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.

30-Day Subsystem Challenge

PLAYBOOKS/TEMPLATES

11/23/20251 min read

🔹Objective

Improve ONE marketing decision based on a real buyer shift and prove it through SQL in 30 days.

Segment: _______________________________

Decision being updated: __________________

🔹Core Assumption Check

We believe our buyer is currently: _______________________________

But signals suggest they have shifted in this way: _______________________________

Main outdated assumption we are replacing: _______________________________

Checkboxes:

[ ] Role change
[ ] Priority change
[ ] Language change
[ ] Urgency change
[ ] Decision criteria change

🔹Buyer Signal Selection

List signals you observed (max 3):

_______________________________
_______________________________
_______________________________

Circle the ONE signal with volume:

Selected signal: _________________________

Why this one matters: _______________________________

🔹Cohort Definition

We will test this change on (choose ONE):

Demo requests from: _____________________
Inbound leads from: ______________________
Email list segment: _______________________
Other: ____________________________________

Test cohort size (last 30 days): _____________________

🔹Baseline SQL

SQL count last 30 days for this cohort: _____________________

Notes on SQL quality: _____________________

🔹Decision Change

We will change ONE decision related to:

[ ] Messaging
[ ] Offer framing
[ ] CTA
[ ] Audience targeting
[ ] Activation email
[ ] Landing page
[ ] Other: __________________

Describe the change (1 sentence): _____________________

Expected effect on SQL: _____________________

🔹Deployment Checklist

We will:

[ ] Apply change ONLY to test cohort
[ ] Leave control untouched
[ ] Track SQL daily

We will NOT:

[ ] Adjust multiple assets
[ ] Add channels
[ ] Change multiple signals
[ ] Redesign strategy
[ ] Add campaigns/content

🔹Daily SQL Tracker

Date: _____________________
SQL Count: _____________________
Notes: _____________________

🔹Diagnostic Interpretation

SQL moved?

[ ] Yes
[ ] No
[ ] Inconsistent

Objections changed?

[ ] Yes
[ ] No

Demo notes reflect new identity?

[ ] Yes
[ ] No

🔹Calibration Decision

If SQL movement ≥5% and rising:

[ ] Refine decision

If flat or negative:

[ ] Revert
[ ] Note incorrect assumption

Reasoning:
______________________________________

🔹Scale Decision

SQL lift after 30 days: _______ %

If ≥10%:

[ ] Apply to next touchpoint

Which one: ____________________________

If <10%:

[ ] Identify deeper buyer shift

New suspected shift: ____________________

🔹Final Summary Output

Buyer shift surfaced:
____________________________

Decision updated:
____________________________

SQL result:
____________________________

Next step:

🔹Reflection (critical insight)

What assumption was wrong?
____________________________

What surprised us?
____________________________

What decision proved most effective?
____________________________

🔹What to do if we saw lift

[ ] Repeat decision on next cohort
[ ] Explore message architecture
[ ] Expand segment
[ ] Run second loop

If you saw lift, scaling correctly is where the real gains compound.

Let’s talk.