The North Star framework calls for the defining of a single, core and meaningful metric along with a handful of contributing inputs which have a bearing on it.

Sean Ellis says it is the crucial metric that "best captures the core value your product delivers to customers". 

Companies are now moving away from being totally focused only on numbers like the total number of tickets closed in customer support. Many of these metrics have made way for a new holistic way of looking at customer 'success'.

One that is more obsessed with maintaining and strengthening relationships with customers rather than activity-based metrics.

Customer engagement is one thing that changes rapidly year on year. In this article, Jackson Noel, Co-founder and CEO of Appcues, lists out 10 strategies to help your B2B SaaS company take customer engagment to the next level in 2020. 

Objectives & Key Results (OKRs), the goal-setting framework, has gained a lot of popularity over the past few years but for some Product Managers - new and old - it remains an area of confusion.

In this article, Sachin Rekhi, founder and CEO @ Notejoy, provides a great rundown on how OKRs should be implemented and provides a bunch of best practices that need to be followed.

As more and more companies deeply integrate AI into their development process, it has become the need of the hour for Product Managers to evolve and leverage this change to improve extraction of consumer insights, speed to market and testing of new ideas.

How should Product Managers deal with this change?

Typically, there are four specific product cultures dominate tech companies: engineering-driven, data-driven, design-driven, and sales-driven product culture.

Engineering-driven product cultures often start with a unique technical insight that becomes the basis for their products. Larry Page and Sergey Brin's Page Rank algorithm, for example, was the unique insight that enabled them to build the world's most successful search engine.

While retention is framed primarily as the percentage of users returning to the product itself, it is also useful to understanding specific product features and subpopulations of users. 

For example, you can examine how retention varies by geography, gender or behavioral characteristics (e.g., daytime vs. nighttime use) to paint a clearer picture of your users. Similarly, at the feature level, you can examine how users interact with individual features and then use that knowledge to guide prioritization and the product roadmap.