This week’s toy: quote tweet games generated by AI. An example: “quote tweet this with a photo of you with badgers printed on it.” I didn’t write these things, AI did. I took a week off from writing last week, so my old streak of 54 in a row is over. Starting a new streak today. Edition No. 55 of this newsletter is here - it’s August 15, 2021.
The Big Idea
The “North Star” of any product is an idea that customers, stakeholders, and employees align on to build a vision for the future. When anyone has questions, they are able to look at this vision to determine what to do next.
The software company Amplitude describes it this way:
A north star metric is the key measure of success for the product team in a company. It defines the relationship between the customer problems that the product team is trying to solve and the revenue that the business aims to generate by doing so.
So building a north star means tying the business goals of the business to the “why” sought by the customer and the thesis the product team wants to achieve. Great! But what happens when that product idea itself depends upon an underlying idea that is different at any company?
On Data Products and Establishing a North Star
Some products are simple, requiring one or two use cases that consumers complete. Others rely on a slightly different thesis where a business user might need a very specific template (for example, efficiently completing a business payment - which requires initial configuration and then is relatively similar in the future). What happens when the underlying product is itself complex?
Eric Weber writes on the challenge of building a north star for a data product.
He explains it well here:
The challenge with identifying a data product’s north star is that we are dealing with some unique challenges: the products are often internal facing, the products don’t always have an obvious connection to revenue and we are more likely to measure external user behavior carefully than our internal users.
When the organization itself does not yet know its goals, how do you build a product that helps that organization achieve its goals? At least part of the answer needs to be a consultative process that itself is a product to create the output you need to build your data product.
The North Star looks different for each customer
When part of your own top line metric is helping a customer achieve their goals, one of the inputs to that process needs to be a way to help them uncover the biggest “why” about their business.
That insight might look fundamental, like:
How do we know we’re dealing with the correct account in every system?
How do I know how much a customer has spent with us over all time
When someone contacts me, how do I know which company they work for?
The guts of the problem are a lot messier, and once the customer believes that making this pain go away is important, you are aligned in your own Go-to-market to solve that problem. The North Star is about helping them to establish their own North Star. When defined, co-creating this future is possible.
What’s the takeaway? Building shared consensus on a data product where the underlying components could change for every customer is easier if part of that product is a process to align the customer with the customer’s own needs and document them as a precursor to building.
A Thread from This Week
Twitter is an amazing source of long-form writing, and it’s easy to miss the threads people are talking about.
This week’s thread: on the origin of ideas through cross-industry innovation
Links for Reading and Sharing
These are links that caught my eye.
1/ Turning opinions into 🏆- Customer interviews are a really valuable part of any product process. But they are opinions. The customer ends up being an n=1 situation much of the time. So how do you take qualitative information and turn it to directional or actionable information? This framework from the Nielsen Norman group has bread crumbs to turn interviews into actionable insights.
2/ On Motivation 🦸🏻 - this quote really stood out for me. The startup game (really, any buisness) is hard. And having a tribe of people who are really talented at what they do and who like to talk about the way they do it is rare and valuable.
3/ Delta Thoughts Δ - Statistics about risk are hard to understand. They are doubly hard to parse when a pandemic has been going on for almost two years. Why are we so bad at assessing risk? (p.s. whatever you think your risk level is, please get vaccinated.)
On the Reading/Watching List
I found this list of Logical Fallacies a good way to remind myself of the holes in my thinking. There is a probably a logical fallacy in here about depending upon a list of logical fallacies, and I’ll let you decide whether that is important … or not.
You can never have enough communication, so I’m reading “Communication Skills for Analytical Thinkers” as a refresher to remind myself that simplifying the story is essential to communicating value. It doesn’t matter how great your idea is if it stays in your head.
What to do next
Hit reply if you’ve got links to share, data stories, or want to say hello.
I’m grateful you read this far. Thank you. If you found this useful, consider sharing with a friend.
Want more essays? Read on Data Operations or other writings at gregmeyer.com.
The next big thing always starts out being dismissed as a “toy.” - Chris Dixon
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