163: Year in Review: Looking Back at Forecasting

Charlotte Ward: 0:12

Hello and welcome to episode 163 of the Customer Support Leaders Podcast. I'm Charlotte Ward. This week I continue to look back at 2020. Back in March of 2020, I wrote an article about forecasting in support, or rather, the lack of our ability to forecast for the rest of 2020. I would have bet that at the start of 2020 support leaders out there would have understood the load their customers might place on the business at each point this year. I would bet that they had implemented a relatively sound forecast for growth and ongoing load. I would bet that they knew how they planned to serve their customers and that they had the tools, resources and processes in place to do so or had plans to do so. If their support business was a game, it was Monopoly or Scrabble. It was well defined, precise and predictable. You knew the rules, even if they were some house rules you'd made up about double money on passing go or only using words in the compact dictionary. But a couple of months into twenty twenty that game box got upturned and the contents were shaken around, pieces were lost, the box misshapen, and the rule book damaged. The game became suddenly unplayable. Yet so many seemed to stare forlornly at the broken game, waiting for it to write itself. The Internet suddenly provided innumerable copies of the rule book, but few seemed to recognise that this was an entirely new game this year. It's been a year of waiting, a year of holding our breath, a year of waiting for the world to resume, counting down the days and weeks until we can go back to normal. But that time passed. We're entering a new world. The old rules. That old rule book just doesn't apply anymore. It's very true for every part of our lives and every part of business. In the customer support world this year, I've seen lives and businesses turned on their heads in support. We're almost always in a state of responsiveness to the needs of others. It drives and shapes our days, our business, and our relationships. It's compassion on tap, codified and shaped into business protocols and paradigms that are well established and well understood. But now, with the rules of this game unknown and the world changing shape beneath our feet, practical advice often ceases to be useful or at best outdated very quickly. We're making up this game as we play it, if we would only admit it. Take away all those protocols and what do you have left in customer support? You have what you always had, you have humans, humans supporting other humans. And now as we look forward to 2021, we must continue to bear that in mind. We must continue to remember that our teams are humans, our customers are humans. And humans are unpredictable, messy creatures, and this world is an unpredictable, messy world. And yet to function, we must do our best to forecast for the future. Let's listen to some other leaders earlier this year talking about support forecasting.

Craig Stoss: 4:12

I generally start with uh finding out the load usually by hour. I try to break it into maybe hours initially uh by day. So for example, what is load at 7 p.m. on a Monday compare to load at 3 a.m. on a Wednesday? And you can start to see a set of trends. And you know, you see a gradual increase from 8 a.m. in the morning on a weekday until it peaks around 11, 11:30, and then it goes back down over the lunch hour. And so I start with looking at that and I start by understanding what is the level of service we want to provide around SLAs and at what times of those days are we beating SLAs or or are we missing SLAs? Uh you can also say when do the most number of frowny face uh responses on your results come in. And if you see a definite pattern that at you know, two in the afternoon you get the most frowny faces, you can start to assume that the service level is declining uh at 2 p.m. for some reason. Maybe that is that you're breaching SLAs, maybe it's um you know the staff is is overworked for some reason at that time, or there's a team meeting at that time. And you just have to decide what is that base level of service that I want to provide. And if you aren't providing it at a period of time, you then change the staffing model to meet that period of time.

Charlotte Ward: 5:26

Um, I hadn't really thought about frowny faces being relevant to forecasting, but there we go. Um you learn something every day. And of course, this model of breaking down uh a coverage period into hours or two or four-hour chunks or whatever it may be, and understanding how the load maps out over a working day extends quite nicely as you go into other territories, doesn't it? So this really just almost extends naturally into a 24 by seven model, and all you're doing is really extending the hours and extending the, you know, your hiring locations or your shift patterns accordingly, right? So there's very little extra work in actually building that into a bigger coverage model.

Mike Redbord: 6:05

I think one of the most valuable functions that the ultimate leader of support can serve is to both benefit their team and their customers by like kind of solving, quote unquote, solving the math problem that is forecasting. When you don't solve it, bad things happen. You run really long queue times and your customers are upset and your team's upset and your boss is upset too. When you maybe solve it in the opposite direction, you have people sitting around on their hands and there's not enough work to go around, and that's also bad, right? So kind of finding that really fine grain uh of balance in the forecast and the budget and then resourcing and aligning it all is actually really, really important to team health and to customer health. The way to think about this at its core is it's sort of an economics problem. It's just supply and demand. Your customers have a certain amount of demand for help, and you have a certain amount of supply of help you can get. I think one of the keys for support leaders in here to get this right and serve their team and serve their customers is to understand that supply and demand equation in a really, really quantified way. And this is going to involve a spreadsheet. This is going to involve a little bit of math, and you're gonna have to do it in order to serve everybody better.

Charlotte Ward: 7:07

So, in terms of that spreadsheet, then it's something that I see come up quite often, is people go looking for a spreadsheet that they can lift from another support organization and use. How specific to the business do you think that spreadsheet needs to be?

Mike Redbord: 7:22

I think that the first version of this, like baby's first, you know, forecasting, budgeting, or resourcing spreadsheet, uh, is 101. Yeah, 101 is like not that specific. In a given month or quarter or week, you've got you know, number of customers that you're gonna have. And that should come from your finance team or your sales team. It's a good opportunity to have a conversation with those guys about their forecast. This month, we're gonna have X number of customers. And then you can look at your own numbers and say, on average, how often do our customers need help? How often do they contact us? And then for each case that you get, how long does that take on average? Look at your team and say, all right, how many cases actually get done in a day? And kind of back it out from the reality. Just kind of look at the behaviors that are happening on the team and ease your way into it and say, oh, it takes us 35 minutes to do a case. Whatever the number is, is what it is. That's your reality. So, okay, we've got 500 customers. Each contact you, you know, 2.5 times a month, then they each case is going to take a certain amount of time. Great. Now you have a supply number on one side of this equation that is measured in you know minutes. That's how much your customers actually want from it. Your job is to give your customers what they need, right? And then think about how many actual working hours you have that your team can really push towards your customers.

Charlotte Ward: 8:33

I think I think it's really interesting there that the way you calculate the demand is based on reality, right? And that's important.

Mike Redbord: 8:41

Yeah, and I think that number of how long, you know, how many minutes it takes us to do a case, that's a number that a lot of people care about because it has a huge influence on this whole math problem, right? If you take five percent less time to do a case, then you need five percent less people, right? Or you can serve five percent more customers. Like it's a lot of leverage in there. And I think when support leaders look at that number, they tend to get embarrassed. The emotion is, oh my gosh, like I can do a ticket faster or something like that. And they they feel they feel an element of shame or oh, I should performance manage my team better. When finance people look at that number, they get excited. They say, Oh, if we can make it less, wouldn't that be useful? And so there's a there's a really interesting handshake here between support and finance where the support folks can say, okay, if we had this tool or if we improved our knowledge base, we could actually drive that number down. And it enables a support person to make a deal with the finance person. And that deal can involve new tools, new technology, uh, sometimes even new hires. It's a really cool dynamic.

Charlotte Ward: 9:41

I you just brought back so much past embarrassments over how long tickets actually take to solve. We both are a long tenure in support. We both know that no two tickets are equal. I've worked in organizations on the front line as an agent myself. I've had 20 plus tickets a day, and then I've done one ticket a day in a different organization. So no two tickets are created equal. And I, and particularly in one company I was with, where there was a a lot of acquisitions and a lot of different products were brought together. It's a very, very difficult conversation. And I remember where tickets were taking more than a day, and you know, another arm of the support team were solving 10 plus a day or whatever. And it's there is that cringe that you feel, that embarrassment that you feel, particularly in bigger organizations, to say actually it does take us this long. Yeah, that trade-off you have to make between being realistic about the numbers and but being and being honest, but actually standing your ground as well and saying it it genuinely takes this long.

Greg Skirving: 10:39

It's uncanny how our business uh resembles the grocery store. You walk into the grocery store, there's six cashiers standing there doing nothing, and uh you go around, you do your shopping, and you go to checkout, and there's two working, and the lines are all the way to the back. I mean, at the end of the day, we really, really can't uh forecast uh accurately to uh to the place that we would really like to. What we really need to do is uh look at the things that we actually know. Obviously, we have uh historical trending. That's that's really the first thing that we need to look at. I think there's a couple of other things that uh uh I know that I look at. Um obviously the release cycle. So uh that, depending on when your release cycle is, that can sway your seasonal trending. So you have to be careful about that.

Matt Dale: 11:28

Yes, so we work in education, a public school, uh, the K-12 school market. And for us, that means that in the fall, during back to school season, things get really crazy. We basically go from kind of our normal in May and June to nothing during July because everybody shuts down and goes on holiday. Um, and in August, people come back having forgotten everything they knew about our product, um, and they're supposed to be using it on a day-to-day basis. And so we see volume on most of our products double. Um, and for a very short period there were on a couple products we see it almost triple. Um, and that's a challenge, as you can imagine, from a forecasting perspective, knowing what is that, what is double acting, is it actually gonna be double? And and how many people, how many agents do we need to have staff so that um we have reasonable call time, call wait times? Obviously, we can't staff for 100% a peak, um, but you know, at what point are we going to be causing real frustration with our customers? The other challenge that we have, I think, that that may be unique or at least different than some of your listeners is that our products are really complex. So it takes quite a bit of learning curve to get people staffed up on that. One of the things that many of my peers look at is they say, hey, you we've got a peak season. Let's let's say I've got I've got a buddy in e-commerce. He knows during you know Black Friday to Christmas that his his business is going to be very busy. And they have folks that they'll bring in for a few weeks to handle you know the basic questions and stuff. And his onboarding usually takes about three days. Um, our normal onboarding process is about three weeks for the basics. And we really joke with our new agents that, hey, you know, you're basically useless for the first six months. And we're okay with that. You need to be okay with that. But from a staffing perspective, that's a real challenge because I can't just bring in temporary folks or part-time folks to help us with that high peak volume. I have to figure out a way to still provide good support for our customers, but but do it in a cost-effective way that doesn't break the budget the rest of the year.

Ash Rhodes: 13:16

You know, you asked me to to come and talk about this, and I immediately had a little bit of a chuckle because to me it is educated guessing for a lot of cases. I mean, it is very well educated, but it is still a kind of guessing. You're making a gamble that all of your previous experiences with other companies is going to stay the same, and that you'll be able to base your hiring decisions and your firing decisions off of that previous experience. And of course, I mean, man, talk about interesting times that we live in. It we could not more perfectly demonstrate that that rule books get tossed out of the window every single day.