Skills CX operations
Arrival pattern analysis
Staffing models need two inputs: how many contacts, and how they arrive. Forecasting gets the first; this skill gets the second.
npx rulebase-skills install cx-arrival-pattern-analysisWhen to use it
Reach for this when someone says any of these — they are the phrases the skill itself triggers on:
- “arrival pattern analysis”
- “are arrivals Poisson”
- “Erlang assumptions”
How it works
The method, in the order the skill runs it. The full procedure — tables, worked examples and the edge cases — is in the skill itself.
What you are testing
For each channel and interval length you schedule to (usually 15 or 30 minutes): Distribution shape, Independence across intervals, Censoring, Batch structure.
Poisson and Erlang: when they apply
Poisson arrivals are a reasonable working assumption when: No systematic batch import or campaign aligns to the clock.
Burstiness
Burstiness means arrivals clump — variance exceeds the mean. Sources: Campaigns and notifications, Outages and status pages, Billing and payroll cycles, Retry behaviour.
Batch arrivals (especially async)
Email, tickets and back-office queues often arrive as discrete batches: Mailbox imports overnight, API retries dumping failed creates, Partner file drops, Auto-routing releasing held tickets at hour boundaries.
Abandonment-censored arrivals
In voice and chat, only contacts who waited appear in handled-arrival counts. High abandonment makes the next interval look lighter than demand was — customers left, they did not disappear.
Implications for model choice
When in doubt, simulate with your empirical arrival trace for a sample of peak days rather than debating formula elegance.
Related skills
Free and open source, and vendor-neutral — it reads the conversations from whichever helpdesk you already run. Browse all 149 skills · connect your helpdesk over MCP · source on GitHub
