North Star Seduction: Why Startups Are Shunning Metrics-Led Product Strategies
Why are today’s best startups shunning metrics-led product strategies? And what can we learn from the infamous North Star failures of the 2010s to inform a better approach to data-driven product management?
By Daniel Kyne • 5 min read
"We’ve overwhelmingly used our wealth to make the world cheaper instead of more beautiful, more functional instead of more meaningful.” — From David Perell’s The Microwave Economy
Substack is a black sheep in Silicon Valley.
Unlike almost every major startup, Substack doesn’t care about having a usage-based North Star Metric. They don’t obsess over the number of newsletters someone subscribes to, how many newsletters they read each week, or a writer’s publishing frequency. Instead, they just prioritize Gross MRR.
That begs the question — why is a company that focuses on maximizing revenue the outlier?
Nathan Baschez, Substack’s former Head of Product, explains that “The point [of Substack] isn’t just to make money — it’s to change the systems that human attention flows through… The Substack model [isn’t] just a business strategy, [it’s] a political philosophy.”
To understand how this philosophy permeates Substack and its approach to building product, you should start by comparing it with its ideological antithesis; Medium.
The Allure of Engagement Maximization
Medium launched in 2012 with a mission to be the easiest way for anyone to publish their writing on the Internet.
It triggered a new wave of blogging popularity by removing a bunch of technical barriers. At the center of Medium was its recommendation engine — unlike traditional blogs, you didn’t need an existing audience to get started on Medium. If your writing was interesting enough to grab people’s attention, Medium would help more readers to discover your stuff.
But to figure out which articles were most engaging, Medium used an algorithmic widget on the side of every article to recommend other posts to readers. That was strike one — luring readers away from the content and writers that drove traffic to Medium in the first place.
To compete for these discovery opportunities, writers increasingly leaned into clickbait. By 2015, 7 of the 9 most recommended articles on Medium were clickbait listicles. Medium had become the dumping ground for “easy” rather than “great” content. Strike two — shallow content became the winning format, turning Medium into a distraction for readers rather than a source of rich information.
Then Medium added advertising in mid-2015. Rather than pushing clickbait content people thought they wanted to read, Medium’s priority was now to promote content that readers didn’t even want to see at all. Strike three — monetization drove a decrease in value for both readers and writers.
Almost as quickly as they had adopted the platform, top writers fled for personal blogs free from distraction and misaligned incentives. Medium succeeded in building the easiest way for anyone to publish online, but forfeited their aim of being the best solution for anyone — readers or writers.
What drove this string of poor strategic decisions? Look no further than Medium’s North Star Metric.
The Road Less Travelled
Like most startups, Medium’s employees collectively worked to increase the company’s North Star Metric: “Total Time Reading”.
A 2013 article from Medium’s Product Lead (at the time) Pete Davies paints a pretty clear picture of how the team interpreted this North Star:
“When a user engages with your platform, you have their attention. And attention is the precious commodity of the super-connected era. I think of competing for users’ attention as a zero-sum game. Thanks to hardware innovation, there is barely a moment left in the waking day that hasn’t been claimed… At Medium, we optimize for the time that people spend reading.”
To drive ad views and content discovery, Medium focused on maximizing the time users spent on-site. But ‘Total Reading Time’ has nothing to do with building the best blogging tool for writers. As is so often the case, their North Star Metric was disconnected from customer value.
Substack doesn’t have a usage-based North Star Metric because it isn’t trying to maximize engagement like Medium. Instead, Substack has focused on solving the highest-priority problems that writers experience when publishing online. Founders Chris Best and Hamish McKenzie make this writer-centric perspective SUPER clear in their 2017 post explaining the company’s vision:
“Twitter makes money from your attention, so they need to compel your attention. Sometimes that leads to good things, like connecting you to people and ideas that matter. But it also means that the addiction, abuse, and outrage that thrive on Twitter and other social platforms may be impossible to eradicate. So what’s left to do? You can change the rules. That’s why we started Substack: when readers pay writers directly, it’s a whole new game.”
At its core, Substack’s founders understood the big problems experienced by online writers. The founders started with the biggest problem — the underlying monetization model for publishing on the Internet — and then moved down the ‘problem stack rank’ over time, solving pain points related to list ownership, recommendations, and content distribution.
Substack is winning because it cares the most about solving customer problems. And they didn’t need a North Star Metric to do that — making online writers’ lives easier led to more great writers signing up, which led to more readers, which in turn fed revenue growth.
This doesn’t seem that hard to grasp, so why do so many companies fall into the trap of optimizing for engagement over impact? Because engagement has become stupidly easy to measure.
Our Collective Addiction to Engagement Data
Everybody knows quantitative data will beat opinions and anecdotes at any startup. Using data to inform key decisions leads to better commercial outcomes and enables better cross-function collaboration. Data is the undisputed mother tongue — and language of power — in tech.
What most people don’t realize, however, is that all of today’s most popular product analytics tools trace their origins back to advertising-based businesses.
The founders of Amplitude, Mixpanel and Heap all worked in social media directly before launching their startups. Google Analytics — used by over 28 million websites, including 74% of the most popular 10,000 sites — is [obviously] owned by the largest ad-revenue company in the world.
These products were built to measure engagement because that’s what advertising-based businesses were dependent on. They’ve basically spent the past 27 years and countless hundreds of millions making this data collection as simple and smooth as possible.
So let’s say you want to build a contrarian company like Substack. Rather than maximize user engagement, you’re going to focus on solving high-priority problems and increasing customer impact. Do you really think you’ll be able to bring your ideas to fruition if all you’ve got are qualitative quotes and storyboards? Even if you’re on the founding team, that’s unlikely to be true over time.
If we want to build customer-centric startups, then we need to find new sources of data that quantitatively represent our customers’ highest-priority problems and most impactful unmet needs. That’s where People Analytics comes in.
Product Analytics → People Analytics
"We shape our buildings; thereafter they shape us.” — Winston Churchill
Now, I’m not saying we get rid of product analytics. Measuring onboarding bottlenecks, activation rate, and feature adoption are all essential for building a great product. But that’s all product optimization — not product strategy.
Product strategy is about how we marry our company objectives together with our available opportunities to impact customers. To do that, we need more than just data on what people are doing in our products — we need data that can tell us what people are thinking and feeling when they pick our products to solve their problems. Only when we can have this type of data can we build truly data-driven customer-centric companies.
This is the mission that we’re working on at OpinionX. We see a future where product teams can measure — with real data — what matters most to their customers.
And we’re making progress building that mission. Over 3,000 teams already measure customer priorities using OpinionX. Research methods like Customer Problem Stack Ranking and The Discovery Sandwich are being implemented today by thousands of teams around the world.
But the part that gets me most excited isn’t even what we’ve built so far.
I can’t help but think about what’s possible when we combine stack-ranked data with a company’s Customer Data Platform. Teams will have needs-based segments that can be used to power their entire marketing and tech stack.
Can you imagine powering your email campaigns, product personalization, or proactive education with real data on what each customer cares about most? It’ll be the first time that true needs-based segmentation will actually be possible at scale.
That’s why I’m excited about OpinionX. The majority of software products today are just trying to siphon away a fraction more of your time, attention and energy. But there’s an alternative future where our products help us to accomplish more in less time. That’s a world where people are the top priority, not the products.
Daniel Kyne is the Co-Founder and CEO of OpinionX — a free research tool for stack ranking people’s priorities. Over 3,000 teams use OpinionX to measure what matters most to their customers, giving them real data to inform their big product decisions. Create unlimited free stack ranking surveys at app.opinionx.co.
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