Snow Day Staffing: When a Fifth of Your Team Can’t Get In

Snow day staffing is a scheduling problem your tool can’t solve for you, because the schedule was right on Sunday and wrong by 7am Monday. What decides how the day goes is whether you can see, within the first hour, who isn’t coming in, why, and which skills just walked out with them. Britain’s snow on 2 February showed how big that gap can get.

Last year I wrote about call centre scheduling software and whether Excel is enough. My answer was that a spreadsheet can hold a schedule but can’t defend it. A snow day is the purest test of that sentence I can think of, because nothing about the schedule was wrong. The world just stopped matching it.

How big was the snow day?

Big enough to show up in national figures. According to Channel 4 News, the Federation of Small Businesses estimated that about one in five people, some 6.3 million, didn’t make it into work, at a cost to the economy of £1.26bn. The same coverage reported a row over whether people who stayed home should lose a day’s pay or a day’s holiday.

One in five is a useful number for a scheduler, because it’s roughly what a snow day does to your team too. If you run a 50-seat centre, that’s ten people. In a team of 200, it’s 40. You can’t absorb that with the shrinkage allowance in your normal plan.

Why doesn’t the schedule just flex?

Because a schedule is built from averages, and a snow day isn’t average. Your planned shrinkage covers ordinary sickness, holidays, training and meetings, spread fairly evenly. A snow day concentrates a month’s worth of absence into one morning and, worse, it isn’t random.

Think about who doesn’t make it in. The people with the longest commutes. The people who rely on one rail line or bus route. The parents whose children’s schools have closed. Those groups aren’t spread evenly across your skills. If your second-line team mostly lives out of town, or your French speakers all came from the same recruitment drive near the same station, you may lose 60 percent of one skill and 5 percent of another. Your overall attendance figure will look survivable. Your queue for that skill won’t be.

That’s the first thing a scheduling tool should show you on a bad morning: not “how many people are in,” but “which skills are now short, by how much, and for which hours.”

Does demand change too?

Usually, and not always in the direction you’d hope. Some contacts disappear because your customers are snowed in too. Others appear because the weather has created new problems: missed deliveries, cancelled appointments, people working from home for the first time and calling about access.

As I covered in mind the gap, the schedule only means something next to the required-agents line. On a snow day you have to redraw both. Re-forecast the morning from what’s actually arriving in the first hour, not from last Monday. Then run the Erlang C numbers again on the new volume. You may find that a day which looked like a disaster is merely bad, or that the one queue you weren’t watching is the one that will break.

What should your tool capture when someone calls in?

More than “absent.” A one-word absence code tells you nothing you can act on. For a weather day, capture three things for each person:

• Why they’re not in: transport, childcare, or illness. Each recovers on a different timeline. Transport may improve by lunchtime. School closures probably won’t.
• Whether they can work from home, and if so, which systems they can actually reach from there.
• When they expect to make it in, if at all.

With those three fields, your absence list stops being a register and becomes a plan. You’ll know who might arrive at 11, who could log in from home if someone sent them the right access, and who’s gone for the day. Without them, you’ll spend the morning ringing round to find out.

What should you decide before the next one?

Most of the snow day response can be decided in advance, on a calm afternoon, by a team that isn’t panicking. Do it now, while February is fresh in everyone’s memory.

• Priority queues. If you can only staff some queues properly, which ones? Write the order down. On the day, nobody should be debating whether billing beats new orders.
• Who covers what. Your skills matrix should show, for every scarce skill, who else can do it at a pinch. If the answer for any skill is “nobody,” that’s your training plan for the spring.
• Message changes. A pre-written recorded message and website note explaining longer waits and suggesting self-service options. Approve the wording now.
• The call-round. Who phones whom, by when, and where the results go. The answer should be one shared sheet, not five supervisors’ notebooks.
• The pay question. Decide your policy on staff who can’t make it in before it happens, so supervisors aren’t making it up one call at a time.

The pay question surprises people, but it matters for staffing. Teams who believe they’ll be treated fairly on a snow day are far more likely to try to get in, or to log in from home, than teams who expect to be docked whatever they do.

How fast should you re-plan on the day?

Within the first hour, and again at about 10:30. The first re-plan uses absence calls and the first 30 minutes of volume. The second uses the actual pattern of arrivals: who made it in late, which queues are building, what the customers are actually calling about.

After that, re-plan every two hours or whenever something big changes. A leading indicator, such as transport updates, school closure lists, or a rising number of “where’s my delivery” calls, will usually tell you what the afternoon looks like before the queue does. Use it.

What about the people who do make it in?

They carry the day, and they notice how it’s handled. The agents who left home at six to get through the snow are the ones taking longer queues, covering skills they don’t normally do, and hearing from customers who are frustrated by the wait. If the only message they get from management is about the people who didn’t arrive, you’ll lose goodwill you need for the next bad day.

Say thank you early, on the floor, not in an email at five o’clock. Relax the targets that no longer make sense: nobody should be measured against a normal service level on a day when a fifth of the team is missing. Make sure breaks still happen, because a team running flat out with no breaks will be slower by mid-afternoon than one that stopped for twenty minutes.

And if you’re moving people onto unfamiliar queues, give them something to work from. A one-page crib sheet for each scarce skill, written in advance, turns “I’ve never done billing” into “I can handle the basics and pass the rest on.” It won’t make anyone an expert in an hour. It will stop the queue from filling with transfers.

What should you measure afterwards?

Not just the service level, which will be bad and tells you nothing new. Measure how long it took to know who was missing, how long it took to re-plan, and which skill ran out first. Those three numbers tell you what to fix before next winter.

Can Excel do any of this?

Some of it. A well-built spreadsheet can hold the skills matrix, the absence fields and the revised required-agents line. What it can’t do easily is show you all of them together, by interval, as they change during the morning. That’s exactly the moment a proper workforce tool earns its licence fee.

If you’re still on spreadsheets, that’s fine. Build a single “bad day” sheet in advance: skills down the side, intervals across the top, required and available in each cell, with the gap highlighted. Practise filling it in once. On the morning, the person who’s done it before will be faster than any software you haven’t configured.

What’s the real lesson from 2 February?

That shrinkage isn’t a percentage, it’s people, and on the worst days they don’t go missing at random. Your schedule will always be built on averages. Your snow day plan has to be built on names.

The weather decides who can’t come in. You decide whether the customers notice.