No metric matches that. Try a shorter word, or the abbreviation.
Average Handle Time AHT
Talk time plus hold time plus after-call work, divided by contacts handled.
(talk time + hold time + after-call work) ÷ contacts handled
What it's forCapacity planning. It is the input to how many people you need on the floor. It is not a performance target, and treating it as one is where most of the damage starts.
How it goes wrongTwo ways. After-call work gets excluded or logged inconsistently, so the number understates how much capacity each contact really consumes. And the moment AHT becomes an agent target it improves while first contact resolution quietly degrades, because the fastest way to end a call is to transfer it or promise a callback. You get a better AHT and more contacts. Averaging across mixed contact types compounds it: a password reset and a billing dispute in the same average describes neither.
What to checkSegment by contact type before you look at it, then put first contact resolution beside it on the same chart. If AHT falls while repeat contacts rise, nothing was saved.
Service Level SL%
The share of contacts answered within a target time, usually written as two numbers such as 80/20, meaning eighty percent answered within twenty seconds.
contacts answered within target ÷ contacts offered
What it's forWhether you staffed correctly against demand, interval by interval.
How it goes wrongIt is measured per interval and reported as a period average, which hides the shape of the day. A month at eighty two percent can contain two hours every afternoon at forty percent and the rest at ninety five. The customers who sat in that afternoon queue are the ones who leave, and the average says you hit target. The target itself is usually inherited from a contract nobody has revisited rather than chosen from what the business actually needs.
What to checkLook at the worst interval, not the average. Plot service level by half hour across a full week and see whether the failures cluster. If they cluster, you have a scheduling problem, not a headcount problem, and hiring will not fix it.
Service Level Agreement SLA
The contractual commitment wrapped around service level and the other targets, including how they are measured, what is excluded, and what happens when they are missed.
contracted target, measured over the agreed window, minus the agreed exclusions
What it's forSetting expectations between a client and an operation, and defining the consequence when the operation misses. It is a document, not a metric, which is the source of most of the confusion around it.
How it goes wrongSLA and service level get used as if they were the same thing. They are not. Service level is what you measured; the SLA is the agreement about which measurement counts. The details that decide whether you pass sit in the window and the exclusions, not in the headline number. Measured monthly, an SLA can be comfortably met by an operation that failed its customers on eleven individual days. Exclusion clauses for outages, volume spikes and force majeure routinely mean the number you report and the experience your customers had are different things. And any penalty attached to a metric agents can influence directly will eventually be met by changing agent behavior rather than by improving service.
What to checkRead the measurement window and the exclusion list before you read the target. Then measure the same thing at a daily and interval level for yourself, so you know the difference between passing the agreement and serving the customer.
Abandonment Rate ABA%
Share of contacts that disconnect while waiting, before reaching an agent.
contacts abandoned ÷ contacts offered
What it's forThe customer-side cost of your queue, which service level on its own does not show.
How it goes wrongShort abandons get counted. Someone who misdials and hangs up after three seconds is not an abandoned customer, but most reports include them, which inflates the figure and buries the trend you actually wanted to see. It also moves when you change your own queue messaging, so a longer greeting raises abandonment without anything about staffing having changed.
What to checkSet a short-abandon threshold, exclude anything below it, and then never change the threshold. Compare abandonment against wait time rather than against volume.
First Contact Resolution FCR
Share of contacts resolved without the customer needing to come back.
contacts with no related repeat within the window ÷ total contacts
What it's forThe one operational metric that tracks whether the work was actually done. Nearly every other metric on this page can be improved by doing the work worse. This one cannot.
How it goes wrongThe definition is where it fails. A repeat within what window, twenty four hours, seven days, thirty? Same channel or any channel? Most operations measure same-channel repeats within a week, which misses the customer who emailed, gave up, and then called. And FCR taken from agent disposition codes is close to worthless, because you are asking agents to mark their own homework on the metric they are judged by.
What to checkBuild it from contact records across every channel, not from disposition codes. Then print the window on the report itself, so nobody compares two numbers built on different windows and draws a conclusion from the difference.
Occupancy OCC
Share of logged-in time an agent spends handling contacts, including after-call work.
(talk + hold + after-call work) ÷ logged-in time
What it's forWorkforce health. It is a risk metric, not an efficiency metric, which is the opposite of how it is usually filed.
How it goes wrongHigh occupancy reads as good on a report and predicts burnout. Held in the high eighties, attrition follows a quarter or two later, and you pay for it in recruitment and ramp cost rather than in the operations report where the cause sits. It is also routinely confused with utilization, which measures against scheduled hours rather than logged-in hours, so two sites reporting the same number can be measuring genuinely different things.
What to checkPut occupancy and attrition on one timeline with attrition offset by a quarter and see whether the shape matches. Write down which denominator you use and make every site use the same one.
Schedule Adherence
How closely agents follow their scheduled activity through the day.
time in scheduled activity ÷ total scheduled time
What it's forWhether the forecast you built can be trusted to turn into actual coverage on the floor.
How it goes wrongMeasured to the minute, it punishes agents for things outside their control. A call that runs past the end of a shift lowers adherence for doing exactly the right thing. Once it is attached to performance reviews, agents optimize for it by wrapping up calls near breaks, which moves the cost into repeat contacts where nobody is looking for it.
What to checkBuild in a tolerance window and exclude failures caused by contact overrun. If adherence looks strong and service level still misses, your problem is the forecast, not the floor.
Shrinkage
Share of paid time that is not available for handling contacts.
(training + meetings + breaks + absence + leave + other off-phone time) ÷ total paid hours
What it's forTurning a headcount requirement into an actual hiring number. Get this wrong and every staffing calculation downstream of it is wrong by the same margin.
How it goes wrongThere is no standard definition, and that is the whole problem. Whether annual leave is included, whether breaks are in or out, whether training counts, all vary by site and by vendor. It is the most disputed number in outsourcing contracts, because two parties can each report shrinkage honestly and still be thirty percent apart. Comparing shrinkage across vendors without first agreeing the components is arithmetic without meaning.
What to checkWrite the component list into the contract, or into an internal standard, before the number is reported even once. Then publish the components alongside the total so anyone can recompute it their own way and see where you differ.
Customer Satisfaction CSAT
Score from a post-contact survey, usually reported as an average or as the share giving a top rating.
satisfied responses ÷ total responses
What it's forDirectional signal on experience, useful in aggregate and over time rather than case by case.
How it goes wrongResponse bias. The delighted and the furious answer surveys. The merely served do not. So CSAT samples the two edges of your distribution and gets reported as the middle of it. Surveying immediately after the contact also measures how the agent handled the conversation rather than whether the problem was solved, and those two questions frequently have different answers.
What to checkShow the response rate next to the score, always. A rising CSAT on a falling response rate is usually not an improvement. Where you can, survey after resolution rather than after contact.
Cost per Contact
Total cost of running the operation divided by contacts handled.
total cost to serve ÷ contacts handled
What it's forComparing channels against each other and, handled carefully, comparing vendors.
How it goes wrongWhat sits in the numerator varies enormously. Technology, facilities, quality assurance, workforce management, supervision, recruitment and training may each be in or out. A vendor quoting an unusually low cost per contact has generally excluded something you will pay for regardless. Driving the number down by deflecting contacts to self-service only helps if the deflected contacts stay away, and when the self-service does not work they come back harder and longer.
What to checkFix the cost components before comparing anything to anything. Then look at cost per resolved issue rather than cost per contact, because resolution is what you were buying.
Attrition
Share of staff leaving over a period.
leavers in period ÷ average headcount in period
What it's forThe real constraint on both quality and cost in most operations, and the one that takes longest to recover from.
How it goes wrongA single blended number hides two unrelated problems. People leaving inside their first ninety days is a recruitment and training failure. People leaving after a year is a management and progression failure. Report them together and whichever is worse conceals the other, so you fix the wrong one. Monthly figures also get annualized by multiplying by twelve, which overstates badly whenever leaving is seasonal, and in this industry it usually is.
What to checkSplit at ninety days and report the two separately, permanently. Track both against occupancy and against quality scores adjusted for tenure.
Forecast Accuracy
How close forecast contact volume came to actual, usually expressed as mean absolute percentage error.
average of |forecast − actual| ÷ actual, across intervals
What it's forWhether the staffing plan built on top of it ever had a chance of working.
How it goes wrongGranularity. Measured daily it looks excellent, because overstaffing in the morning and understaffing in the afternoon cancel each other and the errors net out to nearly nothing. Measured at the interval you actually schedule to, the same forecast can be badly wrong. Reporting daily accuracy while scheduling to half hours is how an operation ends up with a healthy forecast report and a failing service level in the same week.
What to checkMeasure at the interval you schedule to, not the one that flatters you. If daily accuracy is strong and interval accuracy is weak, your volume forecast is fine and your arrival-pattern assumption is not.
Recognize any of these in your own reporting?
Most operations have two or three of these running quietly in the background. Finding them is the first thing I do on an engagement, and it is usually the cheapest thing that changes a decision.
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