During my time as Tech Lead for candidate recommendations at LinkedIn, I witnessed firsthand how the right approach to data-driven development can transform not just outcomes, but the entire experience of building products. This story isn’t just about what we built – it’s about how we discovered a better way to build.
The Challenge: Cracking the SMB Hiring Market
LinkedIn had a fascinating puzzle on its hands. Our data showed that small and medium-sized businesses (SMBs) collectively did just as much hiring as large enterprises. Yet our flagship product, LinkedIn Recruiter, had relatively low penetration in this market. The sophisticated boolean search capabilities that power users loved were overwhelming for occasional recruiters. We had a hypothesis: SMBs needed something fundamentally simpler – more akin to consumer products they were familiar with.
The opportunity was clear, but the path forward wasn’t. How do you take the complexity of professional recruiting and make it accessible to someone who might hire only a few times a year? How do you preserve the power while radically simplifying the experience?
A Tale of Two Approaches
I’ve seen this story play out dozens of times across different companies, and it usually goes something like this: The team starts with grand ambitions and a complex vision. They spend months building what they think users want. Every other week brings new feature requests and priority shifts. Six months in, nobody’s quite sure if they’re making progress. A year later, they have a product that’s neither here nor there – too complex for casual users but not powerful enough for professionals.
Our experience with Candidate Recommendation was dramatically different. Instead of feeling like we were constantly changing direction, we had clarity. Instead of wondering if we were making progress, we could see it in our metrics. Even when we were wrong about specific approaches, we knew we were learning and moving closer to our goal.
The key difference? We had established a clear business thesis early on: “SMBs will recruit more effectively if we can automate the expertise of professional recruiters.” This wasn’t just a nice-sounding mission statement – it was a testable hypothesis with a clear metric: InMail Accepts. If we were right, this metric would improve as we got better at automating recruiting expertise.
This simple foundation changed everything. When Product came with new feature ideas, we could ask “How will this improve accept rates?” When Engineering proposed technical improvements, we could estimate the impact on our core metric. Marketing could focus on acquiring users who would benefit most from our automated approach.
We broke down this core metric into four key levers:
- User reach: Getting more SMBs to try the product
- Recommendation quality: Showing the right candidates
- Message volume: Encouraging appropriate engagement
- Acceptance friction: Making it easy for candidates to respond
Every team could see how their work connected to these levers, and how the levers connected to our core metric. This clarity led to fascinating emergent behaviors. Teams started collaboratively brainstorming ways to move their levers. They began sharing data and insights unprompted. The entire organization aligned naturally around our goal.
The results were remarkable. We achieved a double digit percentage point increase in InMail Accepts – a massive improvement in the recruiting space. More importantly, we built something that genuinely helped SMBs hire more effectively. The recommendation system we developed proved so valuable it was adopted across several other LinkedIn products; and along the way we 3x’ed the velocity of our experimentation!
The Playbook That Emerged
Through this experience, I began to see a pattern – a playbook for building effective data-driven organizations. It goes like this:
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Start with a business thesis that is:
- Credible: Based on real market insights
- Simple: Easy to communicate and understand
- Falsifiable: Can be proven wrong
- Self-reinforcing: Creates competitive advantages as you succeed
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Define ONE core metric that best captures customer value. This forces clarity and alignment.
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Identify 3-5 key levers that drive that metric. This creates clear areas of ownership and impact.
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Align your entire organization around this framework. Everyone should understand how their work moves the levers.
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Develop a strong experimentation mindset. Every feature, every change becomes a bet on moving your metric.
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Continuously refine your understanding. Use data to update your models and assumptions.
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Obsess over experimentation velocity. The faster you can test ideas, the faster you learn.
This approach isn’t just about measurement – it’s about creating clarity and alignment. It’s about making it obvious when things are working and when they aren’t. It’s about building confidence through clear progress rather than through force of personality or political maneuvering.
The Broader Impact
What’s fascinating is how this approach solves so many common organizational problems:
- No more endless debates about priorities – the metrics tell the story
- No more uncertainty about progress – you can see what moves the needle
- No more misaligned teams – everyone understands how they contribute
- No more analysis paralysis – rapid experimentation shows the way forward
I’ve since applied this playbook at multiple organizations, and while the specific metrics and levers change, the fundamental approach remains powerful. It’s not just about building better products – it’s about building better teams and organizations.
The key insight? When you align your organization around clear, measurable business value and make your betting/testing cycles as fast as possible, you naturally optimize for what works while quickly abandoning what doesn’t. Success becomes less about individual brilliance and more about systematic learning and adaptation.
That’s the real power of becoming truly data-driven – it transforms not just what we build, but how we build it.
Looking to apply this playbook in your organization? Let’s talk.