Who is the Buyer Flywheel designed for?

It is designed for Growth GTM teams in B2B SaaS where Marketing, Sales, CS and Product operate on different buyer definitions. This misalignment creates Marketing Lag.

What problem does the Buyer Flywheel solve?

It solves Marketing Lag. Marketing Lag appears when buyer movement is faster than GTM decision updates. Calibration removes this delay.

What metrics matter in this system?

SQL and Conversion. These two metrics confirm whether calibration is accurate. All other metrics are secondary.

What is the Buyer Flywheel?

It is a correction layer that keeps GTM aligned to buyer movement. It enforces a loop of Signals, Calibration and Feedback.

How is this different from Buyer-Led GTM?

Buyer-Led GTM governs timing. The Buyer Flywheel governs accuracy. Timing without accuracy still creates drift.

How is this different from traditional marketing or GTM?

Traditional GTM optimizes activity. The Buyer Flywheel optimizes learning speed. Decisions improve only when calibrated to buyer signals.

What are the pillars of the system?

Signals identify reality. Calibration aligns every vertical to one buyer definition. Feedback verifies accuracy and exposes drift.

What is the 1-1-1 Calibration protocol?

One signal. One cohort. One controlled change. This isolates variables so buyer echo can be read cleanly.

Why does 1-1-1 Calibration matter?

It removes ambiguity. If Conversion moves, the change caused it. If it does not, the assumption fails. There is no guesswork.

How do we know if calibration worked?

Buyer echo must confirm the interpretation and SQL plus Conversion must lift. Without both, calibration is invalid.

What is the rule for the 30-day calibration cycle?

Run one full cycle. If SQL and Conversion do not improve, the assumption is rejected and replaced.

What happens when a calibration test fails?

Revert the change. Hold the assumption. Retest only when new buyer signals justify it.

What emotional position does this system address?

It replaces repeated drift with a learning loop that creates clarity in decision making.

What is the Buyer Flywheel not?

It is not a tactic. It is not a campaign. It is not a GTM identity. It is the operating system that governs alignment.

Why is the system learning-focused?

Buyer behavior changes faster than internal planning cycles. Systems that do not learn drift by default.

How should leadership interpret revenue in this model?

Revenue is downstream of accuracy. SQL and Conversion show alignment in real time.

What do executives expect when adopting the Buyer Flywheel?

They expect system correction that appears in SQL and Conversion rather than vanity metrics.

Calibration Operational Working Template

A recurring cadence system for keeping GTM decisions accurate.

PLAYBOOKS/TEMPLATESKPIS

Pre-Read: Assumption Quality Card

Good assumptions

Describe buyer behavior, not internal intent
Show up in live GTM execution
Can be contradicted by early signals
Would force a decision if proven wrong

Bad assumptions

Restate the plan (“We’re targeting mid-market”)
Beliefs are vague (“Buyers care about value”)
Only show up in dashboards
Can fail without changing behavior

NOTE: If an assumption feels “safe,” it’s probably useless.

Session Setup (Set Once)

Cadence: ☐ Weekly ☐ Bi-weekly ☐ Monthly
Duration: ☐ 30 min ☐ 45 min
Owner: (role / name)
Decision authority present: ☐ Yes ☐ No
NOTE: If “No”, do not run the session.

Active Assumptions (Max 3)

Only assumptions that currently shape GTM behavior.

Assumption (state as a claim) | Where it shows up

A3: (e.g., “Mid-market buyers enter with problem awareness”) | (e.g., "Homepage headline, outbound opener")
A2:
A3:

Signals Observed (This Period)

Leading signals only.

ID: A1
Signal Observed: (e.g., "Buyers ask basic “why does this matter?” questions")
Source: (e.g., "Sales discovery calls")
Signal Status: ☐ Confirms ☑ Contradicts ☐ Unclear

Drift Check

Loss of clarity, not performance.

Assumption ID | Drift type
A1 | ☑ Confusion ☐ Friction ☐ Delay ☐ Resistance
A2 | ☐ Confusion ☐ Friction ☐ Delay ☐ Resistance

Related template: Drift Detection - Early-warning system for GTM decay (The Buyer Flywheel | Notion)

Decision (Once Only)

One decision. No bundling.

Decision category:
(Examples: Change message)
________________________________
Concrete change:
(Example: Replace homepage and outbound opener to lead with problem framing instead of solution claims)
________________________________
Assumption addressed:
(Ex: A1) ________________________________
Reversibility:
☐ Easy
☐ Moderate
☐ Hard

Validation Signal

How this decision will be proven right or wrong.

Primary signal:
(Example: Reduction in “what is this / why do I need it?” questions in first sales calls)
Where it appears:
(Example: Sales call notes, call recordings)
Review date:
(Example: Next cadence)

Session Output

(Logged Each Time)

Assumption tested: (Example: Buyers enter problem-aware)
______________________________________________________
Decision made: (Example: Shift message to problem framing)
______________________________________________________
Signal to watch: (Example: Discovery confusion rate)
______________________________________________________

System Integrity Check (Monthly)

Answer Yes / No only.

Assumptions explicitly named? ___
Decisions made in ≥3 of last 4 sessions? ___
Signals leading (pre-revenue)? ___
Fewer surprises than last month? ___

NOTE: 2+ “No” = cadence failure. Investigate, don’t optimize.

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Notion template
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