How I Repositioned a Floundering Startup

When I joined Third Axiom as the founding product marketer, the flagship product — an AI transportation analytics platform — had ballooned to 40+ use cases. We had a handful of customers, but none of them were using it as intended.

Part of the problem was how we were positioned: as end-to-end data infrastructure that could do anything. That's a hard sell to defend, and it's an even harder product to use — it leaves customers to invent their own use case instead of giving them one.

That's exactly what happened. Our largest customer was running the platform as a data warehouse. Another asked for invoicing, and the product team built it — even though there was no market signal to justify the investment. Others were generating basic reports. Meanwhile, the advanced capabilities that actually differentiated us went untouched.

There was no repeatable ICP, no repeatable sales pattern, no real signal on what actually sold the product.

My first job wasn't marketing. It was finding out what was worth marketing in the first place.

My goal was to find the 3–5 use cases that actually sold the product, and position around those. To get there, I borrowed a methodology from my time at an innovation management startup, where nothing gets funded until there's evidence behind it.

With a lean budget — roughly $4K/month — I ran a series of GTM Sprints: time-boxed cycles built around a simple tempo — hypothesize, test, learn, tighten, repeat. Each cycle followed the same five moves.

Here's how one sprint played out for Carrier Selection — one of the three use cases that ended up winning.

1. Hypothesize — 3PLs and freight brokers struggle to choose the right carrier for the right load. Carrier cost is their single largest line item, and getting it wrong cuts into margin and hurts customer satisfaction. Hypothesis: give them the analytics to choose carriers with confidence, and the product sells itself.
2. Test — Launched cold outbound promoting the ability to choose carriers with confidence — the fastest way to get signal at low cost. Tracked demos booked as the single success metric, no vanity metrics.
3. Learn — Carrier Selection got replies and booked demos where other use cases went quiet. The signal was clear: this was a real pain point, not just a feature.
4. Tighten — Combined learnings from sales demos and signal from the outbound campaigns to sharpen the message around data-driven carrier selection that lowers costs, protects margin, and improves customer satisfaction.
5. Repeat — Ran the sprint again with the improved messaging, then reinforced it with LinkedIn ads to extend reach beyond outbound.

Carrier Selection earned continued investment and became the primary use case we built our positioning around. Use cases without traction were cut fast, allowing us to increase marketing ROI and only invest in the use cases that increased pipeline.

In about 9 months, we narrowed 40+ use cases down to three that reliably resulted in demos booked.

Optimizing carrier mix stood out as the primary use case — the one with the strongest signal and the clearest fit with 3PL and freight broker pain points. We built our core positioning around it.

Two others continued to show promise, so we kept testing them through secondary campaigns rather than dropping them:

• Automating manual freight reporting, freeing hours previously lost to Excel and reducing reliance on error-prone spreadsheets
• Spotting customer churn risk early, before volume decline turns into lost accounts

After we repositioned around carrier selection, we landed our biggest customer — UPS Healthcare — and continued to develop capabilities based on new findings.