These Are the Metrics Great Product Managers Track: Thinking through AI as the next new platform opportunity

These Are the Metrics Great Product Managers Track: Thinking through AI as the next new platform opportunity

In the world of product management, data is everything. Product managers rely on data to make informed decisions, understand user behavior, and drive product improvements. Tracking the right metrics is essential for success, and in this article, we will explore some of the most important metrics that great product managers track.

One of the key benefits of being a data-driven product manager is the ability to gather important user feedback on your product. Metrics such as Monthly Active Users (MAU) and Daily Active Users (DAU) provide a great overview of a digital product's overall health. These metrics help product managers understand how engaged users are with the product and identify any potential issues or areas for improvement.

Another crucial metric that product managers track is the Customer Conversion Rate. This metric measures how many people who land on your website or app actually do what you want them to do. Understanding the conversion rate helps identify key drop-off points for users and features that may not be working effectively. It also provides insights into the discoverability of new features. For example, if a small percentage of users find a new feature, but the conversion rate is high, it indicates that the fault lies in discoverability rather than the feature itself.

Churn and Customer Retention Rate are also important metrics for product managers. As the Airbnb Growth Product Manager points out, companies that focus solely on user acquisition without understanding retention often lose all their users quickly. Churn rate indicates whether your product is delivering what it promises, while customer retention rate measures the ability to keep users engaged and satisfied. These metrics are crucial for product managers to ensure the long-term success and sustainability of their products.

Net Promoter Score (NPS) and Customer Satisfaction Score (CSAT) are two additional metrics that great product managers track. These metrics help measure the sentiments of users and provide insights into their satisfaction levels. A high NPS and CSAT score indicate that users are happy with the product and are likely to recommend it to others. Conversely, a low score may indicate areas for improvement and potential churn risks.

Customer Lifetime Value (CLTV) is a metric that helps product managers put a price tag on their users. By understanding the value that each user brings to the business, product managers can make informed decisions about customer acquisition and retention strategies. CLTV can be further refined by calculating it for different user segments, allowing for more targeted marketing and product development efforts. On the other hand, Customer Acquisition Cost (CAC) is a metric that product managers should track alongside CLTV. If the average CLTV is low, a high acquisition cost could put the business at risk, especially for freemium models.

Moving beyond product management metrics, let's delve into the world of AI as the next new platform opportunity. When it comes to AI platforms, there are two critical questions to consider. Firstly, how much value will the underlying platform retain for itself? History has shown that platforms often extract the bulk of the value, leaving little for the companies and developers building on top. However, in the case of AI platforms, competition between companies like OpenAI (with Microsoft as a partner), Google, and Facebook may limit a single company's ability to maximize profits at the expense of others.

The second question is who will capture the most value generated by AI: incumbents or start-ups? Incumbents have the advantage of unique data sets, large user bases, and strong financial resources, especially in the consumer market. However, there is an opening for start-ups to create value in enterprise software with vertical-specific AI products. Start-ups can differentiate themselves by designing interaction models that minimize friction and risk, leveraging proprietary data, and addressing regulatory and privacy constraints. By doing so, they can protect their brands and create unique value for their customers.

In conclusion, tracking the right metrics is crucial for product managers and AI platform builders alike. By focusing on metrics such as MAU/DAU, customer conversion rate, churn and customer retention rate, NPS/CSAT score, CLTV, and CAC, product managers can make data-driven decisions and drive product improvements. Similarly, AI platform builders should consider the value retained by the platform and the potential for competition and differentiation. Ultimately, understanding and leveraging data and metrics are essential for success in today's digital age.

Actionable Advice:

1. Regularly track and analyze MAU/DAU, customer conversion rate, churn and customer retention rate, NPS/CSAT score, CLTV, and CAC to make informed product decisions.
2. Consider the value retained by AI platforms and the potential for competition and differentiation in order to maximize value for your business.
3. Leverage unique data, design interaction models that minimize friction and risk, and address regulatory and privacy constraints to create value and protect your brand in the AI space.