervention and higher retention rates.
CSMs ask Ask Composed for an account summary and receive a sourced answer in seconds. QBR prep goes from hours to minutes.
Every conversation is backed by evidence. CSMs know exactly what's working, what isn't, and what the account needs — before they pick up the phone.
Support, product, and CS teams all see the same account signals from the same shared Knowledge Base. No information silos.
Connect your CRM, product usage exports, support platform, and onboarding records. Composed AI structures everything into a shared Knowledge Base.
Within hours, Composed AI surfaces the first account health patterns from your existing data. CSMs immediately see which accounts warrant attention.
Make it a habit: open Ask Composed before every customer call and ask for an account summary. Arrive prepared.
As the signal library grows, CSMs can monitor more accounts with less manual effort. Focus on the relationships that matter.
Connect your account data, detect churn patterns early, and equip every CSM with evidence for every conversation.