CN109214874B - IP product operation data processing method, device, equipment and readable storage medium - Google Patents
IP product operation data processing method, device, equipment and readable storage medium Download PDFInfo
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Abstract
The invention discloses a method for processing operation data of an IP product, which comprises the following steps: carrying out big data statistics on the IP access times of sold products to obtain all IP access statistical data; performing correlation analysis on the client IP purchase data and each IP access statistical data to obtain client access statistical data; adding corresponding product data purchased by the client on the basis of the client access statistical data to obtain client basic data; carrying out big data clustering analysis on the basic data of the client according to a preset rule to obtain an overall analysis result; and analyzing the specific purchasing potential of the customer according to the overall analysis result, and outputting purchasing prompt data according to the analysis result. The method can be used for mining the potential of the client according to the business data, improving the directivity and pertinence of the sales process and being beneficial to the improvement of the sales performance. The invention also discloses an IP product operation data processing device, equipment and a readable storage medium, which have the beneficial effects.
Description
Technical Field
The invention relates to the field of big data analysis, in particular to a method, a device and equipment for processing operation data of an IP product and a readable storage medium.
Background
The IP product refers to a network-related product, such as a server, and when the IP product is sold, an IP address is allocated to a client after the client purchases the IP product successfully, and the client can access the product through the IP address.
When the IP product is sold, a salesperson can only passively wait for a client to buy the product, the selling process is passive, and the potential requirements of the client on the IP product cannot be obtained, for example, after the client purchases the current popular VPS for 1 year, the flag-based VPS is required after visiting for 100 times; or the client invests 2400 yuan in two lower version VPS servers at present, and may need 1300 yuan or so of higher version VPS servers after 20 times of access. According to the current passive selling mode, the remarkable improvement of the IP product selling achievement is difficult.
Therefore, how to mine the potential of the client according to the business data and improve the directionality and pertinence of the sales process is a technical problem to be solved by those skilled in the art.
Disclosure of Invention
The invention aims to provide an IP product operation data processing method, which can be used for mining the potential of a client according to business data, improving the directivity and pertinence of the sales process and being beneficial to the improvement of the sales achievement; another object of the present invention is to provide an IP product operation data processing apparatus, device and readable storage medium.
In order to solve the above technical problem, the present invention provides a method for processing operation data of an IP product, including:
carrying out big data statistics on the IP access times of sold products to obtain all IP access statistical data;
performing correlation analysis on the client IP purchase data and the IP access statistical data to obtain client access statistical data;
adding corresponding product data purchased by the client on the basis of the client access statistical data to obtain client basic data;
performing big data clustering analysis on the customer basic data according to a preset rule to obtain an overall analysis result; wherein the cluster analysis comprises: at least one of product type analysis, configuration parameter analysis and price analysis;
and analyzing the specific purchasing potential of the customer according to the overall analysis result, and outputting purchasing prompt data according to the analysis result.
Preferably, performing big data clustering analysis on the customer basic data according to a preset rule, including:
dividing the clients into a plurality of classes according to the access times of the clients in the client basic data;
and carrying out big data clustering analysis on the basic data of each category of customers according to a preset rule to obtain an overall analysis result.
Preferably, the big data clustering analysis is performed on the basic data of each category of customers according to a preset rule, and the big data clustering analysis comprises the following steps:
acquiring product price data of various types of clients in the client basic data;
and performing big data analysis on the product price data of the various types of clients to obtain consumption price analysis results of the various types of clients.
Preferably, the big data clustering analysis is performed on the basic data of each category of customers according to a preset rule, and the big data clustering analysis comprises the following steps:
acquiring the purchased product configuration data of each category of customers in the customer basic data;
and performing big data analysis on the product configuration data of the various types of clients to obtain common configuration analysis results of the various types of clients.
Preferably, before analyzing the targeted purchasing potential of the customer according to the overall analysis result, the method further comprises:
potential customers are screened out according to the overall analysis result;
correspondingly, the specific purchase potential analysis of the customer according to the overall analysis result is as follows: and carrying out targeted purchase potential analysis on the potential customers according to the overall analysis result.
Preferably, the analyzing the targeted purchasing potential of the customer according to the overall analysis result, and the outputting the purchasing hint data according to the analysis result includes:
screening out peripheral products of the products purchased by the customers which are matched with the overall analysis result;
and when a corresponding client is online, outputting a recommendation prompt of the peripheral product to the corresponding client.
The application discloses IP product operation data processing apparatus includes:
the IP access counting unit is used for carrying out big data counting on the IP access times of sold products to obtain each IP access statistical data;
the client access statistical unit is used for carrying out correlation analysis on the client IP purchase data and the IP access statistical data to obtain client access statistical data;
the basic data acquisition unit is used for adding corresponding product data purchased by the client on the basis of the client access statistical data to obtain client basic data;
the cluster analysis unit is used for carrying out big data cluster analysis on the customer basic data according to a preset rule to obtain an overall analysis result; wherein the cluster analysis comprises: at least one of product type analysis, configuration parameter analysis and price analysis;
and the potential analysis unit is used for carrying out targeted purchase potential analysis on the customer according to the overall analysis result and outputting purchase prompt data according to the analysis result.
Preferably, the cluster analysis unit includes:
the client dividing subunit is used for dividing the clients into a plurality of classes according to the access times of the clients in the client basic data;
and the category analysis subunit is used for performing big data clustering analysis on the basic data of each category of customers according to a preset rule to obtain an overall analysis result.
The application discloses IP product operation data processing equipment includes:
a memory for storing a program;
and the processor is used for realizing the steps of the IP product operation data processing method when executing the program.
A readable storage medium having stored thereon a program which, when executed by a processor, implements the steps of the IP product operation data processing method.
The IP product operation data processing method provided by the invention has the advantages that the IP access times of sold IP products are counted by using a big data technology, the client IP purchase data generated in the product sale process and the corresponding product data are correlated, the client basic data comprising the client data, the product data and the access condition are generated, the cluster analysis is carried out on the client basic data by using the big data technology according to the preset data analysis type, the client potential is analyzed according to the cluster result after the flow and the service are combined and analyzed, the client value is mined, the corresponding product purchase prompt is carried out on the client according to the mined data, the targeted active promotion to the client is realized, and the IP product sale performance is favorably improved.
The invention also provides an IP product operation data processing device, equipment and a readable storage medium, which have the beneficial effects and are not described herein again.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a flowchart of a method for processing operation data of an IP product according to an embodiment of the present invention;
fig. 2 is a block diagram of an IP product operation data processing apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an IP product operation data processing device according to an embodiment of the present invention.
Detailed Description
The core of the invention is to provide a method for processing the operation data of IP products, which can mine the potential of customers according to the business data, improve the directionality and pertinence of the selling process and is beneficial to the promotion of the selling achievement; the other core of the invention is to provide an IP product operation data processing device, equipment and a readable storage medium.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. 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.
A large amount of business sales data can be generated in the IP product sales process, but the IP product sales data are not effectively analyzed at present, so that a large amount of data waste is generated.
Data mining is a process of revealing meaningful new relationships, trends and patterns by carefully analyzing a large amount of data, is a new field with high application value in database research, and integrates theories and technologies in multiple fields of artificial intelligence, database technology, pattern recognition, machine learning, statistics, data visualization and the like. Due to the rise of big data technology, all industries use big data technology to perform data mining. According to the method and the system, the great data analysis is carried out on the flow and the service data in a combined mode, the potential of the client is mined, and the product sale initiative is improved.
Referring to fig. 1, fig. 1 is a flowchart of a method for processing operation data of an IP product according to the present embodiment; the method can comprise the following steps:
and step s110, carrying out big data statistics on the IP access times of the sold products to obtain the IP access statistical data.
After the product transaction is completed, the customer is assigned an IP address, and the IP address assigned to the customer will store records at the transaction end, such as 155.123.0.2 for customer 1, 155.123.0.3 for customer 2, etc., that is, customer IP purchase data. When a user accesses through the IP, corresponding access records exist in an IP product at a transaction end, the IP access times are counted by using a big data technology, the statistics can be carried out within a specified time range, all access data from the sale of the product can also be counted, the counted time range is not limited, and the access records can be set as required.
The description is made here with reference to statistics of access data since sale, one type of IP access statistics being for example:
IP: 1.1.1.1, number of visits: 10
IP: 1.1.1.2, number of visits: 50
……
IP: 1.1.1.201, number of accesses: 60
IP: 1.1.1.202, number of accesses: 100
And step s120, performing correlation analysis on the client IP purchase data and each IP access statistical data to obtain client access statistical data.
The customer IP purchase data, i.e., a record of the IP assigned to each customer after purchasing a product, is updated after each customer transaction is successful.
One type of customer IP purchase data is for example,
customer: a, IP: 1.1.1.1
Customer: b, IP: 1.1.1.2
……
Customer: x, IP: 1.1.1.201
Customer: y, IP: 1.1.1.202
It should be noted that, in the process of processing the operation data of the IP product, the corresponding operation data is processed for each sold product, so that the same product IP is processed in the data processing process. Since the number of the IP purchased by the client can be more than 1, when the client access condition is counted, all the IP purchased by the client can be directly counted uniformly, for example, the IP of the client A in the client IP purchase data comprises: 1.1.1.1, 1.1.1.2 and 1.1.1.3, and by counting the access times of the respective IPs, the number of times of access of 1.1.1.1 is 10, the number of times of access of 1.1.1.2 is 50, and the number of times of access of 1.1.1.3 is 2, then when the customer IP purchase data is associated with the statistical data of the respective IPs, the number of times of access of the three IPs addresses is counted, and the number of times of access of the customer a is 62.
The statistical service data (customer IP purchase data) is associated with the number of times the IP is accessed to obtain the number of times the user is accessed, and in the case of the customer IP purchase data, a statistical result is generated, for example:
customer: a, access times: 10
Customer: b, access times: 20
……
Customer: x, number of accesses: 100
Customer: y, number of accesses: 110
And step s130, adding corresponding product data purchased by the client on the basis of the client access statistical data to obtain client basic data.
When big data analysis is carried out, generally, only basic customer data is taken as analysis basis, various types of customer purchased product data can be added to facilitate analysis, and the customer purchased product data can comprise product names, versions, various configuration parameters, support languages, prices and the like.
And on the basis of the customers, adding corresponding data of the products purchased by the customers on the basis of the customer access statistical data to obtain customer basic data.
One type of underlying data is for example:
customer: a, access times: 10, product: VPS, price: 1000
Customer: b, access times: 20, product: VPS, price: 1000
Customer: c, access times: 10, product: VPS, price: 2000
……
Customer: x, number of accesses: 100, product: VPS, price: 1500
Customer: y, number of accesses: 110, product: VPS, price: 2000
In this embodiment, taking the above customer basic data as an example, the process of performing data analysis on the customer basic data generated according to the obtained product data of other types is not described herein again, and reference may be made to the description of this embodiment.
And step s140, performing big data clustering analysis on the basic data of the client according to a preset rule to obtain an overall analysis result.
Setting a clustering algorithm according to different clustering analysis types to realize clustering analysis of customer basic data, wherein the types of the clustering analysis are not limited, and different types of clustering analysis are used for realizing different analysis purposes, generally speaking, the clustering analysis can comprise: at least one of the product type analysis, the configuration parameter analysis and the price analysis, and of course, other types of analysis can be included, such as product performance, energy consumption, browsing times and the like. The analysis of the product types commonly used by the customer can be realized through the big data analysis of the product types, the orientation of the product types of the customer is known, the corresponding product types are recommended to the customer, or the targeted recommendation is performed on the user when the product types are updated, and the like. Through big data analysis of the configuration parameters, the common configuration of the client can be analyzed integrally, and the configuration orientation of the client is known, so that the configuration recommendation conforming to the requirements of the client is provided. The acceptable interval range of the price of the product by the client can be known through the big data analysis of the price, so that the product with proper price is recommended to the client, and one or more types can be selected for comprehensive analysis through the cluster analysis.
Since the user access times reflect the attention degree of the user to a certain product, preferably, the cluster analysis can be performed through big data records in association with the user access times. The user access times are used as the abscissa, the category (such as price, product parameter and the like) of the cluster analysis is used as the ordinate, the user access times are analyzed to be closer to the customer requirements, accurate customer potential analysis is realized, and the process of performing big data cluster analysis on customer basic data according to preset rules can be as follows: dividing the clients into a plurality of classes according to the access times of the clients in the client basic data; and carrying out big data clustering analysis on the basic data of each category of customers according to a preset rule to obtain an overall analysis result.
Specifically, the process of performing cluster analysis on the price may be: acquiring product price data of various types of clients in the client basic data; and performing big data analysis on the product price data of the various types of clients to obtain consumption price analysis results of the various types of clients.
Specifically, the process of performing cluster analysis on the product configuration may be: acquiring the purchased product configuration data of various types of clients in the client basic data; and performing big data analysis on the product configuration data of each category of customers to obtain a common configuration analysis result of each category of customers.
Other clustering analysis processes are not described herein, and reference can be made to the description of this embodiment.
The result of big data analysis of the consumption amount according to the number of times of accessing a certain product by a user is obtained as follows: the access times are as follows: 10-20, should consume: 1300 yuan; the access times are as follows: 100-: 1700 yuan.
It should be noted that, in the cluster analysis algorithm in the present application, the existing algorithm may be referred to, and the operation data of the IP product obtained by statistics is substituted into the existing big data cluster analysis method, so that the operation data of the IP product is effectively combined with the business sales, which supplements each other, and the pertinence and reliability of the business sales are improved by the analysis of the big data.
And step s150, performing targeted purchase potential analysis on the customer according to the overall analysis result, and outputting purchase prompt data according to the analysis result.
And performing potential analysis on the client according to the result of the cluster analysis, wherein the potential analysis comprises the budget number of the client for a certain product, the configuration pursuit of a certain product and the like. For example, if a customer with access times exceeding 200 times is found to be sensitive to configuration update through overall analysis and pursues a new product with high configuration, the customer a with access times exceeding 200 times finds that the configuration of the product 1 purchased by the customer is not latest, then the customer may have the purchase potential of the newly configured product 1, and then a purchase link of the newly configured product 1 is output to the customer.
Specifically, the process of analyzing the targeted purchasing potential of the customer according to the overall analysis result and outputting the purchasing prompt data according to the analysis result may be as follows: screening out peripheral products of the products purchased by the customers which are matched with the overall analysis result; and when the corresponding client is on line, outputting a recommendation prompt of the peripheral products to the corresponding client.
For example, the sum of money generally used for VPS of a customer who obtains 50 access times through statistics is about 1500 yuan, and if the customer A consumes 500 yuan of VPS products at present, the consumption potential of the customer is about 1000 yuan, and some high-end VPS products and accessories can be recommended to the customer. Customer B currently consumes about 1200 yuan of VPS product, and the customer has a consumption potential of about 300 yuan, and may recommend some VPS product accessories purchased before, etc. And analyzing all products, analyzing the customers according to the clustering result, calculating the potential total value of the customers, and recommending proper products in a targeted manner.
In addition, after the big data analysis, the consumption potential of part of the customers is possibly small, so that the potential customers can be screened out according to the overall analysis result before the targeted purchase potential analysis is performed on the customers according to the overall analysis result in order to avoid the adverse effect caused by excessive computing resource consumption of the part of the customers; correspondingly, the specific purchase potential analysis of the customer according to the overall analysis result is as follows: and carrying out targeted purchase potential analysis on potential customers according to the overall analysis result. The targeted analysis can be carried out only on potential customers obtained by the analysis (such as larger consumption space, higher configuration pursuit and more comprehensive product type pursuit), and corresponding purchase prompts are output.
In order to deepen understanding of the IP product operation data processing method provided in this embodiment, the VPS client is analyzed as an example to integrally introduce a flow.
And counting the number of times of visiting all VPS sold IPs by using a big data technology, converting IP addresses into customers according to the customers corresponding to all IPs counted during sale, and correlating to obtain the number of times of visiting VPS products by all the customers who purchase VPS respectively. And associating the customer service data with the counted VPS access times to obtain customer basic data, performing consumption amount big data analysis on the customer basic data according to the customer access times to generate a two-dimensional coordinate graph of the customer access times and the consumption amount, and obtaining consumption amounts corresponding to different access times categories. And acquiring the consumption capacity of all customers, for example, the purchase amount of the customers with the access times of less than 100 is less than 500 yuan, and controlling the price of the recommended purchased commodities to be less than 500 yuan as much as possible. Potential customers with large consumption potential (high consumption amount) are screened out, consumption amount pertinence analysis is carried out on the potential customers in all the customers to obtain the potential total value of each potential customer, pertinence preference analysis is carried out on the potential customers, and corresponding products are recommended to the potential customers according to the obtained potential total value and preference (for example, updated versions of purchased commodities are sold, and data of the updated versions of the commodities are output within the acceptable range of the customers).
Based on the introduction, the IP product operation data processing method provided by this embodiment uses a big data technology to count the IP access times of the sold IP product, associates the client IP purchase data generated in the product sale process with the corresponding product data, generates client basic data including the client data, the product data and the access condition, performs cluster analysis on the client basic data by using the big data technology according to a preset data analysis type, analyzes the client potential according to a cluster result after merging and analyzing the traffic and the service, excavates the client value, and performs corresponding product purchase prompt on the client according to the excavated data, thereby realizing targeted active promotion on the client and facilitating improvement of the IP product sale performance.
Referring to fig. 2, fig. 2 is a block diagram of an IP product operation data processing apparatus provided in this embodiment; the method can comprise the following steps: IP access statistics unit 210, client access statistics unit 220, basic data acquisition unit 230, cluster analysis unit 240, and potential analysis unit 250. The IP product operation data processing apparatus provided in this embodiment may be contrasted with the above IP product operation data processing method.
The IP access statistical unit 210 is mainly used for performing big data statistics on the number of times that the IP of the sold product is accessed to obtain statistical data of each IP access;
the customer access statistical unit 220 is mainly used for performing correlation analysis on the customer IP purchase data and each IP access statistical data to obtain customer access statistical data;
the basic data obtaining unit 230 is mainly used for adding corresponding product data purchased by the customer on the basis of the customer access statistical data to obtain customer basic data;
the cluster analysis unit 240 is mainly used for performing big data cluster analysis on the customer basic data according to a preset rule to obtain an overall analysis result; wherein the cluster analysis comprises: at least one of product type analysis, configuration parameter analysis and price analysis;
the potential analysis unit 250 is mainly used for performing targeted purchase potential analysis on the customer according to the overall analysis result and outputting purchase prompt data according to the analysis result.
Preferably, the cluster analysis unit may specifically include:
the client dividing subunit is used for dividing the clients into a plurality of classes according to the access times of the clients in the client basic data;
and the category analysis subunit is used for performing big data clustering analysis on the basic data of each category of customers according to a preset rule to obtain an overall analysis result.
Preferably, the category analysis subunit may specifically include:
the price data acquisition subunit is used for acquiring the product price data of each category of customers in the customer basic data;
and the consumption price analysis subunit is used for carrying out big data analysis on the product price data of the clients of all categories to obtain the consumption price analysis result of the clients of all categories.
Preferably, the category analysis subunit may specifically include:
the configuration data acquisition subunit is used for acquiring the purchased product configuration data of each type of client in the client basic data;
and the configuration analysis subunit is used for performing big data analysis on the product configuration data of the clients of each category to obtain a common configuration analysis result of the clients of each category.
Preferably, the IP product operation data processing apparatus provided in this embodiment may further include: the output end of the client screening unit is connected with the input end of the potential analysis unit,
the client screening unit is mainly used for: potential customers are screened out according to the overall analysis result;
accordingly, the potential analysis unit is specifically configured to: and carrying out targeted purchase potential analysis on potential customers according to the overall analysis result.
Preferably, the potential analysis unit may specifically include:
the peripheral product screening subunit is used for screening out peripheral products of products purchased by the customer, which are matched with the overall analysis result;
and the recommendation prompt output subunit is used for outputting recommendation prompts of peripheral products to the corresponding clients when the corresponding clients are online.
The IP product operation data processing device provided by the embodiment can be used for mining the potential of a client according to the service data, improving the directivity and pertinence in the sales process and being beneficial to the promotion of the IP product sales performance.
The embodiment provides an IP product operation data processing device, including: a memory and a processor.
Wherein, the memory is used for storing programs;
the processor is configured to implement, for example, steps of the IP product operation data processing method when executing the program, and reference may be made to the description of the IP product operation data processing method.
The present embodiment discloses a readable storage medium, on which a program is stored, and the program, when executed by a processor, implements the steps of the IP product operation data processing method, for example, which can be referred to the above description of the IP product operation data processing method.
Referring to fig. 3, a schematic structural diagram of an IP product operation data processing apparatus provided in this embodiment is shown, where the processing apparatus may generate a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 322 (e.g., one or more processors) and a memory 332, and one or more storage media 330 (e.g., one or more mass storage devices) storing an application 342 or data 344. Memory 332 and storage media 330 may be, among other things, transient storage or persistent storage. The program stored on the storage medium 330 may include one or more modules (not shown), each of which may include a series of instructions operating on a data processing device. Still further, the central processor 322 may be configured to communicate with the storage medium 330, and execute a series of instruction operations in the storage medium 330 on the IP product operation data processing apparatus 301.
The IP product operation data processing apparatus 301 may also include one or more power supplies 326, one or more wired or wireless network interfaces 350, one or more input-output interfaces 358, and/or one or more operating systems 341, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, and the like.
The steps in the IP product operation data processing method described in fig. 1 above may be implemented by the structure of an IP product operation data processing apparatus.
The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The method, the device, the equipment and the readable storage medium for processing the operation data of the IP product provided by the invention are described in detail above. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
Claims (7)
1. An IP product operation data processing method is characterized by comprising the following steps:
carrying out big data statistics on the IP access times of sold products to obtain all IP access statistical data;
performing correlation analysis on the client IP purchase data and the IP access statistical data to obtain client access statistical data;
adding corresponding product data purchased by the client on the basis of the client access statistical data to obtain client basic data;
performing big data clustering analysis on the customer basic data according to a preset rule to obtain an overall analysis result; wherein the cluster analysis comprises: at least one of product type analysis, configuration parameter analysis and price analysis;
analyzing the specific purchasing potential of the customer according to the overall analysis result, and outputting purchasing prompt data according to the analysis result;
carrying out big data clustering analysis on the customer basic data according to a preset rule, wherein the big data clustering analysis comprises the following steps:
dividing the clients into a plurality of classes according to the access times of the clients in the client basic data;
carrying out big data clustering analysis on the basic data of each category of customers according to a preset rule to obtain an overall analysis result; carrying out big data clustering analysis on the basic data of each category of customers according to a preset rule, wherein the big data clustering analysis comprises the following steps: acquiring the purchased product configuration data of each category of customers in the customer basic data; and performing big data analysis on the product configuration data of the various types of clients to obtain common configuration analysis results of the various types of clients.
2. The IP product operation data processing method of claim 1, wherein performing big data clustering analysis on the basic data of each category of customers according to a preset rule comprises:
acquiring product price data of various types of clients in the client basic data;
and performing big data analysis on the product price data of the various types of clients to obtain consumption price analysis results of the various types of clients.
3. The IP product operation data processing method of claim 1, wherein before performing the targeted purchase potential analysis on the customer according to the overall analysis result, the method further comprises:
potential customers are screened out according to the overall analysis result;
correspondingly, the specific purchase potential analysis of the customer according to the overall analysis result is as follows: and carrying out targeted purchase potential analysis on the potential customers according to the overall analysis result.
4. The IP product operation data processing method according to claim 1, wherein the specific purchase potential analysis is performed on the customer according to the overall analysis result, and the outputting of the purchase prompting data according to the analysis result comprises:
screening out peripheral products of the products purchased by the customers which are matched with the overall analysis result;
and when a corresponding client is online, outputting a recommendation prompt of the peripheral product to the corresponding client.
5. An IP product operation data processing apparatus, comprising:
the IP access counting unit is used for carrying out big data counting on the IP access times of sold products to obtain each IP access statistical data;
the client access statistical unit is used for carrying out correlation analysis on the client IP purchase data and the IP access statistical data to obtain client access statistical data;
the basic data acquisition unit is used for adding corresponding product data purchased by the client on the basis of the client access statistical data to obtain client basic data;
the cluster analysis unit is used for carrying out big data cluster analysis on the customer basic data according to a preset rule to obtain an overall analysis result; wherein the cluster analysis comprises: at least one of product type analysis, configuration parameter analysis and price analysis;
the potential analysis unit is used for carrying out targeted purchase potential analysis on the customer according to the overall analysis result and outputting purchase prompt data according to the analysis result;
wherein the cluster analysis unit includes:
the client dividing subunit is used for dividing the clients into a plurality of classes according to the access times of the clients in the client basic data;
the category analysis subunit is used for carrying out big data clustering analysis on the basic data of each category of customers according to a preset rule to obtain an overall analysis result; carrying out big data clustering analysis on the basic data of each category of customers according to a preset rule, wherein the big data clustering analysis comprises the following steps: acquiring the purchased product configuration data of each category of customers in the customer basic data; and performing big data analysis on the product configuration data of the various types of clients to obtain common configuration analysis results of the various types of clients.
6. An IP product operation data processing apparatus, comprising:
a memory for storing a program;
processor for implementing the steps of the IP product operation data processing method according to any one of claims 1 to 4 when executing said program.
7. A readable storage medium, characterized in that the readable storage medium has stored thereon a program which, when executed by a processor, implements the steps of the IP product operation data processing method according to any one of claims 1 to 4.
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