It might be controversial, but I tend to agree with Kax Uson point about managing feature requests from stakeholders:

I have learned that it’s perfectly alright for stakeholders to have solution requests. And to expect them to bring problems to us is unrealistic.

The post gives some good advice on how to translate solution requests into problems/opportunities that teams can solve:

Speak the language they speak. Talk about the benefits and impact of their requests. If you do build the feature they’re asking for, what value would it provide for the customers/users and the company? Speak in outcomes.

There’s some really good advice for job seekers in Carilu Dietrich’s post 10 Most Important Metrics For Evaluating a Company:

Net Dollar Retention (NDR) is one of the best ways to see if a tech company is healthy and growing. It measures the expansion or contraction of a company’s existing customer revenue over a given period. NDR takes into account the revenue lost from churn and increased revenue from upselling, cross-selling, and renewals from existing cusomers.

I would also add Gross Revenue Retention to that list. It describes how much revenue a company keeps from its existing customers over a specific period, not including any new revenue from new customers. So, if a company started the year with $100 from its existing customers and ended the year with $90, even if they lost some customers but upsold others, the gross revenue retention would be 90%. It helps guage how good a company is at keeping its current customers happy and continuing to spend money, without taking into account any new business they’ve gained.

I promise this isn’t going to become an AI blog, but Marty Cagan’s latest on Preparing For The Future has some solid points on how generative AI might change our profession. Here he talks about the impact on QA:

The new generation of AI-based test automation tools has the promise to revolutionize our approach to ensuring the product is behaving properly. On the other hand, our current approach to quality is largely based on deterministic products.  This means that given a set of inputs we can predict what the appropriate output should be, and we can count on that being true indefinitely.  

Yet for many new products built on generative AI, our products are no longer deterministic, but rather probabilistic.  We can no longer count on the same inputs generating the same outputs.  For many contexts, this is not a problem.  But for other contexts, especially when safety is involved, this will require different approaches to ensure appropriate behavior.

Also, yes please:

For those product managers of empowered product teams, the time spent creating artifacts such as written narratives, roadmaps, PRD’s, and acceptance criteria, as just a few examples, should be significantly assisted by the new generation of tools, some of which are already starting to appear. Even at the very rudimentary level, if the new generation of tools can significantly reduce the time a product manager (or engineer) spends dealing with tools like Jira, that will be a substantial win.

Aniket Deosthali (Head of Product for Conversational Commerce at Walmart) provides a great framework for evaluating AI product opportunities in How to Build AI Products People Want:

The most efficient way to evaluate AI opportunities and unlock the advantages of AI is by using the Consideration x Context framework. Let’s start with some baseline definitions.

Y-Axis = Consideration: The amount of effort required to make a decision.

The more thought you put into a decision, the higher consideration it is. For example, choosing a dish detergent is “low consideration” for most shoppers, compared to buying a car which is “high consideration.” Consideration can be represented as a function of the number of compelling alternatives and the stakes - users’ tolerance for errors, for example.

X-Axis = Context: The volume of abstract concepts AI needs to know. 

Context refers to how many abstract concepts a model needs to know in order to provide a useful response. Does it only need to understand a small batch of data points (like a product catalog), or does it need to understand the entire internet (like ChatGPT)?

He goes on to explain how to plot different solutions on a “Survival Curve.” If you work on AI products this is one of the rare actually helpful articles for PMs in the area of AI and LLMs.

The first part Sara Ramaswamy’s Product-Led Growth and UX is just a summary (a good one!), but the “How UX Can Help” sections has some really great insights and ideas, like this one:

While macro conversions (high-level conversion tied to the primary purpose of the site) are often the first success indicators considered, it is, however, important to define and revisit micro conversions, which measure incremental improvements to the user experience. In product-led growth, products are competing at the micro-conversion level. Analyze the conversion user journey and create milestone micro conversions that capture progress toward primary macro conversions. Also identify secondary user actions on site that are correlated with macro conversions.

“Compete at the micro-conversion level” is a really good lens to keep in mind as we improve our products.

I saw two deeply personal posts this week, each related to the dangers of chasing after vanity metrics. First, Justin Andersun tells us about The Ski Lesson, and concludes with this:

We should not lose touch with the spirit of what we’re doing. A job’s essence is to serve the needs of others, and friendship is to support the people we love. Metrics become vanity when they lose touch with that spirit.

Second, fio dossetto writes this about being mindful of vanity metrics:

Vanity metrics are easy to pick and hard to let go of. They can subtly but significantly damage the system for a long time before you spot them, at which point you’ll need to take a hard look at your actions and decide how to course correct. Fast.

Both posts highly recommended!

I think this is the best definition I’ve seen so far of that elusive term “product sense”:

I define great product sense as the ability to do two things without having extensive data (i.e. without running lengthy research upfront):

• Generate many solid, highly profitable ideas for ways to make money
• Intuit whether a product is likely to be successful with a high degree of confidence

The detail of having this sense without extensive data is important. Anyone can get to a great product via guess-and-check. The best product minds reliably take a more direct path.