The second best time to plant a tree.

Komoroske’s “How organizations are like slime molds” has quickly become one of my most referenced pieces of digital media this decade. It talks about how subtly coordination costs at a growing organization slow down decision making – even for the most strategically significant effort. 

Cost of Delay is one way to inject some prioritization, if not urgency, into strategic decision making. Cost of Delay asks: 

“What does it cost us if this is delayed by 4 weeks?”

Cost of Delay = Total Expected Value / 52 weeks in a year

Let’s apply it to an initiative frequently delayed — a pricing increase.

The “Wait Tax”: A Case Study

Unlike product improvements, where the value may diffuse across usage patterns and never be explicitly attributable, a pricing change shows up in revenue on the next sale. No adoption curve. No behavior change required. No hoping customers notice. It’s the clearest possible Cost of Delay calculation, which is exactly why the hesitancy around it is so expensive.

This simplicity also helps keep our math simple.

Imagine a $30M B2B SaaS with 3,000 customers considering a pricing reset as part of an overall effort to increase ARR 50%. The pricing increase is responsible for half the target, a 25% ARR lift or $7.5M/year in new annual revenue.

Monthly value once live: $625,000.

Lets say defining and rolling out the new pricing typically takes 8-12 weeks:

Weekly Cost of Delay: $144,231.

The Wait Tax is the amount of revenue leakage from not launching even sooner.

4 Week Lag
231 Customers at Old Price
$576,923 Wait Tax

8 Week Lag
462 Customers at Old Price
$1,153,846 Wait Tax

12 Week Lag
692 Customers at Old Price
$1,730,769 Wait Tax

The ‘Customers at Old Price’ is the one to sit with. 

At $10K ACV with a 25% increase, each customer onboarding at the old price represents $2,500/year in unrealized revenue. Over an 8-week decision cycle, ~462 customers sign up at the old price. At best they move to the new price at renewal and get counted in next year’s revenue goals?

This changes the question from “What does it cost us if this is delayed by 4 weeks?” to “What’s the ceiling we could spend and still come out ahead?”

At 12 weeks of deliberation, any investment under $1.73M is cheaper than the delay.

However, 

“What If They Aren’t Into You?”

All of this assumes sufficient demand overall, and demand at the new price.

750 net new customers simply may not exist at the new price – or any price. So, finding and selling to them is not a foregone conclusion. 

We can also risk-adjust the Cost of Delay.

Risk-Adjusted Cost of Delay = (Total Expected Value * Success Probability) / 52 weeks in a year

(This is what I instinctively do anyway)

Let’s says lift is half of the projected 25%, so $3.75M/year (300 customers at $12,500).

Risk-Adjusted Cost of Delay = ($7.5M * .5) / 52

Weekly Cost of Delay is still $72,115.

8-week Wait Tax is $576,923.

Any investment under $577K still clears the math at half the expected return.

The numbers overwhelmingly favor action.

Substantial price changes like this are best rolled out segment by segment, beginning with the largest and least-price sensitive, though sometimes with the highest volume. Uncertainty about demand isn’t an argument for waiting. It’s an argument for segmentation and prioritizing rollout.

Pricing is not precious, it’s one of the most impactful and cost-effective growth levers available — and it’s the one most often treated as untouchable. Often because it triggers every organizational anxiety at once — 34 of them by my count; Internal money beliefs. Customer intelligence gaps. The assumption there’s only one way to price or one price for everyone. The discomfort of saying large dollar amounts out loud in front of prospects.

These are real, but they’re not reasons to delay. They’re reasons the delay is so expensive — because the friction is emotional, not operational.

Even with AI assistants, product improvements can take weeks to build, require adoption, and may never be explicitly attributable to revenue. Even in sophisticated tech companies 85% of tested features failed A/B testing, with a 15% chance of false positives. 

Yet teams agonize over pricing while comfortably investing in comparably expensive new features that we hope can justify a price increase some day .

“Features need to pay their own freight, or they’re stealing from the ones capable of doing so.” – Don Reinertsen

How does Cost of Delay look at your organization?