CN113642945B - Client management data processing system and method for multi-source data fusion - Google Patents

Client management data processing system and method for multi-source data fusion Download PDF

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CN113642945B
CN113642945B CN202111200838.1A CN202111200838A CN113642945B CN 113642945 B CN113642945 B CN 113642945B CN 202111200838 A CN202111200838 A CN 202111200838A CN 113642945 B CN113642945 B CN 113642945B
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徐步海
朱敉
张宣一
余桂香
许晓燕
王希民
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Yancheng Gangzheng Scientific Innovation Development Co ltd
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Abstract

The invention discloses a client management data processing system and method for multi-source data fusion, the system comprises a client group classification module, a client group maintenance module and a client group connection establishment module, the client group classification module is used for displaying the positions of the clients, judging whether the distribution condition of the clients on the map is a weak area or a concentrated area, the customer group maintenance module is used for judging the loyalty of the customers to the enterprise according to the strength of purchasing products and the purchasing interval time of different customer groups, and maintains similar customer groups, the customer group contact establishing module is used for further establishing new customer relations in the customer distribution weak areas through the customer relations established in the customer concentration areas and managing the new customer relations, and the loyalty of different customer groups is maintained at different levels for the customers through the customer relationship group maintenance module.

Description

Client management data processing system and method for multi-source data fusion
Technical Field
The invention relates to the technical field of data processing, in particular to a client management data processing system and method for multi-source data fusion.
Background
The customer management refers to the management of customer relations, and refers to the improvement of the competitiveness of an enterprise by obtaining the satisfaction degree of the enterprise through deep analysis of detailed information of customers.
In a manufacturing enterprise, products manufactured in the enterprise need to be sold outwards for the enterprise to operate; and products manufactured in manufacturing enterprises are all required by customers, so that the relationship between the enterprises and each customer needs to be maintained, and meanwhile, the customer relationship needs to be sorted so as to ensure that the customer relationship can be fully utilized to obtain more achievement for the enterprises.
The method is characterized in that customers distributed by a manufacturing enterprise are wide, distributed regions are wide, the grades of the customers need to be classified, and the customers are divided into big customers, small customers, ordinary customers and costal customers, when the customers are subjected to relationship maintenance, the customers are forbidden to be treated as same as each other, the big customers are generally core customers of a company and have certain loyalty to the company, the customers can be saved only if the requirements on the customers are continuously met, otherwise the big customers can become enterprises of other competitors; for small customers, the method cannot be ignored easily, the small customers are induced to buy a large number of products, the small customers can become next large customers, for general customers and the chicken rib customers, which are commonly called scattered households, the chicken rib customers buy less times and the price required by the customers is lower than that of the general customers;
therefore, a need exists for a customer managed data processing system and method for multi-source data fusion that addresses the above-mentioned problems.
Disclosure of Invention
The present invention aims to provide a client management data processing system and method for multi-source data fusion to solve the problems in the background art.
In order to solve the technical problems, the invention provides the following technical scheme: the system comprises a client group classification module, a client group maintenance module and a client group contact establishment module;
the customer group classification module is used for displaying the positions of customers and judging whether the distribution condition of the customers on the map is a weak area or a concentrated area, so that the distribution of managed customers is more effective, and customer relations can be further established in the weak area of the customer distribution.
Further, the customer group classification module comprises a two-dimensional code display unit, a customer position acquisition unit, a two-dimensional plane display unit, a customer demand analysis model establishing unit and a customer characteristic judgment unit;
the two-dimensional code display unit is used for displaying order progress and quality inspection conditions of purchased products to a client, so that the client can know dynamic conditions of the purchased products and show the attention degree of an enterprise to the client, the client position acquisition unit is used for acquiring the accurate position of the client by browsing the two-dimensional code, so that the enterprise can know the distribution condition of the client, the two-dimensional plane display unit is used for displaying the position of the client on a map and knowing the distribution condition of the client, the client demand analysis model establishment unit is used for classifying the client according to the quantity of the purchased products of different clients, dividing the client into small clients and large clients, so that the enterprise can classify different clients, further manage the client, the client characteristic judgment unit is used for identifying the characteristics of different clients and setting the client group to increase the enterprise preference, and the order quantity of the enterprise can be shared, so that the enterprise can obtain more order quantity, the profit obtained by the enterprise is increased, and the delivery cost in the way is reduced.
Further, the customer group maintenance module comprises a batch purchasing strength acquisition unit, a time interval determination unit, a related product recommendation unit, a customer loyalty determination unit and a similar customer maintenance unit, wherein the batch purchasing strength acquisition unit is used for acquiring the number of products purchased from an enterprise by a first customer and recording purchasing data, so that the enterprise knows the times of the customer products and knows whether the customer is satisfied with the products and arranges the products in time, the time interval determination unit is used for calling the interval of the time when the first customer purchases the enterprise products in the historical data for calculation so as to analyze the persistence of the customer on the enterprise products, the related product recommendation unit is used for judging the correlation between the new products of the enterprise and the products purchased in the historical data by the customer through the included angle between vectors and judging whether the customer recommends the new products of the enterprise to a second customer recommended by the first customer, therefore, the love degree of the first customer to the enterprise is analyzed, the image of the enterprise is increased, the customer loyalty judging unit is used for analyzing the loyalty degree of the first customer to the enterprise according to the behavior of the first customer and sending the analyzed loyalty degree to the remote control end, the similar customer maintaining unit is used for distinguishing a customer group with the loyalty degree higher than the preset loyalty degree and maintaining the customer, so that the enterprise can manage the customer group and take time to maintain the connection, and the stable development of the enterprise is guaranteed.
Further, the client group connection establishing module comprises an enterprise type data retrieving unit, an enterprise type correlation degree determining module, a client relationship expanding unit and a client database management unit, wherein the enterprise type data retrieving unit is used for retrieving information of a second enterprise with the same type as the first enterprise in the weak area, the enterprise type correlation degree determining module is used for judging the correlation degree between the second enterprise and the first enterprise product manufacturing type through the obtained second enterprise information so as to analyze whether the second enterprise and the first enterprise establish a cooperative relationship or an enterprise competitive relationship, the client relationship updating unit is used for judging whether the second enterprise and the first enterprise have a cooperative relationship or not when the correlation degree is detected to be low, and traversing whether the enterprise and a client of the second enterprise have a cooperative relationship from the existing client database of the first enterprise, therefore, the purpose that the first enterprise expands the clients is achieved, the client database management unit is used for managing and updating the client relationships stored by the first enterprise, and therefore the relationships among the clients can be called in time, and the relationships among the clients can be concise and clear.
Further, the system comprises the following steps:
z01: the method comprises the steps of positioning the positions of customers according to two-dimensional codes shared by enterprises for the customers, judging the distribution condition of the customers according to the positions on a map, dividing the map into weak areas and concentrated areas according to the distribution of the customers on the map, and classifying the customers according to the quantity of products purchased by different customers, so that the customers are effectively managed;
z02: analyzing the satisfaction degree of a customer to a product according to the strength and the interval time of the first customer purchasing the product in a first enterprise, analyzing the correlation degree of the product purchased by the customer in the enterprise and the product updated by the enterprise in historical data through a product vector included angle, and analyzing whether the product updated by the purchasing enterprise is a second customer recommended by the first customer, thereby analyzing the loyalty degree of the enterprise to the customer and maintaining the customer relationship;
z03: and according to the established customer relationship in the customer concentration area, acquiring the information of the enterprise manufactured products in the weak area, judging the correlation degree between the products to analyze the relationship between the enterprises, traversing whether the enterprise has a cooperative relationship with the customer of the second enterprise through the customer database of the first enterprise, and further expanding and managing the customer relationship.
In step Z01, the conditions for classifying the customer are:
z011: by customer data set R = { R = }1,r2...rmR is the customer data set, MiIs the standard distance between the client and the center, M is the distance between the client and the center, Z is the profit value obtained by the client group enterprises, Zq is the profit value obtained by a single client enterprise, ni、nyRefers to the amount of product purchased by the customer; i.e. ioI refers to the unit price given to the customer; i.e. ikD is the unit price of the product, POriginal transportationIs a cost of individual transport to each customer, POn-site transportationIs the cost of shipping to multiple enterprises;
z012: randomly searching k customers in a customer data set R, and taking one of the k customers as a center D;
z013: any one of the clients s in step Z012i(i =1,2.. t), calculating the distance M between any customers<Mi,
Z014: making the profit earned by the individual client enterprise < the profit earned by the client group enterprise;
wherein: the coordinates of the client in the two-dimensional plane model are W = { (x)1,y1),(x2,y2),(x3,y3)...(xi,yi) Coordinates of customer center W' = (x)k,yk）,
Figure 100002_DEST_PATH_IMAGE001
Profit value obtained by customer group enterprises
Figure 986783DEST_PATH_IMAGE002
Profit value Z obtained by a single customer enterpriseq=(n*i-c*d)-PAnd (5) original transportation.
In step Z02, the first customer takes the data information of the unpurchased product as the starting point of the vector and takes the data information of the product purchased by the first customer to the enterprise as the ending point of the output vector, so as to form the data vector
Figure 100002_DEST_PATH_IMAGE003
(ii) a The second customer takes the data information of the unpurchased product as the starting point of the vector, and the second customer outputs the end point of the vector to the data information formed by the product purchased by the enterprise, thereby forming the data vector
Figure 99096DEST_PATH_IMAGE004
(ii) a The data information of the products manufactured by the enterprise is used as the starting point of the vector, and the data information after the manufacture of the products of the enterprise is used as the end point of the output vector, thereby forming the data vector
Figure 100002_DEST_PATH_IMAGE005
；
Separately computing data vectors
Figure 886792DEST_PATH_IMAGE003
Data vector
Figure 209189DEST_PATH_IMAGE004
And a data vector
Figure 960107DEST_PATH_IMAGE005
Cosine value of (1)
Figure 989243DEST_PATH_IMAGE006
And
Figure 100002_DEST_PATH_IMAGE007
；
Figure 941981DEST_PATH_IMAGE008
；
Figure 100002_DEST_PATH_IMAGE009
；
wherein,
Figure 486095DEST_PATH_IMAGE010
to represent
Figure DEST_PATH_IMAGE011
And
Figure 837442DEST_PATH_IMAGE005
the included angle between the two parts is included,
Figure 845718DEST_PATH_IMAGE012
to represent
Figure DEST_PATH_IMAGE013
And
Figure 912900DEST_PATH_IMAGE005
the included angle between the two parts is included,
Figure 147572DEST_PATH_IMAGE014
refers to a data vector
Figure 240293DEST_PATH_IMAGE011
The die of (a) is used,
Figure DEST_PATH_IMAGE015
refers to a data vector
Figure 165393DEST_PATH_IMAGE013
The die of (a) is used,
Figure 685367DEST_PATH_IMAGE016
refers to a data vector
Figure 531969DEST_PATH_IMAGE005
The mold of (4);
Figure DEST_PATH_IMAGE017
refers to a data vector
Figure 225119DEST_PATH_IMAGE011
And a data vector
Figure 473566DEST_PATH_IMAGE005
The similarity between them;
Figure 164442DEST_PATH_IMAGE018
refers to a data vector
Figure 373706DEST_PATH_IMAGE013
And a data vector
Figure 57497DEST_PATH_IMAGE005
The similarity between them;
when in use
Figure DEST_PATH_IMAGE019
Representing a data vector
Figure 442342DEST_PATH_IMAGE011
And a data vector
Figure 824825DEST_PATH_IMAGE005
The similarity is high, and products related to the enterprise are purchased on behalf of the first customer; when in use
Figure 396752DEST_PATH_IMAGE020
When, it means that the first customer has not purchased products associated with the enterprise;
when in use
Figure 494021DEST_PATH_IMAGE021
Representing a data vector
Figure 451482DEST_PATH_IMAGE013
And a data vector
Figure 749739DEST_PATH_IMAGE005
The similarity is high, the second client recommended by the first client purchases the products related to the enterprise, and the relevance of the second enterprise to the manufacturing type of the first enterprise products is low; when in use
Figure 100002_DEST_PATH_IMAGE022
Representing a data vector
Figure 589388DEST_PATH_IMAGE013
And a data vector
Figure 365714DEST_PATH_IMAGE005
The similarity is low, and represents that the second client recommended by the first client does not purchase the product manufactured by the enterprise, and the correlation between the second enterprise and the product manufactured by the first enterprise is high.
In the step Z02, detecting that the customer database of the first customer is KU = { KU = { (KU) }1,ku2,ku3...kunWhen detecting that the first client recommends the client to purchase the enterprise products for a plurality of times and the times C of the first client purchasing the products in the enterprise>CI and Z>When ZI, it means the loyalty Du of the first customer is high, otherwise the loyalty Du of the first customer is low, the customer group PL = { PL) that will be the same as the loyalty of the first customer1,pl2...plnThe first client is divided separately and stored in a client database management unit and updated in real time, and a client group PL is maintained, wherein CI means that the first client purchases at an enterpriseThe number of times of the standard of the product,
Figure 443260DEST_PATH_IMAGE023
which means a preset standard profit value.
The loyalty, the formula of specific calculation is as follows:
Du=siloi+tji(i+1)loi(i+1);
wherein: siMeans the number of times, tj, that the ith customer purchases a producti（i+1）Means the number of times, lo, that the ith customer recommends a product to the (i + 1) th customer to purchase the productiRefers to the loyalty weight, lo, of the ith customer purchasing a producti(i+1)Recommending the loyalty weight value of products in the enterprise to the (i + 1) th customer by the ith customer;
and ordering the calculated loyalty Du from high to low, and then performing maintenance connection according to the loyalty of the customer group.
In the step Z03, the step of expanding the customer relationship is as follows:
z031: searching enterprises of which the client weak areas are related to manufacturing and selling products of the first enterprise, classifying the enterprises with higher relevance to the first enterprise and the enterprises with lower relevance into different sets, and analyzing the enterprises with lower relevance, wherein the relationship between the enterprises with higher relevance and the first enterprise is a competitive relationship;
z032: in step Z031, it is determined whether a third enterprise having a supply-demand relationship with an enterprise having a low degree of correlation exists in the customer database, and if not, the third enterprise is taken as a customer enterprise to be analyzed; if so, establishing a cooperative relationship with a third enterprise;
z033: modeling an enterprise similar to the third enterprise, obtaining more customer relationships, and updating the customer database.
Compared with the prior art, the invention has the following beneficial effects:
1. through the client group classification module, the distribution condition of the clients on a map can be judged according to the two-dimensional codes sent by an enterprise to the clients, the clients of the same group are established according to the number of the products purchased by the clients in the enterprise and are divided into large clients and small clients, and the small clients meeting the conditions are set as the group, so that more profits can be obtained for the enterprise, and partial expenditure is reduced;
2. the method comprises the steps that through a customer group maintenance module, the number and the corresponding time of products purchased by a customer in an enterprise each time are judged, whether the customer recommends the customer related to the customer to purchase a new product in the enterprise is judged, the loyalty of the customer to the enterprise is judged, modeling is carried out on the customer, and the relationship maintenance is carried out on the customer, so that the enterprise can obtain the support of more customers;
3. the method comprises the steps of obtaining a second enterprise with the same property as a first enterprise through a client group contact establishing module, judging the degree of correlation between the second enterprise and the first enterprise, judging the enterprise cooperating with the second enterprise when the degree of correlation is detected to be low, and judging whether the enterprise related to the second enterprise has a cooperative relationship or not in a client relationship database, so that more clients can be obtained according to the existing client relationship, and the relationship between enterprise products and more clients is increased.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram of the steps of a customer management data processing system and method for multi-source data fusion in accordance with the present invention;
FIG. 2 is a schematic illustration of the region distribution of the customer management data processing system and method of multi-source data fusion according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, the present invention provides the following technical solutions:
the system comprises a client group classification module, a client group maintenance module and a client group contact establishment module;
the customer group classification module is used for displaying the positions of customers and judging whether the distribution condition of the customers on the map is a weak area or a concentrated area, so that the distribution of managed customers is more effective, and customer relations can be further established in the weak area of the customer distribution.
Further, the customer group classification module comprises a two-dimensional code display unit, a customer position acquisition unit, a two-dimensional plane display unit, a customer demand analysis model establishing unit and a customer characteristic judgment unit;
the two-dimensional code display unit is used for displaying order progress and quality inspection conditions of purchased products to a client, so that the client can know dynamic conditions of the purchased products and show the attention degree of an enterprise to the client, the client position acquisition unit is used for acquiring the accurate position of the client by browsing the two-dimensional code, so that the enterprise can know the distribution condition of the client, the two-dimensional plane display unit is used for displaying the position of the client on a map and knowing the distribution condition of the client, the client demand analysis model establishment unit is used for classifying the client according to the quantity of the purchased products of different clients, dividing the client into small clients and large clients, so that the enterprise can classify different clients, further manage the client, the client characteristic judgment unit is used for identifying the characteristics of different clients and setting the client group to increase the enterprise preference, and the order quantity of the enterprise can be shared, so that the enterprise can obtain more order quantity, the profit obtained by the enterprise is increased, and the delivery cost in the way is reduced.
Further, the customer group maintenance module comprises a batch purchasing strength acquisition unit, a time interval determination unit, a related product recommendation unit, a customer loyalty determination unit and a similar customer maintenance unit, wherein the batch purchasing strength acquisition unit is used for acquiring the number of products purchased from an enterprise by a first customer and recording purchasing data, so that the enterprise knows the times of the customer products and knows whether the customer is satisfied with the products and arranges the products in time, the time interval determination unit is used for calling the interval of the time when the first customer purchases the enterprise products in the historical data for calculation so as to analyze the persistence of the customer on the enterprise products, the related product recommendation unit is used for judging the correlation between the new products of the enterprise and the products purchased in the historical data by the customer through the included angle between vectors and judging whether the customer recommends the new products of the enterprise to a second customer recommended by the first customer, therefore, the love degree of the first customer to the enterprise is analyzed, the image of the enterprise is increased, the customer loyalty judging unit is used for analyzing the loyalty degree of the first customer to the enterprise according to the behavior of the first customer and sending the analyzed loyalty degree to the remote control end, the similar customer maintaining unit is used for distinguishing a customer group with the loyalty degree higher than the preset loyalty degree and maintaining the customer, so that the enterprise can manage the customer group and take time to maintain the connection, and the stable development of the enterprise is guaranteed.
Further, the client group connection establishing module comprises an enterprise type data retrieving unit, an enterprise type correlation degree determining module, a client relationship expanding unit and a client database management unit, wherein the enterprise type data retrieving unit is used for retrieving information of a second enterprise with the same type as the first enterprise in the weak area, the enterprise type correlation degree determining module is used for judging the correlation degree between the second enterprise and the first enterprise product manufacturing type through the obtained second enterprise information so as to analyze whether the second enterprise and the first enterprise establish a cooperative relationship or an enterprise competitive relationship, the client relationship updating unit is used for judging whether the second enterprise and the first enterprise have a cooperative relationship or not when the correlation degree is detected to be low, and traversing whether the enterprise and a client of the second enterprise have a cooperative relationship from the existing client database of the first enterprise, therefore, the purpose that the first enterprise expands the clients is achieved, the client database management unit is used for managing and updating the client relationships stored by the first enterprise, and therefore the relationships among the clients can be called in time, and the relationships among the clients can be concise and clear.
Further, the system comprises the following steps:
z01: the method comprises the steps of positioning the positions of customers according to two-dimensional codes shared by enterprises for the customers, judging the distribution condition of the customers according to the positions on a map, dividing the map into weak areas and concentrated areas according to the distribution of the customers on the map, and classifying the customers according to the quantity of products purchased by different customers, so that the customers are effectively managed;
z02: analyzing the satisfaction degree of a customer to a product according to the strength and the interval time of the first customer purchasing the product in a first enterprise, analyzing the correlation degree of the product purchased by the customer in the enterprise and the product updated by the enterprise in historical data through a product vector included angle, and analyzing whether the product updated by the purchasing enterprise is a second customer recommended by the first customer, thereby analyzing the loyalty degree of the enterprise to the customer and maintaining the customer relationship;
z03: and according to the established customer relationship in the customer concentration area, acquiring the information of the enterprise manufactured products in the weak area, judging the correlation degree between the products to analyze the relationship between the enterprises, traversing whether the enterprise has a cooperative relationship with the customer of the second enterprise through the customer database of the first enterprise, and further expanding and managing the customer relationship.
In step Z01, the conditions for classifying the customer are:
z011: by customer data set R = { R = }1,r2...rmR is the customer data set, MiIs the standard distance between the client and the center, M is the distance between the client and the center, Z is the profit value obtained by the client group enterprises, ZqRefers to the profit value, n, obtained by a single client enterprisei、nyRefers to the amount of product purchased by the customer; i.e. ioI refers to the unit price given to the customer; i.e. ikD is the unit price of the product, POriginal transportationIs a cost of individual transport to each customer, POn-site transportationIs the cost of shipping to multiple enterprises;
z012: randomly searching k customers in a customer data set R, and taking one of the k customers as a center D;
z013: any one of the clients s in step Z012i(i =1,2.. t), calculating the distance M between any customers<Mi,
Z014: making the profit earned by the individual client enterprise < the profit earned by the client group enterprise;
wherein: the coordinates of the client in the two-dimensional plane model are W = { (x)1,y1),(x2,y2),(x3,y3)...(xi,yi) Coordinates of customer center W' = (x)k,yk）,
Figure 37053DEST_PATH_IMAGE001
Profit value obtained by customer group enterprises
Figure 52413DEST_PATH_IMAGE002
Profit value Z obtained by a single customer enterpriseq=(n*i-c*d)-POriginal transportation;
the method comprises the steps of judging position information and distance of a client on a map through a Euclidean distance formula, setting the client as a core, forming different client groups according to the geographic position and set distance conditions, enabling enterprises to obtain more resources, subtracting actual quoted prices according to the quantity and unit price purchased by the client by setting profit values obtained by client group enterprises, wherein transportation cost also necessarily forms the profit values of the client group enterprises, and setting the profit values obtained by a single client and distribution cost of single-pass delivery and the client groups simultaneously, so that the benefit of setting the client groups is shown.
In said step Z02, the first client isThe data information of the unpurchased products is used as the starting point of the vector, the data information formed by the products purchased by the enterprise of the first client is used as the end point of the output vector, and the data vector is formed
Figure 147277DEST_PATH_IMAGE024
(ii) a The second customer takes the data information of the unpurchased product as the starting point of the vector, and the second customer outputs the end point of the vector to the data information formed by the product purchased by the enterprise, thereby forming the data vector
Figure DEST_PATH_IMAGE025
(ii) a The data information of the products manufactured by the enterprise is used as the starting point of the vector, and the data information after the manufacture of the products of the enterprise is used as the end point of the output vector, thereby forming the data vector
Figure 361221DEST_PATH_IMAGE026
；
Separately computing data vectors
Figure DEST_PATH_IMAGE027
Data vector
Figure 781706DEST_PATH_IMAGE028
And a data vector
Figure DEST_PATH_IMAGE029
Cosine value of (1)
Figure 81101DEST_PATH_IMAGE030
And
Figure 182918DEST_PATH_IMAGE031
；
Figure 985789DEST_PATH_IMAGE032
；
Figure DEST_PATH_IMAGE033
；
wherein,
Figure 842755DEST_PATH_IMAGE034
to represent
Figure DEST_PATH_IMAGE035
And
Figure 629446DEST_PATH_IMAGE036
the included angle between the two parts is included,
Figure DEST_PATH_IMAGE037
to represent
Figure 490215DEST_PATH_IMAGE038
And
Figure 6647DEST_PATH_IMAGE026
the included angle between the two parts is included,
Figure DEST_PATH_IMAGE039
refers to a data vector
Figure 909881DEST_PATH_IMAGE040
The die of (a) is used,
Figure DEST_PATH_IMAGE041
refers to a data vector
Figure 433135DEST_PATH_IMAGE042
The die of (a) is used,
Figure 158645DEST_PATH_IMAGE043
refers to a data vector
Figure 919797DEST_PATH_IMAGE044
The mold of (4);
Figure 931615DEST_PATH_IMAGE045
refers to a data vector
Figure 896160DEST_PATH_IMAGE011
And a data vector
Figure 284416DEST_PATH_IMAGE005
The similarity between them;
Figure 900074DEST_PATH_IMAGE018
refers to a data vector
Figure 958160DEST_PATH_IMAGE038
And a data vector
Figure 800214DEST_PATH_IMAGE044
The similarity between them;
when in use
Figure 851216DEST_PATH_IMAGE046
Representing a data vector
Figure 72113DEST_PATH_IMAGE040
And a data vector
Figure 956892DEST_PATH_IMAGE036
The similarity is high, and products related to the enterprise are purchased on behalf of the first customer; when in use
Figure 879717DEST_PATH_IMAGE047
When, it means that the first customer has not purchased products associated with the enterprise;
when in use
Figure 485142DEST_PATH_IMAGE048
Representing a data vector
Figure 950759DEST_PATH_IMAGE049
And a data vector
Figure 131073DEST_PATH_IMAGE036
The similarity is high, the second enterprise recommended by the first client purchases the products related to the enterprise, and the second enterprise is related to the manufacturing types of the products of the first enterpriseLow; when in use
Figure 26348DEST_PATH_IMAGE050
Representing a data vector
Figure 825677DEST_PATH_IMAGE051
And a data vector
Figure 10714DEST_PATH_IMAGE052
The similarity is low, the second client recommended by the first client does not purchase the manufactured products of the enterprises, and the relevance between the second enterprises and the manufactured types of the first enterprise products is high;
through the calculation and judgment of the cosine value, whether the purchased enterprise products are the customers or the products of the related customers purchased enterprises are analyzed, the cosine similarity is used for setting vectors for purchasing more enterprise products, more similar enterprises can be set according to the vectors for maintenance, and the enterprise image is increased.
In the step Z02, detecting that the customer database of the first customer is KU = { KU = { (KU) }1,ku2,ku3...kunWhen detecting that the first client recommends the client to purchase the enterprise products for a plurality of times and the times C of the first client purchasing the products in the enterprise>CI and Z>When ZI, it means the loyalty Du of the first customer is high, otherwise the loyalty Du of the first customer is low, the customer group PL = { PL) that will be the same as the loyalty of the first customer1,pl2...plnThe client database management unit is divided and stored in the client database management unit separately and updated in real time, and maintains a client group PL, wherein CI refers to the standard times of products purchased by a first client in an enterprise,
Figure 847082DEST_PATH_IMAGE023
which means a preset standard profit value.
The loyalty, the formula of specific calculation is as follows:
Du=siloi+tji(i+1)loi(i+1);
wherein: siMeans that the ith customer purchases the productNumber of times of (tj)i（i+1）Means the number of times, lo, that the ith customer recommends a product to the (i + 1) th customer to purchase the productiRefers to the loyalty weight, lo, of the ith customer purchasing a producti(i+1)Recommending the loyalty weight value of products in the enterprise to the (i + 1) th customer by the ith customer;
ordering the calculated loyalty Du from high to low, and then performing maintenance connection according to the loyalty of the customer group;
the loyalty is set, the times that different customers recommend the customers to buy the enterprise products are used as one of the judgment modes, and the judgment mode forms the loyalty of the users.
In the step Z03, the step of expanding the customer relationship is as follows:
z031: searching enterprises of which the client weak areas are related to manufacturing and selling products of the first enterprise, classifying the enterprises with higher relevance to the first enterprise and the enterprises with lower relevance into different sets, and analyzing the enterprises with lower relevance, wherein the relationship between the enterprises with higher relevance and the first enterprise is a competitive relationship;
z032: in step Z031, it is determined whether a third enterprise having a supply-demand relationship with an enterprise having a low degree of correlation exists in the customer database, and if not, the third enterprise is taken as a customer enterprise to be analyzed; if so, establishing a cooperative relationship with a third enterprise;
z033: modeling an enterprise similar to the third enterprise, obtaining more customer relationships, and updating the customer database.
For example, in the description of fig. 2, the businesses distributed on the map are specifically A, B, C, D, E, F and G, resulting in A, B, C, D, E, F and G being businesses distributed in the weak area; acquiring a first enterprise which purchases products in the enterprise from the weak area as G, and acquiring areas with high correlation with the manufactured products of the first enterprise, such as A, B and C; and the A, the B and the C are in competition relation with the first enterprise, whether the first enterprise has a cooperative enterprise and an enterprise D, E, F which have a supply and demand relation is judged, if so, the cooperative relation is established, the client database is updated, and if not, the first enterprise is marked as an enterprise to be analyzed.
Example 1: the profit value obtained by the customer segment is compared with the profit values obtained by the individual customers:
the coordinates of the client in the two-dimensional plane model are W = { (x)1,y1),(x2,y2) } = { (500, 1000), (700,2500) }, coordinate of client center W' = (x)1,y1) The price per unit given to the customer is 400, the price per unit of the actual product is 300, 1500 is required for individual transportation to the customer group, and the freight rate given to the customer group is 1000
Figure 619866DEST_PATH_IMAGE001
=1513；
M is within the range of the preset distance,
profit value obtained by customer group enterprises
Figure 81941DEST_PATH_IMAGE053
=（30*400）-（30*300）+500=3300
Profit value Z obtained by individual client enterpriseq=(n*i-c*d)=（30*400-30*300）=3000；
3300>3000, indicating that the profit value obtained by the customer base is higher than that of a single customer.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (8)

1. The client management data processing system for multi-source data fusion is characterized in that: the system comprises a client group classification module, a client group maintenance module and a client group contact establishment module;
the customer group classification module is used for displaying the positions of customers and judging whether the distribution conditions of the customers on a map are weak areas or concentrated areas, the customer group maintenance module is used for judging the loyalty of the customers to enterprises according to the product purchasing strength and the purchasing interval time of different customer groups and maintaining similar customer groups, and the customer group connection establishment module is used for further establishing new customer relations in the weak areas of the customer distribution through the customer relations established in the customer concentrated areas and managing the new customer relations;
the customer group maintenance module comprises a batch purchasing power acquisition unit, a time interval determination unit, a related product recommendation unit, a customer loyalty judgment unit and a similar customer maintenance unit, wherein the batch purchasing power acquisition unit is used for acquiring the quantity of products purchased from an enterprise by a first customer and recording purchasing data, the time interval determination unit is used for calling the interval of the time for the first customer to purchase the products of the enterprise in historical data for calculation, the related product recommendation unit is used for judging the correlation between new products of the enterprise and the products purchased in the historical data by the customer through the size of an included angle between vectors and judging whether the customer recommends new products of the enterprise to a second customer recommended by the first customer or not, the image of the enterprise is increased, and the customer loyalty judgment unit is used for analyzing the loyalty of the first customer to the enterprise according to the behavior of the first customer, and the similar customer maintenance unit is used for distinguishing a customer group with the loyalty higher than the preset loyalty and performing customer maintenance.
2. The multi-source data-converged customer management data processing system of claim 1, wherein: the client group classification module comprises a two-dimensional code display unit, a client position acquisition unit, a two-dimensional plane display unit, a client demand analysis model establishing unit and a client characteristic judgment unit;
the two-dimensional code display unit is used for displaying order progress and quality inspection conditions of purchased products to a customer, the customer position acquisition unit is used for acquiring the accurate position of the customer by browsing the two-dimensional code, and the two-dimensional plane display unit is used for displaying the position of the customer on a map and knowing the distribution conditions of the customer; the customer demand analysis model establishing unit is used for classifying customers according to the quantity of products purchased by different customers and dividing the customers into small customers and big customers; the customer characteristic judging unit is used for identifying the characteristics of different customers, setting customer groups to increase the enterprise preference and sharing the order quantity of the enterprise.
3. The multi-source data-converged customer management data processing system of claim 1, wherein: the client group connection establishing module comprises an enterprise type data calling unit, an enterprise type correlation degree determining module, a client relationship expanding unit and a client database management unit, wherein the enterprise type data calling unit is used for calling information of a second enterprise with the same type as the first enterprise in the weak area, the enterprise type correlation degree determining module is used for judging the correlation degree between the second enterprise and the first enterprise product manufacturing type according to the obtained second enterprise information, the client relationship updating unit is used for judging whether an enterprise and a client of the second enterprise have a cooperative relationship or not from an existing client database of the first enterprise in a traversing manner when the correlation degree is detected to be low, and the client database management unit is used for managing and updating the client relationship stored in the first enterprise, thereby being capable of calling the relation among the clients in time.
4. The client management data processing method for multi-source data fusion is characterized by comprising the following steps: the client management data processing method comprises the following steps:
z01: the method comprises the steps of positioning the positions of customers according to two-dimensional codes shared by enterprises for the customers, judging the distribution condition of the customers according to the positions on a map, dividing the map into weak areas and concentrated areas according to the distribution of the customers on the map, and classifying the customers according to the quantity of products purchased by different customers, so that the customers are effectively managed;
z02: analyzing the satisfaction degree of a customer to a product according to the strength and the interval time of the first customer purchasing the product in a first enterprise, analyzing the correlation degree of the product purchased by the customer in the enterprise and the product updated by the enterprise in historical data through a product vector included angle, and analyzing whether the product updated by the purchasing enterprise is a second customer recommended by the first customer, thereby analyzing the loyalty degree of the enterprise to the customer and maintaining the customer relationship;
z03: according to the established customer relationship in the customer concentration area, obtaining the information of the enterprise manufactured products in the weak area, judging the correlation degree between the products to analyze the relationship between the enterprises, traversing whether the enterprise has a cooperative relationship with the customer of the second enterprise through the customer database of the first enterprise, further expanding the customer relationship and managing;
in step Z01, the conditions for classifying the customer are:
z011: by customer data set R = { R = }1,r2...rmR is the customer data set, MiIs the standard distance between the client and the center, M is the distance between the client and the center, Z is the profit value obtained by the client group enterprises, Zq is the profit value obtained by a single client enterprise, ni、nyRefers to the amount of product purchased by the customer; i.e. ioI refers to the unit price given to the customer; i.e. ikD is the unit price of the product, POriginal transportationIs a cost of individual transport to each customer, POn-site transportationTo multiple enterprisesThe cost of transportation;
z012: randomly searching k customers in a customer data set R, and taking one of the k customers as a center D;
z013: any one of the clients s in step Z012i(i =1,2.. t), calculating the distance M between any customers<Mi,
Z014: making the profit earned by the individual client enterprise < the profit earned by the client group enterprise;
wherein: the coordinates of the client in the two-dimensional plane model are W = { (x)1,y1),(x2,y2),(x3,y3)...(xi,yi) Coordinates of customer center W' = (x)k,yk）,
Figure DEST_PATH_IMAGE001
Profit value obtained by customer group enterprises
Figure 982295DEST_PATH_IMAGE002
Profit value Z obtained by a single customer enterpriseq=(n*i-c*d)-PAnd (5) original transportation.
5. The multi-source data-converged customer management data processing method according to claim 4, wherein: in step Z02, the first customer takes the data information of the unpurchased product as the starting point of the vector and takes the data information of the product purchased by the first customer to the enterprise as the ending point of the output vector, so as to form the data vector
Figure DEST_PATH_IMAGE003
(ii) a The second customer takes the data information of the unpurchased product as the starting point of the vector, and the second customer outputs the end point of the vector to the data information formed by the product purchased by the enterprise, thereby forming the data vector
Figure 666043DEST_PATH_IMAGE004
(ii) a The enterprise starts to manufacture the product data information as the starting point of the vector, and the enterprise product manufacture nodeThe post-processed data information is used as an end point of the output vector, thereby forming a data vector
Figure DEST_PATH_IMAGE005
；
Separately computing data vectors
Figure 830177DEST_PATH_IMAGE006
Data vector
Figure 367468DEST_PATH_IMAGE004
And a data vector
Figure 427697DEST_PATH_IMAGE005
Cosine value of (1)
Figure DEST_PATH_IMAGE007
And
Figure 401338DEST_PATH_IMAGE008
；
Figure DEST_PATH_IMAGE009
；
Figure 41267DEST_PATH_IMAGE010
；
wherein,
Figure 964224DEST_PATH_IMAGE011
to represent
Figure 867458DEST_PATH_IMAGE006
And
Figure 390712DEST_PATH_IMAGE005
the included angle between the two parts is included,
Figure DEST_PATH_IMAGE012
to represent
Figure 240856DEST_PATH_IMAGE004
And
Figure 539025DEST_PATH_IMAGE005
the included angle between the two parts is included,
Figure 144319DEST_PATH_IMAGE013
refers to a data vector
Figure 92552DEST_PATH_IMAGE006
The die of (a) is used,
Figure DEST_PATH_IMAGE014
refers to a data vector
Figure 277546DEST_PATH_IMAGE004
The die of (a) is used,
Figure 768570DEST_PATH_IMAGE015
refers to a data vector
Figure 826656DEST_PATH_IMAGE005
The mold of (4);
Figure DEST_PATH_IMAGE016
refers to a data vector
Figure 324502DEST_PATH_IMAGE006
And a data vector
Figure 391815DEST_PATH_IMAGE005
The similarity between them;
Figure 861980DEST_PATH_IMAGE017
refers to a data vector
Figure DEST_PATH_IMAGE018
And a data vector
Figure 418863DEST_PATH_IMAGE005
The similarity between them;
when in use
Figure 607268DEST_PATH_IMAGE019
Representing a data vector
Figure 478272DEST_PATH_IMAGE006
And a data vector
Figure 412730DEST_PATH_IMAGE005
The similarity is high, and products related to the enterprise are purchased on behalf of the first customer; when in use
Figure DEST_PATH_IMAGE020
When, it means that the first customer has not purchased products associated with the enterprise;
when in use
Figure 796307DEST_PATH_IMAGE021
Representing a data vector
Figure 755165DEST_PATH_IMAGE018
And a data vector
Figure 164281DEST_PATH_IMAGE005
The similarity is high, the second client recommended by the first client purchases the products related to the enterprise, and the relevance of the second enterprise to the manufacturing type of the first enterprise products is low; when in use
Figure DEST_PATH_IMAGE022
Representing a data vector
Figure 343458DEST_PATH_IMAGE018
And a data vector
Figure 101199DEST_PATH_IMAGE005
The similarity is low, and represents that the second client recommended by the first client does not purchase the product manufactured by the enterprise, and the correlation between the second enterprise and the product manufactured by the first enterprise is high.
6. The multi-source data-converged customer management data processing method according to claim 4, wherein: in the step Z02, detecting that the customer database of the first customer is KU = { KU = { (KU) }1,ku2,ku3...kunWhen detecting that the first client recommends the client to purchase the enterprise products for a plurality of times and the times C of the first client purchasing the products in the enterprise>CI and Z>
Figure 529775DEST_PATH_IMAGE023
When it is indicated that the loyalty Du of the first customer is high, otherwise the loyalty Du of the first customer is low, the customer group PL = { PL) that will be the same as the loyalty of the first customer1,pl2...plnThe client is divided and stored in a client database management unit independently and updated in real time, and a client group PL is maintained; CI refers to the standard number of times the first customer purchases a product at the business itself,
Figure DEST_PATH_IMAGE024
which means a preset standard profit value.
7. The multi-source data-converged customer management data processing method according to claim 6, wherein: the loyalty, the formula of specific calculation is as follows:
Du=siloi+tji(i+1)loi(i+1)；
wherein: siMeans the number of times, tj, that the ith customer purchases a producti（i+1）Means the number of times, lo, that the ith customer recommends a product to the (i + 1) th customer to purchase the productiRefers to the loyalty weight, lo, of the ith customer purchasing a producti(i+1)Recommending the loyalty weight value of products in the enterprise to the (i + 1) th customer by the ith customer;
and ordering the calculated loyalty Du from high to low, and then performing maintenance connection according to the loyalty of the customer group.
8. The multi-source data-converged customer management data processing method according to claim 4, wherein: in the step Z03, the step of expanding the customer relationship is as follows:
z031: searching enterprises of which the client weak areas are related to manufacturing and selling products of the first enterprise, classifying the enterprises with higher relevance to the first enterprise and the enterprises with lower relevance into different sets, and analyzing the enterprises with lower relevance, wherein the relationship between the enterprises with higher relevance and the first enterprise is a competitive relationship;
z032: in step Z031, it is determined whether a third enterprise having a supply-demand relationship with an enterprise having a low degree of correlation exists in the customer database, and if not, the third enterprise is taken as a customer enterprise to be analyzed; if so, establishing a cooperative relationship with a third enterprise;
z033: modeling an enterprise similar to the third enterprise, obtaining more customer relationships, and updating the customer database.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7912751B1 (en) * 2007-08-27 2011-03-22 Haytham Issa Allos System and method for customer loyalty system utilizing referrals
WO2012138561A2 (en) * 2011-04-05 2012-10-11 Mobilaurus, Llc System and method of administering a loyalty-based incentive rewards program
CN105303415A (en) * 2015-12-02 2016-02-03 济南塑研塑料制品有限公司 Drinking barrel client location and distribution management method
CN106354848A (en) * 2016-08-31 2017-01-25 成都数联铭品科技有限公司 System for analyzing and displaying geographic information of associated enterprises
CN111242573A (en) * 2020-01-08 2020-06-05 江苏智谋科技有限公司 Customer relationship management system based on big data and knowledge management

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7912751B1 (en) * 2007-08-27 2011-03-22 Haytham Issa Allos System and method for customer loyalty system utilizing referrals
WO2012138561A2 (en) * 2011-04-05 2012-10-11 Mobilaurus, Llc System and method of administering a loyalty-based incentive rewards program
CN105303415A (en) * 2015-12-02 2016-02-03 济南塑研塑料制品有限公司 Drinking barrel client location and distribution management method
CN106354848A (en) * 2016-08-31 2017-01-25 成都数联铭品科技有限公司 System for analyzing and displaying geographic information of associated enterprises
CN111242573A (en) * 2020-01-08 2020-06-05 江苏智谋科技有限公司 Customer relationship management system based on big data and knowledge management

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