Vanity metrics like total revenue can be misleading. A startup might acquire many low-priced, low-usage customers without solving a core problem. Deep, consistent user engagement statistics are a much stronger indicator of genuine, 'found' demand than top-line numbers alone.

Instead of focusing solely on conversion rates, measure 'engagement quality'—metrics that signal user confidence, like dwell time, scroll depth, and journey progression. The philosophy is that if you successfully help users understand the content and feel confident, conversions will naturally follow as a positive side effect.

"Adoption" can be a superficial metric of initial use. The term "absorption" forces a higher standard, implying a feature has become an indispensable, natural part of a user's regular workflow. This reframing focuses teams on creating lasting behavioral change, not just clicks.

Features follow an S-curve of value. Early effort yields little, then a steep rise, then diminishing returns. Use this model to determine if a feature needs more investment to become valuable or if you've already extracted its maximum worth and should stop investing.

Data and metrics are essential but incomplete; they lack insight into user motivation. To truly understand the 'why' behind user behavior, PMs must engage in qualitative research to uncover users' feelings, thoughts, and wants, which dashboards cannot capture.

When prioritizing features, don't just ask what percentage of your current customers will use it. Sometimes, it's strategic to build features that very few existing users need, specifically because those features will attract a new, more desirable customer segment. This is a risk, but it's a calculated bet on moving your business upmarket or into a new vertical.

Unlike passive consumption apps, where getting many users to try a feature once is key, high-intent products like Google Search measure success by user intensity. The critical question is not "how many people used it?" but "are individual users using it more intensely over time?"

Data isn't just for tracking metrics; it's a direct reflection of how users interpret your product's design and guidance. It highlights the gap between the intended use and the actual use, providing crucial feedback for product development beyond simple usage statistics.

StatusGator discovered a core use case by observing user inaction. When customers turned off the primary alert feature, the founders realized the 'single pane of glass' dashboard had standalone value, which led to the development of public status pages.