Kaan: We'll go through, a couple of them now, this is the rest of the live call monitoring and reporting here, but then additionally we have things around what's actually happening historically. So when we jump into inbound here for instance, we can actually see over the past seven days that this company has received 1500 calls, right?
As a manager it's good to know just a raw metric, but wouldn't it be more valuable to understand how many of those calls are missed or abandoned, and you can do that here. You can understand that roughly 30-40% of these calls aren't getting to our reps, and they're calling in, and because they are being dropped, it's either because we're missing revenue, or we're having angry customers that are leaving us. And as a manager you can understand are these calls related to sales, are they related to support, are they related to a specific phone number, and all this information flows in here into this dashboard.
Additionally, because this is showing that 30-40% of the calls are abandoned, as a manager you can jump in here and say "why is that happening", and when you look at the data, it's overwhelmingly showing us that these calls are abandoned in waiting queue, which plain and simple means that we don't have enough people to pick up the call when people are calling in, and our customers are getting frustrated. So what we can do with this data is say, A) we need to hire more people, and B) because we do have a time understanding, can say we need to hire more people and bring them on between a 12 and an 8pm shift, because that's when our peak abandonment occurs, and we have that data in front of us, so it's a really easy way to make a business case.
Jimmy: Very cool. So, how deep can we go into the abandoned waiting queue? Could we figure out how much time triggered the abandon?
Kaan: Good question. So, we do have some understandings over here around service level and reporting metrics, so understanding when people are abandoning, what's the average abandonment time? What's the average wait time? And this is actually a really good case of showing that benchmarking can happen inside of Talkdesk, and you can actually see that as you add more team members, service level increases. What I mean by that is basically that our internal threshhold picked up every call within 120 seconds, or two minutes, is hit 62% of the time, today, but historically it looks like we were actually lower. So being to understand benchmark, understand where we've come from and where we're heading across the entire team is available here.
Jimmy: Okay. This is very cool to see, this is interesting. It really adds that context about what's going on with what's happening over the phone.
Kaan: Exactly, and so this is all macro, kind of high level, what's happening from the top down. But many customers also want to know "What is Chris doing on the phone today? What is Mike doing?" You can actually jump in here and say "Well Tom, in the past seven days has actually made 365 calls." How has Tom been spending his time? Has it been on a call? Has it been after work? Has he been away from his desk? We can actually see all that information here directly in real time, and you can also start to understand across multiple agents and reps how they're performing, and understand what does a good, successful rep look like, and start to model and build off of that. So this is, again, top down, bottom up, whatever you prefer, you have here. It's a really good way to understand what's going on with your team over time.