79: Forecasting in Support with Matt Dale

79: Forecasting in Support with Matt Dale

Matt Dale leads a large team, looking after a complex set of products, with a hugely variable customer base. Oh, and he has to cope with seasonality, too. How does he forecast for any of that?


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Charlotte Ward 0:13
Hello, and welcome to Episode 79 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 to the podcast against a Matt Dale. Matt, it’s lovely to have you back this week we’re talking about forecasting in support. Now, I know that you work in a business that has a fair amount of seasonality and I think that brings its own particular set of challenges, doesn’t it?

Matt Dale 0:49
It doesn’t. It’s great to be back. Charlotte, always happy to join you on the podcast here. Yes. So we work in education, a public school, 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 in June to nothing during July because everybody shuts down and goes on holiday. And an August, people come back having forgotten everything they knew about our product. And they’re supposed to be using it a day to day basis. And so we see volume on most of our products double. And for a very short period, though, on a couple products, we see it almost triple. And that’s the challenge. As you can imagine, from a forecasting perspective, knowing what is that? What is why is it actually going to be doubling? And how many people how many agents do we need to have staff so that we have reasonable call timeout call wait times, obviously, we can’t staff for 100% of Pete. 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 maybe a 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, we’ve got a peak season. 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 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. 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.

Charlotte Ward 2:39
And you have a sense of of that seasonality from previous seasons, but how accurate is your your perception of what the load might be next peak season would you say based on last peak season,

Matt Dale 2:51
so right now all bets are off? This whole COVID thing has messed up all of our models and I think that’s an important thing to think about too. If you’re you’re listening to this and you’re thinking about it. I’ve got this model, or I’m trying to figure out how it works. I’m gonna compare last year last quarter, that’s really difficult because we don’t know what our contracts are gonna look like, last year or in a normal year at this time, we’d be able to look at the sales pipeline and say, Hey, here’s what we’re expecting to sell AR is one of the metrics we look at the other is the number of students that we’re bringing on. And the third is the number of school districts and each one of those shows us a little bit different picture, a small school district tends to actually need more help, because they don’t have the staff to do the work that they need to do. So under a certain size, they’re a little bit more of a challenge. But But typically, I would take a look at the sales forecast and say, Hey, this is where we’re at. I can look at what we did last year, I can look at, you know, this many new dollars in AR is going to roughly represent you know, 20% increase, and this is going to be a 12% increase in tickets. And I can kind of back into the numbers and get an idea of a camp, you know, in the middle of September. I’m expecting this kind of volume and based on what we did last year at our team functioning at this level. You know, I think we need to meet a couple new people Or, you know, whatever it is a lot of stuff is changing all the time. And so you can build a model. In fact, I’ve been working on our model for last six or seven years, and every year it gets a little bit better. But every year there’s a curveball, there’s a change and I think it’s important that when we think about forecasting that we’re making sure that you have that your your understanding your how good your model is, but also that it’s there’s gonna be a level of uncertainty and when you

Charlotte Ward 4:22
you’re talking specifically about how you extract likely growth, and likely upcoming numbers, how in broad strokes do you do that? You talked a little bit about knowing specific types of customer and specific student numbers.

Matt Dale 4:39
Really, we’re basically trying to find the correlation between whatever the number is whether it’s dollars in new sales or schools or number of schools and certain size and and what what sort of volume you’re going to see with that. Also, you have to kind of take into account what sort of volume you you know, things tailing off. So for example, for us a second or third year It’s been with us three years, they’re going to have different support needs than a brand new first district, they don’t drop off in our case as quickly as you might expect, because as they finish their initial implementation, and they say cool, we’d like to have this part or we’d like to add that part. But understanding your customers and understanding that relationship between Hey, we got a new customer or we have we have many customers that are two years this many customers that are three years old, based on historic data, we can see that assuming the the engineering team doesn’t you know, release a new feature that doesn’t work that everybody’s frustrated that we can expect this sort of volume and and kind of getting a rough range there. So you know, working with the sales team, making sure that Salesforce numbers are accurate, and or understanding the level of accuracy. If you make decisions based on knowledge that you think is true, but but isn’t then then you can be in a real tough situation. So understanding what sort of model you’re working under understanding what your your leadership in the company is trying to accomplish, and then trying to kind of fit that in with the reality that you’ve been given. We had a really bad year to came to me and said, what’s the minimum number of people we can hire for support this year? And I’m like, I don’t know. And I didn’t ask a question. They didn’t say what are our sales look like this year? What are we doing from a product perspective? Are we making any big changes? Those are those are questions I should have asked and information I should have had before I said any number, I said, Hey, it feels like we need probably eight or 10. Like I’ll give you for August 15. We realised that we were grossly understaffed, but because of our onboarding process and your inability to train people while you’re trying to triage stuff. It meant it from August until December, we just got hammered. It was terrible. That year, I said, Okay, this, I can’t do this. Again, we can’t have bad information, I need to do a better job and that’s when I started developing the model.

Charlotte Ward 6:41
That’s it for today. Go to customersupportleaders.com/79 for the show notes, and I’ll see you next time.


Transcribed by https://otter.ai


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