How Product Analytics Maturity Feeds a Product-Led Startup Strategy

Founders: Let’s do a quick thought exercise. Take a moment to ask yourself how well you can articulate things like:

  • Your product’s “aha moment”: The light bulb moment when someone realizes just how helpful your product is.
  • The characteristics of your most passionate users: The power users who will champion your product, and what makes them tick.
  • The factors behind user success (and drop-off): The barriers in your users’ path to value—and how to tweak those barriers to cultivate delight.

It’s every founder’s dream to build a product that sells itself. But success hinges upon being able to first answer some essential questions about your product.

What is a product-led startup strategy?

As readers of this publication know well, product-led growth (PLG) is an end user-focused growth model that relies on the product itself as the primary driver of customer acquisition, conversion and expansion.

PLG startups get the product directly to the end user (at any level or role in the company) with minimal friction, and then use the product’s value to drive growth. These early adopters refer their colleagues, and their delight is what fuels further adoption.

“The fundamental point is that you can appeal to the end user and solve their pain versus just executive-level pain, which opens up a larger audience of champions,” says Kyle Poyar, OpenView’s VP of Growth.

Notably, this practice also turns the traditional marketing and sales-led go-to-market strategy on its head. With an incredible product, sales executives act more as guides, offering upsell and cross-sell opportunities, to users who already know the product is the right fit for them. As Atlassian says, sales executives become enterprise advocates.

But the success of a product-led startup depends on having a crystal clear understanding of the value your product drives for users in the first place. And that depends on—you guessed it—having access to the right data.

“Layering in a data-driven approach will help you validate your hypothesis and ensure that the story you’re telling is rooted in reality,” said Wes Bush, founder of The ProductLed Institute. “With a solid understanding of what motivates people to purchase your product (and how to measure it effectively), you’re well on your way to building a product-led foundation.”

Perfecting the pitch: Product analytics and quantitative data as a differentiator

In our work with startups, we all too often hear things like “we don’t have the data” or “we don’t have the resources.” If we read between the lines on these statements, what they really mean is this: “Product analytics maturity is not a priority.”

This is a risky attitude. If you don’t have the data, you can’t follow it—so every decision you make is some variant of “fingers crossed… hope it works!”

That’s why data is the foundation of building great products and accelerating product-led growth. It helps you reconcile what users say they do with what they actually do.

At Mixpanel, we’ve helped companies analyze millions of data points about what actions users are taking in their product, and understand what those actions mean for their product’s retention, engagement and growth. Startups that adopt a thoughtful Goal → Hypothesis → Validation → Deduction → Experimentation → Result framework are able to drive a continuous feedback loop that’s responsive to users’ needs, adding further fuel to their PLG engine.

Take Postman, a collaboration platform for API development, for example. CEO and co-founder Abhinav Asthana built a “user-driven feedback loop where [they] would observe what’s going on, what things people are doing in the product, bake that back into additional capabilities, and then repeat.”

Sounds like a dream state, right? But for many startups, embedding product analytics into their organizational culture can seem a daunting task. There are lots of decisions, big and small, that must then be followed by many tweaks and optimizations. Like most things in life, a one-size-fits-all approach is almost never going to cut it.

To advance your product analytics capabilities, you first need to know where you stand today.

The stages of analytics maturity for startups

An effective PLG strategy requires a deep understanding of your target audience and their pain points, aided by unbiased insights into what actions users are taking in your product. Though there’s a commonly held belief that early-stage startups don’t have the data volumes required for meaningful quantitative analysis, no company is too small to prepare to collect, organize, and analyze—and ask questions of—their data.

(Also, low data volumes can be tracked for free on analytics tools, so there are few barriers to get started).

The good news is that startups can advance quickly as long as they have a commitment to using data to improve their product. The more buy-in you have at all levels in data-driven decision making, the easier it will be to move to more advanced stages.

The first step: Know where your team is. Use this framework to get a better understanding of your analytics maturity. Note that we’ll be using a fictional B2B SaaS startup with a single product to help you understand what this looks like in practice: a digital whiteboard.

Stage: Non-existent to basic

Key indicators:

  • Some teams have a baseline understanding across the organization of what drives value discovery in the product (i.e., the “aha” moment).
  • Data is rarely measured with a product analytics tool.
  • Analytics may be “hacked” by talking to your best users and retracing their discovery steps (versus digging into their data).

A PLG indicator:
With minimal tracking in place, a startup that’s operating with the basic principles of PLG in mind might be thinking about creative ways to scale inside their accounts. For example, perhaps they’re considering launching a “team” offering at a price that an end user could pay for without needing budget approval. The thinking goes that when one team purchases the product, other teams will start asking for it as well.

Example:
In the non-existent to basic stage, the digital whiteboard startup has launched its product and decided to track certain metrics. They know they should be keeping track of various custom events, though they may not know which ones are most important.

Their data tells them that most people log in on Tuesdays, and that most customers come from an invitation link. They have a handle on some numbers (e.g. total signups and new users as of this week), but the data they have isn’t yet driving product development.

To progress: When getting started with product analytics, it’s important to note that some of the work involved is technical (i.e., wrangling data, implementing a tracker), while other tasks are strategic (goal setting, creating a tracking plan), and still other tasks are analytical (building and interpreting dashboards reports). There’s plenty of overlap in these areas such that achieving success requires tight collaboration between a number of departments, from Product to Engineering. Start bygetting a team of diverse stakeholders in the room (or Zoom).

Stage: Intermediate

Key indicators: 

  • People in the organization have an idea of what drives value in the product (i.e. the product’s “aha” moment), but it’s not always validated by data.
  • Individuals and teams are empowered to look at data but not to run analysis; instead, they’re trained to send questions to the queue. This can lead to bottlenecks in getting necessary information to Product, Design, and other departments.
  • Startups here might be using multiple solutions across the user journey, including for attribution, engagement, and experimentation—though they’re not always integrated and some manual workflows lead to delays and mistrust.

The modern data stack

A PLG indicator:
A startup in this stage might be looking at optimizing different pathways in the customer journey, both from a self-serve perspective within the product, and from a more traditional sales lens. They need to decide which strategy to lean into (product-led or sales-led). This isn’t a seamless experience for users yet. Startups here might consider whether there are “product offerings” they can provide to end users to encourage adoption, like demo certifications.

Example:
The digital whiteboard company recognizes that it’s time to step up their game. They begin to gather more sophisticated data—types of templates used (brainstorm, feedback, OKRs, roadmap) and popular features (sticky notes, icons, drawings, text)—and find a tool that supports advanced metrics and analysis. They understand the sequences of actions that lead their users to upgrade, and how engagement and retention varies by company or account.

Most importantly, product analysis is beginning to guide future changes to the product. They regularly consult the data to make sure their hypotheses are rooted in reality and inform decision-making.

To progress: 
Bringing your product analytics maturity to the next level requires a fundamental shift toward data democratization, with data pulled into every product decision. With critical user journeys tracked, you’ll have access to much deeper datasets, but careful planning is critical at this stage in order to leverage the data instead of getting overwhelmed by it.

Stage: Advanced to expert

Key indicators: 

  • Teams use product analytics to validate how the “aha” moment is experienced, and the factors that impact whether users get there successfully (or not).
  • The product is continually being optimized based on the data discovered. For example, it’s known that users who do X are likely to churn, while those who do Y are likely to upgrade. Startups here ask: how can we add stickiness into the product, and have it drive the behaviors we want? Also, what are the characteristics of our most passionate users, and how can we cultivate that?
  • A sophisticated product stack with data feedback loops drives the end-to-end user experience based on a “single source of truth” (attribution, messaging, A/B testing, data warehouses and CDPs). Tools are integrated and require few, if any, manual workflows.

A PLG indicator:
Startups at this stage are delivering an automated experience so every single user can have a seamless path to value, regardless of who they talk to or how they purchase the product.

Example:
The digital whiteboard company is on fire: Their product stack is sophisticated and helps users realize a straight line path to value; they’re harnessing advanced capabilities (e.g. experiment and impact reporting); and the insights they can derive are nearly limitless. In practice, this might mean welcome emails for freemium users of their product highlight a key usage tip—say, drag-and-drop visuals to enhance a collaboration session—that leads to delight for the user, bringing them deeper into the product and priming them to become a paid user down the road.

In the advanced and expert stages, everyone understands how various combinations of user / group behaviors and attributes affect 
things like engagement and retention, how quickly users reach their activation metric, and the actions likely to convert activated users to power users.

To progress: 
You’ve done it! But don’t let yourselves slide. Keep using insights derived from product analytics to align your roadmap with customer priorities, experiment with new features, and more. Make sure new team members are onboarded to your product stack as deeply and early as possible to live out the data-informed culture you’ve built.

The Holy Grail: Using advanced product analytics to fuel the product-led growth engine

Adopting a product-led startup strategy is one of the most efficient ways to go to market as a startup today. Though we’ve shared a number of indicators to help you determine whether you’re on the right track, we’ll leave you with a few high-level “vibe checks”—when you’re truly using the full muscle of a mature product analytics strategy to fuel your PLG engine, you’ll know it because:

  • You’re building the right things based on data (onboarding flow, etc.).
  • Your team can move fast and meet market demands.
  • You’re metrics-driven and able to report on KPIs in real-time (to leadership, board and investors, etc.).

Whether you’re just getting started or are more advanced in your analytics maturity, if you’re working to get a better holistic understanding of how and why users engage with your product, you’ll scarcely go wrong.

Get the guide on advancing your product analytics strategy by Mixpanel in partnership with OpenView and Product Collective.

Hannah Maslar
Hannah Maslar
Sr. Product Marketing Manager
Mixpanel

Hannah is a Senior Product Marketing Manager at Mixpanel, a market-leading product analytics solution. She is passionate about building programs and activities that drive awareness and create customer advocates. Hannah has been at Mixpanel for 3 years and prior to that has worked in Enterprise SaaS, Consumer Startups, and Consulting.
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