You launched one product or feature and are ready to move on to the next. Before you do that, be sure to revisit the product health metrics you specified at the early stages of the product. Ensure that you have a process in place to measure key metrics, extract insights and refine your product strategy accordingly.

The recent launch you led your team to execute is a great leap forward towards meeting your company’s business objectives and delighting your users. Once complete, however, it is vital to measure and analyze its impact on the organization’s “North Star” metrics. These insights will allow you to evaluate performance and identify potential areas for improvement. Given the seemingly infinite number of product metrics available to product leaders, where do you start? And how do you equip your product to report upon these metrics? 

This post will guide you through this daunting process through three key steps:

  • Identifying Metrics to Focus On
  • Instrumenting for Metrics Reporting
  • Assessing Performance and the Path Forward

Identifying Metrics to Focus On

Before diving into selecting specific product metrics, it’s helpful to consider the various types of metrics at a macro level. The Product Equation formulated by SC Moatti is a great starting point. The Equation groups metrics into three categories: acquisition, engagement, and monetization. Generally one of these categories will be the most relevant for your product depending upon its phase in the product life cycle. For example, acquisition may be the focus of a recently launched product. For more mature products, however, engagement may be of primary importance to maintain strong customer retention. Choose the category that best applies to your specific case.

Then, ideate upon metrics within the selected category. Consider the customer onboarding and conversion experiences outlined during the Design stage to guide this brainstorming.  For instance, when considering the engagement category for a B2B product, you can measure the number of daily/weekly/monthly active users per customer account. A higher active user count indicates that your product is stickier within a given account. Another metric to consider is the usage of key features that have historically correlated with higher retention rates. This provides valuable insight into accounts that appear to be on track for retention versus those that are not. For further information on these metrics and more refer to this Digital Product Management webinar.

Instrumenting for Metrics Reporting

Next, work with your team to ensure the appropriate product instrumentation is in place to collect the required data points. A product analytics tool (e.g., Amplitude, Heap, Pendo) equips your product with the capabilities to track customer logins, feature usage, and survey responses. Additionally, a solution such as FullStory will allow you to view actual user session recordings to gain first-hand insight into how your customers are using your product. Finally, combine these insights into a visualization tool such as Looker or Tableau. If your organization has a Product Operations team, they can help to level up the customer insights gleaned from these data sources and more.

Remember that all of these product insights hold no power if they only reside in team members’ inboxes. Consider what reporting channels work best for your company’s culture. For instance, is an internal product newsletter the team’s preferred method of communication for reviewing product trends, or are Slack notifications more effective? The simple act of democratizing product analytics within the organization encourages colleagues to engage in critical dialogue and contribute towards optimization initiatives.

Assessing Performance and the Path Forward

Ultimately, evaluating how your product metrics are performing depends upon how well they support overall business objectives. The “North Star” metric framework helps to set this context. For example, the number of active users per account is a lever to support reducing churn and a key influencer in increasing recurring revenue. Applying this lens to your analysis is essential for not only outlining how product performance impacts the business but also for quantifying that impact. 

Finally, investigate the current baseline values for your metrics and how they have been trending since your launch. Is the performance improving or not? How is the metric performing when zeroing in on users that are engaging with the launched product or feature? This line of questioning determines if the launch is succeeding in meeting its intended objectives and guides further improvements. As you undergo this exercise, be sure to place priority on optimizing the product metrics that have the highest impact on the “North Star” metric.

Wrapping Up

Completing these steps will remove the blindfolds blocking visibility into product performance against both business objectives and customer satisfaction. This will in turn shine a light on the metrics in need of improvement and the associated return from investing in each. With these insights on hand, you can make data-driven decisions, identify growth opportunities and focus the team’s efforts to conquer the “North Star” metrics.

About the speaker
Bharat Manglani ZEFR, Senior Product Manager Contributor

Bharat Manglani is a Product Manager at ZEFR, which focuses on powering the age of responsible marketing. He started his career with 10 years as a technology strategy consultant and then pivoted into the technology sector to pursue his passion for managing the end-to-end product lifecycle. In his prior role at HUMAN (formerly White Ops), he managed the customer facing portal which empowers users to mitigate sophisticated fraud across their advertising, marketing and application ecosystems.

One Responce
  1. Great job Bharat! In Vimeo, we began to focus more on setting up organizational level OKR on the quarterly basis and ensure product level KPI aligns to the OKR so product execution in line with the strategy.

    I think metric can also help with product experimentation. It gives PM the proof of concept of if certain feature release cross the chasm of adoption, so we can make further decision informed by data.

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