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AI for Customer Support

Customer Churn Signals

Churn is diagnosed in a spreadsheet at renewal and caused months earlier in a support queue. The account that does not renew in November had a bad escalation in August, filed the same complaint three times, and its main user quietly stopped logging in. Every one of those facts existed in a system you own. None of them were ever assembled into a sentence that said this account is drifting, because assembling them was nobody’s job and lived in four tools.

Skynet is well suited to exactly that assembly. Support history, account data, and product usage sitting in unified memory means the agent can see the pattern across sources instead of one symptom at a time. It reads the whole relationship and tells you which accounts look like the ones that left last year — while there is still a quarter left to fix it.

How it works

step 01

Learn from who already left

Point the agent at accounts that churned and accounts that renewed. It finds what actually preceded departure in your business, which is usually more specific and less obvious than the generic list of risk factors.

step 02

Watch the live accounts

The agent scores open accounts against that pattern continuously — ticket volume and tone, unresolved escalations, repeated requests, a champion gone quiet, usage sliding. Signals from different tools, read together.

step 03

Explain each flag

A risk score alone gets ignored. The agent gives the specific reason and the evidence: this account, these three tickets, this promise we missed. Something a human can act on rather than worry about.

step 04

Propose the save, then stop

The agent drafts the move — the follow-up on the bug that was never closed, the honest note about the feature request. Your team reads it, decides, and sends. It never contacts a customer or offers anything on its own.

Build it from a prompt

Describe the risk you care about and the agent watches for it.

Retention stops being a renewal-week scramble and becomes something your support team can see coming. The accounts you save are the ones where somebody noticed in September, and the intervention was a real answer to a real complaint rather than a discount offered in a panic.

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