Contact center queueing (Erlang C)
4 queues, 2,400 half-hour intervals over a full business month, where agents_staffed is what Erlang C (1917) says is actually required to clear each queue's own SLA target, not a plausible-looking number.
A call_volume column and an agents_staffed column with no relationship between them can't test a workforce-management pipeline, because the whole point of WFM is that staffing is derived, not chosen independently. Here, agents_staffed is reverse-solved from Erlang C (computed via the numerically stable Erlang B recursion) to be the smallest agent count that clears the queue's declared SLA target, plus a real 20% shrinkage buffer. Wait probability, average speed of answer, and service level are the exact deterministic Erlang C formulas, recomputable from the raw columns. Abandonment follows the same expected-vs-realized split as the credit-risk dataset's expected_loss/realized_loss: a deterministic rate from a competing-exponentials wait/patience model, and a real Binomial draw for what actually happened.
- contact center
- Erlang C
- workforce management
- queueing theory
- WFM
Explore
Every table, profiled
Each column's type, spread, empties and most common values, measured from the CSVs in the download. Switch to the first rows to see the data exactly as it sits in the file.
2 tables
intervals.csv
2,400 rows · 15 columns · 1 foreign keys
Keys
Every join resolves
1 foreign key, 2,400 references checked against the table each one points at. None points at a row that does not exist.
intervals
- queue_idqueues.queue_id2,400 rows checked0 orphans
Checks
What we checked
- every queue clears its own declared SLA target (75-90%) on 100% of its 600 intervals
- service_level_pct matches an independent Erlang C recomputation on all 2,400 intervals
- calls_answered + calls_abandoned equals offered_calls exactly, on all 2,400 intervals
- occupancy_pct and service_level_pct correlate at -0.31 (higher load, worse service)
- aggregate abandonment stays under 4.0% for every queue over the full month
- 0 orphaned foreign keys joining intervals to queues
The same checks ship in the zip as INTEGRITY.txt, so you can rerun them in any SQL engine. How we verify
Questions
Worth asking it
- 1Does higher occupancy actually correlate with worse service level, the real Erlang relationship?
- 2How much staffing buffer (shrinkage) does it take to keep abandonment in an industry-plausible range?
- 3Does a realistic intraday call-volume curve break a naive flat-staffing assumption?
- 4Which queue's SLA policy is hardest to hit given its volume and patience profile?
Use it
Take it, or make one shaped like yours
Rebuild it byte for byte
pip install misata curl -O https://raw.githubusercontent.com/rasinmuhammed/misata/main/examples/contact_center_queueing.py python -c "from contact_center_queueing import build, verify; t = build(n_business_days=30, seed=17); verify(t)"
This dataset reverse-solves staffing from Erlang C and models abandonment via a competing-exponentials wait/patience relationship, neither of which a per-column declared schema can express, so it ships as a runnable script rather than a schema.yaml a bare misata generate could regenerate honestly.
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