What the source specifies
Three posters describe a message–attention–desire–differentiation–investment–enrollment product. The detailed edition adds component formulas, a quality integral, closing probability, expected revenue and customer-value ratios.
Equations and variants
Every visible complete mathematical relation has a linked record, including parameter definitions, constraints, decorative corner equations and repeated formula variants. The original images and full reading copies are available through the source links.
Source findings and limits
The six-factor product appears in both the main line and the multiplication-effect strip. A trophy stands for a commissionable client.
The two D factors mean desire and differentiation. The bottom illustrative result-versus-implementation curve has no data, axis values or printed function.
Claims about multiplying results exponentially and predictable commissions are promotional prose, not an empirical conversion model or a displayed exponential equation.
The graph labeled Results versus Implementation is illustrative and has no ticks, data values, or displayed function. The accompanying claim about exponential multiplication is prose; no exponential mathematical formula is given.
This product is duplicated in the detailed Client Conversion poster, where D_desire and D_diff are separately named.
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.
Equations in this build
17 linked mathematical records. Each opens its notation, interpretation and source.
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.
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…
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.