Meaningful Metrics for Product Analytics

Dive Into The World Of Metrics In Product Analytics And Understand Their Pivotal Role In Steering Business Strategies.
Travis Strickland
Principal Solutions Engineer
Meaningful Metrics for Product Analytics | Kubit Product Analytics


In the digital age, data is the lifeblood of any business. It can transform a company’s trajectory, inform strategic decisions, and predict customer behavior. But data alone isn’t enough. It’s the application of relevant metrics that can truly drive business growth. When created and measured appropriately, metrics can help illuminate the path to better customer experiences, optimized products, and business success. However, not all metrics are created equal. The key lies in selecting ones that are meaningful, actionable, and tied to your specific business objectives.

In this blog post, we dive into the importance of metrics in product analytics, how to set the right ones, and when to measure and evolve them.

Understanding Quality Metrics

Quality metrics provide actionable insights that are specific to your business. They’re quantifiable, easy to understand, and directly linked to your key performance indicators (KPIs).

For instance, an essential metric is Viewing Time in seconds if you’re a streaming media business like ViX. That heartbeat metric is directly tied to the business goals of driving more watch time and directly impacts revenue. Please check out this case study for a more detailed overview of how ViX teams use Kubit to support and enhance their daily work.

Setting Quality Metrics

Identifying the right metrics is vital for your product’s success. Here are some common categories of metrics to consider:

  • Acquisition: how users find and start using your product. Examples include marketing campaign efficacy, traffic source breakdowns, and customer acquisition cost (CAC).
  • Activation: how effectively your product engages users after they sign up. Examples include time to the first essential action and completion of the onboarding process.
  • Engagement: how active are your users? Examples include the Viewing Time per Session or Likes per Day. Typically, these metrics should have a time factor instead of just simple counts.
  • Conversion: how effectively your product drives users towards desired actions. Examples include free to premium signup, checkout, and referring a friend. Typically a Funnel or Path (Sankey diagram) can provide visibility of the users’ journey.
  • Impact: demonstrates the effect of your product on user behavior. Examples include the impact of new product releases on user behavior and engagement.
  • Retention: how well your product keeps users coming back. Examples include how often and at what rate users are returning and how that retention rate changes over time.
  • Churn: Churn metrics help you understand why users stop using your product. Examples include churn rate, reasons for churn, and average time before churn.

Choosing the right metrics depends on your business type, product, and specific goals. There’s no one-size-fits-all approach, but keeping these categories in mind will guide you toward meaningful metrics that reflect your product’s performance and user behavior.

Measuring and Evolving Metrics

Once you’ve identified the right metrics, the next step is to measure them regularly to understand the baselines and make informed decisions. The frequency of measurement depends on the specific metric and your business needs.

For instance, DAUs (Daily Active Users) might be measured daily, while churn rate or retention might be measured monthly or quarterly. Reviewing and updating your metrics periodically ensures they remain relevant as your product and market evolve.

Also, remember that metrics should be seen as tools for learning and improvement, not just reporting. If a metric is consistently underperforming, use it as an opportunity to investigate, learn, and iterate on your product.

Metrics with Kubit

Kubit stands out in the crowded data analytics space due to its unique ability to seamlessly handle a comprehensive spectrum of data types, including but not limited to online, offline, transactional, operational, and behavioral data. Our warehouse-native approach ensures that organizations have the ability to access, analyze, and assimilate ALL of their data with Zero-ETL. This sets a new standard for creating, measuring, and adjusting metrics, offering unparalleled flexibility and precision. Unlike other solutions that mandate predefined data models in their data silos or limit the scope you can view, Kubit’s platform empowers you to explore every facet of your data and gain deep, actionable insights. This differentiation unlocks improved data-driven decision-making and gives you a competitive edge in today’s data-centric business environment.


Meaningful metrics are the guiding compass in navigating the expansive realm of product analytics. They provide a clear direction, enable informed decisions, and drive business success. By understanding what good metrics look like, how to set them, and when to evolve them, product managers and data analysts can increase their positive impact on business outcomes.

Remember, numbers tell a story. Ensure your metrics tell a story that matters to your business. Happy analyzing!

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