A simple framework for diagnosing a product-led growth customer journey
For when you've built it but they haven't come yet
Product-led growth (PLG) has become a popular go-to-market strategy for selling software, as people look for the buying experiences in their business lives to catch up to the buying experiences in their personal lives, where they do their own research, bypass traditional salespeople and look for instant gratification.
While not a cure-all for driving revenue, PLG can be a very cost-effective way to acquire customers if your product primarily solves the pain points of individual end users (rather than managers and corporate overlords) and if the impact can be delivered quickly without handholding from a support person.
Through helping out a few PLG businesses over the last couple of years, I noticed that the self-serve customer journey they rely on to acquire and retain customers has a lot of parallels to the sales-assisted customer journey that sales-led business rely on, especially when it comes to converting prospects from one stage to the next.
I developed the framework in this post to help those businesses diagnose and fix their conversion issues, so I think you’ll find this post useful if you have an existing self-serve channel and are trying to improve the results from it, or if you are considering adding a self-serve channel to an existing sales-led business and want to avoid costly mistakes. If you are a paying subscriber, scroll to the bottom to download the framework in Google Slides.
A 4-stage framework for a self-serve customer journey
The framework is a two-sided funnel, spanning 4 stages of a customer journey: Education, Trial, Impact and Expansion. Each stage has a goal, a set of volume and conversion metrics and a set of key documents and processes:
The goal of the Education stage is to persuade qualified prospects to try out your product.
The key volume metrics are the number of visitors to your website and the number of leads of people who sign up for a trial.
The key conversion metrics are the conversion rate from visitors to leads and the percentage of leads that match your ideal customer profile. This latter metric is often overlooked but can have a negative impact on the downstream conversion of leads to customers in a similar way to how MQLs that don’t match your ICP have a poor conversion in a sales-led channel.
The key docs and processes are your ideal customer profile, website messaging and your demand generation playbook.
The goal of the Trial stage is to convert leads who have tried your product into paying customers.
The key volume metrics are the number of leads who sign up for a trial and the number of leads who convert into customers.
The key conversion metrics are the conversion rate from lead to customer, the % of leads that reach your first impact milestone and the % of customers that match your ICP. The latter two metrics are important because if leads aren’t reaching your first impact milestone, it’s unlikely they will buy and if they are converting but not matching your ICP they are more likely to experience remorse and churn.
The key docs and processes are your first impact milestone, your onboarding messaging and your trial playbook.
The goal of the Impact stage is to create loyal customers by demonstrating recurring impact.
The key volume metrics are the number of customers at the start of the time period, the number of customers churned in the period and the number of customers with low/no activity in the period (or in a recent subset of the time period).
The key conversion metrics are the % of customers retained in the period, the % of revenue retained in the period (aka gross revenue retention) and the % of customers with low or no activity in the period (aka at-risk users).
The key docs and processes are your recurring impact milestones / metrics, your recurring impact messaging and your recurring impact playbook for delivering messaging at the milestones.
The goal of the Expansion stage is to drive additional customer impact by expanding usage and solving more use cases.
The key volume metrics are the number of customers in the period and the number of customers expanding their spend in the period.
The key conversion metrics are the % of customers who expanded their spend in the period and the impact on overall revenue growth (aka net revenue retention).
The key docs and processes are your expansion signals/thresholds, which are typically based on usage patterns, your expansion messaging and your expansion playbook.
The common problems found in a self-serve customer journey
As you lay out your conversion metrics you’ll find one or more of the following issues:
A low conversion from visitor to lead
A low conversion from lead to customer
A low % of customers retained (i.e. high churn)
A low % of customers expanded (i.e. low upsell)
Here are the top reasons that drive each of these issues and advice on how to fix them:
1) A low conversion from visitor to lead
Cryptic, generic or seller-centric messaging
This is very common when your ideal customer profile is not clearly defined. You try to be all things a broad range of customer types and find that the only common denominator across them is you, so your messaging ends up being seller-centric — “who we are”, “what we do”, “our platform” — and littered with jargon like “next-gen”, “seamless”, “real-time” and “integrate”.
The fix for this in a self-serve channel is to develop messaging that centers more around the job your self-serve customer needs to get done.
Focus on the problem you solve, the steps they typically have to go through to solve it and how your product automates or removes those steps.
Get on the phone with 20-30 of your existing customers and ask them, “how has your day to day to changed since you started using our product?”. You’ll hear them describe the job they use your product for, the pain of doing it in the past, the impact they are getting and best of all it will be entirely jargon-free.
Map your features to your customers’ mental model of the solution, so that you build instant credibility and wow them with how you’ve figured out such an elegant solution.
For more on this go read How to make your messaging more effective.
Over-optimizing for top of funnel traffic
This is very common when organic search is a major source of your visitors. It is very easy to get sucked into chasing high volume Google queries with generic content that is relevant to the persona who uses your product but not to the problem that your product solves for them, and then justifying the traffic as both “top of funnel branding” and “free so it doesn’t really matter”.
The fix for this is to look at your bounce rate and visitor to lead conversion rate by landing page:
You’ll find that the pages with the most sessions and highest bounce rates typically have generic content that ranks nicely on high-volume informational queries but generates no conversions, so stop creating more of this type of content — its the PLG equivalent of chasing MQLs.
Then find the pages with lower bounce rates (or longer visit lengths) and low conversion rates and try to drive more traffic from them into the highest converting pages on your site, such as your homepage, pricing page, product demo page etc.
2) A low conversion from lead to customer
Giving too much away for free
This is very common when you offer a free version of your product alongside your paid version because it creates internal tension between acquiring free customers and acquiring paying customers.
It’s easy to get sucked into chasing free users because the numbers are bigger but it leads to loading up your free version with too much value and failing to differentiate it sufficiently from your paid version.
One of the strongest signals that you are giving too much away for free is when you see a significant % of your customers downgrade from your paid version to your free version.
The fix is to find what features people are willing to pay for and then limit the access to them:
Identify the features that are used most heavily by the most active users and least heavily by the least active users. These features will generally align with tasks being completed and are the features to limit access to.
Figure out how many times a user needs to use a feature (or complete a task) before becoming a heavy user and set this as the usage limit. This will also help you decide whether to have a usage limit or a time limit.
Update your messaging to reflect the new limit and start testing it on new users.
Having an unclear first impact milestone