Source identity
| Field | Recorded value |
|---|---|
| Source type | project equation-poster image |
| Original record | The Maddie Equation: Client Conversion Formula.png |
| Date | Jul 15, 2026 |
| Path or archive member | Equation posters/The Maddie Equation: Client Conversion Formula.png |
| Original SHA-256 | 2ec6640a14e314b61f3fe465fa7340101c7b309f4dee5c3336727a8d739932fb |
| Reading-copy SHA-256 | 279bb16910de889aeb80f640edceaeb6225e9354efe5b6c66248d220d8e4c239 |
The original equation poster
Recovered source transcript
Download complete source text ↓618 lines · 13.0 KB
The MADDIE Equation — Client Conversion Formula
===============================================
SOURCE: The Maddie Equation: Client Conversion Formula.png
SOURCE ID: libsrc-a7c47b90b5d4
ORIGINAL SHA256: 2ec6640a14e314b61f3fe465fa7340101c7b309f4dee5c3336727a8d739932fb
REVIEW: 2026-10-17. The original PNG is authoritative. This reading copy separates manually inspected mathematical transcriptions from raw OCR. It is not a verbatim embedded-text extraction.
VISUAL REVIEW NOTES
- The smaller multiplication-effect graph is an illustrative curve of Results (Q) versus Execution Quality, not a displayed analytic equation or measured dataset.
- The same letters are deliberately overloaded among component boxes; the transcript preserves local meanings instead of imposing a global symbol dictionary.
- Claims of predictable commissions and conversion are the poster’s intended application. This review establishes visible source content only, not empirical effectiveness.
MANUALLY INSPECTED FORMULAS
MADDIE six-factor product
Image region: Large top equation and smaller repeated row above total formula
M × A × D × D × I × E = [trophy / commissionable client]
Legibility: Legible
Editorial mathematical explanation: Restates the six-factor conversion framework.
Editorial note: Desire and Differentiation share the displayed D glyph in this schematic product; component boxes distinguish them.
Editorial note: Duplicates the simple MADDIE clients poster.
Message weighted pain-point score
Image region: Message component box
M = Σ_{i=1}^{n} w_i · p_i
Legibility: Legible
Editorial mathematical explanation: Forms message from weighted pain-point quantities.
Editorial note: The image defines pain points and emotional weights but does not specify their measurement scales.
Message-weight normalization
Image region: Bottom of Message component box
Σ_i w_i = 1
Legibility: Legible
Editorial mathematical explanation: The message weights sum to one.
Editorial note: The displayed normalization does not state bounds or nonnegativity for individual weights.
Attention score
Image region: Attention component box
A = (R × U × C) / N
Legibility: Legible
Editorial mathematical explanation: Combines relevance, uniqueness, and curiosity and divides by noise/competition.
Editorial note: R = Relevance; U = Uniqueness; C = Curiosity; N = Noise (competition).
Editorial note: The image does not define zero-noise handling.
Desire score
Image region: Desire component box
D_desire = E × V × I
Legibility: Legible
Editorial mathematical explanation: Multiplies emotional intensity, perceived value, and identity alignment.
Editorial note: These E and I symbols are local component meanings; E and I in the overall MADDIE product denote enrollment and investment.
Desire range
Image region: Bottom of Desire component box
0 ≤ D_desire ≤ 1
Legibility: Legible
Editorial mathematical explanation: States a range for the desire score.
Editorial note: No explicit normalization of E, V, or I is supplied to ensure this bound.
Differentiation score
Image region: Differentiation component box
D_diff = P_you − (1/k) Σ_{j=1}^{k} P_j
Legibility: Legible
Editorial mathematical explanation: Subtracts mean competitor proof strength from your proof strength.
Editorial note: k = Number of competitors; k = 0 is not addressed.
Editorial note: The displayed subtraction can produce a negative result; the poster gives no clipping rule.
Investment score
Image region: Investment component box
I = V_p / (C_e + F_r)
Legibility: Legible
Editorial mathematical explanation: Divides perceived value by emotional/financial cost plus friction to take action.
Editorial note: The denominator must be defined to evaluate the ratio; the image supplies no units or zero-denominator convention.
Enrollment score
Image region: Enrollment component box
E = [T × (S + A)] / F
Legibility: Legible
Editorial mathematical explanation: Scales simplicity plus actionability by trust and divides by friction.
Editorial note: Local T = Trust, S = Simplicity, A = Actionability, and F = Friction. These symbols are reused with other meanings elsewhere on the same poster.
Master commission-quality integral
Image region: Lower-left THE MASTER FORMULA panel
Q = Ψ · ∫_0^T [ C(t) · Φ(G(t)) · ( ||∇H(t)|| / (1 + ||∇H(t)||) ) ] dt
Legibility: Legible
Editorial mathematical explanation: Accumulates customer clarity times perceived integration over the contact-to-enrollment period, modulated by the bounded gradient-norm factor and conscious alignment.
Editorial note: The poster calls Q a probability, but the displayed unrestricted time integral and multiplier do not themselves impose a 0–1 range.
Editorial note: No units, measurements, operational definition of G or H, integration data, or fitted model are provided in the image.
Editorial note: The visibly displayed gradient argument is H(t), not a Hamiltonian definition supplied elsewhere.
Conscious-alignment function
Image region: CONSCIOUS ALIGNMENT (Ψ) panel
Ψ = f(A · X · S)
Legibility: Legible
Editorial mathematical explanation: Makes conscious alignment an unspecified function of agency, experiential connection, and substrate.
Editorial note: The function f is not defined. The nearby Master Formula legend also describes Ψ as conscious alignment (A · X · S), without explicitly writing the f.
Total MADDIE quality product
Image region: Lower-right boxed total formula
∴ Q_total = Ψ · (M × A × D_desire × D_diff × I × E)
Legibility: Legible
Editorial mathematical explanation: Multiplies alignment by the six explicitly distinguished conversion components.
Editorial note: The poster does not derive equivalence between this algebraic product and the separate time-integral Q. Treat them as related source formulations.
Lead value
Image region: Bottom strip LEAD VALUE (L)
L = Σ_{i=1}^{n} v_i · p_i
Legibility: Legible
Editorial mathematical explanation: Adds each outcome value weighted by its probability.
Editorial note: In this formula p_i denotes an outcome probability, unlike the pain-point p_i in the Message component.
Editorial note: No probability distribution, outcome set, or measured outcome values are given.
Closing probability
Image region: Bottom strip CLOSING PROBABILITY (P_c)
P_c = 1 / [1 + e^{−(αS + βT + γV − δF)}]
Legibility: Legible
Editorial mathematical explanation: Uses a logistic mapping of solution fit, trust and value, with friction subtracted.
Editorial note: S = Solution Fit; T = Trust; V = Value; F = Friction.
Editorial note: The coefficients α, β, γ, δ are displayed but not estimated or defined numerically.
Expected revenue
Image region: Bottom strip EXPECTED REVENUE (ER)
ER = L × P_c
Legibility: Legible
Editorial mathematical explanation: Multiplies lead value by closing probability.
Editorial note: The source provides a formula but no data with which to validate or compute expected revenue.
Customer lifetime value
Image region: Bottom strip CUSTOMER LIFETIME VALUE (CLV)
CLV = ARPU × GM × R × L_t
Legibility: Legible
Editorial mathematical explanation: Combines average revenue per user, gross margin, retention and lifetime as the poster specifies.
Editorial note: The source labels both R (retention) and L_t (lifetime) in years. Their separate operational definitions and resulting dimensions are not resolved in the image.
Editorial note: GM is labeled Gross Margin %, but whether it is entered as a fraction or a percent number is not stated.
Return on marketing
Image region: Bottom strip RETURN ON MARKETING (ROM)
ROM = [(CLV − CAC) / CAC] × 100%
Legibility: Legible
Editorial mathematical explanation: Expresses the difference between customer lifetime value and acquisition cost relative to acquisition cost.
Editorial note: CAC = Customer Acquisition Cost. No zero-cost convention or observed values are given.
SOURCE-LOCAL SYMBOL DEFINITIONS
M = Message score. In its component formula p_i denotes pain points and w_i emotional weight.
A (main product) = Attention score.
D_desire = Desire. E denotes emotional intensity; V perceived value; I identity alignment within this component.
D_diff = Differentiation. P_you denotes your proof strength; P_j competitor proof strength; k number of competitors.
I (main product) = Investment score. V_p is perceived value; C_e emotional plus financial cost; F_r friction to take action.
E (main product) = Enrollment. T is trust; S simplicity; A actionability within this component. F is friction.
Q = Commission quality, described by the poster as probability of a closed, high-value client.
Ψ = Conscious alignment. In this component A is agency (you guide, they decide), X experiential connection, and S substrate (market + message + offer).
C(t) = Customer clarity over time.
Φ(G(t)) = Perceived integration: proof and differentiation strength.
∇H(t) = Momentum gradient: urgency, alignment, trust.
T (integral) = Time from first contact to enrollment.
L = Lead value. In this component v_i is value of outcome i and p_i probability of outcome i.
P_c = Closing probability. S is solution fit, T trust, V value, and F friction.
ER = Expected revenue.
CLV = Customer lifetime value.
ARPU = Average revenue per user.
GM = Gross margin percent, as labeled.
R = Retention, labeled years.
L_t = Lifetime, labeled years.
CAC = Customer acquisition cost.
ROM = Return on marketing.
RAW OCR READING AID — NOT A VERIFIED EQUATION TRANSCRIPT
The following preserves the complete Tesseract output, including recognition errors, decorative glyphs and layout artifacts. Use the manually inspected records above and the original PNG for mathematical notation.
— THE FORMULA FOR TURNING STRANGERS INTO COMMISSIONABLE CLIENTS —
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NOT MAGIC. NOT LUCK.
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U = Uniqueness
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mpetitor Proof Strength
Cz = Cost (Emotional + Financial)
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C = Curiosity
I = Identity Alignment
k
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A = Actionability
(Sw = 1)
N= Noise (competition)
(0 < Daesice $1)
lumber of Competitors
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HOW IT WORKS (THE CUSTOMER JOURNEY)
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THE MASTER FORMULA
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THE MULTIPLICATION EFFECT
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ustomer Clarity overtime
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LEAD VALUE (L)
CLOSING PROBABILITY (P¢!
RETURN ON MARKETING (ROM)
THE GOAL
| EXPECTED REVENUE (ER)
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|ARPU = Aug, Revenue Per User
ROM =
CLV — CAC
100 %
Q, P., and CLV
Build a system where
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GM = Gross Margin %
CAC
increase over time
= value of outcome i
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R= Retention (years)
(ee)
while F (friction)
= probsbilty of outcome
| ER= "
Le = Lifetime (years)
CAC = Customer Acquistion Cost
decreases.
ee
_MASTER THE EQUATION. MASTER YOUR F FREEDOM.
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Precision. Doofits
Mathematical records from this source
0102030405060708091011121314151617
Agency–experience–substrate coupling
The source defines a coupling term as a function of agency, an experiential lattice and substrate scale. It specifies which quantities are intended to matter, but leaves the…
MADDIE six-factor client framework
The product presents a six-part framework: message, attention, desire, differentiation, investment and enrollment. The trophy is the desired commissionable-client outcome.…
Message score as a weighted sum of pain points
The Message component assigns a numerical quantity p_i to each pain point and multiplies it by an emotional weight w_i. Adding those products produces M, an additive score: each…
Normalize the total emotional weight in the Message score
This equality constrains the emotional weights in the Message formula to add to one. It fixes their combined scale, so changing the allocation among pain points need not change…
Attention score
Combines relevance, uniqueness, and curiosity and divides by noise/competition.
Desire score
Multiplies emotional intensity, perceived value, and identity alignment.
The declared zero-to-one range of the desire score
The two inequalities require D_desire to lie in the closed interval from zero to one, including both endpoints. The adjacent component equation defines D_desire as emotional…
Differentiation score
Subtracts mean competitor proof strength from your proof strength.
Investment score
Divides perceived value by emotional/financial cost plus friction to take action.
Enrollment score
Scales simplicity plus actionability by trust and divides by friction.
Master commission-quality integral
Accumulates customer clarity times perceived integration over the contact-to-enrollment period, modulated by the bounded gradient-norm factor and conscious alignment.
Total MADDIE quality product
Multiplies alignment by the six explicitly distinguished conversion components.
Lead value as a probability-weighted outcome sum
For each listed outcome i, multiply its value v_i by its probability p_i, then add those contributions from i=1 through n. When these are mutually exclusive outcomes covering the…
Closing probability
Uses a logistic mapping of solution fit, trust and value, with friction subtracted.
Expected revenue from lead value and closing probability
The equation scales lead value L by the closing probability P_c. For a nonnegative L and a probability between zero and one, the product ranges from zero when a close is…
Customer lifetime value
Combines average revenue per user, gross margin, retention and lifetime as the poster specifies.
Return on marketing
Expresses the difference between customer lifetime value and acquisition cost relative to acquisition cost.