Nicolas Druelle leads GTM Ops at Melio, a B2B payment software company. With experience in both the big picture of company development thanks to a stint in VC and in building a company from its early days, Nicolas has been both an eyewitness to and a participant in the ongoing evolution in business-oriented data operations. 

He spoke with Captain Data co-founder and CEO Guillaume Odier about how his data team at Melio is structured, how the “buy vs. build” discussion goes today, and a range of other operational topics.

GO: It’s maybe a bit obvious, since we’ve only recently started talking about operations as a business function, but you didn’t start out as a GTM Operations specialist. Could you tell us about how you got here?

ND: My background was in business finance, and I started working with an internship at a VC firm. That’s where I really got obsessed with trying to understand how you can grow and scale companies to get great outcomes. But in VC I was just observing that process, and I decided I wanted to actually be part of it. So I left and started working with small companies aiming to grow. 

My first was at Storefront, a marketplace for popup stores, where I was a business analyst. That got me deep into data, with the goal of giving leadership the information they needed to understand what was working and what wasn’t working. As I kept having those discussions, I broadened my understanding and scope, working on what we now call go-to-market operations.

GO: And now you’re working at Melio, a payment platform operating in over 70 countries. Can you tell us a bit about how things work there, how the teams are structured?

ND: So Melio is a B2B payment software company, we help SMBs and their accountants to pay their bills. Our customer-facing market is in the US for now, where lots of payments are still made via check. We work on both sides of the equation, letting the payer decide how they want to pay and the payee decide how they want to receive the money. From a GTM standpoint, that leads to a lot of interesting topics. 

Plus, although our customers are in the US, our tech center is in Israel. So we built our operations around that geographical structure. Our GTM and enablement teams are in the US, working closely with sales and customer success that are also in the US. We make sure they are extremely well-connected with the tech team in Tel Aviv. Within that tech team, there’s the data team that brings together data analytics, data engineering, data science, and also GTM technology, which is the team I’m leading.

My team’s goal is to make sure that our teams in the States have the right tools powered by the right data; my role is to connect those two key functions and thus enable the growth of our business.

We work on both sides of the equation, letting the payer decide how they want to pay and the payee decide how they want to receive the money.

GO: In a team of roughly 600, how many people are we talking about?

ND: In the States, we have 4 people in our enablement team working to ensure adoption, orchestration, etc. Then there are another 4 people in enablement working closely with sales and customer success. In Tel Aviv, our data team is serving different departments within the company, but we have 3 data analysts dedicated to GTM and customer-facing problematics as well as 2 data engineers working on the technical aspects of our data pipeline. 

I also have a 4-person GTM tech team that works on things like Salesforce and back-end processes, and finally we have one data scientist who’s working purely on prediction-oriented GTM topics. So we’re talking about almost 20 people working on various aspects of GTM operations.

GO: Looking back at the past five years, what have you seen that’s changed in terms of operations, and specifically GTM operations?

ND: One key trend lately has been the “platformization” of Ops. A lot of SaaS tools have been developed to build the sales stack, the GTM stack. Before, there was Salesforce or Hubspot as the central repository of operations, but as GTM strategies have gotten more complex with different business models, different markets, a very granular vertical has developed to support that function. So now, an Ops person really needs to understand the actual requirements of the business model – depending on the stage, the vertical, the market – and implementing the right tools.

That’s why the “buy vs. build” philosophy has become a central part of what we do, just because you can pretty much find whatever you need to fit your particular use case. And at the same time, I think that technical people are still very big players in Ops because there are possibilities like Product-Led Growth, where companies are capturing so much data, that it’s become even more important to have people who are able to handle that large amount of data and understand it.

GO: If you compare GTM Ops to Revenue Ops, what differences do you see?

ND: GTM is in some ways a subset of RevOps, but it also depends on your stage. Early-stage companies don’t really want to have a strict RevOps policy necessarily, but you will want GTM Ops working from early on, right from the seed stage.

But there might be a lot of things that get done from seed to Series B that aren’t necessarily scalable. You need to find PMF, your lead generation will change, your KPIs will evolve, pricing will move... That’s where your GTM Ops can move fast and implement a monthly or quarterly strategy.

As you scale further, that’s when RevOps comes into play. You’ll need to start aligning your different funnels: marketing, sales, customer experience, etc. At that point, GTM might be very top-of-funnel, whereas RevOps will want to keep an eye on the entire user lifecycle to minimize churn and optimize revenue.

GO: You mentioned Product Led Growth before, is GTM Ops mainly useful for this kind of

SaaS? Or is it useful in other contexts as well?

ND: [smiles] There are a few criteria needed to be a PLG company. SaaS companies pretty much invented PLG, since that’s the growth channel they used to be so successful. And really, as soon as you have some kind of freemium quality, where you can deliver value without the customer needing to give you their credit card, you can think in a product-led direction.

To do that, you have to develop a very strong customer data strategy. You have to be able to measure the right intent data on your platform and then relay that information to the right teams.

I do think we’ll only see more PLG strategies put in place. One thing to remember, too, is that PLG kind of blurs the line between B2B and B2C strategies, since aiming at PLG for a B2B company creates a situation where you’re not selling to “the company”, you’re selling to the end user.

Still, it is true that the magic recipe is going to be found largely in SaaS companies, and specifically those that can provide value to users without the need to pay for it.

GO: Let’s get specific: what does it take to be successful in GTM Ops?

ND: GTM Ops has to start off by being very close to the people holding the vision of where the company is going to go. That means founders, marketing leadership, sales leadership, product leadership.

Because as an organization scales and changes quickly, there’s a big risk of getting trapped in certain tactics, developing a tool stack, a data strategy, a team that’s focused on one specific product offering. That might help for the next 6 months, but will it actually fit the vision for where the company will be in 2 years?

After that, more than any particular tool or strategy, a good Ops person needs to both have a playbook and be able to iterate on it. At the beginning, you’ll always have constraints, you can’t buy all the tools, you can’t hire all the personnel. You need to be able to set things up in a way that works now and that is ready to evolve into the next stage.

GO: Given the experience in growing Melio over the past few years, are there things you

would have done differently?

ND: Two big things come to mind. First, I’d have wanted us to do a better job planning for the best case scenario. When I started, we had a few hundred customers, now we have over 100,000. And back then, I didn’t clearly think through what would happen, in terms of data and go-to-market, in that high growth scenario. Some of that isn’t predictable, but I still think it would have been good to be even more careful and thoughtful about how we’d treat a very large volume of data.

That leads to the second thing, which would be to avoid pushing the entire database into Salesforce or whatever CRM you’re using. I’ve spent a lot of time working to make our data warehouse more efficient, taking only the required, clean data within the relevant system. That was one pitfall we fell into, pushing too much data into the CRM, which created data orchestration and data hygiene issues that had to then get cleaned up afterwards.

For a fast-growing company with a large volume of prospects, handling the data as high

upstream as possible and making sure it’s clean, creative data headed to your sales,

marketing, customer success teams, the better off you’ll be.

GO: Closing with the big takeaways: what’s working out best for you in terms of data best

practices and tips, operationally speaking?

ND: My favorite question! First, you need to have strong data governance that makes sure teams are seeing what they need to see, and only what they need to see. This means having a company-wide framework of what metrics are being defined and what they mean.

Then remember that besides internal data, enriched data and third-party data is a big part of everything we do. I learned this the hard way, since US-based SMBs aren’t necessarily online, they might not have a website, so we needed lots of creativity and sophistication to get the necessary data. Having those third-party enrichment capabilities properly managed, meaning they aren’t going into silos within your organization, is key.

After that, you have to be able to blend your internal, first-party data that you’re collecting on users with that enriched third-party data to give you very robust user scoring.

Just remember that there’s the baseline for data operations, which you can get to with the right tools and providers, and then there’s that extra level, the one you arrive at from being creative and thinking beyond what everyone else does.

‍This interview appears in Captain Data’s white paper “The Rise of Operations”. To go further on the development of specific functions such as Sales Ops, Go-to-Market Ops, Product-Led Ops and Revenue Ops, download the full white paper here.