"Numbers have an important story to tell." – Stephen Few

Product managers strive to be effective decision-makers and create products that resonate with users. However, the path is often fraught with challenges—understanding complex user needs, balancing conflicting team priorities, and aligning stakeholder expectations. When “gut feel” and qualitative insights fall short of understanding what users genuinely want, product analytics steps in with data-driven insights, empowering product managers to make strategic decisions with confidence and effectively chart the product’s course.

To deepen my understanding of product analytics, I recently completed a Product Analytics Certification course offered by Pendo and Mind the Product - and it far surpassed my expectations! In this first installment of a three-part series, I’ll share key insights and practical steps from the course for product managers to leverage product analytics and unlock its transformative potential.

Introducing: The Product Analytics Hierarchy of Needs 📣

Becoming a data-informed company, also known as achieving Data Actualization, requires leveraging a powerful framework that Pendo and Mind the Product call the Product Analytics Hierarchy of Needs. This framework consists of five sequential steps, and much like Maslow's Hierarchy of Needs, each step must be completed before advancing to the next. Along this journey, product managers work closely with cross-functional teams, engineering, and stakeholders establishing clear product goals and metrics, fostering alignment, and driving the organization's transformation into a data-informed culture. Let’s explore each step in detail:

Step 1: Collect Data 🪣

Collecting data is the foundation for understanding users and their actions. It involves setting up a pipeline to gather actionable information, which will later be transformed into metrics and dashboard reports to be shared across the organization. A key consideration in this step is selecting the right product analytics tool. With numerous options available, it’s crucial to choose a platform that aligns with the behaviors of the software users and needs of the organization.

While every organization is unique, beginning with collecting foundational data provides an effective way to get started. Here are two types of data to consider:

User Information: Visitors, accounts, segments, and metadata (e.g., first visit, last visit, time on site).
Behavior Information: Events, page loads, clicks, and feature usage.

When determining which data to collect, target information that supports revenue features, reflects product adoption, and captures user experiences.

Step 2: Refine Data Into Metrics 🎯

This step focuses on defining meaningful performance metrics to evaluate product success and user behavior. The chosen metrics should align with key aspects of the business and provide actionable insights. The course recommended helpful product usage metrics centered around Breadth, Depth, and Frequency:

Breadth: Measures the total number of users and number of users per account.
Depth: Captures how extensively users engage with the product - specifically key features.
Frequency: Tracks how often users interact with the product, i.e. daily, weekly, or monthly.

The goal is to develop clear metrics that align with an organization’s goals and objectives. This may involve identifying a north star metric—a single, overarching measure of success—or multiple complementary metrics that support it.

Consider this approach:

Identify Stakeholders - Determine who will be affected by and benefit from the metrics.
Collaboratively Define Metrics: Outline product metrics that are meaningful and achievable.
Set Success Criteria: Clearly define how success will be measured for each metric.

Following this path establishes a strong metrics framework that the entire organization can rally around.

Step 3: Build Metrics Into Reports 📊

This step focuses on creating reports from metrics that track product usage over time inside the analytics platform. These reports allow product managers to identify trends and issues by observing how metrics fluctuate. By analyzing these fluctuations, a product manager can uncover the underlying factors driving trends and pinpoint areas for improvement or action. Some foundational reports to consider are:

Paths: Connects the user journey before and after engaging with specific features.
Funnels: Tracks how users navigate a defined series of steps, highlighting user drop-off or successful completion.
Retention: Measures users continue to engage with the product over time.

While these foundational reports provide valuable insights, custom product analytics are equally important. They reveal how users interact with identified key metrics, revealing whether things are improving or declining. Having multiple reports allows for meaningful comparisons and a clearer picture of product performance.

Step 4: Take Action ☎️

With insights appearing through reports, this step involves analyzing them and taking action. To do so, there are several tools available to drive meaningful changes in collaboration with cross-functional teams:

Analysis: Uses insights to build hypotheses and uncover answers to key questions.
Dashboards: Dashboards drive collaboration, inform strategy, and guide next steps.
Storytelling: Communicates insights to influence and align the organization.

Taking action relies on securing support and buy-in from key stakeholders. As the product manager proceeds, the organization moves closer to becoming data-informed, with a sharper focus on prioritizing or sunsetting product features.

Step 5: Data Actualization ✨

Just as Maslow’s concept of “Self-Actualization” represents reaching one’s full potential, “Data Actualization” reflects an organization fully embracing and leveraging data to inform decisions. The course describes this as “less of a step and more of a state of being” where the right data is always available and integrated into decision-making processes.

Achieving Data Actualization requires establishing regular reporting processes that empower cross-functional teams. The organization operates with a shared language of metrics and data, ensuring alignment at every level. Product usage data becomes a cornerstone of business decisions, present in board discussions, revenue planning, and beyond.

At this stage, product managers can answer complex questions with confidence, providing clear direction on what actions to take and why—backed by data.

Final Thoughts 🎬

Pendo and Mind the Product’s Product Analytics Hierarchy of Needs provides a structured approach to collecting, refining, and leveraging data to achieve meaningful product outcomes. While creating a data-informed culture may appear daunting at first, taking even small steps toward this goal can yield significant benefits, equipping product managers to guide their products toward sustained growth and success. Remember, every organization’s journey is unique— find a starting place, iterate, and do what’s best for the team.

Ready to learn more? Sign up for the course here.

Product Analytics - A Product Manager's Superpower