Rank support load by account

Combine errors, complaints, affected users, and plan data to show which accounts are consuming the most support attention.

Published

difficulty intermediate · time to value 5 minutes
#analytics #errors #customer-support #account-health #b2b #revenue

Start from this 

Rank accounts by support load for the last 30 days using errors, affected users, complaint events, and plan or revenue tier.

Why this matters

Support load is not evenly distributed. One account can dominate support because of one unresolved bug, bad onboarding, or a product mismatch.

What you get

  • Accounts ranked by tickets, errors, and affected users
  • Top topics or fingerprints per account
  • Revenue-aware prioritization
  • Follow-up action for CS or engineering

Walk through it

You

Which accounts are generating the most support load?

Agent

I’ll join support events and error impact by account.

POST /v1/projects/:project_id/analytics/query {
  project_id: "default",
  hogql: "SELECT properties.account_id AS account, countIf(event = 'support_ticket_opened') AS tickets, countIf(event = '$exception') AS errors, count(DISTINCT distinct_id) AS affected_users FROM events WHERE timestamp > now() - INTERVAL 30 DAY GROUP BY account ORDER BY tickets DESC, errors DESC LIMIT 30"
}

The output

The agent returns accounts by support load and explains whether the likely fix is product, engineering, documentation, or CS.

Setting it up

Send support-ticket events from your help desk webhook or backend. Use the same account_id as analytics and errors.

Variations

  • “Only show enterprise accounts.”
  • “Which support topics correlate with churn?”
  • “Create a weekly support-load dashboard.”

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Try this recipe in your own agent.

Ask your agent to adapt the starter prompt to your saved signal map and live events, then run it against your data.

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