Building in the open
Short pieces on revenue operations, building trajecktory, running a job search like a pipeline, and the small engineering problems that quietly matter. Each one stands on its own and is written to be shared.
RevOps & SDR series
new work regularly
Cornerstone
The five silent failures breaking your revenue engine
Five quiet failures break revenue predictability, and most teams are living with all five without naming any. The pillar the RevOps series is built around, with a self-assessment to score your own engine.
Revenue operations
9 piecesDiagnosing and fixing the engine underneath the forecast.
Scorekeeper or operator: four questions to ask before you accept a RevOps seat
RevOps leadership titles are among the fastest-growing in the US, and most of those seats come without a clear mandate. Four questions that tell you which job you're actually being offered.
The RevOps stat nobody can trace
'Companies with RevOps grow 19% faster and are 15% more profitable.' I traced it to a 2015 conference talk about something else. The original page now returns a 404.
The RevOps job description doubled. Nobody wrote the new one down.
98% of RevOps leaders say their scope grew last year. 89% say the function lacks clearly defined strategic goals. The job got bigger, and nobody wrote down the new one.
The forecast nobody scores
Four in five sales and finance leaders missed a quarterly forecast in a single year. The miss isn't the problem. Never scoring the forecast is.
The deals in your forecast that are quietly fiction
Fluff, sandbagging, and unqualified commits turn a forecast into a hope with a number attached. How to spot each one.
A dashboard that doesn't change a decision is decoration
Reporting exists to trigger an action, not to display data. Two questions separate a dashboard that changes behavior from one that just looks busy.
Six regions, six definitions of pipeline
When every team computes the basics differently, leadership argues about whose report is right instead of what to do. The fix is cheaper than you think.
The quote approval that costs you three days a deal
An approval routed to an exec who rubber-stamps 98% of quotes adds a hidden cycle-time tax nobody measures. The workaround your reps invented is the diagnosis.
The five silent failures breaking your revenue engine
Five quiet failures compound into a forecast you cannot trust, and most teams have all five without naming any. The pillar of the series.
Sales development
7 piecesBuilding and coaching SDR teams that produce pipeline, not noise.
Sales development after the cuts: what the surviving teams are measured on
The median SDR meeting quota is down 40% since 2018, and fewer reps hit it than ever. The surviving teams are more experienced and ramp faster. The scorecard is the part that hasn't caught up.
Buyers want to skip the rep. Until they need to trust something.
67% of B2B buyers prefer a rep-free experience. 69% want a rep to validate what AI tells them. That isn't a contradiction. It's the new job description for sales development.
You can't build an SDR org by throwing bodies at it
A third of B2B companies cut sales development headcount last year. The teams that win designed the role instead of scaling it.
Coach your SDRs like market detectives
The best reps are not the best talkers. They are the best investigators. Coaching them as detectives changes what they find and what closes.
Activity is not the outcome
Dials and emails are inputs teams manage because they are easy to count, not because they predict revenue. Measure what actually leads to qualified pipeline.
Ramp that actually sticks
Most SDR onboarding is a week of orientation, then the phones. Ramp that works is a system with checkpoints, so you see where a rep is stuck.
AI SDRs and the human in the loop
AI is taking over the mechanical top of funnel exactly as human headcount shrinks. That does not end the SDR role. It elevates it to the work AI cannot do.
Lab notes
2 piecesExperiments from my home AI lab, measured against a rule written before the test.
Every challenger lost, including the one that won
Four challenger models tried to replace the AI model my job-search filter runs on. One edged it on the headline number and still lost. The scoreboard, and the rule that decided it.
The fine-tune that looked fine
My fine-tuned AI model matched the original on the headline metric and halved false alarms. It found 6 of 14 strong roles to the original's 10. What that means for anyone buying AI.
Job search & building
4 piecesRelaunching a career by building trajecktory in the open.
The invisible characters hiding in your AI-written text
The invisible Unicode that rides along when you paste from an AI tool, why it breaks forms and resumes, and the open-source cleaner I built.
Relaunching my career by building the tool I needed
I could not find the job-search tool I wanted, so I built it. What building it gave back during a career relaunch.
I built a system to run my job search like a pipeline
Stages, a scoring step at the front, and a follow-up cadence that does not depend on memory. The whole system, in plain terms.
What tracking every job application actually taught me
Fewer applications, more responses, and a warm reply rate about 2.8 times the cold one. The lessons the data made impossible to ignore.
More on the way. In the meantime, explore the Revenue Engine Audit, see the trajecktory case study, or connect on LinkedIn.