CUSTOMER SUPPORT
KB Article Deflection-Decay Monitor (Zendesk)
Weekly, computes each help-center article's deflection rate from Zendesk views and ticket-creation events, flags articles whose deflection has dropped past a threshold.
How it runs
The automated pipeline, trigger to output.
- TriggerWeekly schedule fires
- ActionPull article views + deflection events from ZendeskZendesk
- LogicCompute deflection slope vs baseline
- LogicKeep articles past decay threshold
- ActionAppend flagged articles to Postgres historyPostgres
- OutputPost ranked refresh queue to SlackSlack
What it does
Tracks the deflection rate of every published Zendesk Guide article — the share of readers who solve their problem without opening a ticket — and catches the moment that rate starts decaying. A decaying article is a leading indicator that ticket volume on that topic is about to rebound, so this routes it for a refresh before the wave hits.
When to use it
Run it when your help center has enough traffic that stale content quietly drives ticket spikes, and you want to fix articles proactively instead of reacting to a queue surge.
How it works
- 1A weekly schedule fires the run.
- 2Pull article view counts and per-article ticket-deflection events from Zendesk for the trailing 4 weeks.
- 3Compute each article's current deflection rate and its 4-week slope, then compare against its own baseline.
- 4A logic step keeps only articles whose deflection dropped more than the configured threshold while views held steady or rose.
- 5Write the flagged set, with deltas and reason codes, into a Postgres tracking table for trend history.
- 6Post a ranked refresh queue to the support Slack channel with owner suggestions.
Set it up
What you configure once, before turning it on.
- 1Connect ZendeskTickets, queues, knowledge base.
- 2Connect PostgresAny Postgres URL — query, write, migrate.
- 3Connect SlackChannels, DMs, threads, mentions.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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