The pipeline metric most sales teams track wrong
Conversion rate by stage looks simple. Most teams calculate it in a way that hides exactly the problem it should reveal.
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Starting, running and growing companies, from people who are doing it.
Conversion rate by stage looks simple. Most teams calculate it in a way that hides exactly the problem it should reveal.
A conversation with our own VP of Sales about a compensation change that felt risky and paid off in an unexpected way.
Original survey data on the first sales hire at early-stage companies: who they hired, what they got wrong, and what they would do differently.
We built a lead scoring model, sold it as a core feature, and shut it off eight months later. Here is what replaced it.
You can have 10+ years in your field, a strong CV, the right certifications, and a track record of actually delivering results and still never get a chance to speak to a human.
An opinion, backed by what our clients' analytics actually show: one honest case study outperforms a quarter of blog posts.
The order of questions matters more than the questions themselves. Here is the structure we use on every call.
The survey reports that get cited for years are built differently from the ones that get read once and forgotten.
We called back every subject from case studies we published in one calendar year to check if the numbers held up. Most did not, in an interesting way.
Not a machine learning model. A basic weighted-stage calculation that outperformed manual forecasts for two straight quarters.
A specific, common pattern in customer quotes that reads as fake even when it is completely true.