My Own Journey + Privacy, Context And Intent + Product Ecosystem + Talking Pirates (PAN #5)

How to Optimize Your Product Using Analytics, by Dan Olsen

If you’re into Lean, Dan Olsen who is the author of the Lean Product Playbook gives a 30 minute course on how to use analytics to grow your product.

In it, he refers to the Pirate Metrics, which for those new to it is a widely adopted framework to think about how to structure your product’s metrics (see image above). Another important concept is the MTMM (Metric That Matters Most), which is really similar to the OMTM (One Metric That Matters) from the invaluable Lean Analytics book.

The interesting bit for me is when he gets into a case study. The scenario is that as a Product Manager, you want to improve acquisition, but which metric should you focus on. That’s where Dan introduces the concept of the Upside Potential of a Metric, which is all about how much you can move that needle to improve acquisition for example.

Taking Action

So, dependant on the stage of growth your product is in, what would you say is the MTMM for you at this point?

For the Product Analytics Newsletter, I would say that right now it’s less about Acquisition than Retention. The reason being that I’m still trying to figure out how to best talk about analytics to Product Managers and what would be the best format to increase engagement.

As for which metric I should focus on, considering the Upside Potential of a Metric, the most gains I could make at this point is on the Click Rate instead of the Opening Rate.

Well look at that, I just published another blog post: Product Ecosystem and Analytics.

What’s it about you ask? Well lately I’ve been writing… a lot. It really helps clarify concepts and ideas around product analytics. And I thought it was time to take a step back and just put all those elements in perspective. That’s where I came up with the graphic above, which maps out what a product ecosystem is and how analytics helps illuminate it all.

Where I’m heading with this? My goal is start applying and refining that model on well-known and loved products such as Slack. What is their product management strategy and how does analytics fit into it.

Taking Action

As with Lean Models or the AARRR framework above, I think the right approach to taking models such as the one I’m proposing and taking action with the help of it, is by focusing on a certain element that seems to be central to your success at your stage of growth.

For me, it has to be foundational. I’m not even sure yet what I should be observing. What are the forces at play with the Product Analytics Newsletter? I think those revolves centrally around reader attention. I know I’m competing for your attention with the thousand other emails you’ve received within the same hour, so that has to be a major force I should be considering (yes, I’m fully aware that I’m stating this while sending you a newsletter that has the length of a novel).

Changing contexts and intents, by Tim O’Reilly

Alright, changing gear here. Going back to privacy. As always, Tim O’Reilly is on point with his contributions, this time on how data should be managed whenever the context of an initial consent has changed.

“We need to think about privacy in terms of when data moves from one context to another, rather than in absolute terms”

That applies to when users are using your product which generates data. What you do with that data afterwards needs transparency and consent.

“What do we do when the contexts themselves change? That’s a question we must work hard to answer. Part of the problem is that contexts change slowly, and that changes in a context are much easier to ignore than a new data-driven application.”

So true!

“One special type of meta-context to consider is intent.”

Meaning that consent provided before may apply to future uses if the intent is in-line with initial consent. But if you overshoot, users should be able to state that you went to far and remove consent.

Take Action

Think back to the process of when your users are joining your product. What explicit/implicit consent have they provided you? What sort of identifiable profile elements and behaviours are you currently harvesting? Was that part of the intent that they initially consented?

For example, when you registered to this newsletter, you consented to receiving a fresh email every 2 weeks and maybe for me to track when you opened this email and clicked on something. What if I started to send you targeted promotions? Or if with some wizardry, I could now start tracking what you do on my website lantrns.co? Those are different contexts, but were those intended in your initial consent?