This is a guest post from Pete Cheslock. Pete has held a number of roles across product and engineering and likes to help companies, from startups to enterprise, learn how to improve their systems and software delivery. You can find more from Pete on his website and Twitter.

For modern developer-first companies, product led growth has become the standard go-to-market strategy. If your product has an open source component, it’s even more critical to include detailed product analytics and metrics to understand where your users are coming from and if they are successful. 

Technical founders often make early investments into their observability stacks, such as metrics/logs/traces, to understand when application errors occur. But often that’s where the investments end, leaving early product teams flying blind when it comes to user acquisition and behavior. 

Finally, in those early years of new product development, the founders ARE the sales team: learning the best time to connect with your user can be critical to converting them. Should you email them on install? Should you reach out only after they hit a critical milestone? Can you identify when they get stuck to provide assistance? When you finally hire your first salesperson, how do you show them how prospects and customers are engaging and what stage they are at in their product journey? 

To my mind the answer is simple: just as we prioritize operational observability when it comes to our product, we must also ensure the same level of visibility is in place for our customers. Make the data visible, and power your entire organization with the insights.

As an early employee at many startups, I often ask the engineers for data to understand more about our users. Sadly, the only way to get this data has been “Query the production database.” This works fine in the early days, but breaks down as you scale. The other problem with using your production database to understand your users is that you only see the point in time view into where they are at today. Unless you’ve set up change data tracking, you can only see where those users are at the moment, not where they were last week. 

Start Small

Implementing some basic product analytics doesn’t need to divert your entire product backlog. Start small by making one specific user action visible to the broader team. New user installs are a perfect way to drop a message into Slack and keep the team informed (and motivated) when folks get started with your app. Alternatively, trigger a message to your customer success teams when a user turns on a new integration so they can test how best to engage with your users. 

Identify when users are successful (or not)

As your startup grows, you’ll start to better understand what actions a user might take that makes them successful. Maybe it’s as simple as doing one or two things in your app. Or maybe you want to get notified when they complete some onboarding steps. Defining your milestones is an important strategy to gauge success. Drive these notifications to the teams that care the most. Re-evaluate your milestones as the product evolves. What may have been an important milestone last quarter could change dramatically with the pace of startup product development. Product market fit is the goal for every early stage company and milestones should be a quantified articulation of that objective.

Building your first milestones

Your initial milestones should be hypotheses about the behaviors that will move the needle for your early users. A product qualified lead (PQL) for instance, should be a set of actions that, when taken, will lead to an "aha" moment and should convert at a very high rate (80%+). From there you can refine, adding or removing actions and changing thresholds (the number of times a specific event should have been performed). For instance, you may initially think a qualified user needs to connect three integrations and then bring that down to two as you gather more data.

Make data visible

What we learned from the DevOps community is that breaking down silos is important for collaboration and improving how teams work. These silos also exist across other parts of the business, like sales, product, and marketing. By taking product analytics, which historically would have been siloed inside a product team, and making this data visible across other teams allows everyone to work with the same frame of reference. 

Integrate and enrich with other data sources

Many modern companies are using upwards of 100 different SaaS applications to power their business. This creates over 100 “single panes of glass” where valuable data is siloed away and unable to provide any value to the business. A modern data stack seeks to make things more visible across the team and power actual data-driven decisions. At a previous company our PLG data stack touched all the parts of the business, for example:

  • Marketing: Understand where our users are coming from and the success of our programs.
  • Sales & Business Development: Gets notified when targets accounts sign up, and tracks milestones for targeted outreach.
  • Customer Success: Tracks customer usage to determine overall company health and identify churn risk.
  • Product and Engineering: Analyzes customer usage data to prioritize roadmap features.
  • Finance: Tracks customer usage and calculates the “cost of a customer” for understanding COGS (Cost of goods sold)
  • Sales: Makes better pricing decisions based on the cost to deliver the software and the average cost per customer. 


For early-stage tech founders, it can be hard to make investments into areas of the business that are not related to building the product. But the most critical stage of every startup is where it needs to start increasing its investment into the go-to-market strategy. Making early investments will have a dramatic impact as you onboard new members of your GTM team. Bootstrapping this new team with all the available data will let them hit the ground running and help run the business in a truly data-driven way.

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