In this episode of Mastering CS: Candid Leader Insights, Irina Cismas sits down with Rachel Weller, Head of Customer Success for the Americas at Canary Technologies, a guest management platform for hotels. Rachel started her career as an analyst at Goldman Sachs, spent close to a decade climbing every layer of CS at Yotpo, and has since built teams across the US, EMEA, and APAC.
She shares what it looks like to build a CS team from scratch in a new region, why doing messy, unscalable things first is one of the most important habits in CS, how she thinks about the balance between technology and headcount when scaling, what the human element of AI adoption really means, and why zooming in and out is the one habit she has carried from Goldman Sachs to every CS role since.
What You’ll Learn
- What Canary Technologies does and what the Head of CS role looks like day to day
- Why building a CS team from scratch should start with the market, not the org chart
- What the messy, unscalable things are that you can’t skip when building from zero
- How Rachel thinks about defining and co-creating value with clients
- How she approaches scaling: people, process, and product, and what signals tell her when to use technology versus hire
Key Insights & Takeaways
Start with the market, not the org chart. Before hiring against a spec or profile, you need to understand the customer base, the buying dynamics, the local culture, and the operational maturity of the region you’re building in.
Do the messy, unscalable things first. Everything you read online pushes you toward playbooks and processes. But before you can build something scalable, you need to learn what works and what doesn’t, and that only comes from doing things manually first.
Value is co-created, not assumed. A product marketing release doesn’t define what value means to a client. The most important conversations happen when CS shows up with humility and asks customers to sanity-check and build on what they’re hearing.
Scale is not just about numbers. It’s about people. The skill sets of your individual contributors, their appetite for change, and the balance between commercial and implementation capability all shape what scale actually looks like for your team.
Podcast Transcript
Intro
Irina (0:06 – 0:42)
Welcome to Mastering CS Candid Leader Insights, the podcast where we dive into the world of customer success with industry leaders. I’m your host, Irina Cismas, and today I’m joined by Rachel Weller, Head of Customer Success for the Americas at Canary Technologies, a guest management platform for hotels. Rachel, I’m really happy to have you here.
Thanks for joining! You just stepped into the Head of Customer Success seat.
Paint us a quick picture. What does the role look like day to day? What kind of customers are you working with?
Rachel (0:43 – 1:54)
Our customers are over 20,000 hotels in 125 plus countries, including some of the world’s biggest and most renowned brands: Marriott, Best Western, Choice, Four Seasons, Wyndham. And we are a verticalized hospitality tech agentic AI platform. That’s about the company and our customers.
Day to day, the role looks like everything in CS that everyone experiences, the dynamic nature that is customer success. For me personally, that means a couple of things. It means zooming in and zooming out a lot, which we’ll go into later I’m sure, and managing two layers of people at once. I’m managing a team lead, a director, and many individual contributors as well.
So I do find myself zooming in and out from the weeds and the day to day, and that honestly keeps it very exciting. In addition to the individual contributors and the work component, there’s of course a high level of cross-functional work, executive-facing work, strategic work, the deep work time, if you will, that needs to happen.
What the Role Looks Like and What Rachel Is Focused On
Irina (1:56 – 2:06)
And right now, what’s the one thing you are trying to move the most between all the things that you mentioned? What’s the number one priority?
Rachel (2:06 – 2:38)
Yeah, I’m really thinking about the human element of AI quite a lot these days, kind of consuming as much as I can about it and paying a lot of attention to it internally as well. But all of that sort of is in the ether on a more tangible level. One thing I’m really thinking about is team capability and scale and how we grow output efficiently.
And it’s something that I would say keeps me up at night or I think about morning, noon and night is a more accurate way of putting it.
Building a CS Team from Scratch in a New Region
Irina (2:39 – 2:58)
Speaking of this, and I want to combine the human part with the scaling thing that you mentioned. I know that you build the MA team from the ground up before you move into America side. And I’m curious when you are building a team from scratch in a new region, what comes first?
Rachel (3:01 – 4:21)
What needs to come first is starting with the market and the environment that you’re working in, as opposed to the org chart. Otherwise, you’re doing things backwards. I definitely learned the hard way in more than one country that it’s important to understand the customer base, the buying dynamics, the language, the local culture, the local pain points, and the ops maturity of wherever it is you’re working within that company. Before you hire against a particular spec or for a particular profile, it really needs to be a bottoms-up approach. That’s one.
Another thing I just want to add, because I think this is probably my hardest-won lesson, and I’m sure every CS operator and leader you interview has learned it, is that doing the messy, unscalable things first is incredibly important and not super intuitive. Everything you read online, there are so many playbooks, there’s this desire to get things right on the first go and build a process. But I’ve learned, and I really believe, that before you can do that, you need to do things that are intentionally messy and intentionally not scalable, so you can learn the details of what works and what doesn’t. That way, when you build a process for scale, it actually makes sense and it’s been tested.
The Messy, Unscalable Things You Can’t Skip
Irina (4:22 – 4:56)
I want to zoom in, actually, and go into details. What are those messy things that you cannot avoid and cannot basically automate from day one or apply a Claude or ChatGPT layer on top of? Let’s give some examples and then walk us through your way of thinking when it comes to scale, because it’s something that I’m hearing a lot about.
Rachel (4:57 – 5:59)
In terms of the messy and unscalable, one thing that comes to mind is defining what value actually means to clients. That is not something that can be guaranteed by a product marketing release or by what we built the product for necessarily. We need to make sure at CS that we are providing a solution to what our clients need, that we’re able to pair whatever it is we’re offering with a real pain point and that it solves something.
You hear a lot about value-based outcomes. To me, the ingredients there are making sure that we are understanding a pain point of a client, backing into what a solution might look like, and then making sure that our solution addresses that directly. And then we can go into the how. If it’s done the other way, where we’re just trying to say, do this thing because we know, we assume that it does X, Y, and Z, we’re missing a really important chance to build directly from our clients’ mouths, as it were.
Irina (6:00 – 6:27)
Value-based is something that I’m hearing a lot. And in some cases, it might be hard to articulate.
How do you nail the value? How do you, what’s the process? Is it just by asking the customers and through the discussions?
Or how do you cluster it?
Rachel (6:27 – 7:38)
I think it has to be a combination of what you know your company’s value is, and comfort with an honest and vulnerable conversation with clients to say, hey, we’ve seen that this solves this. Are you experiencing it that way? Is there anything else that you would add? Is there anything that you wish we were doing more of or less of, or maybe doing differently?
It needs to be paired with a little bit of humility whenever we’re going to market with a, hey, our product does this. Because there is a confidence, and that’s incredibly valuable because we’re consultants, we’re strategists, but there has to be this human element as well of saying, can you validate me? Sanity check me? Does this actually resonate with you? And if so, how can we build on it?
There’s something that is based on research, something that is based on product strategy, and that we build as a company. Any customer-led product strategy will do that, which is incredibly valuable in incorporating the voice of the customer. But it always helps to have a client add to what their definition of value is, so that they’re co-collaborators in this.
How Rachel Approaches Scaling: People, Process, and Product
Irina (7:39 – 8:11)
Awesome. And now let’s tackle the second part, the scaling. How do you approach it?
And is it by leveraging technology?
Is it by increasing the headcount? What’s the balance between the two?
Rachel (8:12 – 10:14)
Doing more with less, doing more with the same, I understand the general idea. One thing is scaling faster than the other, right? That’s the dynamic.
I think it has to be an honest look at what resources you have. What is your tech stack? What is your why? What are your resources on the product side? What does churn look like? What do your cross-sell efforts look like? Who are the people you have? It needs to be a holistic look at process, people, and product, and understanding what’s most urgent.
For example, if there is something urgent around clients not adopting certain products or not retaining a certain product as much as we would like, that would be an immediate thing to assess. And then any scale motion needs to be built with that in mind. If it’s more of a carte blanche or a zero to 100 model where we want one CSM to service all of our clients, then obviously scale is going to look different and there needs to be a bit more risk-taking there. Maybe building a certain journey and seeing how it works or doesn’t and then iterating from there.
It also has a lot to do with the willingness to grow resources that you have and what you’re starting with. The skill sets of your people are going to vary a lot. You can have five individual contributors who are more on the commercial or implementation side, extremely capable of doing discovery and helping you derive insights that you can then build a scaled process from. Or you can have five people who are just absolute operators who can go out and talk to 100 clients in a month, and anywhere in between. So it really is always a matter of knowing your people and then understanding what your North Star is and how quickly you need to get there.
The Signals That Tell You When to Use Technology Versus Hire
Irina (10:15 – 10:40)
Are there different signals that point you toward, this I’m going to solve with technology, this I cannot solve with technology and I need to hire? Do you have a decision tree, or do you follow and monitor some specific signals that help you decide when to go where?
Rachel (10:41 – 11:37)
No matter how much I work or don’t work with ICs, I find that understanding the day-to-day of an individual contributor, listening to their stories from clients, their workload, and what they’re actually dealing with on a day-to-day basis is always going to be an important data signal. It’s not always going to be the only one, and it can’t be the only one. There are the economics involved. But that is always going to be a very important indicator of what the appetite is, because at the end of the day, it’s also about maintaining team balance and success for our clients. There needs to be a balance of how do we keep our people happy, interested, and engaged while also serving the client’s needs. It can’t just be about numbers in terms of scale, because numbers involve people. That’s the inherent complexity of customer success, which I think is one of the more interesting parts about it.
How the Team Leverages AI and What the Human Element Actually Means
Irina (11:39 – 12:01)
I have to ask about AI. How does your team leverage it? Where do you use it and how does it help you save time? What are the agents that your team builds internally and how does that AI layer sit on top of everything you have built?
Rachel (12:02 – 16:24)
I’m a big fan of so many different parts of what AI brings to the CS conversation. I know there are a lot of feelings around AI right now. You read LinkedIn posts and articles and it’s in the back of everybody’s head.
A couple of ways that we’ve baked things into our flow: analysis and forecasting on a leadership level. I find that very helpful. It helps me dive deeper into different factors, back-test things, and test different theories going forward. I find it incredibly instrumental there. It’s also extremely important for call review. And I think that no matter how sophistication grows, AI will be one of the most incredible things of our generation in CS. I am a Gong fan and will stand by that until the day I die. It’s an incredible tool for call review but also for coaching, drafting communications, and just allowing people to be more human in their roles because it saves time on the admin component.
I just want to make a comment about AI because I talk to my team a lot about this. AI literacy is really the new currency. And if you think about it that way, CS has this upside: if we do it right on an individual and org level, then CS wins back time to actually be more human, have more conversations, ask better questions, deploy real strategy, instead of optimizing for time only in spreadsheets or analyzing data without the human component. One caution with that is to make sure we’re keeping humans at the center of this AI change, which can be counterintuitive given the rapid pace of transformation.
One key way we’re using this at the company, which I really love, is as a tool to help you become a better version of yourself, become more efficient, hone your craft, and do what you do in your role better. It’s not really about how can we replace ourselves with robots, even though that’s the scare tactic out there. It really is about how can we use AI in a way that allows us to spend more time with our clients, do deeper investigation, be more human, and advocate for clients internally in more human ways.
A really important conversation around AI is to leave behind a one-size-fits-all adoption model, because AI is this broad-reaching concept, philosophy, and framework we’re all evolving in. It is on us, especially on CS leaders, to make sure that we’re leading by example but also with compassion. I’m actually working on an article with a friend of mine who has an AI consultancy, all about the human challenge of AI adoption, because introducing workflows and tools with an AI element can meet people in a fundamentally nerve-wracking way. It can initially threaten someone’s sense of identity. I’ve done all this stuff here, and now AI is replacing all these things. Who am I? What am I good at? What am I going to do to prove my value if I’m not spending hours sending a follow-up email anymore? There’s a lot that’s natural about that, and we have to be patient with each person, understand the will versus ability matrix, and understand how we can bring people along to find their own entry points for using AI in a way that is fundamentally human, fundamentally client-first, and flexible, because everything’s changing so quickly.
Irina (16:25 – 17:16)
I really love your answer about how you are leveraging AI, and it paints a different perspective from what I’m hearing in other conversations, where CS leaders are basically measured on how many tokens they consume on a monthly basis, because that’s a number management can easily check. So this paints things in a different light. Thank you for that.
I want to talk about Gong, which you mentioned as the thing that helps you stay connected and gives you the customer’s reality through the call transcripts. What else is connected that helps the team take better and more informed decisions?
The Tool Stack Behind CS at Canary Technologies
Rachel (17:17 – 18:13)
Gong is so valuable, as I mentioned, and at my previous company I actually wrote the playbook on all the reasons it can be and is critical, not just for individuals but also for managers to push everyone to use it. But in addition to that, Gong, just like any other tool, is even more powerful when it’s plugged into the operational spine of a company. We have a CRM as our system of record. We have a CS platform for health and different playbooks. You have to have a dashboard, a data layer, and tools there. You can build your own or build things out in various spreadsheets. And then of course Slack for communication.
I think Gong is most useful right now because it’s like an octopus. It has a lot of different tentacles and can shoot out different insights to different channels to meet people where they’re actually working and inform things in a very precise way.
Irina (18:16 – 18:27)
So it’s CRM, it’s Gong, it’s the CS product analytics. Is it also well combined and is it fitting the health scoring and does it help you take decisions?
Rachel (18:27 – 19:05)
In terms of client feedback that comes from Gong, yes, absolutely. We have a really big emphasis at my current company on voice of customer and leveraging all different sides of the business to understand exactly what our clients need, through surveys, through subjective input, and of course through the analytical side, through data and usage as well. All these things need to be seen together, and I think it’s very valuable to have multiple profiles of people attending to what our clients are actually saying. It can’t just be CS.
How Rachel Keeps a Large Distributed Team Focused on What Moves the Needle
Irina (19:07 – 19:34)
You are managing a big team across different regions. How are you making sure that everyone stays in sync and how do you know if they are working on the things that move the needle and not just keeping themselves busy? Because it’s a trap that you can easily fall into when it comes to CS.
Rachel (19:35 – 21:03)
Hiring the right people is a really important ingredient there. I’ve personally been involved with the hiring and interview process of every single person on the team right now, and the promotion path of everyone on the team as well, for the most part. That’s incredibly important to make sure we’re bringing on the right people. So that’s one, because a big part of the answer to your question has to do with trust. Definitely something that is more easily said than done, especially if you’re a new manager or a new leader, or if your sphere of influence has grown and you’re a couple of levels removed from people who are operating on the day-to-day.
But there are always going to be leading indicators. Keeping a close eye on data and understanding what we want to measure as product goals, which is the output, versus process goals, which is more of the input. The output can be things like revenue targets and different KPIs around growth and all the typical CS things. And then the process goals are things like the calls we’re having with our clients, strategic touch points, how we’re preparing for them, what people are contributing to the team, level of internal engagement. That can look a lot of different ways. But that is something I look for when measuring or identifying a high performer. It needs to be both things.
The Habit That Has Carried Rachel Through Every Role
Irina (21:06 – 21:33)
You started out as an analyst at Goldman Sachs and spent close to a decade climbing every layer of CS at Yotpo. And you’ve now built teams across the US, EMEA, and APAC. Is there a habit or a way of thinking, something you learned the hard way early on, that still shows up in how you run CS today?
Rachel (21:35 – 23:48)
I’m going to be vulnerable for a moment. I would say that my anxiety has a lot to do with this. My head is constantly running, whether I want it to or not. And so building this discipline early on to zoom in and zoom out has been something that I’ve come back to time and again since college, really. That is a structured ability to really hone in on granular detail, but then ensure that I have time to zoom out and look at the big picture and not get stuck irrevocably in either.
And that has become a muscle. It’s become a habit that has translated through different regions, different countries, different companies, different products, and different industries entirely. That’s something that started at Goldman, because it was a very intense experience. The analyst program teaches you to think big picture and also in terms of small details, with a very highly developed sense of risk and operational rigor. And that rounded out my intellectually indulgent liberal arts degree that I came into the world with. But the pivot out of finance is where I learned the other half, which is that detail without the larger perspective doesn’t actually help either. You need to have both.
Finding this ability to have detail, time in the weeds, and also be able to say, okay, what did I learn today, what does this mean for the bigger picture, what does this mean for my strategy, what are other avenues of discovery I want to go down further, that was a really massive learning for me. And leaving a well-defined corporate track for a function that at the time was still very much being developed, like it is now but in a different phase in 2015, that really tested the same muscle with a lot of trust and a lot of, okay, where am I, but how can I get to the next place, even though I’m operating in more vaguely defined parameters.
Irina (23:50 – 24:08)
Rachel, this was a really interesting conversation. Thank you so much for joining me and taking the time to share it with the CS community.
And to everyone listening, thanks for tuning in. Until next time, stay curious, keep learning, and mastering customer success.
