5 Go-to-Market Lessons From Building a Recruiting Intelligence Platform
Some founders obsess over quick wins, but in a recent episode of Category Visionaries, Amit Bhatia shared counterintuitive go-to-market lessons learned while building his recruiting intelligence platform. Here are the key insights that shaped their journey:
- Start With Deep Market Research, Even If It Means Delaying Revenue
Rather than rushing to market, Amit spent four years running an unprofitable job search engine to truly understand the market dynamics. “The first four years of this company, I butchered,” he admits. “I started because I knew I wanted to work in this space. I knew I needed data, and I didn’t know how to go about it. So I started by bootstrapping and building a job search engine.”
This research period revealed a crucial insight that would shape their entire product strategy: “I had this epiphany. The same exact job can be written in five different ways with five completely different outcomes,” Amit explains. “Until then, I thought, oh my God, why is LinkedIn so bad? Why is indeed so bad? And then I realized, LinkedIn indeed aren’t bad. The jobs are terrible.”
- Deliberately Diversify Your Customer Base
While most recruiting tech companies chased quick wins with tech startups, Amit’s team took a different approach. “One of the things that we’ve tried to avoid, it was very commonplace in the last ten years in recruiting tech and I think at any part of tech to go and build customers that were other tech startups or other tech companies,” he notes.
This strategy proved crucial during the 2022-2023 market downturn, as their tech customers represented less than 25% of their customer base, allowing them to maintain growth while competitors struggled.
- Challenge Convention in Enterprise Sales
The company found ways to dramatically accelerate traditionally slow enterprise sales cycles. “I’ve been surprised at how much a company can speed up an enterprise sales cycle by making the process data driven and immersive,” Amit shares. “Even with our enterprise customers, with the land and expand motion, we’re able to keep sales cycles down to 30 days, often 40 days, without any struggle, even in tight times.”
- Use Data Leadership to Define Your Category
Instead of just claiming the “recruiting intelligence” category, they’re actively defining it through data leadership. “We sit on an enormous amount of data. And if there’s one thing that recruiting has always been enormously hungry for and starved off, it’s great data sets,” Amit explains. Their research reports have become powerful tools for shaping market perception and driving thought leadership.
- Solve for the Real User, Not Just the Buyer
While most recruiting tools focus solely on recruiters, Amit recognized that hiring managers are the key to transformation. “The only reason the hiring manager is not in the driving seat today is because we don’t want that hiring manager to be using birthday or we don’t want the hiring manager to be using greenhouse. The tools or the processes are too complicated or too arcane and that has to change.”
This insight has shaped their product strategy and go-to-market approach, leading to higher adoption rates and stronger expansion within enterprise accounts. “We end up having hundreds if not thousands of users across the company. We end up becoming one of the most used tools in their stack,” Amit notes.
The central thread connecting these lessons is patience and intentionality. Rather than chasing quick wins or following conventional playbooks, Amit’s team has consistently taken the longer, more thoughtful path – from their initial research phase to their careful approach to category creation.
This strategic patience has positioned them to tackle what Amit calls “the ultimate decision problem” in enterprises: “Today, as an industry, we spend somewhere like $70 billion wastefully on recruitment, marketing and executive search.” By taking the time to truly understand and methodically address these inefficiencies, they’re building something with lasting impact rather than just another point solution in an already crowded market.