NLC · Logistics
How NLC compounded inbound leads 4× in two quarters
Rebuilt NLC's lead engine from cold-list spray to a closed-loop AI-qualified pipeline. Two quarters in, inbound MQLs were up 4× with the same ad spend.
Outcome
Rebuilt NLC's lead engine from cold-list spray to a closed-loop AI-qualified pipeline. Two quarters in, inbound MQLs were up 4× with the same ad spend.
0%
The challenge
NLC, a Nordic logistics SaaS, had a healthy ad budget and a flat lead curve. They were buying clicks from people who weren't going to convert, then asking SDRs to "qualify harder." It wasn't working.
What we did
We took two weeks to read every closed-won deal of the last 18 months and extracted the patterns. From that:
- Built a custom GPT that scores inbound leads against the closed-won pattern before an SDR ever sees them.
- Wired it into HubSpot via a Zapier-Claude bridge so SDRs see a 1–10 score
- reasoning on every form fill.
- Rewrote ad creative from feature-led to operator-led — quotes from real ops managers about specific failure modes the product solves.
- Ran a weekly creative review against the model's score distribution.
The outcome
Inbound MQLs up 4.12× with the same ad spend. SDR time-to-first-touch down 38%. Sales accepted-lead rate up from 22% to 71%.
"We finally trust our funnel. The board meetings are different now." — VP Marketing, NLC
The system kept compounding after we shipped. Six months later they're up another 60% on the same baseline.