CN110503446A - The client segmentation method and decision-making technique of electric business platform based on clustering algorithm - Google Patents
The client segmentation method and decision-making technique of electric business platform based on clustering algorithm Download PDFInfo
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Abstract
The client segmentation method and decision-making technique of present invention relates particularly to a kind of electric business platform based on clustering algorithm, including step 1: purchase commodity probability of the data user of the commodity of acquisition user's purchase in electric business website carries out set and summarizes；Step 2: pre-processing the data set obtained in step 1, to obtain the value vector of each client；The value vector is made of six indexs of LCRFMD: being carried out automatic cluster to client according to six indexs of LCRFMD using clustering algorithm and is divided group, by customer segmentation at k class, corresponding k customers, k is the classification number of setting and is natural number greater than 1.Step 4: the group segmented according to automatic cluster, the Marketing Model that selection is adapted therewith.Step 5: selected Marketing Model is calculated and exports result.The present invention uses the reliability for calculating based on clustering algorithm and improving analysis, and data normalization processing further solves data mode consistency problem, reduces the complexity of system.
Description
Technical field
The invention belongs to data mining technology fields, and in particular to a kind of client of the electric business platform based on clustering algorithm point
Class method and decision-making technique.
Background technique
Under internet+trend, the maximum variation in enterprise market is that intermediate link is met with and squeezes comprehensively that supply chain is not
Disconnected to shorten, the relationship of supplier and direct customer increasingly further.Collect the B2B2C electricity that buyer ensures, seller ensures, air control is integrated
Sub- business model can help trade company to build up mutual trust with buyer, ensure that it securely carries out online transaction under close protection.
Four supplier, purchaser, bank's (payment system) and insurance roles are passed through e-platform one-key operation by B2B2C mode.
Since e-platform scale increases, the difference of client context, behavioural characteristic, reusing product behavior accurately to user is had
Effect is estimated, and is the important evidence of the distribution of optimization of enterprises marketing resource, orientation Push Service advertisement.
Summary of the invention
1, technical problem to be solved:
The present invention needs to provide a kind of client segmentation of electric business platform based on clustering algorithm according to electric business platform commercial product recommending
Method and decision-making technique, by the way that the method increase the reliability of analysis, data normalization processing further solves data shape
Formula consistency problem reduces the complexity of system.
2, technical solution:
A kind of the client segmentation method and decision-making technique of the electric business platform based on clustering algorithm, it is characterised in that:
Step 1: the data of the commodity of acquisition user's purchase, and purchase commodity probability of the user in electric business website is collected
Conjunction summarizes；The data of the commodity of user's purchase include user name, merchandise classification, trade name, price, quantity, time, payment
Mode and browsing time.
Step 2: pre-processing the data set obtained in step 1, including data cleansing, attitude layer and data
Transformation, to obtain the value vector of each client；The value vector is made of six indexs of LCRFMD: L indicates client's note
Number of days before the volume time to this monitoring time, C indicate the classification of user's purchase commodity before from registion time to this monitoring time
Number, R indicate that the number of days of client's this monitoring of the time interval of middle last time purchase before this monitoring, F indicate client at this
Purchase number before monitoring time, M indicate accumulative cost of the client in this consumption classification in analysis observation window, and D is indicated
Client buys the average discount amount that commodity are enjoyed every time before book monitoring time.
Step 3: carrying out automatic cluster to client according to six indexs of LCRFMD using clustering algorithm and divide group, and client is thin
It is divided into k class, corresponding k customers, k is the classification number of setting and is natural number greater than 1.
Step 4: the group segmented according to automatic cluster, the Marketing Model that selection is adapted therewith.
Step 5: selected Marketing Model is calculated and exports result.
Further, the step 1 further includes that default certain time interval classifies to client or default client
The length of time of registration is classified.
Further: the process that data convert in step 2 are as follows: data flow into standardized module, by the data of inflow
It is processed into the data with unified format；Index computing module is carried out according to different value vector calculation methods to meter
It calculates.
It further, include: 31 to form the LCRFMD vector of all clients to the subdivision class process of client in step 3
Sample set selects k LCRFMD vector as cluster centre by calculating under initial situation from sample set；32 one by one by sample
The LRFMD vector of this concentration distributes to k cluster centre by minimal distance principle, forms k population；33 rebuild each kind
The central point of group, makes its cluster centre new as population；If each new cluster centre of population is equal at a distance from old cluster centre
Less than threshold value, then calculating terminates, and using k current population as classification results, otherwise switchs to execute step 32.
3, the utility model has the advantages that
The reliability of analysis is improved by calculating based on clustering algorithm, data normalization processing further solves data mode
Consistency problem reduces the complexity of system.It is segmented by automatic cluster visitor group, further improves the prediction of Marketing Model
Effect.
Detailed description of the invention
Fig. 1 is flow chart of the invention.
In order to more specifically describe the present invention, with reference to the accompanying drawing and specific embodiment is to this hair for specific embodiment
Bright technical solution
It is described in detail.
A kind of the client segmentation method and decision-making technique of the electric business platform based on clustering algorithm, it is characterised in that:
Step 1: the data of the commodity of acquisition user's purchase, and purchase commodity probability of the user in electric business website is collected
Conjunction summarizes；The data of the commodity of user's purchase include user name, merchandise classification, trade name, price, quantity, time, payment
Mode and browsing time.
Step 2: pre-processing the data set obtained in step 1, including data cleansing, attitude layer and data
Transformation, to obtain the value vector of each client；The value vector is made of six indexs of LCRFMD: L indicates client's note
Number of days before the volume time to this monitoring time, C indicate the classification of user's purchase commodity before from registion time to this monitoring time
Number, R indicate that the number of days of client's this monitoring of the time interval of middle last time purchase before this monitoring, F indicate client at this
Purchase number before monitoring time, M indicate accumulative cost of the client in this consumption classification in analysis observation window, and D is indicated
Client buys the average discount amount that commodity are enjoyed every time before book monitoring time.
Step 3: carrying out automatic cluster to client according to six indexs of LCRFMD using clustering algorithm and divide group, and client is thin
It is divided into k class, corresponding k customers, k is the classification number of setting and is natural number greater than 1.
Step 4: the group segmented according to automatic cluster, the Marketing Model that selection is adapted therewith.
Step 5: selected Marketing Model is calculated and exports result.
Further, the step 1 further includes that default certain time interval classifies to client or default client
The length of time of registration is classified.
Further: the process that data convert in step 2 are as follows: data flow into standardized module, by the data of inflow
It is processed into the data with unified format；Index computing module is carried out according to different value vector calculation methods to meter
It calculates.
It further, include: 31 to form the LCRFMD vector of all clients to the subdivision class process of client in step 3
Sample set selects k LCRFMD vector as cluster centre by calculating under initial situation from sample set；32 one by one by sample
The LRFMD vector of this concentration distributes to k cluster centre by minimal distance principle, forms k population；33 rebuild each kind
The central point of group, makes its cluster centre new as population；If each new cluster centre of population is equal at a distance from old cluster centre
Less than threshold value, then calculating terminates, and using k current population as classification results, otherwise switchs to execute step 32.
Although the present invention has been described by way of example and in terms of the preferred embodiments, they be not it is for the purpose of limiting the invention, it is any ripe
This those skilled in the art is practised, without departing from the spirit and scope of the invention, can make various changes or retouch from working as, therefore guarantor of the invention
Shield range should be subject to what claims hereof protection scope was defined.
Claims (4)
1. a kind of the client segmentation method and decision-making technique of the electric business platform based on clustering algorithm, it is characterised in that:
Step 1: the data of the commodity of acquisition user's purchase, and purchase commodity probability of the user in electric business website is collected
Conjunction summarizes；The data of the commodity of user's purchase include user name, merchandise classification, trade name, price, quantity, time, payment
Mode and browsing time；
Step 2: pre-processing the data set obtained in step 1, including data cleansing, attitude layer and data become
It changes, to obtain the value vector of each client；The value vector is made of six indexs of LCRFMD: L indicates client enrollment
Number of days before time to this monitoring time, C indicate the classification number of user's purchase commodity before from registion time to this monitoring time,
R indicates that the number of days of client's this monitoring of the time interval of middle last time purchase before this monitoring, F indicate client in this monitoring
Purchase number before time, M indicate accumulative cost of the client in this consumption classification in analysis observation window, and D indicates client
Buy the average discount amount that commodity are enjoyed every time before book monitoring time；
Step 3: automatic cluster is carried out to client according to six indexs of LCRFMD using clustering algorithm and divides group, by customer segmentation at k
Class, corresponding k customers, k are the classification number of setting and are natural number greater than 1；
Step 4: the group segmented according to automatic cluster, the Marketing Model that selection is adapted therewith；
Step 5: selected Marketing Model is calculated and exports result.
2. the client segmentation method and decision-making technique of a kind of electric business platform based on clustering algorithm according to claim 1,
It is characterized by: the step 1 further includes that default certain time interval classifies to client or default client enrollment
Length of time is classified.
3. the client segmentation method and decision-making technique of a kind of electric business platform based on clustering algorithm according to claim 1,
It is characterized by: the process that data convert in step 2 are as follows:
Data flow into standardized module, and the data of inflow are processed into the data with unified format；
Index computing module carries out vector calculating according to different value vector calculation methods.
4. the client segmentation method and decision-making technique of a kind of electric business platform based on clustering algorithm according to claim 1,
It is characterized by: including: to the subdivision class process of client in step 3
The LCRFMD vector of all clients is formed sample set by 31, is passed through calculating under initial situation and is selected k from sample set
LCRFMD vector is as cluster centre；
LRFMD vector in sample set is distributed to k cluster centre by minimal distance principle one by one by 32, forms k population；
33 rebuild the central point of each population, make its cluster centre new as population；If each new cluster centre of population
Threshold value is respectively less than at a distance from old cluster centre, then calculating terminates, and using k current population as classification results, otherwise switchs to
Execute step 32.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111695941A (en) * | 2020-06-15 | 2020-09-22 | 广州探途网络技术有限公司 | Commodity transaction website data analysis method and device and electronic equipment |
| CN112017062A (en) * | 2020-07-15 | 2020-12-01 | 北京淇瑀信息科技有限公司 | Resource limit distribution method and device based on guest group subdivision and electronic equipment |
| CN113159881A (en) * | 2021-03-15 | 2021-07-23 | 杭州云搜网络技术有限公司 | Data clustering and B2B platform customer preference obtaining method and system |
| CN113781108A (en) * | 2021-08-30 | 2021-12-10 | 武汉理工大学 | E-commerce platform customer segmentation method and device, electronic equipment and storage medium |
| WO2022095864A1 (en) * | 2020-11-05 | 2022-05-12 | 西安邮电大学 | E-commerce platform customer segmentation method based on weighted rfm model |
| CN116205675A (en) * | 2023-04-28 | 2023-06-02 | 华南师范大学 | Data acquisition method and device based on thread division |
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| US20150142521A1 (en) * | 2013-11-20 | 2015-05-21 | Sears Brands, Llc | Customer clustering using integer programming |
| CN106529968A (en) * | 2016-09-29 | 2017-03-22 | 深圳大学 | Customer classification method and system thereof based on transaction data |
| CN107133652A (en) * | 2017-05-17 | 2017-09-05 | 国网山东省电力公司烟台供电公司 | Electricity customers Valuation Method and system based on K means clustering algorithms |
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| US20150142521A1 (en) * | 2013-11-20 | 2015-05-21 | Sears Brands, Llc | Customer clustering using integer programming |
| CN106529968A (en) * | 2016-09-29 | 2017-03-22 | 深圳大学 | Customer classification method and system thereof based on transaction data |
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111695941A (en) * | 2020-06-15 | 2020-09-22 | 广州探途网络技术有限公司 | Commodity transaction website data analysis method and device and electronic equipment |
| CN112017062A (en) * | 2020-07-15 | 2020-12-01 | 北京淇瑀信息科技有限公司 | Resource limit distribution method and device based on guest group subdivision and electronic equipment |
| CN112017062B (en) * | 2020-07-15 | 2024-06-07 | 北京淇瑀信息科技有限公司 | Resource quota distribution method and device based on guest group subdivision and electronic equipment |
| WO2022095864A1 (en) * | 2020-11-05 | 2022-05-12 | 西安邮电大学 | E-commerce platform customer segmentation method based on weighted rfm model |
| CN113159881A (en) * | 2021-03-15 | 2021-07-23 | 杭州云搜网络技术有限公司 | Data clustering and B2B platform customer preference obtaining method and system |
| CN113159881B (en) * | 2021-03-15 | 2022-08-12 | 杭州云搜网络技术有限公司 | Data clustering and B2B platform customer preference obtaining method and system |
| CN113781108A (en) * | 2021-08-30 | 2021-12-10 | 武汉理工大学 | E-commerce platform customer segmentation method and device, electronic equipment and storage medium |
| CN116205675A (en) * | 2023-04-28 | 2023-06-02 | 华南师范大学 | Data acquisition method and device based on thread division |
| CN116205675B (en) * | 2023-04-28 | 2023-09-08 | 华南师范大学 | Data acquisition method and device based on thread division |
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