76: Forecasting in Support with Craig Stoss

76: Forecasting in Support with Craig Stoss

Craig Stoss and I both wish we had the magic formula for forecasting potential load changes when product changes are on the horizon.


I’d love your thoughts on this episode! Comment below, and like/love/share/support if you found this inspiring, thought-provoking, or useful!

Charlotte Ward 0:13
Hello, and welcome to Episode 76 of the customer support leaders podcast. I’m Charlotte Ward.

The theme for this week is forecasting. So stay tuned for five leaders talking about that very topic. I’d like to welcome back to the podcast this week, Craig Stoss Craig, lovely to have you back. Again. I would like to talk to you this week about forecasting in support. Let’s talk about understanding the load and how we we figure out how we staff for that load.

Craig Stoss 0:47
Yeah, absolutely. You need to you need to service your core market with the core set of services you want to provide. And so I generally start with finding out the load. Usually by hour, I try to break Get into maybe hours initially by day so so for example, what is load at 7pm on a Monday compared to load at 3am on a Wednesday, and you can start to see a set of trends and you know, you see a gradual increase from 8am in the morning on a weekday, until it peaks around 11 1130 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 SLA s? And at what times of those days are we beating SLA s? Or are we missing SLA s? And then you can start to determine, well, Mike load this time justifies 10 people covering phones and two or three people covering chats and you start to kind of build a vision of to achieve this level of service. I need to staff it with this level of people. I chunk the day out in, you know, two to four hour chunks depending on the type of support we’re providing. And I staff those chunks accordingly to allow people to have a break from Taking real time cases and focus on other things. So I always start with that type of model in order to try to make sure that I’m confident the core service we want to provide to our core market is, is available.

Charlotte Ward 2:13
Si is something that we often forget about, and particularly in certain segments, like the high tech enterprise industries that I’m often involved with. SLS can be as short as first response within 15 minutes, right. So how do you map the people with the SLS in that particular load segment in that particular hour or whatever it may be?

Craig Stoss 2:35
Well, I try not to map it map. it’s specific to people I map it to what the metrics are telling me so so to your example, if I see that at 11am, every day we tend to miss a higher percentage of SLA s than at say, 10am. I would I would argue that means we’re under not understaffed asleep, but we’re under covered at 11am. So that’s, that’s really the base. I mean, there’s there’s lots of Other things in there. Like, for example, you can also say, when do the most number of frowny face 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 at 2pm. For some reason, maybe that is that you’re breaching SLA s, maybe it’s, you know, the staff 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,

Charlotte Ward 3:34
you then change the staffing model to meet that period of time. I hadn’t really thought about frowny faces being relevant to forecasting, but there we go. You learn something every day. And of course, this model of breaking down 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 there’s 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.

Craig Stoss 4:16
Well, yeah, I worked at a company that had existed for about five or six years and if when I mapped out exactly what I just said, number of case load by hour by day, and then I added another dimension of year on top of it, you could see some clear patterns of when we started to sell more into Europe

Charlotte Ward 4:33
sales into new customer bases is one thing if you if you are talking about something as simple as a number of customers all using essentially the same product set, because that’s fairly predictable. final piece I think here that is more difficult to forecast for is when product changes significantly, and that has a less to fight less well defined potential load on the support team doesn’t You know, a product upgrade or a product feature, roll out any of those things not only increased complexity of your product, but potentially also the likelihood of failure of the product. How do you forecast for any of that when there is so little data as a precedent?

Craig Stoss 5:18
I feel like if you and I could solve that problem, we’d be very wealthy people. Yeah. I you know, I part of part of me when I think about forecasting for new market expansions or or new releases is is the preparedness of support. It’s really about Okay, well, what what do I need to forecast as far as training time where my my, my existing team is taken out to learn this stuff? knowledgebase article creation, you know, helping to ramp up new hires that we may need as part of that too. And how do we determine that new new hire number? It’s really hard, you don’t want to be reactive. I definitely start with talking to you know, the product marketing Usually I talk about the markets they’re going to attack. But it is a really fuzzy number. I always say, in support, we need to, we always need to be fiscally responsible, where I see a new feature release or when I hear about a new market being attacked, I do try to focus on Well, what can we do to maintain the self service side of this? What do I do to make sure my team is prepared for it and be as proactive as with the resources we have? You know, and focus on the hiring as as maybe a secondary level of that. It really is so varied that I don’t think there is a great math equation and me as a stats guy, that that’s a hard sentence to say.

Charlotte Ward 6:42
That’s it for today. Go to customersupportleaders.com/76 to the show notes, and I’ll see you next time.


Transcribed by https://otter.ai

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