Well, today's a big day.

It's a big day for me personally.

It's a big day for EndeavorCX, our company, and I believe for the contact center industry at large.

I've been fortunate enough in my 30-ish year career now to have seen and played a part of multiple market transitions.

Twenty years ago, we were moving from TDM to voice over IP.

Ten years ago, that migration from on-premise to the cloud and CCaaS and SaaS taking over the world began.

And it's no secret or surprise that the arrival of AI represents the largest opportunity for our industry to become what it can be.

The problem is that the vendors that led the last market transition to the cloud are failing to lead this one.

It's a different set of skills.

It's a different competence.

And instead, they've chosen hype over reality.

They've chosen to issue press releases instead of release notes and ship product.

They've chosen to change their messaging every quarter based on whatever the trend is for the day.

This quarter, it’s agentic AI.

They’ve chosen high per-agent-per-month pricing for bundles that are obtuse and don’t drive actual results.

And they’ve also chosen to lock up your data in proprietary formats and models, making it impossible for you to stay in control of your own destiny.

So today we change all that.

Today I’m calling this the great contact center rewrite.

It is unprecedented in the size, scope, scale, and depth of what we are doing.

Today we’re not releasing a new product.

We’re releasing a new architecture for the entire industry that puts you in control.

It puts the data where it belongs: in the center.

CCaaS is no longer the center of the universe. Your data is.

Over the next 12 weeks, we’re releasing 12 products, not a singular product.

We’re shipping 12 new solutions that will transform the way you think about AI, customer experience, agent performance, and operational excellence.

We’ll be announcing some amazing things that you’ve never seen before—things I’ve never seen before.

In my 25 years of building data teams and data products, we’re talking about knowledge bases that construct and write themselves, curate themselves, and deploy knowledge across the entire enterprise.

We’re talking about the ability to have a data scientist equivalent running on open source language models that can pinpoint issues inside of healthcare plans, as an example.

The constraint to realizing our potential in the industry is access to data—and two forms of data in particular.

One is the contact call detail records.

The other is the transcript.

Whether vendors want to tell you this or not, 80% of all use cases in the contact center AI space start with a transcript.

Call summary, sentiment analysis, identifying customer journeys, friction points, automating quality assurance—it all starts with the transcript.

Today, those transcripts are locked up in proprietary systems.

You have to pay a ton of money to get them out, or pay the hyperscalers a ton of money, or deploy a third-party solution, which also costs a lot and requires engineering effort to create an ecosystem to drive your downstream AI strategy.

The first product we’re launching today is Prism.

It is the first to market that is built on an open source model with a whole lot of pre-processing and post-processing know-how wrapped into it.

It can connect to your contact center environment in as short as 15 minutes and start showing you insights.

No code. No developing. No engineering. Nothing.

It’s priced per minute, per talk hour.

We’re at 90% cost reduction over most of the models and transcription engines in the market today.

What that does is it sets the foundation.

Now you own your transcripts. You own your call detail data.

That serves as the library.

It serves as your context.

It serves as compliance.

You now break free from having your data, your call recordings, your transcripts locked up in whatever proprietary system you’re paying a lot for every month.

We’re making it cheap, fast, easy.

This is the foundation.

Over the next 12 weeks, as we release new functionalities and features, you’ll be set.

If you already have transcripts today—awesome.

Let’s bring them aboard.

We can serve as your archival metadata layer now and forever.

Today, my advice to startups is to tattoo the following acronym somewhere where you will see it every single day: J-M-I-E, which means Just Make It Easy.

I came up with this several years ago when I was traveling relentlessly and found myself flying Southwest a lot—not because they were always the cheapest carrier, but because they Just Made It Easy. I could book last minute. I could cancel last minute. I could change a flight last minute without fees, without calling. And so I found myself a very loyal customer because of that.

I started adopting that mindset into our companies—just make it easy for someone to do business with you.

Just this last week, I've reached out to a number of startups that I thought could help us, particularly with emerging AI capabilities that are interesting.

Only two of them actually made it easy to get on the phone, walk through a demo, walk through the product, and then actually made the buying process easy—very clear with their expectations and what they did and didn’t do. And, you know, they fumbled through the PowerPoints or whatever, which is fine. I still do that.

The rest of them made it almost impossible to do business with. I had one dude from a pretty well-funded startup where the soonest appointment they had was five days out. Then he was five minutes late for the demo, so I had already bounced, and he emailed me wondering why.

Just yesterday, I had a sales meeting booked, and it was declined by the founder because he made some assumptions about us as a portfolio instead of actually just taking a 15-minute call and realizing that I probably would have done two sales with him.

And those are just very recent examples. The list is long with folks who, for whatever reasons, are just making it difficult and introduce unnecessary friction just to do business with them.

The point here is to step back and look at all of your processes—whether it’s sales, onboarding, post-sales support—just make it easy to do business with, just make it easy to transact.

And man, you're going to accelerate a lot faster than being a pain in people's ass.

Last week, I did a post about how I felt that AI in the contact center industry had plateaued rather than seen a bubble. We'd run out of innovation, effectively. And the result of that was a lot of companies that look alike.

There was a comment made to that post, which was quite insightful—part of the reason we still see so many companies doing the same thing is because of a lack of M&A activity in the market.

And I think that's quite astute because one of the most important pieces of advice I give a startup or a founder is to know the sea you're swimming in and to know it cold so that you can predict the currents before they change.

Every startup at some point is going to have a liquidity event.

You're either going to run out of cash and die, or you're going to go IPO—which very few companies actually achieve.

Or you're going to exit through acquisition, which is the most common way to realize liquidity, aside from going defunct.

But right now, very few companies are buying.

Rewind five years, ten years, fifteen years, twenty years—the M&A activity, particularly in contact centers, has been quite rich.

You've got NICE and Verint, both roll-ups built over two decades that started out as call recording and then diversified.

Beyond that, back in the day, you had Cisco, Avaya, and Genesys, all of them quite acquisitive.

At some level, Zoom was in the early days as well.

Fast forward to today, and really, in this post-GPT moment, the only companies getting acquired right now are the ones adding material cash or cash flow to the acquirer.

Take NICE—they’re only buying companies that will improve their quarterly earnings.

Look back two years—prove me wrong.

Five9 is interesting. I think they probably overpaid for Aceyus, but that was more of a tuck-in strategy that would have added some level of cash contribution to their earnings.

Zoom hasn’t acquired anyone since Solvvy a few years ago, probably because their organic growth is at a level where they don’t have to make a lot of acquisitions.

The FANGs—the Facebooks, Apples, Netflixes, and Googles—they’re also not buying right now.

They’re either building this stuff in-house or waiting for the market to mature enough.

The point of this is that no matter where you're at in the adoption or growth lifecycle, you need to be thinking about who the acquirers in your space are.

How do you align with them? How do you understand what’s important to them?

And then, at some level, how do you think of your company not as building products, but as the product—so that at some point, you’ll have that liquidity event or some type of acquisition.

Today, let's talk about market bubbles, plateaus, and gray swans.

For the last six months or so, I've been looking at AI in the context of the market, and it has felt pretty bubblicious—like we were hitting a bubble with a ton of companies jumping in. Some of them couldn’t even spell "contact center" a year ago. And some can’t spell "AI" most days. At this point, there are probably hundreds of companies doing pretty much the same thing.

Most of them are either focused on voice automation and chatbots—much easier to build today than even a year ago—or they're working on some flavor of call summarization, quality assurance automation, sentiment analysis, or CSAT. They take a call transcript, evaluate it against criteria through a prompt, generate output data, and put a front end on it—suddenly, it's a product.

We had already seen some of the air coming out of that, especially with the recent struggles of high-profile startups and the noise around deciphering real differentiation.

Then last week, DeepSeek released its R1 model, catching the market by surprise. It wasn’t a black swan event like GPT’s original release, which was a true paradigm shift, but it reset the market in many ways by unlocking new capabilities that didn’t exist a week ago.

Now, it's possible to run one of these high-performance models on commodity hardware, and cloud hosting providers are offering the full model at a fraction of previous costs. This shift opens up new possibilities for tackling complexity.

I no longer think the market was in a bubble. Instead, it had plateaued—everyone reached the same level, running in circles trying to figure out the next differentiation.

Some companies will recognize the opportunities that these new models and paradigms unlock, using them to create new products and categories. Others won’t, simply because these models introduce new capabilities that many don’t have the context to apply.

If a model touts PhD-level benchmarks, but you don’t have the expertise or the right questions to ask, you won’t get significantly different results from R1 than you would from GPT-4 or any other model. But those who understand the potential of models that don’t just generate code but can tell you what code to generate—or that don’t just create data but bring in vertical context to analyze it and surface new trends—will create an entirely new category of solutions.

We're going to see a divide: companies stuck at the plateau, having reached the limits of their competence and vision because they’ve only focused on automating what we already know how to do, and those who keep climbing, separating themselves by bringing entirely new capabilities to market.

Some of this will be driven by open-source models, making it possible to buy a Mac Mini and generate high-quality data 24/7 while leveraging more powerful models for reasoning-intensive tasks. The accessibility of these capabilities is shifting rapidly, and I expect we’ll see a wave of innovation until we hit the next summit.

Few things can be more important to an early-stage company's success—or any company's success, really—than your ability to build trust with the market, with your customers, with your employees, the folks that you're recruiting, and partners in the industry. Exercising that leadership and integrity draws people to place their faith in you.

That includes everything from: Will you be in business tomorrow? If I'm going to write you a check and build parts of my organization around you, how can I trust that you'll have the financial resources and ongoing ability to deliver?

A recent example is this week when the whole DeepSeek controversy hit the internet. The open-source AI model came out from China, and there was a lot of fear and uncertainty in the market about what that meant for people. If it was deployed, would data be going overseas to China? Could it cause some type of security issue?

Then there was the camp we’re in, which embraced this new technology pragmatically to ensure the success and integrity of our business and our customers.

Through all of that, not a single customer reached out to us with concerns about what we might be doing with their data and this new DeepSeek model. I believe the reason for that is trust.

One, contractually, we have a whole lot of safeguards in place to ensure data integrity—making sure data doesn’t leave the United States, strict access levels, HIPAA controls, and SOC 2 compliance. For us to start moving data around would be a breach of trust. It would be a breach of contract.

But beyond that, our customers also know that we have the vision and pragmatism to be good stewards of their interests. That’s what building a reputation for integrity and trust in the industry comes down to—demonstrating that you are a good steward.

I see us as stewards of our customers' data and their money. They invest with us every day, and we are responsible for delivering outcomes and results.

As a startup, you have to demonstrate that level of maturity and leadership day in and day out—whether that’s through contractual agreements that create safeguards for clients if something goes wrong, or, more importantly, by proving every day that you’ve earned their respect.

You need to show that you understand the market and these issues as well as anyone, and that you always have your customers' best interests in mind.

While the world is melting down over what’s the right answer or the wrong answer, sometimes the best thing you can do is reach out to your trusted advisors, get educated, and then relay that back to your customers so you can lead them through the process.

As you do that, you not only build trust and engagement, but you also build long-term relationships. That increases customer lifetime value because they know you're not going anywhere—and they know that you have their best interests in mind.

Today, let's tackle AI readiness and “agentic” readiness, which I guess is the new buzzword in the contact center.

There's a lot of, I think, misguided but well-intended advice that the very first thing you need to do is figure out knowledge management or a knowledge base.

And while that is a critical component for a lot of reasons, it's not the very first thing you need to do. The first thing you need to do is ensure that the underlying technology you're acquiring—meaning your CCaaS, WFM, monolithic QA, or whatever applications or agent assistants you're implementing in your contact center—is open and extensible.

I talk a lot about this: APIs and the ability to move data and knowledge back and forth between those applications.

What will inevitably happen is that each of these use cases or applications in the contact center—whether it's an agent assistant, voice AI, or anything else—will have its own onboard knowledge management. Each one handles knowledge differently with memory and vector embeddings.

So, you need the ability to move your knowledge in and out of these systems in an open and easily facilitated way. This means you don't need a knowledge base; you need knowledge orchestration between these applications.

That starts with the ability to push out knowledge.

As an example, we deployed Zoom's agent assistant. It has its own knowledge base, which is very utilitarian in nature. The only thing it does is power Agent Assist, but it has to work that way because of how it indexes all the knowledge.

We have our own knowledge management platform that is open and extensible. You can create all your knowledge base articles in one place and, with just a few clicks, synchronize them directly into a Zoom knowledge base.

If we didn't have this synchronization capability, you'd have to administer knowledge separately in Zoom, in your IVA, and elsewhere. By the way, we synchronize everything out and back in.

The real point here is that if you're just going out to get a new knowledge management system, you're likely adding another tool in isolation. And if you're procuring and selecting your CCaaS, agent assistants, or any other system that doesn't have open APIs, you're just going to keep proliferating these applications without realizing their full value.

So, coming back to how we've designed this and how we guide our clients: it's about thinking through knowledge orchestration with document management and knowledge articles.

As we select partners to deploy voice AI, agent assistants, or even quality assurance, we can now perform deep quality assurance management. For example, we can embed a script directly inside a QA form or assess agent product knowledge right inside an evaluation using an LLM.

The only way to achieve this is by ensuring those vendors have published API documentation and that you can access and use it.

If you need help with that, let me know.

The following is an snippet from my conversation with John Walter on 1/15/2025

The current conversational AI vendors are doing quite well and are maintaining healthy profit margins. I've heard one mention an 80% profit margin number before. I'm just curious about your perspective on 10 to 15 years from now. When it comes to the ability of conversational AI vendors to maintain profit margins in that ballpark,

I don’t think they will. I think 80% gross margins are achievable because the technology is becoming so cheap so fast.

We were in conversational AI for a while, and back when we were doing it, it was much more expensive and complex. Now it's gotten to the point where there are so many open-source tools, and most of the intelligence is offloaded to the LLMs. You still have to deal with the plumbing, like voice connectivity, but even that is becoming super easy. What’s happening is a big proliferation of startups saying, “Hey, we’re another voice automation company, another conversational AI vendor, or another bot.” The market is saturated, which leads to compression of margins.

In 18 to 24 months, you're going to see CCaaS vendors incorporating this technology into their platforms. I know for a fact that at least three of them are working on it. They haven’t released anything yet due to the risks of hallucinations and wanting to ensure they get it right. Pricing is also a factor since they’re used to charging per seat. Now they might need to adopt pricing models like per minute or per transaction. And, as always, price approaches marginal cost in the long run. Bezos’s famous saying, “Your margin is my opportunity,” applies here. Once something is no longer complex to do, it’s time to move on. Voice automation will likely remain big and noisy for another year, but the momentum is waning because it’s no longer difficult or complex.

When I talk to contact center leaders, the gap between interest in voice AI and its adoption is striking. Everyone’s curious and eager to deploy something eventually, but they’re cautious because of hallucination risks. It’s interesting that CCaaS platforms are developing their own tools. This could align with a point where the technology advances enough to be confidently used with low hallucination risks, just as CCaaS vendors make it available natively.

This ties into the earlier discussion about the delay between interest in a product and its adoption. We’re in the early stages of the technology diffusion curve, with larger brands waiting to see what happens. If you’re a startup in that waiting period, you’ll likely be cash-starved. Early wins might come from digitally native companies, but not from large banks or laggards, which take longer to adopt. There’s a market for niche, vertical applications, like a healthcare benefits bot connected to marketplace APIs with specialization that narrows hallucination risks.

Good general startup advice: if you’re managing complexity on behalf of a customer, they’ll pay you. For example, integrating with systems like Epic in healthcare is a pain. Solving that is less about the voice AI and more about connecting into the infrastructure to facilitate transactions. That’s what you’re doing with ProxyLink—facilitating the transaction effectively.

When I see generic voice bots, I’m not excited. It feels like a CCaaS play or a niche-focused play.

In this interview, John Walter posed the question: “What if we make it an incentive so that if a company is willing to adopt this tool, they are also making these AI assistants more valuable? These AI assistants are becoming more useful to the consumer because they’re able to do more real-world tasks and not just write poetry.”

My response dove into the complexities of market timing, lessons from past ventures, and how redefining our go-to-market strategy turned challenges into a scalable platform and successful exit.

One of the hardest things—and probably not really talked about a whole lot around being a founder—is timing and gauging where the market is and where the appetite is. Then, tapping into that early traction while you build toward the bigger vision.

What this sounds like to me—and I’ll tell you about Experian, which I think is super relevant here—is that the only two times I’ve really lost money in a venture were when I went direct to consumer. Your total acquisition cost or customer acquisition cost compared to customer lifetime value is typically inverted. It’s crazy expensive to acquire a customer because you’re competing for very limited wallet share.

I mean, your $10 a month or $20 a month product is competing against everything else. So, the customer has to have a problem right then in order to pull out the credit card. And then you’ve got to keep them as a customer.

If you look back 10 to 15 years at LifeLock, the identity monitoring company, they were pretending to be a tech company, but they were really a marketing company. They would spend $70 million a year—or per quarter—just on marketing to acquire customers. They also had a big retention problem.

When we launched our second company, we went after social network monitoring for teenagers and kids—for cyberbullying, sexual predation, and reputation management. This was back in the early days of social media—around 2012, I think, when we launched.

We realized that you’re asking your customers and prospects to adapt their value system in order to purchase your software. What happened was we were competing against a couple of other companies. One of them sold—I forget who they sold to—but their COO and I met one day. He said, “Man, you’ve just got to abandon this company. Walk away from it. There’s no business here.”

But what we did—and I don’t call it a pivot as much as just redefining our go-to-market strategy—was I went to the identity monitoring companies and said, “Look, what we’re doing isn’t a standalone product, but it’s a great feature in your product, in your bundle.”

So, what we ended up doing was becoming a platform—a back-end platform. We built out all the APIs so we had all the integrations into the social networks. We had all the algorithms to monitor for stuff. Then we went to these companies and said, “Just embed us. We’ll make it super cheap because our cost is so low.”

And they all did—all of them except LifeLock, which is funny.

We ended up selling that company to Experian because Experian has one of the largest consumer bases for identity theft monitoring. They’re a credit union, but they’ve got all these consumer products you’re talking about, right? So, they ended up rolling us into that.

What that did was take our core product and our mission—which really stayed the same. We wanted to get kid protection, call it, at scale.

DoNotPay, by the way, has raised $26 million, and I suspect a great chunk of that isn’t going into the tech—it’s going into what we’re talking about with acquisition costs. But we found that distribution, right?

So, we focused on the infrastructure, the algorithms, and all of that because it is kind of a two-sided market in some aspects.

Good morning, everyone, and welcome back to For Starters. Today, I want to drop a quick note on not screwing up your pre-sales and post-sales onboarding process. You all spend a lot of money acquiring new customers, burn a lot of calories on LinkedIn talking about acquiring new customers—only to mess it all up during the handoff.

Recently—this week alone, in fact—I’ve had three abysmal experiences where we were ready to buy something from a company, and they completely botched it.

Take Brex, for example. It’s a fintech and banking company. We did a lot of research to find banking services that could meet our needs across multiple entities. We were excited to get started. But then, they involved three different people across support, some startup consultant/advisor thing, and sales. None of them coordinated. All we needed to do was upgrade our account to support more than two entities, but nobody could figure that out. Instead, we got dragged through their script of pre-qualifying questions that had absolutely nothing to do with our business. So, short answer: we’re taking that business somewhere else.

The same goes for online support. We’re trying to embed support into our platform to create an elegant, world-class customer experience. But the company we approached started reading off their list of questions about who we are and how we do things today—which isn’t what I want. I want them to focus on who we’re going to become tomorrow and the level of support we aspire to deliver. When you ask the typical SDR or BDR to reframe their questions or guide them toward how you want to buy, it seems to short-circuit their script. Short answer: I’m not sure we’ll go with that company either.

Another company—this time for payroll—couldn’t even manage to respond about how to get a demo.

The moral of the story? Sit down and figure this out. Your onboarding and initial experience are your first impression with a client. If you can’t get that right, you’ll lose them. Retention will be horrible. Ironically, one of these companies is out there flexing about being in “founder mode.” My advice? Just focus on executing and delivering the type of customer experience you’d want yourself. Do that, and people will pay you—and stick around.

Today I'll wade into the waters and talk about a hot topic these days, which is the future of the BPO industry. There's a lot of varying opinions and thoughts on it, from, "It's the end of the world; everything's gonna be automated," to, "Business as usual," and a whole lot in between.

I'm going to step back and look at it a little differently. First, I'm not distinguishing the difference between a BPO contact center and any other contact center that's in-house, because at the end of the day, we're all trying to accomplish the same thing: delivering compelling and excellent customer experience at the best price point possible. AI or no AI—that’s the end goal.

In BPOs, it's our job to lead the industry in how that gets done.

What I think about every day, when I sit down with my morning or afternoon coffee, is: How do we be the best at that? How do we deliver the best customer experience in the best-run contact center? That's the goal for BPOs on the planet. And that leads to an interesting set of questions. Notice I didn't ask, "How do we survive the AI apocalypse?" or "How do we deploy AI?" or anything like that.

The answer starts with just basic principles in economics and running and growing a business—or a service business, wherever you are.

The first operating principle is that we need to increase our capabilities—what we can bring to our customers and to the market, whether it's through products, services, or better execution of what we already do. We need to increase our capacity to deliver those things, and we need to do it in a way that consumes fewer resources. If you can bring those three things together, you're going to have a comparative advantage in the market.

Last year, we grew faster than we ever have. What we did was look at all of our constraints across the organization and ask: If we landed 1,000 seats, 10,000 seats, or even 100 seats today, where would things break down? Or where are things already breaking down with the business we have now?

Then, how do we leverage new tools and new ways of thinking with AI to solve for those and unlock new potentials? We started with training and recruiting, which is a pain point for every contact center on the planet. Attrition is a problem, no matter how good you are, how great your culture is, or how great your pay is.

You're constantly hiring. If you're growing, leaning into clients, you're hiring and training. And, frankly, our training program was a mess. We were throwing people at it, but finally, we stepped back, refactored the whole thing, and asked: How can we use AI to help us scale, improve the efficacy and outcomes of training, and then scale it so that we can do what I call "scaling in place"?

That means: How do we go from 1, 10, 100 seats, or 1,000 seats of new business without adding incremental headcount—except, really, at the agent level?

So we rebuilt the whole program from the ground up. What we have today is a set of great training modules and world-class content. Now, clients are coming to us asking, "Can you do the same thing for us? Can you build these modules or license yours to us?" That's created a new revenue stream for us and might even create a new business.

With recruiting, we've done the exact same thing. Again, recruiting is something every call center in the world has to do, and ours was painful. I strongly believe that if you can improve your talent acquisition strategy, you're going to improve retention, quality, and everything downstream.

We took a very data-driven and AI-driven approach. We've shrunk the time it takes to fill a new hire class, improved the quality of people in those classes, and improved the retention and quality coming out of that.

We're focusing on everything else before worrying about AI agent assists or any of that. We've unlocked new revenue, increased our capacity, and scaled as we add new logos and headcount.

The next piece is selecting and deploying technologies in production. Every CCaaS player has their AI. Tom Weird calls them "Whatchamacallit bots"—automation tools, voice automation, etc.

Not all BPOs are created equal. The industry isn’t monolithic or homogeneous. If you've rented thousands of seats offshore for simple phone calls, like minimum-wage workers checking order statuses, that’s a high-risk profile in the industry. We're not in that business.

For everyone else, it's about delivering these new capabilities better than yesterday without blowing your margins.

That's why we're big fans of Zoom. Their API documents let us build and deploy whatever we need, and their pricing is clear. They ship new code monthly, so we're constantly getting more for our dollar—and so are our customers.

If I look at Nice, Five9, Genesis, or Amazon Connect—most of the BPO world’s go-to tools—you're paying through the nose. Nice is like $250 per agent per month. Amazon Connect is in the same range. You're adding $1-$1.50 per agent hour just to get out of bed in the morning. No thanks.

With Zoom, we’re running a good piece of our business on their Essential licenses for $65/month. We get transcription, APIs, and analytics. Our analytics make the big kids cry themselves to sleep because we bring that to market.

When we move up the stack, we run on Elite licenses for higher-complexity calls. We wanted agent assist, better accuracy, workforce management, call quality, and screen capture—all in one.

This is how we show up fully loaded to any deal or opportunity. We're delivering capabilities, capacity, and results at a price point nobody else can match.

To me, that's how we're growing over the next 12 months—and forever. It’s an operating model: VXOS. Swap out Zoom or any tool, and you still follow the same principles.

AI is just a tool to eliminate constraints, unlock new capabilities, and scale. These are things everyone should be doing, whether you’re 5 seats or 50,000.

Our role in the industry is to help lead, deliver these capabilities to the market, and share what works. We've got scars from failed experiments—some we built and had to rebuild—but when we find things that work, we hit on all cylinders and scale.

That's our opportunity—even to partner with other BPOs. Some very large BPOs understand what we're doing with our sister company, Endeavor CX, around analytics and operational excellence.

Ultimately, if you’re competing for business, you're being measured on call quality, occupancy, and comparisons to everyone else. Your job is to become the best version of yourself. Stop worrying about competitors. Unlock your own potential and drive up your CX delivery expertise.