Why most experimentation is fake progress
Most marketing teams believe they’re “experimenting.”
In reality, they’re:
Rotating tactics
Renaming optimizations
Running tests that can’t meaningfully change direction
Experimentation has become a comfort blanket — a way to stay busy without making hard decisions.
That’s why this asset exists.
These are 7 experiments worth running this quarter, not because they’re trendy, but because they:
Challenge real assumptions
Create asymmetric learning
Can materially change strategy
If an experiment can’t change what you do next, it’s not an experiment.
It’s activity.
A rule before we begin (non-negotiable)
Every experiment below follows the same criteria:
It tests an assumption, not a preference
It can influence a real decision
It creates learning even if it “fails”
If a test doesn’t meet those conditions, don’t run it.
Experiment 1: Message–Market Fit Stress Test
What you’re testing
Whether your current messaging actually resonates with the audience you think you’re targeting.
The assumption
“Our positioning is clear and compelling to the right buyer.”
Most teams never test this directly.
How to run it
Create 2–3 sharply different value propositions (not copy variations):
One outcome-led
One problem-led
One belief-led
Run them in:
Paid ads
Cold outbound
Landing page hero tests
What to measure
Not CTR alone — but:
Quality of inbound conversations
Objection patterns
Sales cycle friction
Why this matters
If your messaging is off, every downstream optimization is wasted effort.
This experiment can force a repositioning decision, which is far more valuable than a 5% lift.
Experiment 2: Channel Reduction Test
What you’re testing
Whether doing less marketing, better, outperforms broad coverage.
The assumption
“We need to be active across multiple channels to grow.”
Often false.
How to run it
For 30 days:
Pause or deprioritize your weakest channel
Reallocate effort into your strongest one
Increase depth, not volume
What to measure
Engagement quality
Conversion efficiency
Team focus and clarity
Why this matters
Most teams are underperforming not because they lack channels — but because they lack channel mastery.
This test challenges a core resourcing assumption.
Experiment 3: Funnel Shortening Experiment
What you’re testing
Whether your funnel complexity is hurting decisions.
The assumption
“More steps = more persuasion.”
Rarely true.
How to run it
Remove one step:
Skip a form
Collapse two pages into one
Offer direct scheduling instead of gated content
What to measure
Completion rates
Drop-off points
Sales objections post-conversion
Why this matters
Complex funnels hide weak value propositions.
Shorter funnels expose reality faster.
Experiment 4: High-Intent Traffic Bias Test
What you’re testing
Whether lower volume, higher intent traffic outperforms scale.
The assumption
“More traffic gives us more chances to convert.”
Often wrong.
How to run it
Shift spend or effort toward:
Branded search
Comparison keywords
Referral traffic
Retargeting warm audiences
Reduce broad, cold acquisition temporarily.
What to measure
Conversion to opportunity
Time to decision
Customer quality
Why this matters
If high-intent traffic outperforms significantly, your problem isn’t acquisition — it’s relevance.
Experiment 5: Retention-First Growth Test
What you’re testing
Whether improving retention unlocks easier growth than acquisition.
The assumption
“Growth = more leads.”
Short-term thinking.
How to run it
Choose one retention lever:
Onboarding improvement
Lifecycle education
Habit-forming content
Focus on existing customers for one cycle.
What to measure
Engagement depth
Repeat usage
Expansion signals
Why this matters
Retention experiments often outperform acquisition — but teams rarely prioritize them because they’re less visible.
This experiment can shift growth strategy entirely.
Experiment 6: Human vs AI Boundary Test
What you’re testing
Where AI genuinely helps — and where it quietly degrades quality.
The assumption
“Using more AI will make us faster and better.”
Not always true.
How to run it
Run parallel workflows:
One human-led
One AI-assisted
Apply this to:
Ad ideation
Email drafts
Content outlines
What to measure
Output quality
Revision cycles
Performance downstream
Why this matters
AI should compress time, not replace judgment.
This experiment defines your no-AI zones — a critical competitive edge.
Experiment 7: Decision Ownership Test
What you’re testing
Whether unclear ownership is slowing or diluting outcomes.
The assumption
“Collaboration improves decisions.”
Only up to a point.
How to run it
For one initiative:
Assign a single decision owner
Clarify inputs vs authority
Set a clear decision deadline
What to measure
Speed
Quality of execution
Post-launch clarity
Why this matters
Many marketing problems aren’t tactical — they’re organizational.
This experiment tests structure, not channels.
Why these 7 experiments matter more than 50 others
Notice what these experiments don’t focus on:
Button colors
Micro-copy tweaks
Tool features
Vanity metrics
They focus on:
Assumptions
Tradeoffs
Direction-setting decisions
That’s where leverage lives.
How strong teams run experiments differently
Weak teams ask:
“Did it work?”
Strong teams ask:
What assumption did this validate?
What decision does this unlock?
What should we stop doing now?
An experiment that kills a bad idea is a success — not a failure.
How to choose which experiment to run first
Ask:
Where are we most uncertain?
Where is the cost of being wrong highest?
What decision are we avoiding?
Start there.
Experimentation isn’t about being busy.
It’s about reducing uncertainty fast enough to make better decisions.
If your experiments aren’t changing what you do next quarter, they’re not experiments — they’re theater.
These 7 are designed to do the opposite.
