Dear Advisor: How do I do Product-Led-Growth (PLG) as my Go-to-Market (GTM) strategy for Product market fit (PMF)?
September 2021, updated on May 2022
Great question! First, I commend you for thinking through your strategy for product market fit. To get there, I’ll start by decoding the alphabet soup -- and defining the terms, then discuss outcomes, and advise on steps to get there.
Please note: The advice below holds whether you are (as early as) ironing-out an idea, have wireframes, a Minimum Viable Product (MVP) -- or a more mature product. It’s never too early to think about Product Led Growth.
Product Led Growth as a Go-to-Market strategy for Product Market Fit boils down to using data to understand what’s resonating about your product with your customers (as soon as they’re on-boarding), to improve your product, find market fit and grow.
Product-Led (PLG): company’s product is at the core of the customer experience, from acquisition to onboarding to retention -- and how the customers are interacting with the product helps determine the direction of the company‘s focus and priorities (for sales, engineering, etc.) [1, 2]
Go-to-Market (GTM): company delivering a unique value proposition in solving their customers’ pain points, to achieve a competitive advantage 
At what stage do you consider that you’re going-to-market (GTM)? Great question! It means different things to different people, start-ups and VCs. For the purpose of this blog post, I will consider your being in the market, as soon as your product is past the Beta stage and is publicly available for customers.
Product-Market-Fit (PMF): company solving customer pain points with competitive advantage, that’s seeing (1) exponential profits, and (2) a loyal customer base that’s big and profitable. (For a more detailed, actionable roadmap for PMF, please see my slides and video recording here.)
End Goal: Product Market Fit
To reach product market fit, we need to find a big, growing, profitable and loyal customer base, which means we need to:
track how the customers are -- and aren’t -- using the product,
find where (any of) the drop-offs in engagement are and why -- to reduce the friction, and
identify what’s keeping them hooked -- and build on that.
How do we do that from the very beginning?
Step 1: Understand our Customers’ Pain Points and How you Solve Them
I share advice on how to learn which of the 30 most common customer pain points your product solves here.
Step 2: Identify Paths Customers may take in your Product to Solve Their Pain Points
First, let’s write down all of the steps around how we expect our customers to use the product to solve their pain points. Expect is the operative word here.
As an example, we expect our car to get an average of 35 miles per gallon.
Whether you’re in the market already or have an idea in mind, draw out (paper, whiteboard, etc. OK) what are all of the directions/decisions your customers may/should take in your product, to help them reach their end goal. You’re essentially drawing a Wardley map.
Phil Libin's talk argues that the map will have 14 lines to identify and quantify your customer journey. He also reminds listeners to o what works for you; you can start with 14 or a smaller number -- whatever reflects your business and customers better.
I'd argue that every aspect of the business should be summarized by 14 -- different -- lines (e.g. supply by 14, demand by different 14, manufacturing by yet another 14). This way, our goal as business owners is to optimize them. :)
For a marketplace, you have two sets of customers: supply and demand; and will need one journey for each side
For hardware-as-a-service (HaaS), you’ll have one additional chart capturing the journey around the hardware, which may include the paths from:
idea → prototype → manufacturing/production → shipment → delivery
This is great, but how can I start small and iterate here? Great question! To start, try to understand who are your top revenue/profit-generating customers.
What are their paths? That is, where do they come from and what keeps them coming back?
Are there signals that may help you find other customers, most similar to them, that are not yet as high revenue/profit-generating? That is, who are the "look-alike" customers that we can upsell to -- and how -- to get them to be highest revenue/profit-generating?
Once those paths (including upsells) are ironed out, then it's time to focus on the next-largest revenue/profit-generating customers for a similar exercise.
Step 3: Collect Data on Paths and Profits
Now that we have an idea of what behavior we’re expected to see from our customers, it’s time to see if that’s actually the case.
As an example, we expected our car to get an average of 35 miles per gallon. How much did it actually get on your last trip?
As another example, I highly recommend not asking customers who’d recommend this product to a friend, but including referral links in the product and checking who’s referring friends, at what rates, when, etc.
To see our customers’ actual behavior, we’ll need to dig into the data, of when and how our customers interacted with our product, to be able to quantify each of the customer paths/funnels in the customer journey (as shown by lines in the image above). Each line can have its own metric, such as a percentage or profit. (At Evernote, where this grew out of, Phil had one team per line.)
Here’s advice on what should be included in the data you will/are collecting.
Step 4: Optimize Paths to Reduce Friction and Increase Hooks (AKA Product-Led-Growth)
Evaluate the metrics (Step 3 above) to see what’s performing as -- or better than -- expected, and what can be improved?
For example, in the customer journey image above, line 10 is the highest priority to fix ( e.g. when someone goes from high value customer to inactive). How are you doing here?
What does it take for someone to go from low- to high- value customer?
Here are actual examples of how to reduce drop-offs during app on-boarding process. (Notice that data helped the team compare results of experiment and control groups in these A/B tests.)
Step 5: Iterate
Iterate on Steps 1 through 4, with more advice on how to take small, actionable and measurable steps to do so -- and how to prioritize this step within the existing backlog, in this article I wrote for dot.LA.
Here are some (more concrete) topics that you may need to iterate on:
For instance, as you’ll start quantifying the customer journey (Step 3), you’ll see if all of the touch-points are captured in the data, and if all of the product interactions are tracked. If that’s not the case, that’s a task(s) for the backlog.
As you’ll find drop-offs in the on-boarding (or anywhere along the) customer journey, what do our customer preferences tell us about how we can reduce this friction in our product, to retain these customers?
Do we know exactly who the profitable and loyal customers are, and what we’re doing to keep them hooked on the product? How can we recreate this experience for our other customers, (virtually) seamlessly in the product?
Putting it All Together
PLG as a GTM strategy for PMF boils down to using data to understand your customers -- as soon as they’re on-boarding -- to improve your product, find market fit and grow.
If you’re stuck anywhere along the PMF journey, please reach out and schedule a strategy call.
We’ll help you start:
Aligning incentives, between what you offer, what your customers need, and how much your customers are willing to pay for a solution that solves their pain point;
Outlining and quantify your customers’ journeys in the product, based on data available; and
Evaluating your product market fit;
You may also like:
Phil Libin presents his Model to Answer All Startup Growth Questions (talk) by Phil Libin for Founder Institute
Why Investors Can’t Fix Your Company, by Dalton Caldwell and Michael Seibel
Dave Parker's book: Trajectory: Startup: Ideation to Product/Market Fit
How to Craft Your Product Team at Every Stage, From Pre-Product/Market Fit to Hypergrowth, by First Round Review
What is Product Led Growth? (talk) by Lena Verna
How to convert product-qualified leads [PQLs] into revenue, by Alexa Grabell
10 GTM Lessons: Scaling to $100 million: Ramping your cloud GTM engine, by Bessemer Venture Partners
Five lessons we’ve learned from bringing DAO tooling to market, by Otterspace, Rahul Rumalla, Emily Furlong and Ben Dobbrick
Image: from Phil Libin's talk