GovCon Leads Radar — Turning three business goals into a testable build order

The Situation

By the time I finished the product vision for GovCon Leads Radar, I had three business goals locked in: grow subscription revenue, prove that AI-driven tiering meaningfully changes seller behavior, and grow through referrals instead of paid ads. That’s a normal output at this stage of any build.

What comes next is a deliberate step in how I approach every project after the product vision: Impact mapping — turning each goal into the specific, observable user behavior that would prove it’s actually working, and naming the assumption that behavior depends on. “Grow revenue” isn’t a spec, it’s a direction. To build against it, I need to know exactly who has to act differently, what that different action looks like on a given day, and what has to be true underneath it for the goal to move at all. This impact-mapping pass is how I make that translation explicit before any engineering decisions get locked in.

(See: The GovCon Leads Radar product vision)

The Stakes

Every capability I design next inherits a line back to one of these three goals: revenue, tiering trust, or referral growth. Get that line wrong, and I risk building things that feel productive without moving any of the numbers that actually matter.

The subtler risk isn’t picking the wrong feature, it’s sequencing the right ones badly. Shipping trust-building mechanics before there’s outcome data to build trust on, or shipping a referral feature before knowing whether people refer the product organically at all, burns engineering time on assumptions that can’t be tested yet. So the real question driving this whole exercise wasn’t just “what should this product do?” It was “what order do these bets need to be tested in, and what’s the smallest thing that tests each one?”

The Thinking

For each goal, I worked through the same questions: who is the actor whose behavior actually has to change, what’s the smallest observable version of that change, and what has to be true underneath it for the change to happen at all? I also mapped the actors around the primary user: the external systems and other players whose behavior the product depends on but doesn’t control.

Revenue. The primary actor is the GovCon seller, and the target is concrete: $8k–$12k in monthly recurring revenue. The behavior shift breaks into four distinct pieces: opening a tiered dashboard daily instead of re-running manual searches on public contracting sites, exporting qualified leads directly to CSV or CRM instead of hand-copying them, paying for persistent, scheduled searches instead of re-entering the same criteria every session, and relying on surfaced point-of-contact data instead of separately researching contacts. Each of those is a different daily habit, and each one had to earn its place in the first release on its own.

But the seller isn’t the only actor this goal depends on. USAspending.gov and SAM.gov — the external data sources the entire cross-referencing engine is built on — have to stay stable and accessible for any of this to work, and that’s completely outside my control. And there’s an internal actor too: whoever is responsible for data integrity has to keep pace as the pipeline scales, or the tiering quality erodes quietly.

Underneath all of that sit two genuinely risky assumptions: that the cross-referenced data is accurate enough to be worth paying for, and that sellers will perceive enough time-savings value to pay $99–$999+ a month rather than doing it manually for free. There’s also an honest failure mode worth naming directly: sellers might just keep using their spreadsheet-and-manual-search workaround because it feels “good enough,” and never convert into a paying habit at all. That’s the real competitive threat to this goal. . .not a rival product, but inertia.

AI-tiering trust. This goal protects the core product claim: that the tiers mean something. The measurable target is a Tier 1 vs. Tier 3 conversion differential, tracked across at least 70% of active accounts. The primary actor here is the same seller population, but there’s a second, more interesting segment worth calling out on its own: a trust-skeptical seller who defaults to manually reviewing every single lead because there’s no visible proof yet that the AI tiers correlate with real outcomes. That skepticism isn’t a UX detail to smooth over, it’s the central risky assumption the whole goal depends on: that users will shift from distrusting an algorithmic score to relying on it once enough outcome evidence accumulates.

Referral growth. The target: 30%+ of signups from referral or word-of-mouth. This is the thinnest of the three maps — one actor, one capability, one assumption — and it’s also where a competitive actor enters the picture directly: an established competitor could modernize its own offering and blunt the differentiation advantage before organic referral ever gets a chance to compound. The entire goal currently rests on a single, high-risk assumption: that the target market is homogeneous enough that satisfied sellers will refer peers without any dedicated referral mechanism built into the product at all.

The Decision

Not every capability tied to a goal deserves a place in the first version. Some earn it because they’re both high-impact and the fastest way to test a risky assumption. Others get deferred because building them early would mean spending real engineering time on an assumption that can’t be tested yet.

For revenue, the daily habit loop — opening the dashboard, exporting leads directly, relying on persistent saved searches, surfacing point-of-contact data — went into the MVP as a set of four distinct capabilities, not one vague bucket. Those are the behaviors that directly drive paying retention, and they’re also the fastest way to find out whether the accuracy and pricing assumptions actually hold. Data-lifecycle and compliance governance work tied to the internal-operator actor got pushed to Post-MVP; it matters, but its link to MRR is indirect enough to wait without putting the core bet at risk.

For AI-tiering trust, outcome tracking — the mechanism that generates the Tier 1 vs. Tier 3 evidence — made the MVP cut, because without it there’s no way to ever test the claim at all. The feature meant to actively shift skeptical sellers toward trusting the tiers got deferred, for a structurally similar reason to the compliance work above: it depends on an accumulated track record that doesn’t exist on day one, and designing around evidence I don’t have yet would be premature.

For referral growth, the decision was the starkest of the three: there’s no dedicated referral mechanism in the product at launch. Instead, the plan is to observe whether organic word-of-mouth happens on its own before building anything to force it. Given the competitive risk of an established player closing the gap first, this is a bet I’m making with eyes open, and one I’ll revisit with real signup data rather than more speculation.

The Result

What came out of this wasn’t three separate roadmaps, it was one build order, ranked by how directly each piece serves a goal and how risky the assumption underneath it is. The real output of an exercise like this isn’t the map itself. It’s the confidence to not build something yet, with a clear, defensible reason why.

Concretely: the first version of GovCon Leads Radar will center almost entirely on the daily loop — open the dashboard, review the tiers, export what’s qualified, keep the search persistent — plus the instrumentation needed to start proving the tiering claim with real outcome data. Trust-building features and referral mechanics are deliberately absent from that first release, not as oversights, but because building them now would mean designing on top of evidence that doesn’t exist yet.

The Lesson

Three abstract business goals and a list of feature ideas are not the same distance from reality, and treating them as if they were is how roadmaps get bloated with things that feel productive but aren’t tied to anything measurable. Running every goal through the same filter — which actor has to change behavior, what’s the smallest version of that change, and what has to be true underneath it — turned a vision document into a sequenced, testable build order.

The part I’m watching most closely going forward is the referral bet. Testing whether organic word-of-mouth happens before building anything to cause it is either disciplined sequencing or a real blind spot, and with a competitor able to close the differentiation gap in the meantime, that’s not a risk I’m ignoring, just one I’m choosing to validate with data instead of guessing.

Your Turn

If you had to defend one feature in your current roadmap purely by pointing to the specific user behavior it’s supposed to change, could you? Which one would you struggle to defend?