We treat the average gift like gospel – reporting, obsessing and buidling decks on it.  That would make sense if donor behavior followed a nice, clean bell curve, tall in the middle, tapering on both ends. In that world, averages matter. Standard deviation matters. Outliers? They’re noise.

But your donor data probably doesn’t live in that world. It lives in a power curve world.

Bell curves describe things like height, blood pressure, IQ. Averages work because most people fall close to the middle.  But in power curve worlds, the middle is meaningless like these:

• Word frequency (a few words dominate)
• Website traffic (a few pages get most of the hits)
• Healthcare costs (5% of people account for 50% of cost)
• Retail revenue (only a few product sku’s drive bulk of sales)
• Fundraising

A small % of donors give the vast majority of your dollars. A handful of campaigns deliver most of your return. One $10,000 gift makes a mockery of your “average gift” stat.

In this world, the outliers aren’t noise—they’re the signal.

The Real Problem: Normal World Thinking

Despite living in a Power World, most fundraising systems run on a Normal World operating system:

• We test subject lines and timing windows like they apply to everyone equally.
• We treat every donor like a variation of the “average donor.”
• We report on campaign ROI like the last email drop holds the secret to success or failure.

We’ve built our reporting, staffing, systems, even our culture to serve the middle because that’s where efficiency lives. That’s how you optimize processes and forecast neatly.  But in a Power World, efficiency doesn’t scale. Insight does.

If you stop optimizing for the middle and start building for the extremes, you get leverage. Not 2% lift—exponential return.

What Does That Look Like?

Here’s the framework:  In a Power World, all the real action is in the Who, Why, How, and When.

• Who: Build Playbooks from Extremes, Not Averages

That $10,000 donor?  That’s not a distraction, it’s an R&D opportunity.

Stop dismissing edge cases. Start decoding them.

What happened there? Was it timing? Message? Identity resonance?  If you can reverse-engineer what drove one outsized result, you can apply that learning across everything.  One outlier isn’t an exception. It’s a blueprint.

• Why: Test Motivations, Not Just Messages

Most testing looks like this: “Did subject line A beat B?”  It’s tactical, cosmetic.

The real game is testing motives—what drives giving in the first place. That’s what actually generalizes.

You don’t need to know which headline works.
You need to know what belief or emotion makes someone act.

For example, donors high in justice values respond to fairness and righting wrongs. Donors high in security values want protection and order. These aren’t just messaging cues, they’re infrastructure.  Once you know the motive, you can scale it across:

• Messaging
• Channels
• Appeals
• Cadence

It’s not A/B testing. It’s system design around why people give.

• How: Design for One Insight That Scales Everywhere

In a Normal World, you optimize 20 things for 5% each. In a Power World, you find one deep insight that drives everything.

That’s what scale-free means—a simple rule that governs the whole system.

One identity insight. One trait-based rule. One behavioral principle.
Applied everywhere.

You’re no longer tweaking, you’re building a replicable engine.  Deep simplicity beats shallow personalization.

• When: Cadence Should Be Personal, Not Calendar-Based

Most orgs run on a campaign calendar. Fiscal year end. Spring appeal. Q2 push.  The result? Everything gets sent to everyone, all the time.

In a Power World, the real question isn’t “When should we send?”  It’s: When is Jack ready to listen? When is Jill most likely to act?

This isn’t about opens or clicks. It’s about response patterns over time.

Some donors fatigue fast. Some convert after quiet.
Some respond to short bursts, others to long gaps.

Stop comparing Jack to Jill, which is all RFM and any model derivative of it ever do, no matter how much AI you blather on about.  Instead, model each donor’s historical response to solicitations and let them set the tempo.   This is dynamic timing.  It’s personalized cadence at scale.

That’s how you stop fatiguing donors and start respecting their rhythms.

If you keep treating outliers like noise, optimizing for averages, and blasting the calendar you’ll always be chasing efficiency and missing impact.

But if you start building systems for the Who, Why, How, and When that really matter then you don’t just improve performance, you change the whole equation.

Kevin