Assembled initially replaced a manual spreadsheet process. Their success came from understanding the spreadsheet was a symptom of deeper pains like headcount planning, real-time dashboards, and agent utilization. The real value was in solving these complex operational problems, not just digitizing a spreadsheet.

Didi Gurfinkel explains that the long, pre-PMF period was spent building a deep, robust platform connecting Excel to databases. When they finally pivoted to FP&A, this over-engineered platform became a massive competitive advantage that newer, niche-focused competitors couldn't replicate.

In crowded markets, founders mistakenly focus on other startups as primary competition. In reality, most customers are unaware of these players. The real battle is against the customer's status quo: their current tools like spreadsheets, hiring a person, or using an old system. Your job is to beat those options.

Even against other "Excel-based" FP&A tools, Datarails won deals by letting customers connect their existing spreadsheets without rebuilding them. This dramatically lowered the adoption barrier and made the learning curve immediate for finance teams with complex legacy models, creating a powerful competitive edge.

A product has strong market pull when it aligns with the customer's true goal (their "to-do list") far better than their current action (their "calendar"). Automated note-taking app JMP had pull because it perfectly matched financial advisors' hidden goal to minimize time spent on compliance paperwork.

Initially building a tool for ML teams, they discovered the true pain point was creating AI-powered workflows for business users. This insight came from observing how first customers struggled with the infrastructure *around* their tool, not the tool itself.

TeamBridge initially built a scheduling tool, but customers revealed the real problem was workflows and automations stuck in spreadsheets *surrounding* the schedule. Pivoting to solve this deeper, systemic pain led to making more money in one month than the previous two years combined.

Product-market fit can be accidental. Even companies with millions in ARR may not initially understand *why* customers buy. They must retroactively apply frameworks to uncover the true demand drivers, which is critical for future growth, replication in new segments, and avoiding wrong turns.

Users exporting data to build their own spreadsheets isn't a product failure, but a signal they crave control. Products should provide building blocks for users to create bespoke solutions, flipping the traditional model of dictating every feature.