CN1870039A - Large scale One-to-One marketing optimization model building method and device - Google Patents

Abstract

An optimized modeling method of large scale one to one marketing uses linear planning model description format to set up one to one marketing optimum model by utilizing maximum profit, minimum cost and maximum client number as marketing aim and utilizing maximum cost limit, product stock restraint, selling channel restraint, maximum offer number restraint to be received by client, order restraint and single offer restraint as marketing restraint. The automatic modeling-device for realizing said method is also disclosed.

Description

A kind of extensive One to One marketing optimization modeling method and device

Technical field the present invention relates to the modeling that the modelling problem of marketing optimization, particularly analytic type CRM marketing domain have the One to One marketing optimization of constraint condition and objective function.

Background technology One to One marketing optimization is one of analytic type CRM guardian technique, its major function is that auxiliary enterprises to how carrying out One to One marketing activity is made a strategic decision, which client what decision should do to, could effectively improve client's loyalty, make enterprise utilize the client to realize big as far as possible interests as far as possible for a long time.

The CRM software developer still is in the starting stage in the research of One to One marketing optimization problem both at home and abroad at present, its treatment technology mainly comprises by mutual link real-time collecting client to be responded, utilize data mining technology, enterprise's historical data is analyzed, grasp client's hobby, thereby how to optimize conversation content, communication approach and the frequency that exchanges and the information etc. of enterprise and customer communication by the method decision of business rules reasoning.Though yet the optimization method of this rule-based reasoning can marketing activity be optimized to One toOne to a certain extent, but it needs accurately to collect the historical sales record of a large amount of customer informations and enterprise, and have stronger subjectivity and uncertainty, can not carry out reasonable distribution to the enterprise marketing resource objectively, it is maximum to guarantee that really enterprise makes a profit.

If give accurate expression with the form of objective function and constraint condition with One to One Optimization Model, and give accurately to find the solution with linear programming method, then can effectively help enterprise to determine best marketing program objectively, guarantee that enterprise benefits really to reach the maximization on the mathematical concept.Yet for a large and medium-sized enterprise, the foundation existence that One to One optimizes mathematical model involves a wide range of knowledge, and there is diversity in objective function, and constraint condition is also quite complicated, each coefficient is not easy to determine, has various nonlinear situation and in large scalely difficult point such as is difficult for finding the solution.Therefore how simple, the most important condition that extensive One to One marketing optimization linear programming model just becomes optimization One to One marketing activity is described exactly.

Summary of the invention the invention provides the objective function and the constraint condition determination method of an extensive One to One marketing optimization model for addressing the above problem, and an extensive One toOne marketing optimization model building device is provided simultaneously.

The present invention designs definite method of objective function under following four kinds of marketing purposes situations.

(1) reduces the business activity cost, consider that promptly with minimum commercial expense cost be that target is carried out marketing activity.

(2) increase enterprise and make a profit, promptly consider with enterprise's profit to be that purpose is launched marketing activity.

(3) improve client's conservation rate, develop new client as far as possible, be that purpose is carried out marketing activity promptly to attract maximum client source.

Its objective function is determined method when (4) considering above three kinds of marketing purposes situations at the same time or separately.

The present invention designs definite method of constraint condition under following six kinds of marketing restraint conditions.

(1) maximum cost restriction, i.e. the maximum cost expenditure of this marketing activity.

(2) how many inventories product library storage constraint promptly has need to sell.

Whether (3) channel promotion constraint promptly for certain specific client, can market by this channel promotion.

(4) client can receive maximum Offer number constraint, and promptly for certain specific client, he (she) only wishes to receive the Offer below how much quantity.

Whether (5) order constraint promptly has the situation that order is preengage.

(6) single Offer constraint promptly guarantees same client is only promoted same product with a kind of method.

According to the description to extensive One to One marketing optimization simulated target function and constraint condition, the present invention has designed One to One marketing optimization model automatic modeling device, comprises following components:

(1) optimizes MBM.

(2) extensive One to One marketing optimization database.

(3) MPS data output interface.

(4) user selects input interface.

This device adopts modular design, according to user-selected objective function and constraint condition, reads that the company information of storing among the SQLServer carries out One to One marketing optimization modeling automatically and with its result's storage.The MPS data-interface is provided simultaneously, converts this model data to standard MPS linear programming problem descriptor format.

Description of drawings

Fig. 1 automatic modeling device of the present invention workflow diagram.

Fig. 2 automatic modeling device of the present invention marketing database information model figure.

Fig. 3 model building device data flowchart of the present invention.

Embodiment

Below in conjunction with accompanying drawing the specific embodiment of the present invention is described in detail.

At first provide the embodiment of optimization problem describing method.

If the different commodity of enterprise production and management g kind

A ₁，A ₂，Λ，A _g，g＝O(10 ²) (1)

These commodity are intended adopting the different canvasser methods of h kind

S ₁，S ₂，Λ，S _h，h＝O(10) (2)

Then the different canvasser methods to different commodity constitute m=g * h kind Offer altogether.Every kind of Offer has its relevant attribute, comprises cost, profit data and the condition of compatibility etc. of Offer, and these are input quantity.

Suppose to have l attribute, i kind Offer availability vector is expressed as

O _i＝(o _i1，o _i2，Λ，o _il)，i＝1，2，Λ，m (3)

Suppose that enterprise provides this m kind Offer to n relative clients, then is expressed as n client

C ₁，C ₂，Λ，C _n，n＝O(10 ⁶) (4)

Each client also has its relevant attribute, as name, and address, the income situation, client's grades etc. suppose that quantity is p, then j client's availability vector is expressed as

C _j＝(c _j1，c _i2，Λ，c _jp)，j＝1，2，Λ，n (5)

In addition, with the possibility (can obtain) that each client accepts each Offer, use matrix R=(r by the historical sales data being carried out data mining _Ij) _{M * n}Expression,

$R=\left[\begin{array}{cccc}{r}_{11}& r_{12}& \mathrm{\Λ}& r_{1n}\\ r_{21}& r_{22}& \mathrm{\Λ}& r_{2n}\\ M& M& \mathrm{\Λ}& M\\ r_{m1}& r_{m2}& \mathrm{\Λ}& r_{\mathrm{mn}}\end{array}\right]---\left(6\right)$

Therefore, for setting up Optimization Model, relevant client j is input as:

I _j=(c _J1, Λ, c _Jp, r _1j, Λ, r _Mj), j=1, Λ, n; I=1, Λ, m (7) being input as to Offer i:

O _i＝(o _i1，o _i2，Λ，o _il)，i＝1，2，Λ，m (8)

Output data after the optimization, promptly optimum Channel-Offer-Customer embodiment is by optimal allocation matrix Boolean variable matrix X=(x _Ij) _{M * n}Expression:

One to One marketing optimization problem is at client C, according to given data I, O etc., satisfying under the prerequisite of all constraint conditions, determine best Channel-Offer-Customer embodiment X, satisfy enterprise's business activity cost minimum, profit maximum, on the basis that keeps the frequent customer, strive for requirements such as new client as much as possible.

Provide the embodiment of objective function describing method again.

In One to One marketing optimization model, objective function should demonstrate fully the overall goal of enterprise, as reduce the business activity cost, increase enterprise and make a profit, improve client's conservation rate, develop new client etc. as far as possible, and give priority to according to the difference of certain marketing activity objectives.

Consider accuracy and the easy property found the solution, the determining of objective function must be followed certain principle, mainly considers these points.

(1) cost of different Offer (comprising expenses on publicity and merchandise cost etc.) will embody in objective function to some extent;

(2) every client should embody in objective function to some extent to the profit that enterprise brings;

(3) objective function should embody " on the basis that keeps the frequent customer, striving for new client as much as possible " principle.

For above-mentioned (1) individual principle, be at different Offer's.If it is Q that the expenses on publicity of i kind Offer are provided _i, merchandise cost is L _i, then the total cost of enterprise implement allocative decision X is expressed as:

$S\left(X\right)=Q\left(X\right)+L\left(X\right)=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{x}_{\mathrm{ij}}{Q}_i+\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{\λ}}_i{x}_{\mathrm{ij}}{L}_i---\left(10\right)$

Wherein ${\mathrm{\λ}}_i=\frac{\underset{j=1}{\mathrm{\Σ}}{r}_{\mathrm{ij}}}{n}$ The expression client is to the response rate of i kind Offer.

Above-mentioned (2) individual principle then is at different clients'.Data shows that 20% gold medal client can bring 80% profit to enterprise, and many clients at cost/profit analysis is actually in loss.Therefore, every client brings how much should embodying to some extent of profit to enterprise in objective function.Suppose that the service that client j obtains i kind Offer will bring Y to enterprise _JiProfit, then implementing allocative decision X will can be expressed as for the gross profit that enterprise produces:

$P\left(X\right)=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{Y}_{\mathrm{ji}}{x}_{\mathrm{ij}}---\left(11\right)$

For above-mentioned (3) individual principle, it has shown the relation balance problem that develops between new client and the maintenance frequent customer, at the old and new customers.Experience shows, develop a new client than keeping the input that a frequent customer has more 5 times, invest in the existing customer, its satisfaction increase meeting Building Customer Loyalty degree is had a direct impact, and then have influence on the bottom line of enterprise, this just needs to embody the principle of " on the basis that keeps the frequent customer, striving for new client as much as possible ".How to keep frequent customer's requirement in constraint condition, to consider.For striving for new client as much as possible, can maximize the number of non-zero column in the embodiment X.If the non-zero columns of embodiment X is Z (X), can be expressed as

$Z\left(X\right)=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}\underset{j=1}{\overset{m}{\mathrm{\Σ}}}{x}_{\mathrm{ij}}---\left(12\right)$

Then strive for enlarging the maximization problems that client's number has just become Z (X) as far as possible.

Through above-mentioned analysis, the objective function that obtains One to One marketing optimization problem is as follows.

(1) when only considering that the pure profit of this marketing activity is maximum, objective function is

J(X)＝P(X)-S(X) (13)

(2) when only considering to develop the client as far as possible, objective function is

J(X)＝Z(X) (14)

(3) when only considering this business activity cost hour, objective function is:

J(X)＝-S(X) (15)

(4) when above-mentioned whole requirements all needed to consider, objective function was

J(X)＝α[P(X)-S(X)]-βS(X)+γZ(X) (16)

Wherein, α, beta, gamma represent this time marketing activity respectively at net profit, and what impact effect and client counted the aspect stresses the degree coefficient, if get 0, then represents this factor not considering.

One to One marketing optimization problem is exactly under the prerequisite that satisfies all related constraint conditions, maximization objective function J (X).

Provide the embodiment of constraint condition describing method below again.

The constraint condition that One to One marketing optimization problem need be considered comprises:

(1) client accepts the constraint of Offer, and this comes from the client aspect, mainly contains that each client accepts the quantity constraint of Offer and the client of specific type has specific requirement etc. to Offer;

(2) come from the restriction of Offer aspect, mainly contain the constraint of constraint, profit of quantity constraint that Offer is provided, expense and adaptability constraint etc.

For the constraint condition of above-mentioned (1) aspect, consider that at first the client accepts the quantity constraint of Offer.There is different restrictions in the quantity that different clients meets Offer, can be expressed as

$\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{x}_{\mathrm{ij}}\≤b_j,j=\mathrm{1,2},\mathrm{\Λ},n---\left(17\right)$

B wherein _jExpression client j can accept the maximum constraints of Offer number.

About the client of specific type, there is certain restriction in it to the Offer that is accepted.This is mainly at the frequent customer.Because new client of development is than keeping the input that a frequent customer has more 5 times, invest in the existing customer, its satisfaction increase meeting Building Customer Loyalty degree is had a direct impact, and then have influence on the bottom line of enterprise, this just need satisfy frequent customer's specific demand as far as possible.For example, frequent customer j only accepts i kind Offer, for keeping this frequent customer, can add following constraint, and wherein D represents the special frequent customer's of this class set.

x _ij＝1，j∈D (18)

In fact, the order constraint of often running in enterprise's marketing process, promptly before enterprise's marketing activity, carry out merchandise sales by certain concrete marketing approach with regard to having signed sales contract with certain client, this also can regard frequent customer's restraint condition as, for example, client j has been signed sales contract, and decision is bought commodity by Offer i, then sets up corresponding constraint condition x _Ij=1.

Certain product is at first considered the quantity constraint of Offer for the constraint condition of above-mentioned (2) aspect.Maximum quantity constraint to every kind of Offer can supply is expressed as

$\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{x}_{\mathrm{ij}}\≤w_i,i=\mathrm{1,2},\mathrm{\Λ},m---\left(19\right)$

W wherein _iIt is the higher limit that i kind Offer can offer the client.

The restriction of marketing cost is expressed as

$\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{Q}_i{x}_{\mathrm{ij}}\≤S---\left(20\right)$

Wherein S represents the maximum budget value of expense.

About the restriction of Offer adaptability aspect, mainly be can different clients to be promoted at different Offer, can retrain

Thereby can get following complementarity condition

x _ij[1-E _i(I _j，O _i)]＝0，i＝1，2，Λ，m，j＝1，2，Λ，n (22)

Being constrained to example with channel fit below describes this constraint condition.

To obtaining the client of certain channel information, enterprise can't adopt corresponding marketing mode to it, therefore should be with its zero setting in constraint condition.Suppose that enterprise lacks the email address of client j, and the sequence number of email channel is k, then with this constraint condition E _i(I _j, O _i), (Λ hk) puts 0 to i ∈ for k, 2k.To satisfy constraint condition (21) formula so, then x _IjBe necessary for 0, promptly in best marketing program result, will can not select this marketing approach for use, can not carry out still making a profit under this marketing mode maximum to this client to guarantee enterprise to j client.And to having obtained the client of this channel information with enterprise, then in addition its corresponding E (I, O)=1, then x get 0 or 1 all can, concrete outcome obtains according to the actual optimization operation result.

In addition, adaptive constraint also is embodied in same client is avoided promoting on the identical product to it by number of ways to Offer, can be expressed as

$\underset{i\∈G_k}{\mathrm{\Σ}}{x}_{\mathrm{ij}}\≤1,j=\mathrm{1,2},\mathrm{\Λ},n,k=\mathrm{1,2},\mathrm{\Λ},g---\left(23\right)$

Wherein, G _kThe Offer collection of the same kind of goods is promoted in expression.

Provide the specific implementation method that Optimization Model is described below.

By above-mentioned analysis to One to One marketing optimization problem, objective function and constraint condition, a kind of One to One marketing optimization model can be described as the formula (24).

$\max J(X,Y)=\mathrm{\α}[P\left(X\right)-S\left(X\right)]+\mathrm{\βF}\left(X\right)+\mathrm{\γZ}\left(X\right)$

$s.t.\mathrm{and}/\mathrm{or}\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{x}_{\mathrm{ij}}\≤b_j,j=\mathrm{1,2},\mathrm{\Λ},n$

$\mathrm{and}/\mathrm{or}{x}_{\mathrm{ij}}=1,j\∈D$

$\mathrm{and}/\mathrm{or}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{x}_{\mathrm{ij}}\≤w_i,i=\mathrm{1,2},\mathrm{\Λ},m$

$\mathrm{and}/\mathrm{or}\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{Q}_i{x}_{\mathrm{ij}}\≤S---\left(24\right)$

$\mathrm{and}/\mathrm{or}{x}_{\mathrm{ij}}[1-E_i(I_j,O_i)]=0,i=\mathrm{1,2},\mathrm{\Λ},m;j=\mathrm{1,2},\mathrm{\Λ},n$

$\mathrm{and}/\mathrm{or}\underset{i\∈G_k}{\mathrm{\Σ}}{x}_{\mathrm{ij}}\≤1,j=\mathrm{1,2},\mathrm{\Λ},n;k=\mathrm{1,2},\mathrm{\Λ},g$

$x_{\mathrm{ij}}=\left\{\mathrm{0,1}\right\},i=\mathrm{1,2},\mathrm{\Λ},m;j=\mathrm{1,2},\mathrm{\Λ},n$

Wherein and/or represents to have multiple constraint may need to consider simultaneously, also may only consider certain several constraint wherein, should make a concrete analysis of according to particular problem.

Below in conjunction with a simple case One to One marketing optimization modeling method is specifically introduced.Suppose that a company produces 5 kinds of commodity, promote to n=100 position client that then the said firm adopts different canvasser methods that m=5 * 3=15 kind Offer can be provided altogether to different commodity through 3 kinds of modes (direct mail, Email and phone).The profit P that brings to company of i item Offer so _iCan be expressed as

P _i＝λ _i(Y _i-L _i)-Q _i i＝1，2，Λ，15 (25)

Wherein, λ _iThe expression client is to the average response rate of i kind Offer, Y _iThe price of representing i kind Offer, L _iThe cost of products of representing i kind Offer, Q _iThe required expenses on publicity of i kind Offer are used in expression.

For general marketing activity, to consider the restriction of marketing cost usually and avoid repeating to promote the same kind of goods to same client.Therefore, the constraint condition of One to One marketing optimization model can be defined as

$\underset{i\∈D_k}{\mathrm{\Σ}}{x}_{\mathrm{ij}}\≤1,j=\mathrm{1,2},\mathrm{\Λ},n,k=\mathrm{1,2},\mathrm{\Λ},5$

$\underset{j=1}{\overset{n}{\mathrm{\Σ}}}\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{Q}_i{x}_{\mathrm{ij}}\≤S---\left(26\right)$

Wherein, S represents the higher limit of all expenses, D _kThe Offer collection of the same kind of goods is promoted in expression, and I=1,2, Λ, m j=1,2, Λ, n.

Like this, if the purpose of certain One to One marketing activity of the said firm is a profit maximization, then Optimization Model can be defined as the formula (27).

$\max \underset{j=1}{\overset{n}{\mathrm{\Σ}}}\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{C}_i{x}_{\mathrm{ij}}$

$s.t.\underset{j=1}{\overset{n}{\mathrm{\Σ}}}\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{Q}_i{x}_{\mathrm{ij}}\≤S---\left(27\right)$

$\underset{i\∈D_k}{\mathrm{\Σ}}{x}_{\mathrm{ij}}\≤1,j=\mathrm{1,2},\mathrm{\Λ},n$

$x_{\mathrm{ij}}\∈\left\{\mathrm{0,1}\right\},i=\mathrm{1,2},\mathrm{\Λ},m;j=\mathrm{1,2},\mathrm{\Λ},n$

Provide the specific implementation method of One to One marketing optimization automatic modeling device below in conjunction with accompanying drawing.

This device has been realized extensive One to One marketing optimization automatic modeling function.It has adopted the modular construction design, the data of each inside modules is encapsulated, to guarantee the complete sum safety of data.The transmission of the data between each module is finished by Data Input Interface and data output interface with calling then, makes data access more convenient, and the data variation in module does not directly have influence on other module.

Fig. 1 is the workflow diagram of this contrive equipment.The user can select different clients, product and channel constraint at different Optimization Model target and constraint condition in this module, sets up Optimization Model.For product and channel constraint, only need the simple kind of selecting to participate in modeling to get final product.For the client, can be by whether selecting continuous client or discontinuous client to classify.For the client of discontinuous ID, again can be by whether selecting once more according to its attribute (credit worthiness, grade).Behind the device generation model, this model relevant information can be saved to the Model table in the database, model data information that should model is kept in the database with the view form.

This device marketing database comprises 7 information tables, is respectively: client's table 1, and product table 2, channel table 3, order table 4, model table 5, marketing program table 6, optimum induce sweat 7.Its information model figure as shown in Figure 2, the relation of the direction indication of line arrow between them between each tables of data, the numerical reference on the line is represented the degree that both get in touch.The attribute that is labeled as pk (Primer Key) in each table is a major key, and the attribute that is labeled as fk (Foreign Key) is an external key.

The data flow synoptic diagram of this device as shown in Figure 3.Enterprise marketing information 8 (comprises customer information, channel information and product information etc.) at first be stored in the database 9, providing three kinds of marketing purposes and six kinds of constraint conditions to carry out the modeling of One to One marketing optimization for the user selects, select from data, to be optimized the excavation of model information and extraction, set up Optimization Model automatically according to the user, and the output and the preservation of realization Optimization Model information, and the systematic function of MPS data.

As the background data base system, its I/O operation to database realizes by ADO data access application DLL (dynamic link library) One to One marketing optimization model building device with SQL Server 2000.The marketing database scale of considering extensive One to One marketing optimization problem is very huge, marketing message can the visit with speed of displaying can be slack-off along with the increase of data scale.For addressing this problem, Optimization Software has adopted the data page technology when design.

The design concept of this device data page technology is that all records in the tables of data are divided into not same page, and program is visited all records in the data page at every turn, has improved data access and inquiry velocity like this.Install simultaneously and on the interface, given respective handling, make the user can visit and inquire about the record on the different pieces of information page or leaf flexibly.Its concrete thinking of implementing is: open the total record number of record set → calculatings → definite branch number of pages → determine record set pointer position → certain page of record of inquiry.This record number that installs each data page is made as 100, can take into account the needs of the sufficient amount record that data query speed is fast and the forms data page shows like this.

The extensive One to One marketing optimization modeling method and the modeling transposition thereof of the present invention's design can be set up One to One marketing optimization model accurately and conveniently automatically.This Optimization Model has taken into full account the commercial object and the rule of One toOne marketing optimization, has solved One to One marketing optimization accurate description problem, has stronger practicality.Its linear programming description form can utilize linear programming method accurately to find the solution to it in addition, thereby it is maximum to guarantee that really enterprise makes a profit.

Claims (12)

1. extensive One to One marketing optimization modeling method is characterized in that: with the linear programming describing method extensive One to One marketing optimization model is carried out accurate description with the form of constraint condition and objective function, may further comprise the steps:

A. set up extensive One to One marketing optimization simulated target function under the several three kinds of marketing purposes situations of maximum profit, minimum cost and maximum client;

B. set up maximum cost restriction, the constraint of product library storage, the channel promotion constraint, the client can receive maximum Offer number constraint, the model constrained condition of extensive One toOne marketing optimization under order constraint and six kinds of marketing of the single Offer constraint restraint condition;

C. set up different user at many marketing objectives, many marketing constraints are extensive One to One marketing optimization model down.

2. extensive One to One marketing optimization modeling method according to claim 1 is characterized in that: the maximum profit objective function that step a sets up is:

J(X)＝P(X)-S(X)

Wherein $P\left(X\right)=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{Y}_{\mathrm{ji}}{x}_{\mathrm{ij}},$ $S\left(X\right)=Q\left(X\right)+L\left(X\right)=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{x}_{\mathrm{ij}}{Q}_i+\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{\λ}}_i{x}_{\mathrm{ij}}{L}_i,$ M is the available Offer number of company, and n is potential customers' number of company, Y _JiFor client j obtains the profit that the service of i kind Offer will bring to enterprise, x _IjBe marketing program variable, Q _i, L _iBe respectively expenses on publicity and merchandise cost that i kind Offer is provided, λ _iThe expression client is to the response rate of i kind Offer.

3. extensive One to One marketing optimization modeling method according to claim 1 is characterized in that: the minimum cost objective function that step a sets up is:

J(X)＝-S(X)。

4. extensive One to One marketing optimization modeling method according to claim 1 is characterized in that: maximum client's number offer of tender number that step a sets up is:

$J\left(X\right)=Z\left(X\right)=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}\underset{j=1}{\overset{m}{\mathrm{\Σ}}}{x}_{\mathrm{ij}}.$

5. extensive One to One marketing optimization modeling method according to claim 1 is characterized in that: the maximum cost restriction marketing constraint condition that step b sets up is:

$\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{Q}_i{x}_{\mathrm{ij}}\≤S$

S represents the maximum budget value of expense.

6. extensive One to One marketing optimization modeling method according to claim 1 is characterized in that: the product library storage constraint condition that step b sets up is:

$\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{x}_{\mathrm{ij}}{\≤w}_i$

w _iIt is the higher limit that i kind Offer can offer the client.

7. extensive One to One marketing optimization modeling method, it is characterized in that: the channel promotion constraint condition that step b sets up is:

x _ij[1-E _i(I _j，O _i)]＝0

Wherein

8. extensive One to One marketing optimization modeling method according to claim 1 is characterized in that: the client that step b sets up can receive maximum Offer and count constraint condition and be:

$\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{x}_{\mathrm{ij}}{\≤b}_j$

B wherein _jExpression client j can accept the maximum constraints of Offer number.

9. extensive One to One marketing optimization modeling method according to claim 1 is characterized in that: the order constraint condition that step b sets up:

x _ij＝1，j∈D

Wherein D represents order client's set.

10. extensive One to One marketing optimization modeling method according to claim 1 is characterized in that: the single Offer constraint condition that step b sets up is:

$\underset{i\∈G_k}{\mathrm{\Σ}}{x}_{\mathrm{ij}}\≤1$

G wherein _kThe Offer collection of the same kind of goods is promoted in expression.

11. extensive One to One marketing optimization modeling method according to claim 1 is characterized in that: the different user that step b sets up is at many marketing objectives, and many marketing constraints extensive One to One marketing optimization model down are:

maxJ(X，Y)＝α[P(X)-S(X)]+βF(X)+γZ(X)

$s.t.\mathrm{and}/\mathrm{or}\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{x}_{\mathrm{ij}}\≤b_j,j=\mathrm{1,2},\mathrm{\Λ},n$

and/or?x _ij＝1，j∈D

$\mathrm{and}/\mathrm{or}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{x}_{\mathrm{ij}}\≤w_i,i=\mathrm{1,2},\mathrm{\Λ},m$

$\mathrm{and}/\mathrm{or}\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{Q}_i{x}_{\mathrm{ij}}\≤S$

and/or?x _ij[1-E _i(I _j，O _j)]＝0，i＝1，2，Λ，m；j＝1，2，Λ，n

$\mathrm{and}/\mathrm{or}\underset{i{\∈G}_k}{\mathrm{\Σ}}{x}_{\mathrm{ij}}\≤1,j=\mathrm{1,2},\mathrm{\Λ},n;k=\mathrm{1,2},\mathrm{\Λ},g$

x _ij＝{0，1}，i＝1，2，Λ，m；j＝1，2，Λ，n

α wherein, beta, gamma is represented this time marketing activity respectively at net profit, and what impact effect and client counted the aspect stresses the degree coefficient, and and/or represents to have multiple constraint, and and represents and need consider simultaneously, or represents only to consider certain several constraint wherein.

12. an extensive One to One marketing optimization model modeling device is characterized in that: comprise

A. handle and store modeling desired data and result's extensive One to One marketing optimization database;

B. this model data is converted to the MPS data output interface of standard MPS linear programming problem descriptor format;

C. user's root retrains the selection input interface of selecting to marketing purposes and marketing;

D. carry out the optimization MBM of data mining according to marketing database enterprise marketing message according to the user.

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