The importance of a data-driven go-to-market

Yotam Yemini, fmr. CRO at Quantum Metric & VP at Turbonomic, on the importance of revenue operations

Today we welcome Yotam Yemini, fmr. CRO at Quantum Metric & VP at Turbonomic, to discuss the importance of revenue operations to scaling your business. Yotam is a passionate student, builder, and coach of high-growth software companies.  He has proven go-to-market leadership experience as an executive and advisor for numerous startups that have been ranked in the top 10% of the Inc. 5000 list of America’s Fastest-Growing Companies for the past decade.  He is a former NCAA D-I men’s college basketball coach and graduate of Tulane University’s School of Science and Engineering with a focus on Industrial-Organizational Psychology. Yotam resides in Bethesda, Maryland with his wife and two children.

Today you’ll learn about:

  • Why you need a data-driven go-to-market
  • How to implement a data-driven process early
  • When you should make your first RevOps hire
  • The ROI of having a RevOps team


CB: You’re a big advocate of creating a strong revenue operations function early in the company-building process to support a data-driven go-to-market strategy. Why?

YY: I think about product commercialization in four key food groups: product, marketing, sales, customer success. Product is deciding what should be built, marketing is working to raise awareness and consideration from the target market, sales is working with potential customers to support the buying process, and customer success is making sure the anticipated value is achieved by the customer. Each motion builds into the next, so you need revenue operations to serve as the control center that binds these teams together.

CB: What best practices can founders incorporate themselves before they hire a sales team and a revenue operations lead?

Before you are ready to start scaling out your go-to-market functions, you need to make sure there’s a pattern in the earliest customers that you bring onboard, and you need to do that yourself as the founding team.  In trying to define this ideal customer profile (ICP) from the early days, a common mistake is getting too hung up on defining the ICP very broadly as if to prove the total addressable market (TAM) is huge.

From a revenue operations perspective, this means the founding team should be focused less on proving the broader TAM, and more on finding a data-driven way to prove there’s a vein of gold within that broader market to serve as the wedge for landing the first 20-100 customers. This is all about categorizing your broader TAM into smaller, consumable chunks, and thoughtfully demonstrating how to efficiently prioritize how you will grow your TAM over time.

Another mistake that you want to avoid is making sure that you’re using reliable, available data to help provide a data-driven foundation for defining this TAM and the various segments within it. This will pay great dividends as you build out the go-to-market functions and need to deal with how to build and split territories as well as how to execute account-based marketing and sales programs.

So in summary, make sure to define your ICP with common data points across your customers that have a high willingness to pay for the early version of your product, then correlate that with publicly available company data that you can easily find.

CB: In your experience, what advice do you have to help founders design the right go-to-market strategy in the early days?

YY:  One of the most important decisions you need to make early on is whether your initial product and value prop are going to get adopted top-down (from executives who sell this down to individual users) or bottom-up (from individual users who sell this up to executives).  The tradeoffs are obvious, selling top-down should have larger landing deal sizes but longer sales cycles and selling bottom-up should be easier to do with a freemium, try-before-you-buy offer.

A mistake to avoid is trying to make this decision without understanding the more natural way for your product to gain traction, and an even worse mistake is trying to do both of these at the same time. You’ll benefit from picking one side, top-down or bottom-up, and you don’t necessarily have to stick with that side forever, but you will at least have a clear blueprint for how to go-to-market in the early days.

Your go-to-market approach will naturally become more diverse and evolve over time – if you’re going to build a really big company, you’re going to have to use a variety of go-to-market motions – but it’s very important to commit to one motion early on and master it. Once you pick a side, the actual “how” becomes so much easier. There’s a wealth of information and expertise out there to help you scale an enterprise sales team, or build a product-led-growth engine, but you have to figure out which side of the spectrum you’re on first.

CB: When do you think a founder should start thinking about hiring a revenue operations person?

YY: I look at revenue operations to drive data-driven decisions on how to run go-to-market as you scale the business. If you have someone in the early founding team who’s experienced and passionate about data-driven decision making for go-to-market, then you can afford to kick the can down the road for a bit. In the early days, when you’re pre-revenue, you should have someone that owns this as part of their full-time job, but maybe it’s not 100% of their role.

In general, I would argue RevOps is one of the most underrated early hires. When a company doesn’t have someone doing the basics, like setting up initial systems to capture go-to-market data, the tendency is to wait way too long to make the hire. If you don’t have that data-driven infrastructure, reporting, and decision making in place as you scale, it can lead to some disastrous consequences. It’s not enough to want to be data-driven two or three years down the road into building a business, because then it’s too late and you don’t have the infrastructure required to be truly data-driven. I’ve seen all kinds of problems come about as a company grows and matures because this infrastructure wasn’t put in place early enough.

CB: What are some of the issues or consequences you’ve run into where a lack of data made it more difficult to scale?

YY: In planning forward 12-24 months, you should be doing win/loss analysis about not only your product-market fit but also your pricing and packaging.  At a previous company we were using word documents for all of our proposals, which are not only error-prone but they also make it very hard to learn from trial and error from your sales process. It’s valuable to have data on all of the proposals you send out over the years and you don’t want this data to be too fragmented across people’s individual computers and inboxes, but missing from your CRM. You don’t necessarily need to go buy a CPQ tomorrow, but it’s important to build your proposal process into your CRM so it’s a data point you can track over time.

Data issues can be more basic than that, too. For example, if you go up the funnel to qualification stages, it’s incredibly important to do a real scoping of the customer’s requirements and track that in the CRM. You have to be tracking that information accurately, and you can’t just be using the default jargon that product and marketing are teaching to the salespeople, you have to use the customer’s actual feedback and context. If you build up that data set over time you should begin to understand that if the customer’s requirements are “X” then we have an 80% probability of closing the deal, and if the customer’s requirements are “Y” then we have a 20% probability of closing the deal. That’s when you really can start to predictably scale your sales engine. This data is valuable to the entire go-to-market organization because it informs how the product team prioritizes features in the roadmap, how marketing messages the value of the product, and how sellers prioritize accounts.

Another very basic yet important topic is data definition and classification. Everyone in go-to-market wants to be running account-based sales and marketing processes, but if you don’t have a shared understanding across your company of what an account is, you can’t get there. Everyone needs to be speaking the same language. What’s the difference between a contact, lead, account, opportunity, marketing qualified lead, sales qualified lead, etc. I’ve seen so many instances where people in marketing ask for data from sales and they’re not getting what they ask for because people are speaking different languages. This can be a major alignment deficiency and performance drag on the go-to-market functions.

CB: How do you think about justifying the value or showing the ROI of a revenue operations team to executives or founders who may not understand it?

YY: Revenue operations is the glue that binds all the business units of go-to-market together, meaning product, marketing, sales, and customer success. The team helps you define what to measure, how to measure it, what the data is telling you and, based on this, how best to scale each of these functions proportionally and in unison. The value of revenue operations has to start with the CEO; the ultimate leader of the business either places a high amount of value on data-driven decision making or they don’t. In my experience, the decision-making processes are so much smoother when everyone believes in a data-driven process. I have seen someone go so far as to put in their signature line, “in G-d we trust, all others bring data.” One of the only reasons I could see a CEO being against data-driven decision making is because they don’t trust the data, and that’s even more reason to invest in high-quality data infrastructure and RevOps from the start.

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