Focusing solely on a single metric for product success can lead to poor decision-making and a lack of overall product quality.

Be cautious of measuring retention over too long a time period, as this can artificially inflate your retention metrics and mislead you about user engagement.

To determine if users are getting value from a product, simply measuring engagement, like watching more content, can be misleading; for example, watching fight videos may not reflect true user satisfaction.

Measuring retention is critical; if you don't have a product that retains users, you shouldn't focus on growth yet.

Product metrics for AI should focus on the depth of engagement rather than just frequency, as meaningful interactions may not always show up in traditional metrics.

I value metrics like time on site and engagement over page views, as they indicate how well content resonates with the audience.

It's important to choose meaningful actions for measuring user engagement; simply tracking app opens can lead to misleading retention metrics.

Balance product development with marketing and customer engagement; being overly focused on product can lead to user dissatisfaction.

Setting a metric and goal for your team allows for data-driven operations, where you can monitor progress daily and adjust strategies accordingly. A low retention rate, like 6.25% after four weeks, indicates poor product-market fit; aim for at least 20-30%.