👋 Hey, I’m Lenny and welcome to a 🔒 subscriber-only edition 🔒 of my weekly newsletter. Each week I tackle reader questions about product, growth, career, and anything else else that’s stressing you out about your work.

If you’re just joining us, make sure to read part one of this series, where I cover the first three steps of starting your PLG motion:

• Step 1: Map your funnel

• Step 2: Pick a starting point

• Step 3: Anticipate the most common pitfalls

Now let’s continue with step 4.

You can find Hila Qu 曲卉 on LinkedIn, Twitter, and Substack. Make sure to check out the Reforge courses she teaches, Advanced Growth Strategy and Experimentation & Testing, and sign up here to get updates on Reforge’s new PLG program, which launches in fall 2023.

Step 4. Set up the infrastructure

PLG tooling is a fast-evolving space. At a high level, there are two categories to consider:

1. PLG infrastructure

2. PLG tool stack

The infrastructure tools are must-haves for running a PLG motion, while the tool stack includes other optional solutions that make PLG easier.

1. PLG infrastructure

All PLG companies need three important pieces of infrastructure to enable the motion:

a. Data infrastructure

In a way, product-led growth is data-led growth. You give away your free product in exchange for distribution to a large group of users, plus the insights into how they use your product. Without data, you are flying blind. You may be getting free users, but you have no idea how they are using your product and how to activate and convert them.

The first component of your data infrastructure is product analytics tools and data instrumentation. Tools such as Amplitude and Mixpanel are commonly used here, but, as the saying goes, “garbage in, garbage out.” Companies need to dedicate engineering resources to instrument tracking properly. Many B2B companies are significantly lacking in product analytics—watching product usage closely is less important when you sell via human touch—but without a strong foundation of product analytics, PLG will never work. 

At GitLab, to address this gap, we set up a telemetry team with a dedicated PM and engineers, responsible for creating data collection frameworks and policies and coordinating all product teams to implement tracking, as well as navigating customer and community communication.

The second component is a customer 360 database. You want to connect various data sources, such as product usage, marketing campaigns (e.g. Marketo, HubSpot), sales activities (e.g. Salesforce), and third-party firmographic data (e.g. ZoomInfo, Clearbit) into a central database so that you can answer complex questions like “Which features do users A and B from company X use, how many emails did we send them, and is our sales team engaging with them?”

Then you can also ingest relevant data back into the tools above to empower smart actions—for example, sending feature usage data to Salesforce so that your sales rep can talk about the features the prospect used in a sales call, or to HubSpot to trigger targeted emails based on feature usage.

To achieve this, you need a centralized database and a two-way data pipeline to and from your marketing/sales/product analytics tools. Companies usually achieve this through a combination of homegrown systems and third-party tools (e.g. data warehouse, CDP, ETL, reverse ETL). Follow these links for three examples of PLG data infra built by teams at Netlify, Mixpanel, and Unity.

An emerging category of product-led sales/PQL tools—such as Pocus, Endgame, Toplyne, and Pace—can also help connect multiple data sources, identify PQLs, and send them to CRM platforms like Salesforce.

b. Experimentation platform

Once you have data infra in place, the next important piece of PLG infrastructure is a product experimentation platform. Many companies go through a journey like this:

The most common mistake I see is that companies skip buying and jump right into building. In other words, they bypass the option of using a third-party experimentation tool, often because the engineering and product teams feel like they can build anything. 

But building an experimentation platform requires not only engineering resources but also data science and statistical expertise. The time and labor it takes to build and maintain a homegrown platform properly is not a small commitment, so I strongly suggest that smaller teams try not to reinvent the wheel, and rather look into tools such as Optimizely, VWO, and Eppo.

c. Lifecycle marketing tools

Many SaaS products that originated from sales-led motions use Marketo or HubSpot as their email marketing tools. In this context, emails are primarily leveraged as a channel to nurture leads and generate MQLs.

For a PLG product, however, you’ll leverage channels like email, in-app messages, push notifications, and SMS to drive product usage and user engagement—so lifecycle marketing tools such as Customer.io and Braze are commonly used to trigger the right message to the right user at the right time.

2. PLG tool stack

Once you have the critical PLG infrastructure in place, you’ll find that there are also many specialized tools you can leverage in specific focus areas.

Below are some commonly used PLG tool categories. As a disclaimer: I haven’t used all of them personally, and I stumbled upon many tools through extensive reading and research with other PLG experts. My intention is not to recommend specific tools but rather to highlight some tool categories and examples that can help you solve specific PLG problems. ...