6 Things An ML Practitioner Should Know About Revenue
Learnings about revenue from being an ML practitioner for over a decade
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If you're an ML practitioner, you should know that generating revenue is a key component of your company's operations. Sales and marketing teams are usually closest to it, especially in enterprise SaaS companies. ML practitioners frequently find themselves in the middle of the sales process. And there are opinions on how involved an ML practitioner should be in this process. They didn't sign up to do sales, so why should they be sales-savvy?
As an ML practitioner, understanding how your company generates revenue and how your sales team works could be very beneficial to you. But on the other hand, getting too deep into the sales process is not the best use of an ML practitioner's time.
After doing this for many years, I can tell you this: An ML practitioner who understands sales is way more effective than someone who doesn't. And what does it lead to? Faster career growth. You'll get more comfortable with it as you work more with sales teams. Every company is structured differently, so your mileage may vary.
But wanting to understand revenue has to come from within. I've been an ML practitioner for a long time. That's all I ever did for years and I could foresee my trajectory. But once I understood the mechanics of revenue generation, my trajectory really changed. In this post, I'll talk about 6 things an ML practitioner should know about revenue in the context of enterprise SaaS.
Thing #1: Your company's revenue model
Understand how your company makes money. This is less about the exact dollar amount and more about the mechanics of it. If you don't know, ask people and participate in discussions. Here are the key questions you should know the answer to:
Who is the ideal target customer: You should know who the ideal target customer is. What criteria does your company use to identify customers? What makes a customer well suited for your product? Is your product for very large companies or very small companies? Is it for fintech or healthcare or manufacturing? You should know it clearly.
Who is the user within that company: Once a target company is identified, who is the person using your product? Is it the data analyst? Or marketing manager? Or a salesperson? Are they technical or non-technical?
What's the pricing model: This refers to unit economics of your business. Does your customer pay per seat? Or for the volume of data processed? Or for the number of active code repositories? Or for the number of active servers being used?
How much are customers paying for your product: The mechanics of a company that charges $10 per month vs $10,000 per month are very different. You should know what bucket your product falls under.
What makes a customer come back and pay more: Your product has to create business value for your customer. They have to be successful with your product. Customer success is a crucial part of modern SaaS companies and you should know how customer success works within your company. If a customer is paying for 5 users, what will make them come back and pay for 50 users?
Thing #2: Your sales process
Now that you know your company's revenue model, you should understand how your company approaches potential customers. How do they get them interested in the product? And how do they sign deals with customers?
Knowing your company's sales process gives you a great view of the business. You'll understand where your potential customers hang out and what their status quo is. How do they think about solving business problems? Once you know your company's sales process, find out where the bottlenecks are. Where do potential customers tend to slow down before signing up? If you're the person who can remove sales bottlenecks, you'll be a superstar.
Thing #3: Identifying tractable problems
Revenue generation is a function of how your product creates business value for your customers. To do that, you need to identify tractable problems that can be solved with your product. Your product can be used in a million different ways. That's great! But you need to specify exact use cases so that your customer can visualize how your product can be used in their context.
When you talk to a customer, listen to them and understand what they're trying to solve with your product. The goal is to identify tractable problems that can be solved with your product.
Thing #4: Cost of delivering your product
I'm not referring to salaries here. I'm talking about the cost of cloud computing, storage, payment processing, and the associated infrastructure. Let's say you signed up a customer. And they're excited about it. You've decided to charge $10,000 per month. But as it turns out, you'll have to spend $50,000 per month to deliver it. This is not a sustainable business. You need to know how much your company is spending to deliver the product to your customer.
Thing #5: Measuring the business value created by your product
The whole point of your product's existence is to deliver business value to your customer. It's not to "save time", "be more organized", or "increase efficiency". If your customer puts in $X into your product, they need to get $Y back. And Y has to 4-5 times bigger than X. You need to internalize this fact: You need to create business value for your customers and be disciplined about measuring that value.
Thing #6: Contributing to your company's growth
This is one of the fastest ways to grow your career regardless of the field you're in. If you can find ways to contribute to your company's growth, you'll be irreplaceable. This means you have to understand your company's operations. You need find ways to be useful. Can you bring in customers? Can you hire talented teammates? Can you increase the speed at which new users sign up? The best ML practitioners out there are constantly finding ways to make things happen. You should too.
Where to go from here
The goal of this post is to show what ML practitioners need to know about revenue. Many of them think that knowing about revenue is optional, so they don't focus too much on it. And many years later, they wonder why they aren't growing as fast as that colleague of theirs. In this post, we discussed 6 aspects of revenue that you should know. Here's another key learning: Knowledge of revenue will make you better at your own craft. You'll be a better ML practitioner if you know how your product is creating value for your customer and generating revenue for your company.