Summary

Recap

In this chapter, we focused on the tracking, marketing, promotion, and selling of the AI/ML product. We covered the various ways that a product manager can benchmark and track their product and its success using metrics and KPIs, as well as what that means for the greater organization and the successful adoption of that product among its user base. We also contextualized this benchmarking against the overarching product strategy and vision that powers what gets tracked and measured. All these activities help product managers get internal signals into whether or not the product they’ve built works for their active customers and users.

Then, we discussed the greater work of getting external signals on what is and isn’t working using the various tools in the growth tech stack that directly connect to the UX. We went over the elements of growth hacking. Whether we’re looking to optimize how we acquire, engage, or retain customers, we’re going to have to find a way to gather that data, analyze it, and use it to make real decisions about how to add, remove, or improve features. Overarching, long-lasting trends of organizations becoming increasingly data-oriented and data-driven have been massively influential drivers of creating a new culture in the software world.