Frequently asked questions
The ten questions workforce planners and contact-centre managers ask most often. Short, opinionated answers — with a link to the deeper article on each.
No fluff, no consultancy-speak. Where the honest answer is “it depends”, we say so — and tell you what it depends on.
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What service level should I set?
There’s no industry-standard answer — but there is an industry-standard mistake, which is to inherit 80/20 from a previous operation without checking whether it fits yours. The right SL target is the one that matches what your customers will tolerate, what your unit economics will support, and what your operation can sustainably deliver. For most retail and service operations 80% in 20 seconds is reasonable; regulated and complaint-handling queues often need higher; back-office and lower-urgency queues often justify lower.
The bigger question is whether SL is still the right primary metric at all. Some operations are moving toward outcome metrics — first-contact resolution, abandonment, segment-level service — and using SL as a secondary check rather than the headline.
Read: how to set a service level target →
Read: is service level a dead KPI? → -
What’s a normal shrinkage rate?
Including paid leave, holidays, training, coaching, breaks, meetings, and unplanned absence — a healthy operation runs at 28%–35%. Operations above 40% are usually carrying hidden time the agents themselves don’t realise they’re consuming. Operations below 25% are usually under-investing in training or coaching and will pay for it later in quality and attrition.
The most useful exercise is to break the number down. The single composite shrinkage figure obscures whether the problem is structural (holiday, training) or behavioural (adherence, ACW). Each needs a different response.
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What’s a normal attrition rate for a contact centre?
The honest answer is that the headline number is the wrong one. Year-one attrition is typically 35%–55% in retail and service operations; tenured attrition is 8%–15%. The composite hides the actual problem. The operations that meaningfully reduce attrition do it by segment, almost always starting with year-one.
The follow-on question, usually, is what the attrition actually costs. The number most operations carry on the slide (£1,500–£2,000 per leaver) captures about a quarter of the real cost — the all-in figure including vacancy, ramp-up, manager time and quality drift is closer to £6,000 per leaver.
Read: what’s a normal attrition rate? →
Read: the true cost of attrition → -
Is Erlang C still the right tool?
For most voice-only operations, yes. Erlang C is still the right tool when arrivals are roughly Poisson, the queue is single-skill, you have at least 15-minute intervals, and abandonment isn’t a meaningful share of contacts.
It’s the wrong tool for chat and async work (different concurrency physics), for skills-routing problems (Erlang C assumes everyone can take everything), for bursty arrivals (the Poisson assumption breaks), and for outbound (genuinely different model). When in doubt, simulation is the answer.
Use: the Erlang C calculator →
Read: chat staffing isn’t Erlang → -
How accurate should my forecast be?
More useful than the accuracy target is the question “against what?” A weekly forecast at total-operation level should hit 3%–5% MAPE on a stable operation. A daily interval-level forecast is closer to 8%–12%. A 30-minute interval forecast on a small queue can be 15%–25% and still be doing a useful job — because the noise floor at that granularity is high.
The mistake most operations make is benchmarking forecast accuracy against an aspiration instead of against a naive baseline. If your fancy model isn’t beating “same day last week” by a meaningful margin, the sophistication isn’t adding value.
Read: forecast accuracy metrics →
Read: your forecast is probably more accurate than you think →
Read: Poisson and natural noise → -
How much will AI improve my forecast?
Less than vendors claim, more than sceptics expect. A well-tuned ML model with external drivers typically delivers a 15%–25% relative MAPE improvement over a strong statistical baseline. Foundation models add a small further increment. The bigger gains usually come from getting the data clean, not from a better algorithm.
The honest order of operations is: fix the data, beat a moving-average baseline with a Holt-Winters or Prophet model, layer in driver-based regression for the variables that genuinely matter, then consider AI/ML. Operations that start at the AI step and skip the foundations usually find the model is no better than what they had.
Read: AI for forecasting — what works and what doesn’t →
Read: gen-AI for planners (the everyday productivity use) → -
Does adding agents always improve service level?
Yes, but not linearly. The curve is heavily non-linear: at low staffing, one more agent transforms service; at high staffing, one more agent barely moves it. The most important intuition for a finance conversation is that 14-to-15 agents matters more than 24-to-25 — and that intuition makes the headcount conversation tractable.
This is also why “just throw bodies at it” is a poor strategy past a certain point. Once you’re past the steep part of the curve, scheduling shape, AHT, and shrinkage move SL more than headcount does.
Try: change agents by 1 in the Erlang C calculator →
Read: the power of one → -
Should breaks be fixed or flexible?
Fixed breaks deliver more predictable intraday coverage; flex breaks deliver better workforce experience and lower attrition. The honest answer is a hybrid — fix breaks tightly at the operation’s known coverage troughs (lunch peak, mid-afternoon dip) and flex them elsewhere.
Operations that pick one of the two pure models leave value on the table. The schedule-design question more broadly is one of the cheapest service-level levers most planners under-invest in.
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What should I do when finance gives me a headcount that’s too low?
Operate honestly inside the cap, but document the gap quarterly. Five levers genuinely move outcomes inside a constrained headcount: shrinkage, schedule shape, AHT, channel/demand-shaping, and targeted SL compromise by segment. Avoid the four traps that look like solutions but aren’t — permanent overtime, the unstaffed late shift, hiding in averages, and quiet quality drift.
The political move is to present the gap as a trade-off rather than a deficit. “At this FTE, here’s the SL we’ll deliver; at the modelled FTE, here’s what we’d deliver. The difference costs £X in payroll and delivers £Y in retained customer value.” That framing wins more conversations than arguing the maths.
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What does a planning team look like in a small contact centre?
Below about 60 agents the planning function is usually one person doing forecasting, scheduling, and real-time across the day. 60–150 agents typically supports a 2-person team split between long-range (forecasting/scheduling) and real-time. 150+ supports a proper specialist function with separate leads for forecasting, scheduling, and real-time, plus an MI/insight role.
The structure matters less than the discipline. Even a one-person team can produce serious work if the process is right; a five-person team can produce theatre if it isn’t.
Read: the planning team in a small contact centre →
Read: the minimum viable planning team →
Question we haven’t answered?
Suggest one in the community thread or message ccplanning on LinkedIn. We’ll rotate the list as new questions come in and as new articles publish.
Page reviewed regularly as the article library grows. Last reviewed: May 2026.