In partnership with
The ice cream shop that makes money when it's cold
28 Wishes sells ice cream in Los Angeles. Below 70°F, sales fall about 20%. The weather is out of their hands. Rent isn't.
So the owners started putting about $20 a day into Kalshi weather markets, taking the cold side. The days that keep customers away now pay something back.
This is hedging. Big companies have done it for decades, buying protection against bad weather, fuel spikes and rising rates. It used to take a broker, a trading desk, and an order size no corner shop could meet.
Kalshi opens it up. Contracts on weather, fuel prices, inflation, tariffs and regulation, starting at a few dollars. Take a position on the outcome that would hurt you. If it hits, the payout softens it. If it doesn't, the contract expires and the good month was the point.
GM to the Top 1% ☕
A forecast call I sat in last month had four reps confidently committing deals that all quietly moved a week later. Nobody lied. The CRM data everyone trusted was just wrong, and nobody had checked it in weeks.
New research puts a hard number on how common that is. Only about 7% of sales orgs hit 90% or higher forecast accuracy. The median org sits at 70 to 79%. That gap between confident and correct is where quarters get lost.
💡 THE GAP IS DATA, NOT INTELLIGENCE
Here is the part of the research that should change how you think about your own forecast. Orgs that layer AI onto clean CRM data see accuracy climb to 90 to 98%. That is not a marginal lift. That is the difference between a forecast you can bet a quarter on and one you are guessing at with better vocabulary.
But a separate analysis attributes roughly 70% of forecast error to manual data entry mistakes, not model quality. Wrong close dates. Stale stage assignments. Deals sitting in "commit" for six weeks with no updated evidence. An AI model layered on top of that mess does not fix it. It just produces a more confident-sounding wrong answer.
This reframes the fix. You do not need a better forecasting tool before you need better forecasting inputs. The 83-point swing between the median org and the top 7% is sitting in field hygiene most reps consider someone else's job.
The reps and managers who close that gap first are not the ones with the fanciest AI stack. They are the ones who treated their own CRM entries as the actual product, not the paperwork around the real work.
🔧 THE FORECAST HYGIENE PASS
Four checks to run on your own pipeline before your next forecast call.
1. Check every close date against real evidence: If a close date has not moved in three weeks and there is no note explaining why, it is a guess wearing a date format.
2. Re-verify your own "commit" deals weekly: A deal only stays in commit if something concrete happened this week. No update means it drops a stage until proven otherwise.
3. Write the reason, not just the stage: Every stage change should carry a one-line reason. That reason is what an AI layer actually needs to reason well; the stage label alone is not enough signal.
4. Compare your gut number to your CRM number: If your personal confidence in a deal disagrees with what the CRM says, one of them is lying. Find out which before the call, not during it.
🎯 THIS WEEK'S HOMEWORK
Before your next forecast call, pull your five largest open deals and rewrite the close date and stage reason for each based on actual evidence from the last two weeks, not the number that has been sitting there. Compare the new picture to what you were about to present.
❓ QUESTION OF THE DAY
Which deal on your forecast right now has a close date you would not bet your own money on?
Reply with the deal. That's the one to fix before the call, not after.
See you tomorrow.
Edward
Founder, Morning Sales
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