Part 3: Decide with Data
The Operational Pyramid
By Chris Recio, Director of Contact Center and Advanced Applications and Peter Hornberger, Director of Channel Success of BrightMetrics
How can you assist your team? How can you be helpful?
A lot of what you can do is take data and make it available team‑wide to your organization. We talked about this a little bit from a gamification standpoint in the last one.
We talked about the key to a successful gamification strategy is presenting data transparently so that everyone in the organization understands what the expectations are and how the team is doing in matching up against those expectations.
That accountability creates the opportunity for a team to self‑manage, compete, and understand what levers they can pull to impact their performance.
Segmenting your organization
If we take that out of step, not just from the agents, but to the organization‑wide, one of the best things you can do to let your team know, “We’re in this with you. We are working on this together. We understand the challenges and the problems. We’re trying to figure out the best way to work on these things,” is by getting the right information to the right people.
You have these two data points. What are the most important and influential stats for our organization, and present those to the people that need to be seeing them on a regular basis.
If you think back to Five Metrics Every Call Center Manager Should Master, there are probably hundreds of different metrics you could look at.
What you want to do is, as an organization, identify what are our operational goals? What are we trying to accomplish? What do we think would have the most impact on our operational efficiency and our customers’ experience?
I’ve got a nice little pyramid here that we’re going to talk about.
Segment your call center organization into one of these three personas.
Upper management. That could be C level. That’s your vice presidents, your executives, your call center managers.
Level B: floor leaders. That’s your supervisors, the boots on the ground, the people that are assisting and helping during those challenging spots, but more than anything are sitting at a higher level, trying to influence and assist the agents so that they’re more productive throughout their days.
Then you have your actual agents; level A.
You’ll notice that I’ve circled within there IT. IT is something that is going to come into play a lot in your call center from a system standpoint, but isn’t necessarily sitting in that pyramid. They can fall anywhere along that chain, depending on how IT’s responsibility works within your team.
You’ve got four different personas. I perfectly understand that this is a simplistic example, but I’m sure you can map your own team and understand where these various people fit.
Think about this fluidly and map your own team to it, but in general these are the distinctions that you see operating within a team.
It’s important to understand what kind of data is going to be used by each one of these roles. When we talk about upper management, upper management cares a lot about the strategic perspective. What you want from an upper management standpoint is a definition of, what is our operational goal right now?
These are things that will change over time. You want to be operationally efficient and you want to drive a positive customer experience.
The list of things that you could do to impact that can be exceptionally long. You could probably sit down at a whiteboard and come up with 15, 20, 25 metrics today that you think would have an impact.
What are the high‑value targets? What are the three to five things that you could start to work on today and have the biggest valuable impact in those two areas, operational efficiency and customer experience?
Start at that upper management level, what are our team goals and how do we drive towards that? As that’s developed, that information is communicated to the floor leader. “Here’s what we want to do. Supervisors, team leads, here’s what we’re looking to accomplish.”
That team is going to take that information and use that within their tactical analytics.
They’re going to look at day‑to‑day, “Here’s the dashboard that’s showing that’s showing me our performance. Here’s those three to five stats that we think are most impactful towards the operational goals. That information is passed on to each agent or a group of agents to put into action.
Goals could be things like your average queue time. “We want to make sure that calls are being picked up within 60 seconds or less,” or, “We never have more than four calls sitting in our in our queue”
You’re monitoring that and paying attention to that day to day, and if things start to trend or get out of alignment with those goals, how are you going to react to that? You’re going to use that tactical analytics to figure out a reactionary point.
Let’s look at the agents. You normally don’t expect that the agent is going to be the person who’s blending both of the types of data, but typically, that’s where you see both of these data points being most impactful.
From an agent standpoint, a lot of what you’re going to do at the high level is define, “Here’s the expectations for the agents, the goals we want them driving towards.”
You’re going to do a mix of things. You’re going to do your quarterly performance reviews, but I would argue even more frequently than that, you want to be providing historical score card performance to the agents, maybe even on a weekly basis.
From that, they’re going to see how well they performed and if you are up for it, I would also recommend showing them how they did in relation to everyone else. When they see that, they’re going to say, “OK, well, I got beat by this person, but I did a little bit better than this person.
You want to also give them the tactical perspective, kind of what I talked about when I talked about that sales agent that you were training.
You want to also give them the historical perspective. “Here’s how you stack up,” that strategic analytic, and then you want to give them the tactical perspective of, what can you do differently right now?\
And let’s not forget, there’s one other side to this: the IT side.
If you’re identifying trends, anomalies, and reoccurrences in your data strategically, your system can come to your aid. It’s not always something where you need to solve a problem with people.
There’s a lot of things you can do within the system, the tools, and the context in our application itself to assist with this, and this is where IT comes into play.
IT can tell you about, “Here’s all the different tools and components within our contact center that we have at your disposal. What’s the problem you’re dealing with, and how do we solve for it?”
Look at the information. “How can I put the system to work for me?” Here’s a classic example.
Today you hear a lot about callback in queue, and people believe that if I turn it on, it’s going to solve the world’s problems. The reality is, it’s not. If I had a spike in traffic, and I’m not able to meet my thresholds for queue time…
Let me give you an example. Let’s say we decide we turn it on at the 90‑second mark. That’s a minute and a half, and we decide our call queue is going to announce it. It’s going to say, “Thanks for holding.”
In 90 seconds, you’re going to hear this message that says, “We’d like to offer you to hold your place in queue. Enter your phone number and hang up, and we’ll call you when your turn is available.”
You’re forcing your customer to have to wait 90 seconds, when you know the system knows what’s happening with the metrics now.
What I would suggest is, if you know your system, have it do some intelligence mapping, which is only offer the callback in queue when I hit a certain threshold.
For example, if you know that you’ve got four people in queue, and your average wait time for the day is three minutes, you’re doing a disservice to your client by making them wait that 90 seconds.
If your system is designed and implemented from an IT perspective with intelligence that says, “Before I actually make this person wait in queue, go look at the average queue time, and if it’s over 90 seconds, immediately give them that offer.
“Thank you for calling Inflow Communications. The average wait time is longer than expected. If you’d like to hold your position in queue, press 1.”
The rough translation is, you’ve done two things. You’ve mitigated the circumstance that you’re trying to chase tactically.
Number two, you’ve put a nice dynamic, elastic sensibility in the processing that says, “I’m not going to force my client to wait 90 seconds. I’m actually going to offer it to them on the front side.
Psychologically, if I buy into it, my experience has changed. My perception has changed, and on the flip side, you’ll see a reduction in abandoned calls at that point, because you’re actually making the offer on the front side.
Of course, it takes a little bit of knowledge, knowing what the system is and what it can do, and that’s where we come in to help educate and work with IT.
That’s kind of a big bucket there. Hopefully, that example makes sense to you.
This is the third of a 3-part series of blog posts about Deciding with Data. In this series, we will talk about the analysis and situational applications of strategic and tactical data analysis. Be sure to check outs 1 and 2.
Founded in 1997, Inflow Communications is a national leader in unified communications and Contact Centers. With over to 100,000 endpoints under Inflow’s innovative support plans around the world, their dedication to knowledge, innovation, and unrivaled customer support has landed them in ShoreTel’s top 2% in global customer satisfaction, and as a winner of ShoreTel’s coveted Circle of Excellence Partners award. For two years in a row, Inflow is a ShoreTel Platinum Partner, the highest level of partnership, and is their fastest growing partner globally. In addition, Inflow is one of the few Cloud Contact Center providers that offer implementation, ongoing support, and comprehensive consulting and training programs. Inflow services clients across the globe and has local offices in over 10 major cities in the US.