Professor Charley T

Diary of the Disrupter: Lessons from $1B in Spend
Entry #27

Most ad “testing” isn’t testing. It’s just launching and hoping.

Real creative testing works when you treat it like an experiment, not a guess. That means structure. A hypothesis. A control. One variable. And a clear way to measure the outcome.

Start with a question, not an idea.
“Can a shorter video lower CPA?”
“Does this hook improve conversion rate?”

Then isolate that one change and test it against a control — a proven, stable ad with predictable performance. Keep everything else the same. Same audience. Same optimization. Same placement.

If you change multiple things at once, you didn’t run a test. You created noise. You might get a result, but you won’t know why it happened.

Measurement matters just as much as the creative. “Looks good” isn’t a conclusion. Did it earn spend? Did CPA improve? Did it steal volume from your control or add incremental value?

The goal isn’t to find a lucky winner. It’s to learn what works and why. When you test like a scientist, every experiment gives you insight — and insight is what lets you scale with confidence.

Action Item:
Write one clear hypothesis you want to test in your next campaign. Then build a test that isolates that single variable and runs it against your control. Don’t test five things—test one.
That’s how real learning happens.

18 hours ago | [YT] | 14

Professor Charley T

The Meta Ads industry has a credibility problem.

Everyone has a screenshot.
Everyone has a framework.
Everyone has a secret.

On June 2nd, I’ll be joining Yiqi Wu,Lee Bissonnette, and Sabir Semerkant in NYC to talk about what the data actually says.

We’ll cover:

• How the algorithm really works
• AI and machine learning
• Attribution and signal quality
• What’s actually driving growth for DTC brands in 2026

No hacks.
No gurus.
No photoshopped screenshots.

Just a conversation grounded in data, first principles, and real world results.

Looking forward to sharing the stage with:

• @yiqiw_ , Founder & CEO of @aimerce_ai
• @LeeBissonnette , Co Founder & CEO of Brand X Commerce
• @SabirS , Founder of Growth by Sabir
• And me, Charles Tichenor IV, Founder of Disrupter School

📅 June 2nd
🕔 5:00 PM
📍 1216 Broadway, NYC

Hosted by @Shopify, and @Techweek_

🎟️ Register here: luma.com/tkuch5t5?utm_source=CT

23 hours ago | [YT] | 23

Professor Charley T

Diary of the Disrupter: Lessons from $1B in Spend
Entry # 26

Understanding Customer Journeys as Cash Flow Cycles

Every customer journey is a timeline, not a transaction.

When someone buys, it doesn’t end at checkout — it starts a cycle. The real signal of financial health is how often and how predictably that customer comes back.

A cash flow cycle is simply the time between purchases. Yet many businesses ignore it. They chase new customers but don’t track when, or if, those customers return. That creates dependence on cold acquisition and leaves retention underused.

When you know your cycle, you gain predictability.
If repeat purchases typically happen around day 30, and 10% return, today’s buyers represent future revenue already in motion.

Now growth compounds instead of resets.

You can align campaigns, emails, and offers to match natural buying rhythms. You can forecast with more confidence. And you start seeing acquisition cost differently — because one customer often equals multiple purchases over time.

At that point, marketing isn’t just generating revenue.
It’s creating future revenue streams.

And scalability comes from making those streams predictable.

Action Item:
Identify your current cash flow cycle. Look at when customers typically return. Use your data to calculate the average number of days between first and second purchases — and start building strategy around that window.

1 day ago | [YT] | 16

Professor Charley T

Diary of the Disrupter: Lessons from $1B in Spend
Entry #25

More data doesn’t automatically mean better decisions.

Every business tracks numbers.
Few actually use them well.

A CPA rises and budgets get cut.
Conversion dips and creative gets blamed.

But without context, these moves are just guesses with spreadsheets.

Single metrics rarely tell the full story. A performance spike could be seasonality. A drop could be channel overlap. When you look at numbers in isolation, you risk solving the wrong problem.

That’s how teams fall into reactive mode.
Chasing signals. Launching quick fixes. Disrupting what was already in motion.

You end up constantly adjusting, but rarely building anything stable.

Reports alone don’t solve this. You can know what happened and still not know why. You can see results without understanding how they connect to real business goals.

This leads to wasted spend and slower decisions.
Teams start doubting the data because the story keeps changing.

Silos make it worse. Creative, media, and ops all read the same numbers differently. Alignment turns into debate instead of progress.

Data becomes powerful when it’s viewed together, not separately.
When everyone sees the same picture, patterns make sense. Decisions get faster. Confidence goes up.

At that point, data stops being a scoreboard.
It becomes a strategic tool.

And that’s when it actually drives growth.

Action Item:
Review your last 3 major campaign decisions. Were they made in reaction to isolated metrics? Revisit one and ask: What context was missing—and what would you have done differently if you had it?

2 days ago | [YT] | 21

Professor Charley T

Diary of the Disrupter: Lessons from $1B in Spend
Entry #24

The most underrated growth metric? Your second purchase rate.

If you had to bet on one number to predict business health, it’s not CAC or even LTV.

It’s how many customers buy a second time.

The first purchase is costly. Ads, content, operations, discounts — you often break even or lose money just to acquire the customer.

The second purchase is where margin begins.
It’s also where trust is proven.

When someone buys again, it signals:

• The product delivered
• The experience worked
• The brand stuck
• Your follow-up did its job

And once a customer returns, everything gets easier.
Emails get opened. Offers convert better. AOV grows.

If only 5% come back, you’re stuck chasing new customers forever.
Raise that to 15–20%, and growth starts compounding.

Your CPA drops.
Predictability rises.
Profit expands without spending more on acquisition.

One-time buyers don’t build businesses.
Repeat customers do.

Simple action step:
Check how many customers made a second purchase in the last 90 days.
Then test one focused strategy to increase it in the next 30.

Growth gets easier when customers come back.

Action Item:
Run a report showing how many of your customers made a second purchase in the last 90 days. Pick one strategy to test that specifically aims to increase that number over the next 30 days.

3 days ago | [YT] | 25

Professor Charley T

Looking forward to speaking at an NYC #TechWeek event next week 🍸🌇

We’ll be discussing “Best Meta Ads Campaign Structure for DTC in 2026” together.

Should be a great conversation around Meta ads, AI, attribution, and what’s actually driving growth for DTC brands right now 🚀

And of course, rooftop drinks and networking after the panel never hurt 😄

📅 June 2 | ⏰ 5 PM | 📍 Midtown, NYC

Link to register: luma.com/tkuch5t5

3 days ago | [YT] | 27

Professor Charley T

Diary of the Disrupter: Lessons from $1B in Spend
Entry #23

The Uber Example — A $1M Lesson

Great dashboards don’t always mean real results.

There’s a well-known case where a major ride-sharing company was spending about $100K per month on native ads. The reports looked strong: solid ROAS, healthy engagement, steady conversions. On paper, it worked.

Leadership asked a harder question: Is this actually driving growth?

So they paused the spend. Same markets. Same time period. Same conditions. Just no native ads.

What happened?
Nothing.
No drop in riders.
No dip in bookings.
No revenue hit.

They weren’t buying growth.
They were buying credit.

That decision reportedly saved them over a million dollars a year.

The takeaway is simple:
1. Attribution shows activity.
2. Incrementality shows impact.
3. Clicks and conversions can still happen even if your ads didn’t cause them. When platforms measure their own performance, they naturally claim more credit.


The real question isn’t “Did this ad get conversions?” It’s “Would those conversions happen anyway?”

If the answer is yes, your ROAS is just a comforting illusion.

Smart marketers don’t just trust numbers. They pressure-test them. Because growth comes from what changes the business, not what decorates a report.

Action Item:
Identify one campaign or channel where reported performance seems strong but actual business results feel flat. Could you test a short-term pause to validate the true impact?

4 days ago | [YT] | 15

Professor Charley T

Diary of the Disrupter: Lessons from $1B in Spend
Entry #22

What Incrementality Actually Means

Attribution asks, “Who gets credit?”
Incrementality asks, “Did this create value?”

That’s a big difference.

Incrementality measures the real lift from your marketing — the sales that wouldn’t have happened without it. Not claimed impact. Actual impact.

A simple way to think about it:
If you stop spending and nothing changes, that spend wasn’t driving growth.

There’s a well-known case where a large ride-sharing company paused a major native ad channel that looked great on paper. Attribution said it worked. ROAS looked healthy.

When they turned it off?

No drop in riders. No drop in revenue.

They just saved a huge monthly bill.

That’s incrementality in action.

Here’s the practical lens:
1. Focus on lift, not credit. Marketing should create new demand or unlock conversions that wouldn’t happen otherwise.

2. Use holdouts when possible. Reducing or pausing spend in a channel shows you what’s truly additive.

3. Look at contribution, not just volume. Sometimes the win isn’t more sales, but more efficient ones. Lower CAC. Better margins. Stronger channels elsewhere.

4. Keep it simple. You don’t need fancy tools. Track spend changes and business outcomes. Compare before and after.

5. Most brands confuse reporting with impact. Numbers can look great while doing very little.

Incrementality cuts through that.

It shows what actually moves the business.

Action Item:
Identify one channel where you can run a simple holdout test. Pause spending for 3–5 days and track total revenue. What changes — if anything?

5 days ago | [YT] | 16

Professor Charley T

BRAND NEW TUTORIAL POSTED TO YT

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Comfrt’s cheat code
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6 days ago | [YT] | 18

Professor Charley T

Diary of the Disrupter: Lessons from $1B in Spend
Entry #21

Most creative tests fail before they even start — because there’s no control.

A control isn’t your “best” ad. It’s your most predictable one.
The ad that’s been running long enough to show stable, repeatable behavior.

That stability is what makes learning possible.

Without a control, you’re not testing. You’re comparing new ads to your hopes and expectations — not to reality.

A control gives your results meaning.
If your baseline ad sits at a $35 CPA and your new version comes in at $33, you have context. Without that benchmark, the numbers are just noise.

The other half of real testing is the variable.

A variable is one intentional change.
Not new copy and a new visual and a new audience. That’s not testing — that’s chaos.

One change. One hypothesis. One lesson.

When the only difference between two ads is the headline, and one clearly outperforms the other, the decision is obvious. You don’t debate. You act.

Good testing isn’t about finding random winners.
It’s about learning why something works — so you can repeat it on purpose.

Action Item:
Identify your current control ads. Which ones are stable, consistent performers, even if they aren’t your flashiest creatives? Label them. Then choose one variable to test against them in your next campaign. Track the difference and write down what you learn.

6 days ago | [YT] | 18