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Dalton + MichaelDalton + Michael

How Startups Close Million Dollar Deals

In this episode of Dalton + Michael, they discuss how founders can approach selling six- and seven-figure deals, even when they have no personal experience with deals of that size. Many founders default to building self-service products because those are the kinds of products they are used to buying. But for many startup ideas, limiting yourself to self-service can cap the size of the opportunity. Dalton and Michael talk through what it takes to sell bigger contracts, how founders can learn enterprise sales, and why closing large customers may be more approachable than it seems. Dalton + Michael is brought to you by @Standard_Cap Dalton Caldwell on X: https://x.com/daltonc Michael Seibel on X: https://x.com/mwseibel

Dalton CaldwellhostMichael Seibelhost
Jun 1, 202618mWatch on YouTube ↗

At a glance

WHAT IT’S REALLY ABOUT

How startups win million-dollar contracts by selling outcomes, not tools

  1. Big deals become possible when your pricing and packaging look familiar next to established vendors, so customers can evaluate you side-by-side without confusion.
  2. Selling outcomes (revenue, savings, speed, risk reduction) supports much higher prices than selling developer tools, because executives buy business impact—not implementation details.
  3. Many “tool companies” (e.g., AWS, Stripe) actually win huge contracts via enterprise sales teams and outcome-oriented service/solution packaging that most founders never see.
  4. Founders can learn what outcomes to promise by asking customers how success is measured and by leveraging cross-customer pattern recognition that vendors often have.
  5. Enterprise sales is slow and iterative, so companies should run multiple large-deal cycles in parallel while still hitting short-term growth goals elsewhere.

IDEAS WORTH REMEMBERING

5 ideas

Price like the market, not like your internal costs.

Customers evaluate expensive software by comparing a few vendors with similar packaging; if you price in an unfamiliar unit or structure, you add friction and lose trust even if your logic is “first principles.”

Outcome-based selling is how small teams win huge contracts.

A 20-person startup can beat a 1,000-person incumbent if the purchase feels comparable and the startup can credibly deliver the same business result—buyers care about impact, not headcount.

Executives buy revenue and risk reduction; engineers buy tools.

CEOs are incentivized to move metrics tied to company value, while developers often focus on how the product works; aligning your pitch to executive outcomes is what unlocks six- and seven-figure pricing.

If your category sells outcomes and you sell tools, you’ll struggle.

Tools can be profitable, but mismatching the dominant buying mode in your market is “a recipe for disaster,” because customers will benchmark you against outcome-delivering vendors.

Your view across customers can make you more ‘expert’ than any single customer.

Because you operate horizontally, you can spot repeatable patterns of how companies use your product to make money and then teach those playbooks back to prospects as part of the sale.

WORDS WORTH SAVING

5 quotes

It is really, really, really hard to make billions of billions and billions of dollars-... $10 at a time. It is very, very hard.

Michael Seibel

Understand how other vendors-... that your potential customer pay for or would evaluate, understand how they position and price and sell and, and just be like that.

Dalton Caldwell

Just, just make it so if they look at you side by side with the other vendors-... you don't break their brain.

Dalton Caldwell

I think that when you're philosophy two, you get to charge a lot of money.

Michael Seibel

It's not that they showed up one day and they just got it-... the first time they tried. It's that they kept trying to sell a seven-figure contract.

Dalton Caldwell

Enterprise pricing comparables and normsAvoiding “weird pricing” that breaks buyer expectationsTools vs outcomes positioningExecutive incentives vs engineer preferencesSales teams, quotas, and enterprise motions (AWS/Stripe)AI commoditizing code vs boosting service marginsRunning long sales cycles in parallel and learning via repetition

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