User engagement — sometimes also referred to as product engagement — is a way to measure how actively your users interact with your product/application at a granular level.

Data analysis is one of the cornerstones of product management. What few people understand, however, is that misinterpretation can be worse than just following gut feelings.

Unsegmented, general revenue figures only tell part of the story. Distinguishing between different sources of revenue — namely, new and recurring revenue — is vital for deriving essential insights and fine-tuning your projections.

Time to value (TTV) measures how quickly your customers expect to benefit from the value your product provides.

Mean time to repair (MTTR) measures the time required to identify a product failure and bring the product to its normal operating status.

Data may be collected in different places, but it’s ultimately piped into one place where multiple tools and teams can access it. This is a single source of truth.

Empirical evidence is data or information that can be physically observed or measured and is factual instead of personal opinion.

To put it simply, customer analytics allows the product manager to make informed, data-driven decisions and work in an agile way.

A data-driven insight generation loop is a process that maximizes your chances of finding relevant insights from data.

An altitude map displays levels of product metrics visually. It maps out the most relevant metrics and shows how each influences each other.

Net revenue retention is a metric that helps to measure cumulative revenue retained from existing customers by examining revenue.

While many product managers focus on gaining new customers as fast as possible, it doesn’t do much for overall business growth if they leave as quickly as they arrived.