Every roadmap contains certainties, but the critical question is whether those certainties are grounded in verifiable customer behavior. Many product roadmaps articulate features and initiatives as if their necessity is self-evident, yet a closer look often reveals a lack of direct evidence from customer actions to support these claims. This disconnect can lead to significant wasted effort, building features customers don't truly need or use.
It was Monday morning, and Femi, newly promoted to Head of Product at a Lagos-based fintech, stared at the roadmap his team had inherited. His predecessor, who’d just moved to a larger firm, had left behind a meticulously detailed plan for the next six months. Every item, from "Enhanced Transaction Categorization" to "AI-powered Spending Insights," was presented with an air of unshakeable confidence. Femi scrolled, then clicked into the associated documentation. He’d been an engineer for years, known for his relentless focus on shipping, but this new role felt different. He needed to own these decisions, not just execute them.
He paused at "Personalized Savings Goals." The description promised a feature that would help users save for specific life events. It sounded plausible, even desirable. But as he dug deeper, looking for the underlying data, the rationale began to fray. The "user research" mentioned was a survey from six months ago, asking if users would like a savings feature. There were no actual usage metrics, no observation of current saving behaviors, no direct customer interviews where a user articulated a problem this feature would solve. Just a general affirmation of interest. The implicit assumption was that if you built it, the desire would manifest into usage. Femi felt a cold knot in his stomach. What if they built this, shipped it, and users either ignored it or, worse, found it confusing? What if the next six weeks of engineering time vanished into a feature that collected dust?
When Certainty Outruns Evidence
The most common trap in roadmap planning is confusing a plausible idea with a validated one. A "good idea" is a starting point, not an endpoint. Without observing how customers actually behave, interact with prototypes, or articulate their real-world problems, even the most intuitive features can fall flat. This isn't just about avoiding failure; it's about prioritizing resources effectively, especially in lean startup environments where every sprint counts.
Femi knew his team had limited runway, the kind of constraint that made every decision feel heavy. He remembered a time as an engineer when he'd been given a feature to build, an "in-app chat" for customer support, that everyone assumed was critical. Three months of work later, it was used by less than 1% of the user base, while a simple email button remained the primary channel. The team had built what they thought was best, not what the users actively gravitated towards. What Happens When AI Agents Outpace Your Team’s Review Capacity? discusses how even advanced AI features need to align with existing human workflows to avoid being ignored.
The Cost of Unverified Roadmaps
The expense of building features that don't land isn't just the engineering hours. It's the opportunity cost of what wasn't built, the mental overhead of maintaining unused code, and the slow erosion of team morale when efforts don't translate into impact. For an early-stage founder, this can mean the difference between extending runway or burning through it prematurely. For product builders, it’s the frustration of shipping things that don't move the needle.
In Lagos, with the competitive fintech landscape, Femi couldn't afford to guess. He thought about the investor deck for their next round. How would he justify these roadmap items if pressed on their customer impact? The questions would be pointed: "What specific problem does this solve for which segment?" and "What data proves this is the highest leverage feature for revenue or retention right now?" Without concrete answers, the roadmap wasn't a plan; it was a wish list. This type of scrutiny is critical when evaluating potential partners or hires, as discussed in Can Three Career Pivots Be the Strongest Evidence in a Senior Hire?.
Building a Behavior-Driven Roadmap
Shifting to a behavior-driven roadmap requires discipline. It means moving beyond surveys and internal assumptions to observing actual customer actions. This could involve:
- Usage Analytics: What features are users already engaging with? Where do they drop off? What existing workarounds do they employ?
- Customer Interviews: Not just asking what they want, but observing them in their natural environment and understanding the underlying jobs they're trying to get done.
- Prototype Testing: Putting low-fidelity versions of features in front of real users to see how they interact, before writing a single line of production code.
- A/B Testing: For smaller iterations, testing different versions of a feature to see which drives the desired behavior.
Femi decided his first task wasn't just to execute the roadmap, but to validate it. He called an emergency meeting, not to critique the previous team, but to reset expectations. They would pause development on "Personalized Savings Goals" for one week. That week would be dedicated to rapid customer interviews, observing how current users managed their finances, and presenting a clickable prototype to a small group to gauge genuine interaction, not just expressed interest. He needed to see their fingers move, their eyes track, their frustrations surface, before committing another engineering hour. This small pause, a week for direct customer behavior observation, felt risky with the looming deadlines, but the alternative felt riskier: building in the dark.
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