Quality And Coaching Demo
Apr 10, 2026 06:53
· 11:41
· English
· Whisper Turbo
· 2 speakers
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Speaker 1 (Quality And Coaching Demo)
Hey, team. It's Nick. I run Partner Solution Engineering here at Cresta, and today I'm going to do a quick walkthrough of our quality management and coaching product, which sits as part of our larger Cresta platform.
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Speaker 1 (Quality And Coaching Demo)
So the audience for this particular product is going to be the front line of the contact center. So that's your agents and supervisors slash coaches. And the goal here is to provide better visibility into agent behaviors, as well as better tools for coaches and supervisors to develop healthy coaching to ultimately drive better agent performance and better agent improvement.
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Speaker 1 (Quality And Coaching Demo)
So before we jump into the platform and kind of talk through how we do that, I want to level set kind of the state of quality assurance in a lot of contact centers.
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And that state is based off of a manual process that's existed for quite some time now. So in your traditional kind of manual quality assurance program or quality management program, you have human beings doing manual evaluations based off of random sampling for contact center agents.
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Speaker 1 (Quality And Coaching Demo)
So this particular process has existed this way for often decades, and it has three critical flaws that we're going to address today as we go through the demo.
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So the first is that manual quality management often only reviews maybe one to 2% of interactions per agent, which means over 95 to 98% of interactions simply are not getting reviewed just because it's not viable from a time perspective.
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So that's one flaw that we're going to talk about. The next is that evaluations are often subjective. So because there's a human doing the evaluation, humans may interpret things differently, may hear things differently, may have better understanding of what the criteria is, which makes quality results when done manually inconsistent, which can impact agent adoption.
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And then the third piece that we're going to be in. And then the third piece that we're going to touch on is the accountability structure often leans heavily toward the agent.
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So evaluating specifically agents for their performance, but often programs lack accountability structures for the coaches as well to develop, build and execute on good coaching plans that really drive improvement at the agent level.
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Speaker 1 (Quality And Coaching Demo)
So we're going to talk about how to address a lot of those with Cresta today in the demo.
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Speaker 1 (Quality And Coaching Demo)
So I'm going to jump into the platform here, and we're going to start with what's called Performance Insights.
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So Performance Insights is specifically targeted at supervisors and coaches to help equip them with the data and tools to understand the performance and behaviors of their agents.
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So this all starts from a foundational element that we call opera.
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So opera is where we build individual agent behaviors, and we do that by using natural language prompting and machine learning rather than your traditional either keywords or manual review.
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So the way that we start is we'll build out a behavior using that natural language prompting, and this can be anything from soft skills.
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Speaker 1 (Quality And Coaching Demo)
So show empathy, offer to assist, which we'll talk about.
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Speaker 2 (Quality And Coaching Demo)
It can be sales behaviors.
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It can even be customer indicators on the customer side, and the goal is to build up desired and undesired behaviors, put those into a scorecard that controls waiting and desired outcome, and use that scorecard to provide aggregated performance data that ultimately gets presented to the supervisor in a way that helps expose and provide visibility into where the opportunities might lie.
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So what we're looking at is the result of that process.
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So starting at the top here, we have what we call a performance score.
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So this is dictated by the individual scorecard that I just talked about, and within that scorecard, there are going to be several behaviors, which we'll talk about, that inform that score.
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So the goal here is you have a single number metric that represents the scorecard that contains the behaviors that you're looking for.
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In addition to that, we have some predictive metrics here, such as average handle time, CSAT, and resolution, which comes out of the box, and Cresta provides those as part of our insights platform.
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Speaker 1 (Quality And Coaching Demo)
So between all of these functions, we have a better overview as to our overall team performance, including trending information.
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Now, as we scroll down, we're going to start to see the individual behaviors that comprise that particular performance score.
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Speaker 1 (Quality And Coaching Demo)
So in this example, I have a group of servicing criteria.
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So these are your traditional kind of soft skills that are designed to elevate the experience of the customer.
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So offering to assist, showing empathy when it's relevant, making sure that all the issues are completed.
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Speaker 2 (Quality And Coaching Demo)
And then we also have some sales behaviors.
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So ways for the agent to provide additional value through sales to service motions.
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So these elements comprise my scorecard, and that's what informs that performance score.
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So the better we do all of these behaviors, the higher the performance score is going to be for the team, as well as for the agents.
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So here we can see the individual behaviors, and we can see some information around how well our top agents do, as well as what our average team performance is.
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So if we look at show empathy, the top agent...
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are able to achieve 92% on the interactions, and the average is 88%. And again, that is based off
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of 100% interactions for that custom behavior. So we've defined empathy, and we're also applying
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it to every single interaction rather than just a subset. If we look to the right here, we see our
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trending information based off of week, and I can click on any one of these boxes, and it will pull
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up a stack rank of my agents and how well they're performing for that specific behavior. So in this
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case, I've pulled up show empathy. We have some agents who have really exceptional performance
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with that criteria. If I scroll down to the bottom, we have some agents that are lagging behind their
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peers. If I want to know more, I can click on any one of these agents for any one of these boxes,
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and it pulls up some example interactions where the behavior could have been demonstrated and whether
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or not they did or did not demonstrate it. So here we can see some example interactions where
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they did not do the behavior and also where they did do the behavior. And if I click on any one of
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those, it will pull me to the interaction with our closed conversations tab and show me not only the
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interaction where the behavior occurred or did not occur, but also show me where in the call it was
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demonstrated or should have been demonstrated based off of the opportunity. So here we see an example
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where the agent is demonstrating empathy based off of our criteria, which is informed by our generative
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AI prompt and customization. We can see some other information around the call itself down here at the
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bottom. So how long it was, any other behaviors that were demonstrated, any indicators from the
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customer based off of our predictive emotions. So here we can see the customer exhibited anger, which is why
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the agent showed empathy toward the customer, which is a desired behavior. We can also see over here on the
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right, any additional scoring that happened, including the aggregated score. So this particular
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call got a performance score of 75 based off of our complete scorecard. And you can see how each
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individual behavior was marked as well. So if you think about using this as a supervisor, right, I've gone
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from potentially knowing not a lot about my agents or knowing very little based off of a small sample
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to now understanding how often this particular behavior gets demonstrated for every single call.
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I've been able to identify the agent that needs the most improvement based off of what I selected.
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And I can start to research that particular issue by jumping to the relevant interactions and even the
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point in the call where that behavior should have been demonstrated. So if you think about tools for
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coaches, this is a great way to streamline that coaching process and equip supervisors with the tools
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to identify and execute on a coaching strategy that they can develop.
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So I'm going to jump back to my performance insights tab. And rather than look at it from a behavior
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perspective, we're going to look at it from an agent perspective. So I can stack rank my team based off
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the leaderboard. So I can view my top performers based off performance score. I can view my bottom
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performers based off of that performance score and see all the details of how my agent is performing based
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off of my targets. So in this case, Olivia has the lowest performance score. We can see some of the reasons
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why. So she often doesn't ask if there's anything else she can help with. She's very poor at the
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sales behaviors that we look for, which leads to poor CSAT. So one of the things that I can do as a
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supervisor is click on Olivia's name, which will pull up her coaching dashboard that is dedicated to
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her. So we have the same scorecard being represented here. We can see her performance score and her
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metrics, but specific to just Olivia. Now, one of the things that we can do from here is build out a
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coaching plan. So this is one of the ways that we drive accountability for coaches, which is we want
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coaches to go in and set targets, to document coaching, to use the tools that they're given.
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And that's best done through a documentation and goal setting process. So the supervisor can come in,
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set target and focus criteria for the agent to focus on. This will help track that behavior,
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show how coaching is impacting that particular behavior over time. And all of that rolls up to a
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coaching effectiveness metric that is informed by how well I impact the behaviors I'm targeting
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as a coach. So it's a great way to track kind of the effectiveness of the coaching that I'm having.
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And we also have a full documentation process where supervisors can come in and set targets,
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set goals, document broader, more broad kind of coaching plans, as well as one-on-one sessions,
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and even set individual manager notes. So when you package all of this together,
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there's some accountability on the coaches being had to develop good coaching, actually conduct
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their coachings, and set achievable targets for their agent, which leads to more impactful coaching.
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If we go to the next level of this and think about how we can consume supervisor data,
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someone can come in and view all of the coachings that's been done, that's been done by my supervisor.
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and see who is being effective with their coaching, who is not doing their coaching,
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who is documenting their coaching well.
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And this happens in the coaching report.
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So the audience for this is basically a manager of the supervisors, right?
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So a contact center leadership, maybe a contact center director can come in and say who is
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doing their coaching, who is being effective, and who is not.
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And that's represented here by the top coaching sessions, a full log of all the sessions that
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have happened for each team, overall coaching effectiveness, and a grid that shows all
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of my coaches and where there might be opportunities to improve.
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So who's not doing their coaching, who is not having impact on agents.
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And all of this creates an accountability structure within the contact center that holds coaches
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accountable toward developing good coaching for their agents just as much as it encourages
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good agent behavior.
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So the result of this is a complete vertical alignment of the front line of the contact
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center.
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The coaches are accountable for developing good coaching and aligning those objectives
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based off of the scorecard.
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So again, to reiterate, the goal here is to improve the front line performance with automated
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performance management and better coaching.
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And that's done with better behaviors through generative AI.
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100% of a value of calls get evaluated for whatever criteria you want to look for.
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And it's done objectively without subjectivity because it's being done with unified criteria.
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So the result here is just better coaching, taking advantage of technology and AI to deliver
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more meaningful and more impactful outcomes that improve agent behaviors.
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Speaker 2 (Quality And Coaching Demo)
Appreciate everyone.
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Speaker 1 (Quality And Coaching Demo)
Let me know if you have any questions.
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Please feel free to reach out.
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Speaker 1 (Quality And Coaching Demo)
Thank you and have a good day.
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