Best SaaS metric

The best SaaS metric to measure user engagement should indeed align with your product’s value proposition and how it drives meaningful outcomes for users. Since your AI coach aims to optimize team performance and encourage users to "express their best selves," the metric you choose must reflect this unique dynamic. Here’s a first-principles approach to help you define the ideal engagement metric for your use case:

𝗙𝗶𝗿𝘀𝘁 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲: 𝗖𝗼𝗻𝗻𝗲𝗰𝘁 𝗨𝘀𝗮𝗴𝗲 𝘁𝗼 𝗩𝗮𝗹𝘂𝗲 𝗖𝗿𝗲𝗮𝘁𝗶𝗼𝗻

At its core, a user engagement metric should track behaviors that directly correlate with the value your product delivers to users. This value is maximizing growth efficiency for startups through improved team performance. So, your metric must track how frequently users leverage your AI coach in ways that impact their performance and success.

Here’s how to apply that principle:

1. Identify Key Actions that Signal Engagement and Value

What specific interactions with your AI coach indicate that users are deriving real value? These could include:

• Completion of tasks or daily/weekly goals set by the AI coach.
• Active participation in AI-suggested exercises, feedback, or improvement plans.
• Frequency of checking-in for guidance, reflection, or performance insights.

The goal is to identify actions that are leading indicators of success. For example, if teams that use the AI coach to complete growth-related tasks regularly outperform others, task completion becomes a strong candidate for your engagement metric.

2. Determine an Optimal Usage Frequency

For an AI tool, stickiness (e.g., DAU/MAU ratio) is often a good start, but it needs to be tied to more meaningful outcomes than just app usage. Define optimal frequency of usage: how often should users engage with the AI coach to experience maximum growth efficiency?

For example:

• Teams might need to engage with the AI three times a week for it to significantly impact performance, similar to the A3x7 framework, which measures if users perform key actions three times within seven days.

You could adapt this to fit your AI coach's context, such as C2x7: ensuring users complete at least two key growth tasks per week.

3. Create a Custom Metric that Reflects Performance Outcomes

Here’s where creativity comes into play. Beyond just tracking usage or tasks, think about how this engagement translates to performance outcomes. You could create a hybrid metric that combines:

• Frequency of meaningful actions (e.g., C2x7) with
• Performance impact (e.g., how often teams hit their growth objectives after using the AI coach).

This could result in a custom metric like Performance-Driven Engagement (PDE), where you track how many users complete key AI-suggested actions and improve their performance metrics (e.g., growth targets met).

Example:

• PDE = % of users who complete AI-recommended actions at least 2 times per week and show a measurable improvement in key startup metrics (revenue, users, etc.)

4. Test and Iterate

Once you establish a working metric, continue to validate it by measuring whether higher engagement levels correlate with better startup outcomes. Refine the formula based on feedback and results.

Conclusion:

By following this first-principles approach, you create a metric that’s not just about usage but about value creation through behavior that aligns with your product’s core mission. Stickiness ratios like DAU/MAU or the A3x7 framework offer a solid foundation, but customizing a performance-based engagement metric (like PDE) ensures it fits your specific use case.