Product-Market Fit Engine + Day 2 at Amazon + Grand Tour of the BI Tool Landscape + More (PAN #21)
Edition #21 - September 23, 2019
Good morning product owners 👋
Kids are back to school and reality sets back in. That means publishing a weekly newsletter has become unrealistic again :) So starting today, this newsletter is returning to its original frequency of being sent every 2 weeks.
Second point of order is that I’m leaving for Vietnam this coming week for a 2-week vacation and I sure won’t be doing any work while there 😴📸🍺 So this newsletter is to be the only one that’s going to be sent for the next month or so. But it’s full of goodies. Hope you enjoy it!
Superhuman’s Product-Market Fi Engine
What a great story of how simple analytics can lead a thought process to achieve product-market fit.
For the founder of Superhuman, the question that is at the root of it all was: “what if you could measure product/market fit?” He stumbled on Sean Ellis’ idea that, to have a leading indicator of if you’re on the right path, you need to know how many of your current users would be pissed off if you unplugged that product 🤬. Not exactly how he phrases it, but you get the point.
And it all flows down from that question. Who are your power users? What are the features that rocks their world? Who do they think would benefit the most from your product? How to double down on what users love and what prevents other users from absolutely loving your product?
An insightful journey from beginning to end. Worth the long read.
As you’ll see in the BI Landscape story below, Tableau does have the reputation of being the established player in this field and being a bit outdated. There are some definite disadvantages to using Tableau, such as having to install the software on your machine, dealing with a bunch of data sources, difficulty of having multiple people do development on workbooks and collaborate through Git, etc.
But it’s still an awesome tool to explore and visualize your data imho. And version 2019.3 has some really neat features to better manage your data and prepare it for analysis. There is Catalog that will give you an overview of all your data sources and how they are being used by your dashboards. And there’s the ability to insert R / Python scripts in your Data Prep flows to have even more control on how you’re massaging your data before analysis.
Trouble is, all that is only available or useful if you buy the data management add-on. Catalog is only offered through it, and Data Prep is only convenient if you can automate its execution through Data Prep Conductor. I get why they’re doing it from a business perspective, but… I hate add-ons… 😞
Day 2 at Amazon
Why do I hate add-ons? Cause it’s motivated by a business decision more than by a consumer one. And that’s exactly how Day 2 feels like in the world of Jeff Bezos.
Ben Thompson offers another great analysis of what’s trending in tech by putting into context some of the decisions made by Amazon lately. If Bezos obsesses on Day 1, which is all about keeping things fresh, nimble and consumer-focused, Thompson argues that it’s starting to look as if Day 2 is starting to settle in.
I had never even heard of that Day 1 and 2 concept and it’s actually a great philosophy to adopt whenever growing a business. There are also some warning signs from Day 2 you should keep an eye on and if you don’t want to fall to an unavoidable death.
Mode Analytics With Improved Tables
Not much to say here. If you like Mode Analytics, then all improvements can be really interesting. Here they are introducing cool new features for their tables. Looking forward to trying that out! 🤓
Data-Driven Blunders And How To Avoid Them
“Let’s be clear, good tracking and hypotheses validation with data is essential for any product manager. The problem arises when we expect data to be the “secret sauce” that will immediately improve all aspects of our product, and that the answer to every question is always more (events, dashboards, tests).”
That piece provides context as to why data is essential to grow your product, but also the pitfalls to avoid. It’s all about first having an implementation strategy. And keep in mind that the goal is to have the data that provides insights to support our hypothesis and decisions, not dictate our every decisions.
The Modern BI Tools Landscape
This is a long-read for anyone interested in doing a deep dive into the BI landscape. With the “wave” of super-acquisitions lately (Google buying Looker and Salesforce buying Tableau) in this space, it is a super-competitive landscape with the established players, the disruptive newcomers, as well as the open source rebels.
There is something to be said about the classification though, calling established players “Legacy” solutions, and newcomers “Modern”. I fail to see how Looker is that modern in comparison to Tableau, although I do see some of the advantages of using it over Tableau. But Tableau is “modern” on other fronts, such as with ETL through their highly visual tool Tableau Prep.
That said, very informative overview of the BI landscape if you want to have a good grasp on who are the key players and they differ from one another.
Create a Tracking Plan
“Think of analytics as a feature, ship the MVP, and then continue to invest over time as the data maturity or your organization grows.”
Nicely written and insightful piece by the guys at Iteratively on creating a tracking plan for your analytics. I think we’ve written enough about why having a tracking plan is important, but I do like the simplicity of their approach and I know they’re dedicated to helping their users create and implement the most efficient tracking plan for their needs.