CN110689355A - Client classification method, device, computer equipment and storage medium - Google Patents
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Abstract
The application relates to a customer classification method, a customer classification device, computer equipment and a storage medium. The method comprises the following steps: the method comprises the steps of obtaining historical data of a client, extracting data of classification indexes based on the historical data of the client, wherein the classification indexes comprise the first order time of the client, the last order time, consumption frequency, consumption amount, the average value of re-bubble ratio and premium, establishing a client value analysis model based on the classification indexes, and inputting the extracted data of the classification indexes into the client value analysis model to obtain a client classification result. The client classification method, the client classification device, the computer equipment and the storage medium increase three-dimensional indexes on the basis of the traditional RFM model classification method, and have stronger classification pertinence and more accurate classification.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method and an apparatus for classifying clients, a computer device, and a storage medium.
Background
The coming of the information age changes the marketing focus of enterprises from product centers to client centers, and the management of client relationships becomes a core problem of the enterprises. The core problem of customer relationship management is customer classification, low-value customers and high-value customers are distinguished through customer classification, enterprises make optimized personalized service schemes for different customers, different marketing strategies are adopted, limited marketing resources are concentrated on the high-value customers, and the enterprise profit maximization target is achieved.
The rapid development of the internet, the logistics industry rises rapidly with the help of the e-commerce industry, the status of the logistics industry in the economy of China is steadily promoted, along with the increase of the client scale of the logistics enterprise, the client background and the behavior characteristics are different, the accurate client classification result is an important basis for optimizing marketing resource allocation of the enterprise, and the client classification increasingly becomes one of the key problems to be solved urgently in the client relationship management. With the depth of the machine learning method, statistical analysis and data mining are widely applied to the customer group classification and identification research, however, less research is applied to the identification and classification of the customer group of the piece goods logistics.
The traditional customer classification mainly adopts an RFM (radio frequency memory) model classification method for classification, but indexes used for classification in the RFM model classification method are wide in selection and inaccurate in classification.
Disclosure of Invention
Based on this, it is necessary to provide a client classification method, apparatus, computer device and storage medium for solving the technical problems of wide selection of indexes for classification and inaccurate classification in the RFM model classification method.
A customer categorization method, the method comprising:
acquiring historical data of a client;
extracting data of classification indexes based on the historical data of the customers, wherein the classification indexes comprise the first order time of the customers, the last order time, the consumption frequency, the consumption amount, the average value of the re-foaming ratio and the premium;
establishing a customer value analysis model based on the classification indexes;
and inputting the extracted data of the classification indexes into the customer value analysis model to obtain a customer classification result.
In one embodiment, the customer history data includes customer number, time of first order, shipping volume, shipping cost amount, quote cost, number of days to place orders, maximum interval to place orders, average interval to place orders, weight, volume, industry in place, and shipping cost payment form.
In one embodiment, the extracting data of the classification index based on the customer history data further comprises:
and performing data cleaning, attribute specification and/or data transformation on the historical data of the client.
In one embodiment, before performing data cleansing, attribute reduction, and/or data transformation on the customer history data, the method further includes:
analyzing missing values and abnormal values of the client historical data;
the step of performing data cleansing on the customer history data comprises:
and clearing data comprising missing values and abnormal values in the client historical data.
In one embodiment, the building of the customer value analysis model based on the classification index further comprises:
and carrying out Z-score standardization treatment on the classification indexes.
In one embodiment, the establishing of the customer value analysis model based on the classification index comprises:
selecting an initial centroid from a training set of the classification indicators;
selecting a point with a maximum Euclidean distance from the initial centroid as a second centroid from the training set of the classification index;
and selecting a preset number of centroids on the basis of the initial centroid and the second centroid, and establishing a customer value analysis model according to the selected centroids, wherein the customer value analysis model is used for classifying customer data.
In one embodiment, the establishing a customer value analysis model based on the classification index further comprises:
obtaining a probability density map of each classification index based on the selected centroid;
and establishing a customer value analysis model based on the probability density graph.
A customer categorization apparatus, the apparatus comprising:
the acquisition module is used for acquiring historical data of a client;
the extraction module is used for extracting data of classification indexes based on the historical data of the customers, wherein the classification indexes comprise customer first order time, last order time, consumption frequency, consumption amount, average re-foaming ratio and premium;
the modeling module is used for establishing a customer value analysis model based on the classification indexes;
and the classification module is used for inputting the data of the extracted classification indexes into the customer value analysis model to obtain a customer classification result.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring historical data of a client;
extracting data of classification indexes based on the historical data of the customers, wherein the classification indexes comprise the first order time of the customers, the last order time, the consumption frequency, the consumption amount, the average value of the re-foaming ratio and the premium;
establishing a customer value analysis model based on the classification indexes;
and inputting the extracted data of the classification indexes into the customer value analysis model to obtain a customer classification result.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
acquiring historical data of a client;
extracting data of classification indexes based on the historical data of the customers, wherein the classification indexes comprise the first order time of the customers, the last order time, the consumption frequency, the consumption amount, the average value of the re-foaming ratio and the premium;
establishing a customer value analysis model based on the classification indexes;
and inputting the extracted data of the classification indexes into the customer value analysis model to obtain a customer classification result.
According to the customer classification method, the customer classification device, the computer equipment and the storage medium, the customer historical data is obtained, the data of the classification indexes are extracted based on the customer historical data, the classification indexes comprise the first order time of a customer, the last order time, the consumption frequency, the consumption amount, the average value of the re-bubble ratio and the premium, a customer value analysis model based on the classification indexes is established, the extracted data of the classification indexes are input into the customer value analysis model, and the customer classification result is obtained.
Drawings
FIG. 1 is a flow chart illustrating a customer classification method according to an embodiment of the present invention;
FIG. 2 is a flow chart illustrating a customer classification method according to another embodiment of the present invention;
FIG. 3 is a block diagram of a client classifying device according to an embodiment of the present invention;
FIG. 4 is a block diagram of a client classifying device according to another embodiment of the present invention;
FIG. 5 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The coming of the information age changes the marketing focus of enterprises from product centers to client centers, and the management of client relationships becomes a core problem of the enterprises. The core problem of customer relationship management is customer classification, low-value customers and high-value customers are distinguished through customer classification, enterprises make optimized personalized service schemes for different customers, different marketing strategies are adopted, limited marketing resources are concentrated on the high-value customers, and the enterprise profit maximization target is achieved.
The rapid development of the internet, the logistics industry rises rapidly with the help of the e-commerce industry, the status of the logistics industry in the economy of China is steadily promoted, along with the increase of the client scale of the logistics enterprise, the client background and the behavior characteristics are different, the accurate client classification result is an important basis for optimizing marketing resource allocation of the enterprise, and the client classification increasingly becomes one of the key problems to be solved urgently in the client relationship management. With the depth of the machine learning method, statistical analysis and data mining are widely applied to the customer group classification and identification research, however, less research is applied to the identification and classification of the customer group of the piece goods logistics.
The existing customer behavior classification method is mainly based on an experience classification method, a statistical analysis method and a data mining method. The empirical analysis method generally divides the client into categories according to experience of a decision maker, and has strong subjectivity, and the subdivision result is not objective and lacks persuasion. The customer classification method based on the statistical analysis method is a quantitative research, and the classification of the customer is carried out according to the characteristic statistical result of the customer attribute. The method based on data mining can mine useful information from a large amount of incomplete noisy original data, a common method is a K-means clustering method, a traditional K-means method cannot mine useful information from massive data and a large amount of characteristic attributes effectively, and the algorithm has high requirements on data preprocessing, initial clustering center selection and clustering category number determination.
The RFM model is an important tool and means to measure customer value and customer profitability. Among the numerous modes of analysis for Customer Relationship Management (CRM), the RFM model is widely mentioned. The mechanical model describes the value status of a customer by its recent purchases, the overall frequency of purchases, and how much 3 dollars are spent.
Referring to fig. 1, fig. 1 is a schematic diagram illustrating a customer classification method according to an embodiment of the invention.
In this embodiment, the customer classification method includes:
As can be understood, the obtaining of the historical data of the customer is to obtain the historical order data of the customer to be classified.
And step 110, extracting classification indexes based on the historical data of the customers, wherein the classification indexes comprise the first order time of the customers, the last order time, the consumption frequency, the consumption amount, the average value of the re-foaming ratio and the premium.
Illustratively, the classification index is added with three indexes of a client first time, a re-bubble ratio average value and a premium price on the basis of an RFM model.
And step 120, establishing a customer value analysis model based on the classification indexes.
It can be understood that a customer value analysis model is established based on the classification indexes, and the input of the customer value analysis model is the classification indexes of the customers to be classified, and the output is the customer classification result.
And step 130, inputting the data of the extracted classification indexes into the customer value analysis model to obtain a customer classification result.
According to the customer classification method, the customer historical data is obtained, the classification indexes are extracted based on the customer historical data, the classification indexes comprise the first order time of a customer, the last order time, the consumption frequency, the consumption amount, the average value of the re-bubble ratio and the premium, a customer value analysis model based on the classification indexes is established, the extracted data of the classification indexes are input into the customer value analysis model, and a customer classification result is obtained.
Referring to fig. 2, fig. 2 is a schematic diagram illustrating a client classification method according to another embodiment of the invention.
In this embodiment, the customer classification method includes:
Illustratively, the obtaining customer history data includes obtaining customer number, time of first order, shipping volume, shipping cost amount, quote cost, number of days to place orders, maximum interval to place orders, average interval to place orders, weight, volume, industry in place, and shipping cost payment form. It will be appreciated that the customer history data may also include other relevant data.
Specifically, taking Y logistics company as an example, data is extracted from a background database of the Y logistics company, 2019.3.1 is used as an end time, a time period with a width of one year is selected as an analysis observation window, and detailed data of all clients with waybill records in the observation window is extracted to form historical data. From data tables such as waybill information tables, transportation line tables and customer information tables in the Y logistics company system, detailed data of all customers in 2018/3/1-2019/3/1 are extracted, wherein the detailed data comprise attributes such as customer numbers, first order time, waybill amount, freight charge amount, quotation charge, number of days for placing orders, maximum placing order interval, average placing order interval, weight, volume, industry where the customers are located, freight charge payment form and the like.
And step 210, analyzing missing values and abnormal values of the client historical data.
Illustratively, the analyzing missing values and abnormal values of the historical data of the client comprises exploring and analyzing a training set, and through data observation, a certain list of attributes in the original data is found to have a missing value, namely a missing value, the number of waybills is greater than 0, and the records of the freight amount equal to 0 are abnormal values.
And step 220, performing data cleaning, attribute stipulation and/or data transformation on the historical data of the client.
Illustratively, data cleansing is the discarding of records that have missing values and outliers. The attribute specification is to select attributes related to six indexes of LRFMHI: customer number UserId, first order date startDate, last order date lastDate, observation window end date observeDate, order days orderDays, freight amount fee, weight heavy, volume, heavy bubble ratio heavyVolumeRate, premium insturdPrice, delete attributes unrelated to or redundant to it. And converting the data into a corresponding format by adopting an attribute construction and data standardization mode so as to adapt to the requirements of data mining tasks and algorithms. It will be appreciated that one or more of these pre-processing may be performed on the customer history data.
And 230, extracting data of classification indexes based on the historical data of the customers, wherein the classification indexes comprise the first order time of the customers, the last order time, consumption frequency, consumption amount, the average value of the re-foaming ratio and the premium.
Illustratively, the customer order time is an index L, the last order time is an index R, the consumption frequency is an index F, the consumption amount is an index M, the average value of the re-foaming ratio is an index H, and the premium is an index I. Specifically, the specific calculation method of the classification index is as follows:
L＝observeDate-startDate；
R＝observeDate-lastDate；
F＝orderDays；
M＝fee；
H＝AVG(heavey/volume)；
I＝insuredPrice。
after the data of the above 6 classification indexes are extracted, the method further comprises the step of performing Z-score standardization processing on the classification indexes, namely analyzing the distribution of each index data analysis, wherein the Z-score standardization processing formula is as follows:
wherein, X is a value of a certain attribute of a user, μ is a mean value of all users under the attribute, and σ is a standard deviation of all users under the attribute. It is understood that other normalization processing methods can be used to perform Z-score normalization on the classification index.
Illustratively, the establishing of the customer value analysis model based on the classification index includes adopting a K-means + + clustering algorithm, and establishing the customer value analysis model based on the classification index, and the specific steps are as follows:
1) randomly selecting 5 initial centroids from a customer training set in the following manner:
i. randomly selecting 1 initial centroid from the training set;
selecting a point from the training set that is farthest from the 1 st centroid as the 2 nd centroid;
then selecting a point which is farthest from the existing centroid from the training set as a 3 rd centroid;
repeating step iii until 5 centroids are obtained.
2) The distance to each centroid is calculated for each data point remaining in the training set and is assigned to the closest centroid class.
3) The centroids of the classes that have been obtained are recalculated.
4) And repeating the steps 2) and 3) until the set iteration number is reached.
In the clustering process, the distance measurement adopts Euclidean distance, and the calculation formula is as follows:
i.e. based on the classification index.
It will be appreciated that 5 centroids represent 5 classes, and that a data point assigned to a certain centroid represents which class it is assigned to, and therefore all customers are assigned to 5 classes.
In this embodiment, the establishing of the customer value analysis model based on the classification indexes further includes obtaining probability density maps of the classification indexes according to the selected centroid; the customers are classified into 5 classes based on the probability density map. Specifically, the customers are classified into VIP customers, important retention customers, important development customers, important saving customers and low-value customers based on the classification indexes, and a customer value analysis model is obtained, and the specific steps are as follows:
1) drawing a probability density graph of each characteristic of each customer group according to the clustering result;
2) obtaining the feature description of each customer group according to the probability density graph;
3) the customers are defined as 5 levels of customers according to the characteristics description: VIP customers, important maintenance customers, important development customers, important saving customers and low-value customers, wherein the concrete performance of each customer group is as follows:
vip client: the number of the clients is the lowest, less than 0.5%, the conference time (L) is long, the ordering frequency (F) is high, the freight contribution (M) is large, the heavy foam ratio (H) is reasonable, and the premium (I) is high
important maintenance client: the number of the customers is more than that of the VIP customers, the conference time (L) is long, the ordering frequency (F) is high, the freight contribution (M) is large, and the heavy bubble ratio (H) is reasonable.
important development customers: such customers have short enrollment times (L), high ordering frequency (F), large freight contribution (M), reasonable reshuffling ratio (H), and high quoted prices (I).
important saving clients: such customers are high in order frequency (F) and have a large freight contribution (M), but have not been ordering for a longer time (R).
Low-value customers: such customers have short admission times (L), low ordering frequencies (F), low freight contributions (M), unreasonable heavy-bubble ratios (H), and have not ordered them for a longer period of time (R).
And step 250, inputting the data of the extracted classification indexes into the customer value analysis model to obtain a customer classification result.
It can be understood that the classification result of the customer to be classified can be obtained by inputting the data of the extracted classification indexes into the customer value analysis model and comparing the classification indexes.
Specifically, before inputting the extracted data of the classification indexes into the customer value analysis model, the method further comprises the steps of extracting the data of the classification indexes from historical data of customers to be classified, and carrying out Z-score standardization processing on the classification indexes. It can be understood that the extracted data of the classification index is subjected to the same Z-score standardization processing as the training data, so that the classification result can be more accurate.
It should be understood that although the various steps in the flow charts of fig. 1-2 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 1-2 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 3, there is provided a customer sorting apparatus including: an acquisition module 300, an extraction module 310, a modeling module 320, and a classification module 330, wherein:
the obtaining module 300 is used for obtaining the client history data.
And the extraction module 310 is used for extracting data of classification indexes based on the historical data of the customers, wherein the classification indexes comprise the first order time, the last order time, the consumption frequency, the consumption amount, the average value of the re-foaming ratio and the premium.
And the modeling module 320 is used for establishing a customer value analysis model based on the classification indexes.
And the classification module 330 is configured to input the data of the extracted classification indexes into the customer value analysis model to obtain a customer classification result.
In another embodiment, as shown in fig. 4, there is provided another customer sorting apparatus including: an acquisition module 300, an analysis module 340, a pre-processing module 350, an extraction module 310, a modeling module 320, and a classification module 330, wherein:
the obtaining module 300 is used for obtaining the client history data.
An analysis module 340, configured to perform missing value and abnormal value analysis on the client history data;
the step of performing data cleansing on the customer history data comprises:
and clearing data comprising missing values and abnormal values in the client historical data.
And a preprocessing module 350, configured to perform data cleaning, attribute stipulation and/or data transformation on the client historical data.
And the extracting module 310 is used for extracting classification indexes based on the historical data of the customers, wherein the classification indexes comprise customer first order time, last order time, consumption frequency, consumption amount, average re-foaming ratio and premium.
And the modeling module 320 is used for establishing a customer value analysis model based on the classification indexes.
A modeling module 320 further configured to:
selecting an initial centroid from a classification index training set;
selecting a point with a maximum Euclidean distance from the initial centroid as a second centroid from the training set of the classification index;
and selecting a preset number of centroids on the basis of the initial centroid and the second centroid, and establishing a customer value analysis model according to the selected centroids, wherein the customer value analysis model is used for classifying customer data.
A modeling module 320 further configured to:
obtaining a probability density map of each classification index based on the selected centroid;
and establishing a customer value analysis model based on the probability density graph.
And the classification module 330 is configured to input the data of the extracted classification indexes into the customer value analysis model to obtain a customer classification result.
The above definition of the client classification method is not described in detail herein. The modules in the client sorting device can be wholly or partially implemented by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 5. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of client classification. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 5 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring historical data of a client;
extracting data of classification indexes based on the historical data of the customers, wherein the classification indexes comprise the first order time of the customers, the last order time, the consumption frequency, the consumption amount, the average value of the re-foaming ratio and the premium;
establishing a customer value analysis model based on the classification indexes;
and inputting the extracted data of the classification indexes into the customer value analysis model to obtain a customer classification result.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and performing data cleaning, attribute specification and/or data transformation on the historical data of the client.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
analyzing missing values and abnormal values of the client historical data;
the step of performing data cleansing on the customer history data comprises:
and clearing data comprising missing values and abnormal values in the client historical data.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and carrying out Z-score standardization treatment on the classification indexes.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
selecting an initial centroid from a training set of the classification indicators;
selecting a point with a maximum Euclidean distance from the initial centroid as a second centroid from the training set of the classification index;
and selecting a preset number of centroids on the basis of the initial centroid and the second centroid, and establishing a customer value analysis model according to the selected centroids.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
obtaining a probability density map of each classification index based on the selected centroid;
and establishing a customer value analysis model based on the probability density graph.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring historical data of a client;
extracting data of classification indexes based on the historical data of the customers, wherein the classification indexes comprise the first order time of the customers, the last order time, the consumption frequency, the consumption amount, the average value of the re-foaming ratio and the premium;
establishing a customer value analysis model based on the classification indexes;
and inputting the extracted data of the classification indexes into the customer value analysis model to obtain a customer classification result.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and performing data cleaning, attribute specification and/or data transformation on the historical data of the client.
In one embodiment, the computer program when executed by the processor further performs the steps of:
analyzing missing values and abnormal values of the client historical data;
the step of performing data cleansing on the customer history data comprises:
and clearing data comprising missing values and abnormal values in the client historical data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and carrying out Z-score standardization treatment on the classification indexes.
In one embodiment, the computer program when executed by the processor further performs the steps of:
selecting an initial centroid from a training set of the classification indicators;
selecting a point with a maximum Euclidean distance from the initial centroid as a second centroid from the training set of the classification index;
selecting a preset number of centroids based on the initial centroid and the second centroid, and establishing a customer value analysis model according to the selected centroids
In one embodiment, the computer program when executed by the processor further performs the steps of:
obtaining a probability density map of each classification index based on the selected centroid;
and establishing a customer value analysis model based on the probability density graph.
According to the customer classification method, the customer classification device, the computer equipment and the storage medium, the customer historical data is obtained, the data of the classification indexes are extracted based on the customer historical data, the classification indexes comprise the first order time of a customer, the last order time, the consumption frequency, the consumption amount, the average value of the re-bubble ratio and the premium, a customer value analysis model based on the classification indexes is established, the extracted data of the classification indexes are input into the customer value analysis model, and the customer classification result is obtained. Meanwhile, the traditional K-means algorithm is improved, and the method is more suitable for updating.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method for classifying a customer, the method comprising:
acquiring historical data of a client;
extracting data of classification indexes based on the historical data of the customers, wherein the classification indexes comprise the first order time of the customers, the last order time, the consumption frequency, the consumption amount, the average value of the re-foaming ratio and the premium;
establishing a customer value analysis model based on the classification indexes;
and inputting the extracted data of the classification indexes into the customer value analysis model to obtain a customer classification result.
2. The method of claim 1, wherein the customer history data includes customer number, time of first order, shipping amount, shipping cost amount, quote cost, number of days to place orders, maximum interval to place orders, average interval to place orders, weight, volume, industry in place, and shipping cost payment form.
3. The method of claim 1, wherein said extracting data for a classification metric based on said customer history data further comprises:
and performing data cleaning, attribute specification and/or data transformation on the historical data of the client.
4. The method of claim 3, wherein said performing data cleansing, attribute reduction, and/or data transformation on said customer history data further comprises:
analyzing missing values and abnormal values of the client historical data;
the step of performing data cleansing on the customer history data comprises:
and clearing data comprising missing values and abnormal values in the client historical data.
5. The method of claim 1, wherein said building a customer value analysis model based on said classification metric further comprises:
and carrying out Z-score standardization treatment on the classification indexes.
6. The method of claim 1, wherein the building a customer value analysis model based on the classification metric comprises:
selecting an initial centroid from a training set of the classification indicators;
selecting a point with a maximum Euclidean distance from the initial centroid as a second centroid from the training set of the classification index;
and selecting a preset number of centroids on the basis of the initial centroid and the second centroid, and establishing a customer value analysis model according to the selected centroids, wherein the customer value analysis model is used for classifying customer data.
7. The method of claim 6, wherein said building a customer value analysis model based on said classification metric further comprises:
obtaining a probability density map of each classification index based on the selected centroid;
and establishing a customer value analysis model based on the probability density graph.
8. A customer categorization apparatus, characterized in that the apparatus comprises:
the acquisition module is used for acquiring historical data of a client;
the extraction module is used for extracting data of classification indexes based on the historical data of the customers, wherein the classification indexes comprise customer first order time, last order time, consumption frequency, consumption amount, average re-foaming ratio and premium;
the modeling module is used for establishing a customer value analysis model based on the classification indexes;
and the classification module is used for inputting the data of the extracted classification indexes into the customer value analysis model to obtain a customer classification result.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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| CN114331525A (en) * | 2021-12-27 | 2022-04-12 | 商派软件有限公司 | Multi-dimensional customer type analysis method and system |
| CN116797253A (en) * | 2022-12-13 | 2023-09-22 | 乖乖数字科技(苏州)有限公司 | Classification management method based on client resources |
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