CN110728539A - Big data-based customer differentiation management method and device - Google Patents
Big data-based customer differentiation management method and device Download PDFInfo
- Publication number
- CN110728539A CN110728539A CN201910955704.7A CN201910955704A CN110728539A CN 110728539 A CN110728539 A CN 110728539A CN 201910955704 A CN201910955704 A CN 201910955704A CN 110728539 A CN110728539 A CN 110728539A
- Authority
- CN
- China
- Prior art keywords
- customer
- information
- customers
- category
- score
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/01—Customer relationship services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0609—Buyer or seller confidence or verification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/12—Hotels or restaurants
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Business, Economics & Management (AREA)
- Strategic Management (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Engineering & Computer Science (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Tourism & Hospitality (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Primary Health Care (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- Data Mining & Analysis (AREA)
- Game Theory and Decision Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a customer differentiation management method and device based on big data, a computing device and a computer readable storage medium, wherein the customer differentiation management method based on big data comprises the following steps: the method comprises the steps of collecting relevant information of a customer through big data mining, obtaining a category to which the customer belongs according to the collected relevant information of the customer, and recommending a differential management scheme corresponding to the category to which the customer belongs according to the category to which the customer belongs.
Description
Technical Field
The invention relates to the technical field of internet, in particular to a customer differentiation management method and device based on big data, a computing device and a computer readable storage medium.
Background
With the continuous development of the hotel service industry at home and abroad, the requirement for the subdivision of the business inside the industry is higher and higher. For high-end hotel brands, improving the service satisfaction of the clients who check in, controlling the security risks before and after the clients check in and in the whole process, and further providing differentiated service contents for the clients with different security levels is a main demand for more and more hotel and hotel merchants.
Currently, most hotel industry merchants establish a CRM and business management system, but the existing hotel CRM and business management system lacks a process for grading customers, so that corresponding services cannot be provided for the customers according to the demands of the customers, different security and wind control management measures cannot be provided for the customers according to the demands of the customers or differences among the customers, and the customer dissatisfaction increase on hotels, and the reputation and the service quality of the customers are affected.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The invention aims to provide a customer differentiation management method and device based on big data, a computing device and a computer readable storage medium, thereby overcoming the defects of the prior art.
In a first aspect, an embodiment of the present specification discloses a big data-based customer differentiation management method, including:
collecting relevant information of customers through big data mining;
classifying said customers according to said collected relevant information of said customers;
and recommending a differential management scheme corresponding to the category to which the customer belongs for the customer according to the category to which the customer belongs.
In a second aspect, an embodiment of the present specification discloses an apparatus for differentiated management of hotel customers, including:
a collection module configured to collect relevant information of a customer through big data mining;
a classification module configured to classify the customers according to the collected related information of the customers;
the recommendation module is configured to recommend a differential management scheme corresponding to the category to which the customer belongs to the customer according to the category to which the customer belongs.
In a third aspect, an embodiment of the present specification discloses a computing device, which includes a memory, a processor, and computer instructions stored on the memory and executable on the processor, where the processor executes the instructions to implement the steps of the method for customer differentiation management based on big data when the instructions are executed by the processor.
In a fourth aspect, embodiments of the present specification disclose a computer-readable storage medium storing computer instructions which, when executed by a processor, implement the steps of the above-described method for customer differentiation management based on big data.
Compared with the prior art, the big data-based customer differentiation management method and device, the computing device and the computer-readable storage medium provided by the specification have the following beneficial effects: through analyzing the relevant information of the customers, the customers with different requirements and different risk levels can be classified, so that differentiated services or differentiated risk management and control schemes can be provided for the customers, the satisfaction of the customers is improved, and the reputation of the hotel is improved.
Drawings
FIG. 1 is a block diagram of a computing device of the present specification;
FIG. 2 is a flow chart of one embodiment of a big data based customer differentiation management method of the present description;
FIG. 3 is a flow chart of one embodiment of a big data based customer differentiation management method of the present description;
FIG. 4 is a flow chart of one embodiment of a big data based customer differentiation management method of the present description;
FIG. 5 is a flow chart of one embodiment of a big data based customer differentiation management method of the present description;
FIG. 6 is a flow chart of one embodiment of a big data based customer differentiation management method of the present description;
FIG. 7 is a schematic diagram illustrating an embodiment of an apparatus for big data based customer differentiation management according to the present disclosure;
FIG. 8 is a schematic diagram illustrating an embodiment of an apparatus for big data based customer differentiation management according to the present disclosure;
fig. 9 is a schematic structural diagram of an embodiment of an apparatus for customer differentiation management based on big data according to the present specification.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
In this specification, a method and an apparatus for customer differentiation management based on big data, a computing device and a computer readable storage medium are provided, which are described in detail in the following embodiments one by one.
Fig. 1 shows a block diagram of a computing device 100 for analyzing influence of a person to be analyzed based on a person related to the person to be analyzed according to an embodiment of the present specification. The components of the computing device 100 include, but are not limited to, memory 110, processor 120, and computer instructions stored on memory 110 and executable on processor 120. The processor 110 is configured to perform data processing analysis based on analysis of the influence of the person to be analyzed by the person associated with the person to be analyzed according to the user instructions received by the computing device 100 and the computer instructions stored in the memory 110. The memory 110 and the processor 120 are connected by a bus.
The computing device 110 may also include a network interface through which the computing device 110 communicates with one or more networks. The network interface may include one or more of any type of network interface, both wired and wireless.
Wherein the processor 120 may perform the steps of the method shown in fig. 2. Fig. 2 shows a schematic flowchart of a big data-based customer differentiation management method according to an embodiment of the present specification, including step 202 to step 206.
Step 202: relevant information of customers is collected through big data mining.
In one or more embodiments provided in the present specification, the identity card provided by the customer is scanned to obtain the head portrait or the identity card number on the identity card of the customer or the relevant information of the customer is called according to the identity card number provided by the customer. The processor 120 is connected with a public security database, a user credit blacklist database such as a bank or a payment bank, a business registration information database and the like, and the processor 120 automatically searches the relevant information of the customer from the public security database, the user credit blacklist database such as a bank or a payment bank, the business registration information database, the stored customer check-in information or other network databases according to the obtained customer identity number or the customer head image, and collects the relevant information of the customer, such as identity information, occupation information, academic information, criminal information or income information and the like of the customer.
Step 204: and obtaining the category of the customer according to the collected related information of the customer.
In one or more embodiments provided in this specification, the customers are classified according to the collected relevant information of the customers, and if the identity information of the customer is false or the criminal information is criminal evasion personnel, the customers are classified as a dangerous treatment category, if the industry information of the customers includes negative contents such as illegal loan, default overdue, and third-party industry blacklist, or if the relevant information of the customers includes law complaint and executed information, the customers are classified as a risk management treatment category, such as legal personal income tax and social insurance of the customers, or high management and no bad information of the enterprises in business, the customers are classified as a high-quality treatment category.
Step 206: and recommending a differential management scheme corresponding to the category to which the customer belongs for the customer according to the category to which the customer belongs.
In one or more embodiments provided in this specification, a differentiated management scheme corresponding to a category to which a customer belongs is recommended for the customer according to the category to which the customer belongs, a hotel attendant is prompted to alarm if the customer is treated in a danger category, the hotel attendant is prompted to perform key monitoring precaution on the customer if the customer is treated in a risk management and control category, and a service item required by the customer or a required security level is prompted for the hotel attendant to refer to if the customer is treated in a high-quality level category.
The present specification also provides that a processor may perform the steps in the method shown in fig. 3. Fig. 3 shows a schematic flowchart of a big-data-based customer differentiation management method according to an embodiment of the present specification, including steps 302-308:
step 302: the method comprises the steps of extracting a face image of a customer through face recognition, and calling relevant information of the customer through the face image of the customer.
In one or more embodiments provided in this specification, a camera arranged at a hotel service desk is used to extract a face image of a customer, and the acquired face image is used to search and retrieve relevant information of the customer from a network system connected with a processor, so as to complete big data mining of the relevant information of the customer.
Step 304: and scoring the customers according to the collected related information of the customers to obtain scores of the customers.
In one or more embodiments provided in the present specification, the customer is scored according to the searched related information of the customer, and if the identity information of the customer is false or the criminal information is a person for criminal escape, the customer is scored negatively; if the industry information of the customer comprises negative contents such as violation loan, default overdue, a blacklist of a third-party industry and the like or relevant information of the customer comprises complaint execution information such as court complaint and executed information and the like, the grade of the customer is zero; if the client is allowed to pay personal income tax and social insurance according to law, and is a legal person of an enterprise or a high management of the enterprise without bad information, the client is given a score of 1-10 points or more according to the relevant information of the client.
Step 306: and obtaining the category of the customer according to the grade of the customer.
In one or more embodiments provided herein, the customers are classified according to their scores, and if the scores of the customers are negative, the customers are classified as dangerous customers; if the score of the customer is zero, listing the customer as a risk management and control treatment category; if the scores of the customers are between 1 and 10, the customers are classified into different categories according to the different scores of the customers, if the scores of the customers are between 1 and 5, the customers are classified into a common customer treatment category, if the scores of the customers are between 5 and 8, the customers are classified into a high-quality customer treatment category, if the scores of the customers are between 9 and 10, the customers are classified into a VIP customer treatment category.
Step 308: and recommending corresponding service items or recommending corresponding level security and protection wind control management measures for the customers according to the categories to which the customers belong.
In one or more embodiments provided in the present specification, a security management measure corresponding to a service item or a level corresponding to a recommendation of the security management measure is recommended for a customer according to a category to which the customer belongs. If the customer belongs to the customer who treats the category according to the dangerous customer, the hotel service staff is prompted to give an alarm, and meanwhile, the security wind control management measures of the category of the customer are prompted to be monitored in a key mode. If the customer belongs to the customer who treats the category according to the risk control, hotel service personnel are prompted to carry out high-level security wind control management measures on the customer, service items are not recommended to the customer, and management confusion is avoided. If the customer is a customer of a treatment category according to a common customer, recommending fewer service items to the customer, and prompting to provide lower-level security wind control management measures for the customer; if the customer is a customer of a category treated according to a high-quality customer, recommending relatively more service items to the customer; simultaneously prompting to provide a medium-level security wind control management measure for the customer; if the customer is a customer of the category treated according to the VIP customer, all service items are recommended to the customer, and meanwhile, a higher-level security and wind control management measure is provided for the customer.
The present specification also provides that a processor may perform the steps in the method shown in fig. 4. Fig. 4 shows a schematic flowchart of a big-data-based customer differentiation management method according to an embodiment of the present specification, including steps 402 to 410:
step 402: the method comprises the steps of extracting a face image of a customer through face recognition, and calling relevant information of the customer through the face image of the customer.
In one or more embodiments provided herein, the collected information about the customer includes at least two of academic information, consumption information, income information, occupation information, criminal information, identity information, complaint execution information, voyage information, and occupation information of the customer.
Step 404: and respectively scoring at least two items of the collected related information of the customers to obtain the score of each item of the related information of the customers.
In one or more embodiments provided in the present specification, at least two collected items of the relevant information of the customers are scored respectively, and a score of each item of relevant information of the customers is obtained.
If the collected related information of the customer comprises academic information, professional information and identity information, wherein the identity information is correct, the identity information is 0, the academic information is scored according to the levels of colleges, major schools, major departments, major staffs, doctors and doctors, and the grades and the accepted culture modes of the colleges, major schools and the doctors are comprehensively considered, if the academic history of the customer is the levels of the colleges, the major schools and the doctor schools, the score is 1-6, if the academic history of the customer is the levels of the colleges, the score is 7, if the academic history of the customer is the levels of the colleges, the score is 8, if the academic history of the customer is the levels of the colleges, the score is 9, and if the academic history of the customer is the levels of the colleges, the score is 10; and meanwhile, scoring the professional information according to common workers, middle-level leaders and high-level leaders, and if the client is judged to be the high-level leader, comprehensively evaluating the dimensions of whether the client acts as a company legal person, whether business enterprises exist under the name, the number of the business enterprises, whether the business punishment information exists in the enterprises, the enterprise registered fund level, the actual financing amount of the client and the like. If the professional information is 1-5 minutes for general workers, 5-8 minutes for middle-layer leaders and 8-10 minutes for high-layer leaders.
Step 406: and performing mathematical operation on the obtained scores of all the related information of the customers to obtain the scores of the customers.
In one or more implementations provided herein, the obtained score of each item of relevant information of the customer is mathematically operated to obtain a score for the customer. If the identity information of the customer is 0, the academic score is 8 points, and the position score is 8 points, the identity information, the academic score and the occupation score of the customer can be added, and then the score of the customer is 16 points; if the identity information of the customer is 0, the academic score is 6 points and the position score is 5 points, the identity information, the academic score and the occupation score of the customer can be added, and then the score of the customer is 11 points; if the identity information of the customer is 0, the academic score is 10 points and the position score is 10 points, the identity information, the academic score and the occupation score of the customer can be added, and then the score of the customer is 20 points; if the identity information of the customer is 0, the academic score is 1 score, and the position score is 0, the identity information, the academic score and the occupation score of the customer can be added, and then the score of the customer is 1; .
Step 408: and obtaining the category of the customer according to the grade of the customer.
In one or more embodiments provided herein, the category to which the customer belongs is obtained from a rating of the customer. If the score of the customer is between 1 and 11, the customer treats the category for the common customer; if the score of the customer is between 12-16 points, the customer treats the category as a good customer; if the customer has a score between 17-20 points, the customer treats the category for a VIP customer.
Step 410: and recommending corresponding service items or recommending corresponding level security and protection wind control management measures for the customers according to the category to which the customers belong.
The present specification also provides that a processor may perform the steps in the method shown in fig. 5. Fig. 5 shows a schematic flowchart of a big-data-based customer differentiation management method according to an embodiment of the present specification, including steps 502-514:
step 502: relevant information of customers is collected through big data mining.
In one or more embodiments provided herein, the collected information about the customer includes at least two of academic information, consumption information, income information, occupation information, criminal information, identity information, complaint execution information, voyage information, occupation information, and complaint execution information of the customer.
Step 504: and respectively scoring at least two items of the collected related information of the customers to obtain the score of each item of the related information of the customers.
In one or more embodiments provided in the present specification, at least two collected items of the relevant information of the customers are scored respectively, and a score of each item of relevant information of the customers is obtained. If the collected relevant information of the customers comprises academic information, occupational information and identity information, wherein the identity information is false, the identity information is divided into-1, the criminal information is the person for which the criminal is in the process of escaping, the criminal information is divided into-2, the criminal information is the background of the prior department, differential assessment is carried out according to case level, occurrence time and the number of the prior departments of the prior affair, and the score is-2-0; if the identity information is correct, the identity information is 0, and if there is no criminal information, the criminal information is scored as 0; the industry information comprises negative contents such as violation loans, default overdue, third-party industry blacklists and the like, or relevant information of customers comprises industry negative information such as complaint execution information such as court complaints and executed information and the like, and industry positive information related to good matters are comprehensively evaluated, and a score of-10 to 10 is given; respectively scoring according to the high school, the middle and lower high school, the major, the subject, the master, the doctor and the doctor, scoring 1-6 scores if the academic record of the customer is the middle and lower high school, scoring 7 scores if the academic record of the customer is the major, scoring 8 scores if the academic record of the customer is the subject, scoring 9 scores if the academic record of the customer is the master, and scoring 10 scores if the academic record of the customer is the doctor and the doctor; and meanwhile, scoring the professional information according to the common workers, the middle-layer leaders and the high-layer leaders, and scoring 5, 8 and 10 for the middle-layer leaders and the high-layer leaders if the professional information is the common workers. The consumption information is respectively given for 5-1 according to the amount of money paid from the bank card every month. The flight information selects the taken cabin space and the occupied proportion, the travel destination, the travel frequency and other conditions to comprehensively evaluate according to the number of the monthly trips and the taken transportation, the comprehensive evaluation is given for 10-0 points respectively, and the income information is comprehensively considered for 10-1 points according to the tax payment amount and the social security payment condition.
Step 506: and setting a weight value for each item of relevant information of the customer.
In one or more embodiments provided herein, a weight is set for each item of relevant information of the customer. If the score of the identity information is-1 and the score of the criminal information is-2, the weight values of the identity information and the criminal information are 1000, and the weight values of other information are 0; if the score of the identity information is 0, the score of the criminal information is 0, the weight of the industry information is set to be 100, the weight of the academic information is set to be 20, the weight of the professional information is set to be 20, the weight of the consumption information is set to be 20, the weight of the voyage information is 10, and the weight of the income information is 30.
Step 508: and multiplying the score of each item of relevant information by the weight of the item of relevant information to obtain the customer score of the item of relevant information.
In one or more embodiments provided in this specification, the score of each item of related information is multiplied by the weight of the item of related information to obtain the customer score of the item of related information. If the identity information of a customer is 0; criminal information is 0; multiplying the industry information of the customer by a weight 100 for a score of-1 to obtain an identity information score of the customer of-100; multiplying the academic information of the customer by the weight 20 for 1 to obtain the customer score of the academic information of 20; multiplying the occupation information of the customer by the weight 20 to obtain the customer score of 100; multiplying the score 1 of the consumption information of the customer by a weight 20 to obtain the score of the consumption information of 20 points; the score 1 of the travel information of the customer is multiplied by the weight 10 to obtain a score of 10 points of the travel information, and the score 2 of the income information of the customer is multiplied by the weight 30 to obtain a income score of 60 points.
Step 510: and adding the customer scores of each item of relevant information to obtain the scores of the customers.
In one or more embodiments provided herein, the customer scores for each item of relevant information are added to obtain a score for the customer. For example, the identity information score of a customer is 0, the criminal information score is 0, the industry information score is-400, the academic information score is 20, the professional information score is 100, the consumption information score is 20, the voyage information score is 10, and the income information score is 60, so that the score of the customer is 110.
Step 512: and obtaining the category of the customer according to the grade of the customer.
In one or more embodiments provided herein, if the customer has a score of less than-1000, then the customer is classified as a dangerous customer treatment category; if the score of the customer is greater than-1000 points and less than or equal to 0 point, the customer is classified as a risk management and control treatment category; if the score of the customer is greater than 0 and less than or equal to 500, the customer is classified as a common customer treatment category; if the score of the customer is larger than 500 points and is smaller than or equal to 800 points, the customer is classified as a high-quality customer treatment category; if the customer score is greater than 800, the customer is listed as a VIP customer treatment category.
Step 514: and recommending corresponding service items or recommending corresponding level security and protection wind control management measures for the customers according to the category to which the customers belong.
The present specification also provides that a processor may perform the steps in the method shown in fig. 6. Fig. 6 shows a schematic flowchart of a big-data-based customer differentiation management method according to an embodiment of the present specification, including steps 602-612:
step 602: relevant information of customers is collected through big data mining.
Step 604: grouping the collected information related to the customers.
In one or more embodiments provided herein, the collected information is grouped according to similarity, the similar information is grouped into a group, and at least one group of information among academic information, consumption information, income information, occupation information, criminal information, identity information, complaint execution information, voyage information, and occupation information about the customer is obtained.
Step 606: and scoring the related information of each group according to the number and the group of the related information of the customers contained in the related information of each group.
In one or more embodiments provided herein, each group of related information is scored for the number and group of the related information of the customer included in each group of related information. For example, a score may be given according to a certain standard to each item of the customer related information included in each group of related information, the scores of each item of the related information in the group are added to obtain a basic score of the group, then a certain weight is given according to the group category of the group, and the basic score of the group is multiplied by the weight to obtain a score of the group of related information.
If the identity information of the customer is false, the score of the identity information of the customer is-200; if the criminal information of the customer is a person for criminal escape, the criminal information of the customer is divided into-200 points; if the identity information of the customer is real, the score of the identity information of the customer is 0; if the criminal information of the customer does not exist, the criminal information of the customer is scored into 0 point; comprehensively evaluating according to negative contents such as violation lending, default overdue and a third-party industry blacklist of the industry information of the customer or industry negative information such as complaint execution information such as court complaint and executed information and industry positive information related to good conditions of the customer, and giving a score of-40 to 40 points; scoring by 1-10 points according to high school and below high school, major, this family, Master, doctor and doctor; and meanwhile, giving scores of 5-10 points according to general workers, middle-level leaders and high-level leaders. The consumption information is respectively given to 10-1 points according to the monthly payment amount from the bank card. The travel information is respectively given for 10-0 points according to the number of the monthly travel and the taken transportation means, and the income information is respectively given for 20-1 points according to the tax payment.
Step 608: and adding the obtained scores of each group of related information of the customers to obtain the scores of the customers.
In one or more embodiments provided in the present specification, the obtained score of each set of related information of the customer is subjected to a mathematical operation, so as to obtain a score for the customer. If the identity information of a customer is divided into zero, the criminal information is divided into zero, the industry information is divided into-20, the academic information is divided into 8, the professional information is divided into 8, the voyage information is divided into 5 and the income information is divided into 10, the score of the customer is 11.
Step 610: and obtaining the category of the customer according to the grade of the customer.
In one or more embodiments provided herein, the category to which the customer belongs is obtained from a rating of the customer. If the score of the customer is less than-100 points, treating the customer as a dangerous customer; if the score of the customer is between-40 and 0, the customer is classified as a risk management treatment category; if the score of the customer is greater than 0 and less than or equal to 60 points, the customer is classified as a common customer treatment category; if the score of the customer is greater than 60 points and less than or equal to 80 points, the customer is classified as a high-quality customer treatment category; if the customer's score is greater than 80, the customer is listed as a VIP customer treatment category.
Step 612: and recommending corresponding service items or recommending corresponding level security and protection wind control management measures for the customers according to the category to which the customers belong.
The present specification also provides a big data based customer differentiation management apparatus, as shown in fig. 7, including a collection module 702, a classification module 704, and a recommendation module 706.
A collection module 702 configured to collect relevant information of customers through big data mining. The collecting module 706 is connected with the identity card scanning module and receives the identity card provided by the identity card scanning module to scan the identity card of the customer to obtain the head portrait or the identity card number on the identity card of the customer; the collecting module 706 is further connected with an input module for receiving the customer's identification number input by the user; thereby searching the relevant information of the customer through the head portrait or the ID number of the customer. The collecting module 702 is further connected to a public security database, a bank or payment bank user credit blacklist database, a industry and commerce registration information database, etc., and the hotel management system automatically searches the customer related information from the public security database, the bank or payment bank user credit blacklist database, the industry and commerce registration information database, the stored customer check-in information or other network databases according to the obtained customer identity number or customer head, and collects the customer related information about the customer, such as identity information, occupation information, academic information, criminal information or income information, etc.
A classification module 704 configured to obtain a category to which the customer belongs according to the collected related information of the customer. The classification module 704 receives the collected information about the customers transmitted by the collection module 702, and processes the received information about the customers to obtain the categories to which the customers belong. If the identity information of the customer is false or the criminal information is criminal escape personnel, the customer is classified as a dangerous treatment category, if the industry information of the customer comprises negative contents such as illegal borrowing, default overdue and a third-party industry blacklist or the related information of the customer comprises execution information such as court complaint and executed information, the customer is classified as a risk control treatment category, if the legal complaint of the customer is paid for personal income tax and social insurance, or if the legal complaint of an enterprise is a legal person or a high-management enterprise is an operating enterprise and no bad information, the customer is classified as a high-quality treatment category.
A recommending module 706 configured to recommend a differentiated management scheme corresponding to the category to which the customer belongs to the customer for the category to which the customer belongs. The recommending module 706 is connected to receive the category information of the customer transmitted by the classifying module 704, and recommends the differentiation management scheme corresponding to the category of the received customer for the customer according to the category of the customer. If the customer belongs to the dangerous customer category, the hotel service staff is prompted to give an alarm, if the customer belongs to the risk management and control category, the hotel service staff is prompted to perform key monitoring and prevention on the customer, and if the customer belongs to the high-quality customer category, the service items required by the customer or the required security level are prompted to be referred by the hotel service staff.
The present specification also provides a device for customer differentiation management based on big data, as shown in fig. 8, including a collection module 802, a classification module 804, and a recommendation module 806.
The collecting module 802 is further configured to extract a facial image of the customer through face recognition, and retrieve the relevant information of the customer through the facial image of the customer. The collection module 802 extracts a face image of a customer by a camera arranged at a hotel service desk, searches and retrieves relevant information of the customer from a network system connected with a processor by the acquired face image, and completes big data mining of the relevant information of the customer.
The collecting module 802 is connected with a public security database, a bank or payment bank user credit blacklist database, a industry and commerce registration information database and the like, the collecting module 802 automatically searches the relevant information of the customer from the public security database, the bank or payment bank user credit blacklist database, the industry and commerce registration information database, the stored customer check-in information or other network databases according to the obtained customer identity number or customer head, and collects the relevant information of the customer, such as identity information, occupation information, academic information, criminal information or income information and the like of the customer.
The classification module 804 includes:
scoring submodule 80402 configured to score the customers according to the relevant information of the customers collected by the collecting module 802, and obtain scores for the customers. The scoring sub-module 80402 scores the customers according to the relevant information of the customers searched by the collecting module 802, and if the identity information of the customers is false or the criminal information is the person for the criminal to escape, the customers are negatively scored; if the industry information of the customer comprises negative contents such as violation loan, default overdue, a blacklist of a third-party industry and the like or relevant information of the customer comprises complaint execution information such as court complaint and executed information and the like, the grade of the customer is zero; if the client's legislation is used to pay personal income tax and social insurance, and the client is a legal person of the business or a high-management and non-bad information of the business, the client is given a score within 1-10 according to the relevant information of the client.
A classification sub-module 80404 configured to obtain a category to which the customer belongs based on the rating of the customer.
A classification submodule 80404, configured to classify the customer according to the score of the customer by the scoring submodule 80402, and if the score of the customer is a negative score, classify the customer as a dangerous customer; if the score of the customer is zero, listing the customer as a risk management and control treatment category; if the scores of the customers are between 1 and 10, the customers are classified into different categories according to the different scores of the customers, if the scores of the customers are between 1 and 5, the customers are classified into a common customer treatment category, if the scores of the customers are between 5 and 8, the customers are classified into a high-quality customer treatment category, if the scores of the customers are between 9 and 10, the customers are classified into a VIP customer treatment category.
A recommending module 806 configured to recommend the corresponding service item or recommend the corresponding level of security management measures for the customer according to the category to which the customer belongs.
The recommending module 806 recommends a service item or a security management measure of a level corresponding to the category to which the customer belongs according to the category to which the customer belongs determined by the classification sub-module 80204. If the customer belongs to the category to be treated according to the dangerous customer, prompting the hotel service personnel to give an alarm, and simultaneously prompting that security wind control management measures of the category of the customer are monitored in a key mode. If the customer belongs to the customer who treats the category according to the risk control, hotel service personnel are prompted to carry out high-level security wind control management measures on the customer, service items are not recommended to the customer, and management confusion is avoided. If the customer is a customer of a treatment category according to a common customer, recommending fewer service items to the customer, and prompting to provide lower-level security wind control management measures for the customer; if the customer is a customer of a category treated according to a high-quality customer, recommending relatively more service items to the customer; simultaneously prompting to provide a medium-level security wind control management measure for the customer; if the customer is a customer of the category treated according to the VIP customer, all service items are recommended to the customer, and meanwhile, a higher-level security and wind control management measure is provided for the customer.
The present specification further provides a big data based customer differentiation management apparatus, as shown in fig. 9, including a collection module 902, a classification module 904, and a recommendation module 906.
Wherein the collecting module 902 is configured to obtain the category to which the customer belongs according to the collected related information of the customer.
The classification module 904 includes: a scoring submodule 90402 and a classification submodule 90404.
Wherein scoring submodule 90402 includes:
a grouping unit 904022 configured to group the collected information related to the customers, and obtain at least one group of information of academic information, consumption information, income information, occupation information, criminal information, identity information, complaint execution information, voyage information and occupation information related to the customers;
a scoring unit 904024 configured to score the number and category of the related information of the customer included in each group of related information for each group of related information; the scoring unit 904024 scores the number and the group of the related information of the customer included in each group of the related information for each group of the related information. For example, a score may be given according to a certain standard to each item of the customer related information included in each group of related information, the scores of each item of the related information in the group are added to obtain a basic score of the group, then a certain weight is given according to the group category of the group, and the basic score of the group is multiplied by the weight to obtain a score of the group of related information. If the identity information of the customer is false, the scoring unit 904024 scores-200 points for the identity information of the customer; if the criminal information of the customer is a person for criminal escape, the scoring unit 904024 scores the criminal information of the customer into-200 points; if the identity information of the customer is true, the scoring unit 904024 scores 0 for the identity information of the customer; if the customer has no criminal information, the scoring unit 904024 scores the criminal information of the customer into 0; comprehensive evaluation is carried out according to negative contents such as violation lending, default overdue and a third-party industry blacklist of the industry information of the customer or industry negative information such as court complaints, executed information and the like complaint execution information and industry positive information related to good matters contained in the relevant information of the customer, and a score of-40 to 40 is given by the scoring unit 904024; the scoring unit 904024 gives a score of 1-10 points according to the high school and below high school, major, this family, master, doctor and doctor; meanwhile, the professional information is given a score of 5-10 points according to the general workers, the middle leader, the high leader and the scoring unit 904024. The consumption information is given 10-1 points by the scoring unit 904024 according to the monthly payment amount from the bank card. The air travel information is respectively given 10-0 points by the scoring unit 904024 according to the number of the travel per month and the taken transportation means, and the income information is respectively given 20-1 points by the scoring unit 904024 according to the tax payment amount.
An operation unit 904026 configured to perform mathematical operation on the obtained scores of each set of relevant information of the customer to obtain scores for the customer. The arithmetic unit 904026 performs mathematical operation on the obtained scores of each set of related information of the customer to obtain scores for the customer. If the identity information of a customer is divided into zero, the criminal information is divided into zero, the industry information is divided into-20, the academic information is divided into 8, the professional information is divided into 8, the voyage information is divided into 5 and the income information is divided into 10, the score of the customer is 11.
A classification sub-module 90404 configured to obtain a category to which the customer belongs based on the rating of the customer. The classification sub-module 90404 obtains the category to which the customer belongs based on the rating of the customer. If the score of the customer is less than-100 points, the customer is treated as a dangerous customer; if the score of the customer is between-40 and 0, the customer is classified as a risk management treatment category; if the score of the customer is greater than 0 and less than or equal to 60 points, the customer is classified as a common customer treatment category; if the score of the customer is greater than 60 points and less than or equal to 80 points, the customer is classified as a high-quality customer; if the customer's score is greater than 80, the customer is listed as a VIP customer treatment category.
A recommending module 906 configured to recommend the corresponding service item or the corresponding level of security control management measure for the class to which the customer belongs according to the class to which the customer belongs.
An embodiment of the present specification further provides a computer readable storage medium storing computer instructions which, when executed by a processor, implement the steps of the method for customer differentiation management based on big data as described above.
The above is an illustrative scheme of a computer-readable storage medium of the present embodiment. It should be noted that the technical solution of the storage medium and the technical solution of the above-mentioned method for editing a topographic scene belong to the same concept, and details that are not described in detail in the technical solution of the storage medium can be referred to the description of the technical solution of the method for differentiated management of hotel customers.
The computer instructions comprise computer program code which may be in the form of source code, object code, an executable file or some intermediate form, or the like. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The foregoing descriptions of specific exemplary embodiments of the present invention have been presented for purposes of illustration and description. It is not intended to limit the invention to the precise form disclosed, and obviously many modifications and variations are possible in light of the above teaching. The exemplary embodiments were chosen and described in order to explain certain principles of the invention and its practical application to enable one skilled in the art to make and use various exemplary embodiments of the invention and various alternatives and modifications as are suited to the particular use contemplated. It is intended that the scope of the invention be defined by the claims and their equivalents.
Claims (10)
1. A big data-based customer differentiation management method is characterized by comprising the following steps:
collecting relevant information of customers through big data mining;
obtaining the category of the customer according to the collected relevant information of the customer;
and recommending a differential management scheme corresponding to the category to which the customer belongs for the customer according to the category to which the customer belongs.
2. The big-data based customer differentiation management method according to claim 1, wherein obtaining the category to which the customer belongs according to the collected related information of the customer comprises:
scoring the customers according to the collected relevant information of the customers to obtain scores of the customers;
and obtaining the category of the customer according to the grade of the customer.
3. The big data-based customer differentiation management method according to claim 2, wherein the collected related information of said customer comprises at least two items of academic information, consumption information, income information, occupation information, criminal information, identity information, complaint execution information, voyage information, and occupation information of said customer, and the scoring of said customer according to said collected related information of said customer, and the obtaining of the score of said customer comprises:
at least two items of the collected related information of the customers are respectively scored to obtain the score of each item of the related information of the customers;
and performing mathematical operation on the obtained scores of all the related information of the customers to obtain the scores of the customers.
4. The big data based customer differentiation management method according to claim 3, wherein the scoring of each relevant information of said customer obtained is mathematically operated, and obtaining the score for said customer comprises:
setting a weight value for each item of relevant information of the customer;
multiplying the score of each item of relevant information by the weight of the item of relevant information to obtain the customer score of the item of relevant information;
and adding the customer scores of each item of relevant information to obtain the scores of the customers.
5. The big-data based customer differentiation management method according to claim 2, wherein said scoring said customers according to said collected relevant information of said customers comprises:
grouping the collected related information of the customers to obtain at least one group of information of academic information, consumption information, income information, occupation information, criminal information, identity information, complaint execution information, voyage information and occupation information about the customers;
scoring each group of related information by the number and the group of the related information of the customer included in each group of related information;
and adding the obtained scores of each group of related information of the customers to obtain the scores of the customers.
6. The big-data based customer differential management method according to any of claims 1-5, wherein the collecting relevant information of customers through big-data mining comprises:
the method comprises the steps of extracting a face image of a customer through face recognition, and calling relevant information of the customer through the face image of the customer.
7. The big-data based customer differential management method according to any one of claims 1-5, wherein recommending, for the customer, a differential management scheme corresponding to a category to which the customer belongs according to the category to which the customer belongs comprises:
and recommending the service items corresponding to the class of the customer for the customer according to the class of the customer.
8. The big-data based customer differential management method according to any one of claims 1-5, wherein recommending the customer a differential management scheme corresponding to the category to which the customer belongs according to the category to which the customer belongs comprises:
and recommending security wind control management measures of corresponding levels of the categories to which the customers belong to the customers according to the categories to which the customers belong.
9. An apparatus for customer differentiation management based on big data, comprising:
a collection module configured to collect relevant information of a customer through big data mining;
the classification module is configured to obtain a category to which the customer belongs according to the collected relevant information of the customer;
the recommendation module is configured to recommend a differential management scheme corresponding to the category to which the customer belongs to the customer according to the category to which the customer belongs.
10. The big-data based customer differentiation management apparatus according to claim 9, wherein said classification module comprises:
a scoring submodule configured to score the customers according to the collected relevant information of the customers, and obtain scores for the customers;
a classification submodule configured to obtain a category to which the customer belongs based on the rating of the customer.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910955704.7A CN110728539A (en) | 2019-10-09 | 2019-10-09 | Big data-based customer differentiation management method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910955704.7A CN110728539A (en) | 2019-10-09 | 2019-10-09 | Big data-based customer differentiation management method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110728539A true CN110728539A (en) | 2020-01-24 |
Family
ID=69219739
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910955704.7A Pending CN110728539A (en) | 2019-10-09 | 2019-10-09 | Big data-based customer differentiation management method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110728539A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103325067A (en) * | 2013-05-29 | 2013-09-25 | 广东电网公司佛山供电局 | Service recommendation method and system based on electricity customer segmentation |
| CN105741186A (en) * | 2016-01-22 | 2016-07-06 | 深圳市燃气集团股份有限公司 | Pipeline gas data processing method and pipeline gas data processing system based on user grade |
| CN107256496A (en) * | 2017-05-27 | 2017-10-17 | 上海非码网络科技有限公司 | Customer management method and system, server based on multi-platform data |
| CN109063163A (en) * | 2018-08-14 | 2018-12-21 | 腾讯科技（深圳）有限公司 | A kind of method, apparatus, terminal device and medium that music is recommended |
| CN109409908A (en) * | 2018-10-10 | 2019-03-01 | 北京长城华冠汽车技术开发有限公司 | Customer value classification method and device, computer-readable medium |
-
2019
- 2019-10-09 CN CN201910955704.7A patent/CN110728539A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103325067A (en) * | 2013-05-29 | 2013-09-25 | 广东电网公司佛山供电局 | Service recommendation method and system based on electricity customer segmentation |
| CN105741186A (en) * | 2016-01-22 | 2016-07-06 | 深圳市燃气集团股份有限公司 | Pipeline gas data processing method and pipeline gas data processing system based on user grade |
| CN107256496A (en) * | 2017-05-27 | 2017-10-17 | 上海非码网络科技有限公司 | Customer management method and system, server based on multi-platform data |
| CN109063163A (en) * | 2018-08-14 | 2018-12-21 | 腾讯科技（深圳）有限公司 | A kind of method, apparatus, terminal device and medium that music is recommended |
| CN109409908A (en) * | 2018-10-10 | 2019-03-01 | 北京长城华冠汽车技术开发有限公司 | Customer value classification method and device, computer-readable medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Calders et al. | Why unbiased computational processes can lead to discriminative decision procedures | |
| CN108038696B (en) | Method and system for detecting bill swiping based on equipment identification code and social group information | |
| Politou et al. | Profiling tax and financial behaviour with big data under the GDPR | |
| CN110892442A (en) | System, method and apparatus for adaptive scoring to detect misuse or abuse of business cards | |
| US20050043961A1 (en) | System and method for identification, detection and investigation of maleficent acts | |
| CN111507831A (en) | Credit risk automatic assessment method and device | |
| CN109583966B (en) | High-value customer identification method, system, equipment and storage medium | |
| CN112102073A (en) | Credit risk control method and system, electronic device and readable storage medium | |
| Woods et al. | Towards integrating insurance data into information security investment decision making | |
| US10262371B2 (en) | Automated compliance scoring system that analyzes network accessible data sources | |
| Ghozali et al. | Determinants of fraudulent financial reporting and whistleblowing system: Applying theory of planned behavior | |
| Sultan et al. | Financial intelligence unit of Pakistan: an evaluation of its performance and role in combating money laundering and terrorist financing | |
| Amin et al. | Application of optimistic and pessimistic OWA and DEA methods in stock selection | |
| Blanco et al. | Financial statement comparability and accounting fraud | |
| CN112950359B (en) | User identification method and device | |
| Howell et al. | Voting for the president: The Supreme Court during war | |
| Sukri et al. | Risk Management Analysison Administration System Using Octave Allegro Framework | |
| CN115564591A (en) | Financing product determination method and related equipment | |
| CN110728539A (en) | Big data-based customer differentiation management method and device | |
| Amaliyah et al. | Impact of Digital Shariah Banking Systems on Cash-Waqf amongst Muslim Millennials | |
| CN113743816A (en) | Vehicle rental risk control method and device, computer equipment and storage medium | |
| Brandão et al. | Plus: A semi-automated pipeline for fraud detection in public bids | |
| CN114140245A (en) | Organization risk assessment method, device and equipment and computer storage medium | |
| Ekechukwu et al. | Effect of forensic accounting on the performance of Nigerian banking sector | |
| CN111460280B (en) | Legal service personnel recommendation method based on public legal service platform |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200124 |
|
| RJ01 | Rejection of invention patent application after publication |