Multi-level marketing is a strategy that some direct sales companies use to encourage their existing distributors to recruit new distributors by paying the existing distributors a percentage of their recruits’ sales; the recruits are known as a distributor’s “downline.” All distributors also make money through direct sales of products to customers. Amway is an example of a well-known direct-sales company that uses multi-level marketing.
MLM Matrix
Out of all MLM compensation plans, the matrix model (also known as the forced matrix) is probably the simplest to understand. Looking, dare I say it, somewhat like a pyramid you start off at the top of your matrix and as you grow your organisation fill in allocated spots below you.
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There are of course slight variations that can be applied to the matrix model but overall the functionality is virtually the same. As your organisation grows the levels of your matrix increase and these levels provide you with residual income. This residual income is paid out in commissions via the product sales or recruitment those in your organisation achieve.
Your basic matrix model for an MLM compensation plan involves two size factors, A x B.
Usage: ComplexMatrix aa = new ComplexMatrix(bb); This creates a new instance of ComplexMatrix, aa, with a complex matrix that is a s a deep copy of the matrix in the existing Complex Matrix instance or Matrix instance, bb. See also setTwoDarray and copy.
A (Children Limit) is your frontline and is the number of levels wide your matrix is. It is comprised of members you personally recruit into your organisation.
B (Matrix Depth) is also a number and is the number of levels deep your matrix is. Some compensation plans cap this number whilst others don’t.
Your standard MLM compensation plan matrix looks something like this:
The above is a 3X2 (2 levels deep and three levels wide) matrix and is a simple example. More complex matrix models can extend infinitely wide and deep or have limitations placed on the size so as to either force a matrix cycle or cap commission payouts.
The two biggest variations in matrix models are whether or not the matrix is fixed or not
The fixed matrix model
The fixed matrix model places limitations on your standard matrix, usually in the form of size restrictions. Typically there will be a cap in the form of A x B so as to trigger a cycle event.
A cycle is what happens when the matrix fills up and typically involves the person at the top of the matrix ‘cycling’ out and being rewarded with a commission payment.
After cycling the member is then placed into another existing matrix within the company, or at the top of a new fixed matrix. From here the member attempts to fill up the matrix again for another commission payout.
Fixed matrices are usually capped at small numbers (2 x 3 or 2 x 2 are quite common) so as to reward regular commission payouts for those who mass recruit into the business.
Due to the nature of the fixed model it is commonly affected by what’s called spillover. I’ll explain spillover a little later but the gist of it is your matrix can be faster filled off the work of your upline or those above you in a fixed matrix.
The standard matrix model
This standard variation of the matrix compensation model is a lot freer in that there are no restrictions on the width and depth of the matrix you create. Each person on your frontline spawns off an independent branch of your matrix and over time the idea is to grow these branches several levels deep.
Due to there being no restrictions on the size of your matrix, you can have as many people on your frontline and grow your organisation as many levels deep as you can achieve.
The standard matrix compensation model isn’t affected by spillover as each branch under your frontline run independent of each other. Because of this the unfixed matrix model initially relies more heavily on your own ability to fill it with recruits. In this sense team leverage is diminished in the early stages of building your organisation.
What is spillover?
When you join an MLM company with a fixed matrix compensation plan model, you’re placed at the top of a new matrix. This matrix however is a branch of your upline’s matrix, which in turn is a branch of their upline.
Spillover is what happens when someone above you in a parent matrix fills up a spot on their matrix which corresponds to a spot in your downline on your matrix.
Say for example you’re on the frontline of your uplines fixed matrix and your upline fills their frontline. The next level down (the frontline of your matrix) can then be filled over time by the recruiting efforts of your upline as they fill up the other levels of their own matrix.
Complex Matrix Plugin Wordpress
Here’s an example;
In the above matrix you can see how your own matrix fits in with that of your upline. For spillover to occur, let’s say your upline has filled their frontline (spots A and B), which you are on.
You directly recruit someone to spot C and then your upline manages to recruit another member. Due to your frontline being on their matrix spot D is filled via your upline. Spot D being filled affects both your own frontline and the matrix of your upline.
This affect is called spillover and requires no effort on your part to fill a spot on your own fixed matrix.
UniLevel MLM Plan
The MLM Uni level Plan running many Network Marketing Companies and providing an opportunity for the group or individual to earn huge profit.
One important thing with this MLM Uni level Plan is its simplicity; so that the MLM networker or company can explain Unilevel plan easily to their new comers who willing to join MLM business.
Uni-level Plan permits the affiliates to introduce new comers in its front line. There is no restriction for width i.e. a members can sponsor unlimited in width under his/her frontline and compensation distributed up to the limited depth. Further all frontline also efforts to do the same for earn bonuses or compensation.
To become more attractive Uni level Plan, MLM Company can introduce some rewards or incentives whenever a member introduced a set number of frontline.
Binary MLM Plan
The MLM Binary Plan is a most popular plan among MLM companies, network marketers, part-timers and members who want to earn through MLM business.
MLM (multi-level marketing) companies where new joiners introduced into Binary Tree structure i.e. one on left and another on right sub-tree. Generally, one side sub-tree is referred to as Power leg while other is Profit leg.
Power leg grows with new member placement, even introduced by previously enrolled or ancestors. New members in the power leg placed under a leaf available node of the binary tree, when a member works to grow his Profit leg, some compensation distributed calculated by a formula using certain value matched with Power leg that may be 1:1 or 2:1.
- matrix.xla
- matrix.hlp
- matrix.csv (*)
- FunCustomize.dll (**)
Complex Matrix Plugin Java
Volume 1
Complex Matrix Plugins
- Press Alt F11
- Press Ctrl R
- In the window pane click the 'Thisworkbook' object
- Press F4
- Scroll down this window until you see
- IsAddin
- Change the property to False
- Now save the workbook as xls
- Copy the entire codes to an Excel spreadsheet column
- Sort the contents of the column
- Delete the rows that do not contain procedure declarations
Name | Function Description |
Gauss_Jordan_step | Gauss Jordan algorithm step by step |
Gram_Schmidt | Gram-Schmidt's Orthonormalization |
Interpolate | Interpolation with polynomials |
M_ABS | Euclidean Norm of vector or matrix |
M_ADD | Addition of matrices |
M_BAB | Similarity transform [B]*[A]*[B]^-1 |
M_DET | Determinant |
M_DET_C | Determinant for complex matrix |
M_DET3 | Determinant for tridiagonal matrices |
M_DIAG | Diagonal matrix from a vector |
M_DIAG_ERR | Diagonalization error |
M_EXP | Matrix series expansion e^[M] |
M_EXP_ERR | Truncation error of matrix expansion series |
M_ID | Matrix Identity (I) |
M_INV | Matrix inverse [A]^-1 |
M_INV_C | Complex Matrix inverse [A]^-1 |
M_MULT_C | Complex matrices multiplication |
M_MULT3 | Mutliplication for tridiagonal matrix |
M_POW | Power of matrix [A]^n |
M_PROD | Product of matrices [A]*[B]*[C]*…. |
M_PROD_S | Matrix multiplication for a scalar |
M_RANK | Rank of matrix |
M_SUB | Subtraction of matrices |
M_T | Matrix transpose |
M_TRAC | Trace |
M_TRIA_ERR | Triangolarization error |
Mat_Adm | Returns the Admittance matrix of a linear passive network |
Mat_BlokPerm | Returns the permutation vector of block-partitioned matrix |
Mat_Blok | Returns the block-partitioned matrix |
Mat_Cholesky | Cholesky decomposition |
Mat_Hessemberg | Hessemberg form |
Mat_Hilbert | Returns Hilbert's matrix |
Mat_Householder | Returns Houseolder matrix |
Mat_Leontief | Returns the Leontief inverse matrix of Input Output Analysis |
Mat_LU | LU decomposition |
Mat_QR | QR decomposition |
Mat_QR_iter | Performs the diagonalization with the QR iterative method |
Mat_Tartaglia | Returns Tartaglia's matrix |
Mat_Vandermonde | Returns Vandermonde's matrix |
MatCharPoly | Characteristic polynomial coefficients |
MatCmpn | Companion matrix |
MatCorr | Correlation matrix |
MatCovar | Covariance matrix |
MatDiagExtr | Diagonal extractor |
MatEigenvalue_Jacobi | Eigenvalues of symmetric matrix with Jacobi algorithm |
MatEigenvalue_max | Dominant eigenvectors with powers' method |
MatEigenvalue_pow | Eigenvectors with powers' method |
MatEigenvalue_QL | Eigenvalues of tridiagonal matrix |
MatEigenvalue_QR | Eigenvalues with QR algorithm |
MatEigenvalue_TridUni | Eigenvalues of tridiagonal uniform matrix |
MatEigenvector | Eigenvector of eigenvalue |
MatEigenvector_C | Complex eigenvector of eigenvalue |
MatEigenvector_inv | Eigenvector of eigenvalue |
MatEigenvector_Jacobi | Eigenvectors of symmetric matrix with Jacobi algorithm |
MatEigenvector_max | Dominant eigenvalues with powers' method |
MatEigenvector_pow | Eigenvalues with powers' method |
MatEigenvector3 | Eigenvectors of tridiagonal matrix |
MatExtract | Extract sub-matrix |
MatMopUp | Matrix mop-up of round-off errors |
MatNorm | Vector or Matrix Norm |
MatNormalize | Vectors Normalization |
MatOrtNorm | Orthonormalization |
MatPerm | Permutation matrix |
MatRnd | Random matrix |
MatRndEig | Random matrix with given eigenvalues |
MatRndEigSym | Random symmetric matrix with given eigenvalues |
MatRndRank | Random matrix with given rank or determinant |
MatRndSim | Random symmetric matrix with given rank or det. |
MatRot | Returns the orthogonal planar rotation matrix |
MatRotation_Jacobi | Jacobi's rotation matrix |
Path_Floyd | All-pairs-path of Graph with Floyd's algorithm |
Path_Min | Returns the shortest path of a Graph with Floyd's algorithm |
Poly_Roots | Polynomial rootfinder with Lin-Bairstow method |
Poly_Roots_QR | Polynomial rootfinder with QR method |
ProdScal | Scalar Product (inner) |
ProdScal_C | Complex scalar product |
ProdVect | Vector Product 3D |
REGRL | Linear regression with SVD |
REGRP | Polynomial regression |
RRMS | root mean squares |
Simplex | Linear Optimization with Simplex method |
SVD_D | Singular Value Decomposition [U]*[D]*[V]^t: returns D |
SVD_U | Singular Value Decomposition [U]*[D]*[V]^t: returns U |
SVD_V | Singular Value Decomposition [U]*[D]*[V]^t: returns V |
SYSLIN | Solve Linear System [A]x=b |
SYSLIN_C | Solve a Complex Linear System [A]x=b |
SYSLIN_ITER_G | Solve Linear System with Gauss-Seidel algorithm |
SYSLIN_ITER_J | Solve Linear System with Jacobi algorithm |
SYSLIN_T | Solve triangular linear sistem |
SYSLIN3 | Solve tridiagonal linear system |
SYSLINSING | Solve Singular Linear System [A]x=b |
TRASFLIN | Linear Transform |
VarimaxIndex | Returns the Varimax index of a given Factors matrix |
VarimaxRot | Computes the orthogonal rotation with Varimax Kaiser's |
M_MULT_TPZ | Multiplies a Toeplitz matrix for a vector |
SYSLIN_TPZ | Solve Toeplitz Linear System [A]x=b |
M_TPZ_ERR | Toeplitz matrix error |