Here Are the Vanity Metrics to Forget in AI Product Management and What Real Metrics To Use

Tracking impressive numbers in AI? Vanity metrics like user count or model downloads might mislead you. Focus on real user engagement and impact!

Hey there, AI product leaders! 👋

We're all hustling to build the next big thing in AI.

We track our progress, celebrate milestones, and report to stakeholders.

But are we focusing on the right metrics?

In the fast-paced world of AI, it's easy to get seduced by vanity metrics - numbers that look impressive on the surface but don't actually reflect the health or potential of your product.

I'm talking about things like the number of registered users, total model downloads, or even the raw accuracy of your algorithm.

Sure, these numbers might give you a temporary ego boost, but they can be dangerously misleading.

---

Why Vanity Metrics Can Be Your Downfall

Chasing vanity metrics is tempting.

It might look good for a while, but it's bound to crumble.

Here's why:

• They Don't Reflect User Value: A million registered users means nothing if they're not actively using and/or paying for your AI product.

• They Mask Underlying Problems: High download numbers can hide a churn problem. Users might be trying your product and quickly abandoning it.

• They Distract from Real Goals: Focusing on vanity metrics can divert resources from what truly matters, like improving user engagement and retention.

• They Don't Predict Future Success: A high-accuracy model today doesn't guarantee it will perform well in the real world or adapt to changing user needs.

So, What Should You Be Measuring?

Okay, enough about the problem.

Let's talk solutions.

As a product leader in the AI space, you need to focus on actionable metrics that provide genuine insights into your product's ability to bring value to the user and guide your strategic decisions.

Here's a breakdown of the metrics that truly matter:

1. User Engagement: Are Users Actually Using Your AI?

This goes beyond mere registration or downloads.

You need to understand how users are interacting with your AI features.

Key Metrics:

• Daily/Monthly Active Users (DAU/MAU): How many users are engaging with your AI on a regular basis?

• Session Length: How long are users spending with your AI features in each session?

• Feature Usage: Which specific AI features are users engaging with the most? Which are being ignored?

• Retention Rate: What percentage of users return to your AI after their first use? ‼️ Pay attention - retention is a lagging indicator and can be misleading too!

2. User Satisfaction: Is Your AI Meeting User Needs?

Happy users are the cornerstone of a successful product.

You need to gauge whether your AI is solving their problems and exceeding their expectations.

Key Metrics:

• Net Promoter Score (NPS): How likely are users to recommend your AI to others?

• Customer Satisfaction Score (CSAT): How satisfied are users with specific interactions or features?

• User Feedback: Collect qualitative feedback through surveys, interviews, and in-app feedback forms. Analyze this feedback for recurring themes and pain points.

• Churn Rate: What percentage of users are stopping using your product or specific feature? Why?

3. Model Performance in the Real World: Does Your AI Deliver on Its Promise?

Accuracy on a test dataset is one thing.

Performance in real-world scenarios is another.

You need to track how your AI model is performing in the wild.

Key Metrics:

• Precision and Recall: These metrics are crucial for evaluating the accuracy of your model's predictions in real-world use cases.

• Error Rate: How often is your model making incorrect predictions? What are the consequences of these errors?

• User-Reported Errors: How often do users flag issues with your AI's performance?

• Model Drift: Is your model's performance degrading over time as it encounters new data?

4. Business Impact: Is Your AI Driving Business Outcomes?

Ultimately, your AI product needs to contribute to the bottom line.

You need to connect your AI's performance to key business objectives.

Key Metrics:

• Conversion Rate: Are users taking desired actions after engaging with your AI, such as making a purchase or signing up for a subscription?

• Customer Lifetime Value (CLTV): How much revenue do users who engage with your AI generate over their entire relationship with your product?

• Cost Savings: Is your AI automating tasks or processes, leading to cost reductions for your business?

Example: The Mistake of Focusing on Model Accuracy Alone

Let's say you've built a state-of-the-art AI model for image recognition with 99% accuracy on your test dataset.

Impressive, right?

But what if that accuracy drops to 80% in real-world conditions due to variations in lighting, image quality, and object orientation?

What if users find the model too slow for practical use?

What if it misclassifies images in a way that has serious consequences for your users?

This is a perfect illustration of why relying solely on model accuracy as a vanity metric is an issue.

You need to consider real-world performance, user experience, and business impact to truly assess the success of your AI.

Actionable Tips You Can Implement Today

1. Identify your key business objectives and choose 3-5 actionable metrics that directly relate to them.

2. Set up a system for tracking these metrics regularly, whether it's a dashboard, a spreadsheet, or a specialized analytics tool.

3. Review your metrics at least once a week and discuss the findings with your team.

4. Use your insights to prioritize your product roadmap and make data-driven decisions.

5. Don't be afraid to adjust your metrics as your product evolves and your understanding of your users deepens.

The Takeaway: Focus on What Truly Matters

---

---