Alignment can feel like a bit of a buzzword—everyone in SaaS preaches it, but few really define what it looks like in practice.
And yet alignment across teams is both a requirement and a benefit of product-led growth. To help demystify what we mean when we talk about alignment, let’s take a closer look at one of the more tangible ways that teams can become aligned: metrics.
Metrics provide a common language and reporting system that cross-functional teams can rally around. The right set of metrics can act as a north star that helps all departments navigate toward the same goal. Metrics should not be siloed—they should be reported on and affected by cross-functional teams who can leverage the data to make more informed decisions and enact coordinated changes across your business.
You can use the following framework to your PLG strategy, we see familiar metrics that are important no matter what Go-To-Market strategy we are pursuing.
Time to value (TTV) is the amount of time it takes new users to realize your product’s value. Your goal should be to reduce time to value as much as possible—the sooner users reach their first aha moment or activation event, the better.
To do this, focus on optimizing your user onboarding experience around the key actions within your product that correlate to activation, like inviting colleagues to your platform, importing customer data, or integrating with other tools in their tech stack.
TTV can also be defined as the time it takes a user to move from the activation to adoption phase.
Product-qualified leads (PQLs) are typically activated users—folks who have completed a key action within your product, had their aha moment, and have seen the value that your product can offer first-hand. In other words, they’re probably the warmest leads that your sales team is ever going to get.
Your exact definition of a PQL will differ from other companies. How you define a product-qualified lead will depend on the specific actions that a user takes within your product that indicate they are ready to move on to the next stage of the user lifecycle.
To find your product’s activation event and define what a PQL looks like at your company, you’ll want to use a combination of user interviews, session recordings, and A/B tests to identify the user behaviors that correlate with conversion and retention.
Percent of users that go from a free product to a paying customer. Calculated by number of customers who become paying customers / number of customers trialing or using free version.
Revenue from existing users being cross-sold/upsold. Calculated by MRR / ARR from existing customers through upsell/cross-sell. It’s a lot easier to get more money from happy, paying customers than it is to acquire new ones. It’s more cost-effective, too—it’s roughly 2X cheaper to upsell to an existing customer than to acquire a new one, and over 3X cheaper to generate expansion revenue than the customer acquisition cost (CAC) of a new customer.
That’s why expansion revenue is easily one of the most important levers for sustainable SaaS growth. Also called expansion monthly recurring revenue (MRR), this metric measures the revenue generated from existing customers through upsells, add-ons, and cross-sells.
Revenue you can expect to make from an individual user. Calculated by ARR or MRR/# of customers. Average revenue per user (ARPU) is the amount of money, on average, that you can expect to make from an individual user. It’s a straightforward metric, calculated as total MRR divided by the total number of users.
Money lost after accounting for new and expansion revenue. Calculated by Revenue lost in period – new and expansion revenue / revenue at beginning of period. Net revenue churn is the amount of money that is lost after counting expansion revenue and new revenue. The formula is revenue lost in the time period – expansion revenue divided by revenue in the beginning. Net churn is often calculated as a percentage. It is a useful SaaS product growth metric as it helps understand why customers are leaving the product. This is useful in understand how to increase product-led growth.
Gives insight into % of active users having a positive experience. Calculated by # of positive user survey responses / total responses or traditional NPS calculation.
Product management teams typically have the product adoption rate high on their list of KPIs to track when launching a new product. But you’re missing a major piece of the PLG puzzle if you fail to add the feature adoption rate to that list as well.
Both rates are key metrics that indicate how well your product is received by your target users. The product adoption rate tells you the percentage of active users, while the feature adoption rate (which is also measured as a percentage) clues you into why people continue to engage with your product. You may find that the adoption rate of a new feature is what’s driving your overall product adoption rate to climb.
Getting insight into which features users find the most value in will inform your product positioning and any other product decisions you make.
the number of users that are active in the product for any given time period
If you follow the best practices of product-led growth, you’ve designed your product to benefit the end user. You’ve made it your primary growth channel. Understanding how many users are using the product actively for any given set of time is key to understanding if you’ve been successful in your mission to keep the focus in the right place and nail the user experience.
Learn how to implement and track all these metrics with journy.io. Lets demystify the analytics behind product led growth.
Customer-facing teams at SaaS companies need clear insights on who their customers are, and what they’re doing on your platform. 🙌 With stage view, journy.io provides a SaaS growth pipeline, with accounts/users clearly ordered by lifecycle stage, and which includes key details, from health to triggered PLG signals. 💡
Growth teams at SaaS companies need clear insights on who their customers are, and how they differ with e.g. those who didn’t convert. By analysing data from across an entire stack, from CRM, Marketing Automation, and other engagement platforms —combined 🙌— they are now able to easily determine trends in customer segmentations and aggregations.
Customer teams need product usage insights. To that end, dev teams are being asked maintaining non-platform-critical features. A better approach is to simply send raw events and metadata onto a secure data pipeline, and have a customer data platform compute key metrics!
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