We've got a diverse group of founders with us this year, tackling challenges across a number of industries – both old and new.
First a quick intro, I'm Erin Olsen, Head of Marketing here at Stage 2 Capital. I'll be your guide through the Catalyst 2024 program, giving you an insider's view of the valuable insights and experiences shared during our sessions.
Here's what I'm planning to do for you: Each week, I'll be your eyes and ears on the ground of the 2024 Catalyst Curriculum. I'll capture the key insights, distill the big "aha" moments, and even dish out some homework for you to apply these lessons to your own startup journey. My goal is to bring the Catalyst experience to you and help you learn the Science of Scaling methodology right alongside our cohort.
This week, we're tackling a question that keeps many founders up at night: When is the right time to scale your startup?
How do you know you have PMF? What data do you look at? We'll explore how to make data-driven decisions around Product-Market Fit (PMF) and Go-to-Market Fit (GTM Fit), setting youup for sustainable growth.
You won't want to miss this!
Mark Roberge, Co-Founder of Stage 2 Capital, kicked things off with a simple question:
"When do you know you're ready to scale?"
This sparked many different responses from founders. Some talked about having a product that customers are willing to pay for with promising unit economics. Others shared that it's when existing customers are buying more. Some pointed to customers recommending your products to others as a key indicator. Another interesting perspective was that you're ready when you have double-digit clients signing up consecutively with very similar profiles.
These indicators? They're great, but they're not the whole story.
The takeaway from this discussion is that figuring out when to scale is a tricky question and one that evokes many different opinions. Customer retention is ultimately the best quantifiable indicator of product market fit, but here’s the problem, retention is a lagging indicator - meaning you’re not going to know if you have it until it’s too late. You have to find a leading indicator of retention.
Mark's position is that while each company is different, there is a consistent framework that can help you identify when your company is ready to scale. And he breaks it down into two critical areas:
You’re ready to scale, when you have Product-Market-Fit AND Go-to-Market Fit and you do these sequentially.
Let's get into each of these concepts and how to measure them effectively.
Product-Market Fit is the holy grail of early-stage startups. It's the point at which your product satisfies a strong market demand. But how do you quantify something that can feel so intangible? Enter the concept of Leading Indicators of Retention (LIR).
The LIR is defined by a simple yet powerful formula:
P% of customers do E event every T time
Where:
💡 Pro-tip: Keep your LIR simple. Many companies are tempted to go down the path of mixing multiple variables, something that sounds like "If 80% of customers send X number of messages OR have XYZ number of users AND they are using other XYZ product (you get the idea). Yes, that's cool but Mark reminds us that it's also complicated. When you move into scale, you want it to be a powerful tool across the company. And if it's too tricky for people to easily understand, it's also going to be hard for people to execute it.
This formula helps you identify the key actions that indicate your product is providing real value to customers. Let's look at some examples from well-known companies:
These metrics go beyond vanity numbers like signups or downloads. They reflect actual product usage and customer value.
As Mark Roberge challenges us: "What is the lead indicator of retention for your business?”
Identifying your LIR is crucial because it provides an early signal of customer retention, long before you see it reflected in your annual renewal rates.
Once you've defined your Leading Indicator of Retention (LIR), the next important step is measuring it.
Let's break it down:
For companies acquiring 10+ customers per month:
For example, a company might see:
As you look down this chart, you can see the
This improvement indicates progress towards product-market fit.
💡 Pro-tip: Don't overthink your LIR. Once you have data, you'll start to see how your progress is tracking over time and you'll likely end up changing your LIR based on this data, especially as you scale. The key is to start measuring and iterating rather than getting stuck trying to find the perfect metric from the outset.
As you start to see the data from your cohort analysis, you'll also start to see which customers are hitting your LIR and which ones are not. This is going to help you understand what your ideal customer looks like. You'll start to see patterns emerging, which will be key in refining your Ideal Customer Profile (ICP).
Mark reminds us that you want to be precise around your ICP and used an example of a company that does AI for customer support teams. They defined their ICP based on attributes such as:
The key is to categorize potential customers into three groups:
How does this work in practice? Your ICP serves as a guide for your teams, helping them know when to proceed, when to act opportunistically, and when to politely decline. Here's how it might play out:
Remember, your ICP isn't about proving a billion-dollar market right away. It's about finding the least risky path from zero to $1 million, and then from $1 million to $10 million. You can expand your target market over time, but starting with a precise ICP allows you to build repeatable solutions and achieve product-market fit more efficiently.
Once you've achieved Product-Market Fit, the next step is finding Go-To-Market (GTM) Fit. This is where you optimize the economics around your product and target market.
Mark explains, that just like in Product-Market-Fit where, the best internal statistical metric for you to understand is customer retention, you need leading indicators for your GTM.
The key metric for GTM-Fit is unit economics - the profitability of your go-to-market motion in acquiring and servicing customers. Unlike GAAP accounting profitability, unit economics scales with your customer base.
While there are various ways to measure unit economics (payback period, magic number, burn ratio), Mark used the example of Lifetime Value to Customer Acquisition Cost (LTV to CAC) ratio. The industry has accepted a general standard that your LTV to CAC ratio should be greater than 3. (We'll discuss this further in Module #2, including how a Cohort-CAC-payback approach offers greater accuracy. However, it's valuable to master the LTV to CAC formula, as it remains a common metric investors use for evaluation.)
Here's a simplified breakdown:
For example:
You can plug these numbers into the formula to see if you're achieving the desired LTV to CAC ratio.
Next week, we have an entire Module of the Catalyst Curriculum dedicated to implementing GTM-Fit so keep an eye out for more!
Here are the five essential lessons.
That’s it for this week!
We invite you to apply these concepts to your own startup and encourage you to complete the following exercises:
Stay tuned for upcoming posts in our Inside Catalyst series and pop your questions in the comments section.