Network customer distribution method and device based on customer management system

Abstract

The invention provides a network customer distribution method and device based on a customer management system, and the method comprises the steps: building a customer behavior model, carrying out the calculation through the customer behavior model according to account information, and obtaining a customer score; obtaining a first grade of the customer according to a preset customer grade table corresponding to the customer score; establishing a sales behavior model, and performing calculation through the sales behavior model according to the sales information to obtain a sales score; obtaining a second grade of the sales according to a preset sales grade table corresponding to the sales score; within a set time, first levels of all clients are obtained, then the clients are distributed to corresponding sales according to a distribution strategy, and the account and the account information are encrypted and stored in a server; and efficient and accurate distribution of the clients is realized, so that the satisfaction degree of the clients is improved, and the competitiveness of enterprises is improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent allocation technology, and in particular to a network customer allocation method and device based on a customer management system. Background Art

[0002] Customer allocation is one of the core links of a company's operations. Its work efficiency and accuracy directly affect the actual customer conversion rate. However, the existing customer allocation method relies on manual operations, which leads to a series of problems: First, manual allocation cannot respond to market changes and customer needs in a timely manner. In a fast-paced market environment, customer needs are real-time, while manual allocation often lags behind, causing customers to wait too long and significantly reduce satisfaction. For example, after a customer registers, he or she may have to wait a long time before being assigned to a salesperson, during which time the customer's enthusiasm and willingness to buy may gradually fade.

[0003] Secondly, manual allocation consumes a lot of manpower costs. As the number of customers continues to increase, more human resources are needed to carry out customer allocation work. This not only increases the operating costs of the enterprise, but also may lead to a waste of human resources, because these personnel could have been invested in more valuable business activities.

[0004] Finally, manual operations are prone to errors. The uncertainty of human factors makes it easy to make mistakes in the customer allocation process, such as allocating customers to inappropriate sales personnel, duplicate allocations, or missed allocations. These mistakes not only affect the company's service quality, but may also damage the company's reputation. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a network customer allocation method and device based on a customer management system, so as to achieve efficient and accurate allocation of customers, thereby improving customer satisfaction and enhancing the competitiveness of enterprises.

[0006] In a first aspect, the present invention provides a method for allocating network customers based on a customer management system, comprising the following steps: Step 1: Establish a customer behavior model, calculate the customer behavior model based on the account information, and obtain the customer score; obtain the first grade of the customer according to the preset customer grade table corresponding to the customer score; Step 2: Establish a sales behavior model, calculate the sales information through the sales behavior model to obtain a sales score; and obtain the second level of the sales according to the sales score corresponding to the preset sales level table; Step 3: Get the first level of all customers within the set time, and then assign the customers to the corresponding sales according to the allocation strategy; Step 4: Encrypt the account and the account information, and store them on the server.

[0007] In a second aspect, the present invention provides a network client allocation device based on a client management system, comprising: The customer rating module establishes a customer behavior model, calculates the customer behavior model based on the account information, and obtains the customer rating; the first rating of the customer is obtained according to the preset customer rating table corresponding to the customer rating; The sales grade module establishes a sales behavior model, calculates the sales information through the sales behavior model to obtain a sales score; and obtains the second grade of the sales according to the sales score corresponding to the preset sales grade table; The allocation module obtains the first level of all customers within a set time, and then allocates customers to corresponding sales according to the allocation strategy; The storage module encrypts the account and the account information and stores them in the server.

[0008] One or more technical solutions provided by the present invention have at least the following technical effects or advantages: (I) Improving the reliability and accuracy of allocation: By continuously optimizing and adjusting the score ratio, the reliability and accuracy of allocation can be greatly improved; the score calculation method and allocation strategy can be dynamically adjusted according to the actual operation situation and data analysis results to adapt to the ever-changing market environment and customer needs.

[0009] (II) Improve customer response speed and conversion rate: The original solution required 1 hour of manual work every day. After replacing it with the above-mentioned fully automatic allocation solution, it not only significantly improved customer response speed, but also improved customer transactions and conversion rates. The automatic allocation solution can complete customer allocation work in an instant, ensuring that customers can receive timely service from sales staff. At the same time, through accurate customer level classification and sales score calculation, customers can be assigned to the most suitable sales staff, thereby increasing the possibility of customer transactions and conversion rates.

[0010] (III) Savings on human cost expenditures: Great savings on human cost expenditures. A large amount of human resources are no longer needed to carry out customer allocation work. Enterprises can invest these personnel in more valuable business activities, such as market development, product development and customer service, further enhancing the core competitiveness of enterprises.

[0011] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The present invention will be further described below in conjunction with embodiments with reference to the accompanying drawings.

[0013] Figure 1 is a flow chart of the method in Embodiment 1 of the present invention; Figure 2 It is a schematic diagram of the structure of the device in the second embodiment of the present invention. DETAILED DESCRIPTION

[0014] The embodiment of the present application provides a network customer allocation method and device based on a customer management system to achieve efficient and accurate customer allocation, thereby improving customer satisfaction, increasing the enthusiasm of sales personnel, and improving the competitiveness of the enterprise.

[0015] Embodiment 1

[0016] like Figure 1 As shown, this embodiment provides a network customer allocation method based on a customer management system, comprising the following steps: Step 1: Establish a customer behavior model, calculate the customer behavior model based on the account information, and obtain the customer score; obtain the first grade of the customer according to the preset customer grade table corresponding to the customer score; Step 2: Establish a sales behavior model, calculate the sales information through the sales behavior model to obtain a sales score; and obtain the second level of the sales according to the sales score corresponding to the preset sales level table; Step 3: Get the first level of all customers within the set time, and then assign the customers to the corresponding sales according to the allocation strategy; Step 4: Encrypt the account and the account information, and store them on the server.

[0017] In this embodiment, preferably, the encryption method is: extracting unique identification information from the server's hardware, including the number of CPU cores, memory, hard disk capacity and MAC address of the network card, the memory and hard disk capacity are both in GB; combining the obtained unique identification information in a set order to generate a unique machine code string; then generating 128-bit random characters; combining the machine code string with the 128-bit random characters as an encryption seed; hashing the encryption seed through the SHA256 hash function to generate a 256-bit AES symmetric encryption key, and encrypting with the AES symmetric encryption key; the data is stored on the server, but for security reasons, it needs to be encrypted. The encryption method of the present invention can make it difficult for criminals to decrypt, and when data migration is required later, the encryption method can well ensure the security of communication.

[0018] In this embodiment, preferably, the step 1 is specifically: Customer behavior model: F1=(n1×P1+n2×P2+P3+P4+P5+P6+P7+P8)-(X1+X2); n1: the number of times the purchase page is opened within a set time, P1: 2 points; n2: number of logins within the set time period, P2: 1 minute; Determine the login client type. If the login client type is a computer, P3 is 2 points; if the login client type is a mobile terminal, P3 is 1 point; Whether to click the novice tutorial. If you click the novice tutorial, P4 is 2; if you do not click the novice tutorial, P4 is 0; Whether to click on the novice welfare coupon to use it immediately. If you click on the novice welfare coupon to use it immediately, P5 is 5 points; if you do not click on the novice welfare coupon to use it immediately, P5 is 0 points; Whether the login IP has logged in to other accounts. If the login IP has logged in to other accounts, P6 is 2 points; if the login IP has not logged in to other accounts, P6 is 0 points; Whether the login IP has registered other accounts. If the login IP has registered other accounts, P7 will be 3 points; if the login IP has not registered other accounts, P7 will be 0 points; Whether the customer service staff has been contacted. If so, P8 is 2 points; if not, P8 is 0 points; Whether the mobile phone number has been cancelled. If the mobile phone number has been cancelled, X1 is 5 points; if the mobile phone number has not been cancelled, X1 is 0 points; Whether it has been more than 7 days since the last login. If it has been more than 7 days, X2 is 2 points; if it has been less than 7 days, X2 is 0 points; The customer rating is calculated based on the customer behavior model according to the account information; the account information includes: the number of purchase page openings within a set time, the number of logins within a set time, the last login client type, whether the novice tutorial is clicked, whether the novice benefit coupon is clicked to use immediately, whether the login IP has logged in to other accounts, whether the login IP has registered other accounts, whether the customer service staff has been contacted, whether the mobile phone number has been cancelled, and whether it has been more than 7 days since the last login; First-level rating from customers: If 30≤F1, the customer’s first level rating is S1; If 20≤F1<30, the customer’s first level rating is A1; If 10≤F1<20, the customer’s first level rating is B1; If F1<10, the customer's first level assessment is C1.

[0019] In this embodiment, preferably, step 2 is specifically: Sales behavior model: F2 = (Y1 / Y2) × 100 + (N1 + N2) × 0.5; where Y1 is the number of orders completed by the sales team last month, Y2 is the number of orders allocated last month, N1 is the average of all customers’ final ratings of the sales team; N2 is the average of all customers’ ratings of the sales team’s timely resolution of customer concerns; If 30≤F2, the sales level is S2; If 20≤F2<30, the sales level is A2; If 10≤F2<20, the sales level is B2; If F2 is less than 10, the sales level is C2; by conducting sales ratings once a month, sales personnel can be given opportunities for promotion and become more competitive.

[0020] In this embodiment, preferably, step 3 is specifically as follows: within a set time, obtain the first level of all customers, and then allocate the customers to corresponding sales according to an allocation strategy; the allocation strategy is to evenly allocate the first set percentage value among customers of the same level to sales of the corresponding level, and evenly allocate the second set percentage value among customers of this level to sales of the highest level among unallocated ones; evenly allocate the third set percentage value among customers of this level to sales of the highest level among unallocated ones; evenly allocate the fourth set percentage value among customers of this level to unallocated sales; the sum of the first set percentage value, the second set percentage value, the third set percentage value and the fourth set percentage value is 1.

[0021] For example, divide the customers with the first level of S1 into 50%, 25%, 15% and 10%; then distribute 50% of the customers with the first level of S1 evenly to the sales with the sales level of S2, distribute 25% of the customers with the first level of S1 evenly to the sales with the sales level of A2, distribute 15% of the customers with the first level of S1 evenly to the sales with the sales level of B2, distribute 10% of the customers with the first level of S1 evenly to the sales with the sales level of C2. Through the above distribution method, sales can get more reasonable opportunities, capable sales can get opportunities, improve their sales level, and greatly increase the enthusiasm of sales.

[0022] Based on the same inventive concept, the present application also provides a device corresponding to the method in Example 1, see Example 2 for details.

[0023] Embodiment 2

[0024] like Figure 2As shown, in this embodiment, a network customer allocation device based on a customer management system is provided, comprising: The customer rating module establishes a customer behavior model, calculates the customer behavior model based on the account information, and obtains the customer rating; the first rating of the customer is obtained according to the preset customer rating table corresponding to the customer rating; The sales grade module establishes a sales behavior model, calculates the sales information through the sales behavior model to obtain a sales score; and obtains the second grade of the sales according to the sales score corresponding to the preset sales grade table; The allocation module obtains the first level of all customers within a set time, and then allocates customers to corresponding sales according to the allocation strategy; The storage module encrypts the account and the account information and stores them in the server.

[0025] In this embodiment, preferably, the encryption method is: extracting unique identification information from the server's hardware, including the number of CPU cores, memory, hard disk capacity and MAC address of the network card, the memory and hard disk capacity are both in GB; combining the obtained unique identification information in a set order to generate a unique machine code string; then generating 128-bit random characters; combining the machine code string with the 128-bit random characters as an encryption seed; hashing the encryption seed through the SHA256 hash function to generate a 256-bit AES symmetric encryption key, and encrypting with the AES symmetric encryption key; the data is stored on the server, but for security reasons, it needs to be encrypted. The encryption method of the present invention can make it difficult for criminals to decrypt, and when data migration is required later, the encryption method can well ensure the security of communication.

[0026] In this embodiment, preferably, the customer grade module is specifically: Customer behavior model: F1=(n1×P1+n2×P2+P3+P4+P5+P6+P7+P8)-(X1+X2); n1: the number of times the purchase page is opened within a set time, P1: 2 points; n2: number of logins within the set time period, P2: 1 minute; Determine the login client type. If the login client type is a computer, P3 is 2 points; if the login client type is a mobile terminal, P3 is 1 point; Whether to click the novice tutorial. If you click the novice tutorial, P4 is 2; if you do not click the novice tutorial, P4 is 0; Whether to click on the novice welfare coupon to use it immediately. If you click on the novice welfare coupon to use it immediately, P5 is 5 points; if you do not click on the novice welfare coupon to use it immediately, P5 is 0 points; Whether the login IP has logged in to other accounts. If the login IP has logged in to other accounts, P6 is 2 points; if the login IP has not logged in to other accounts, P6 is 0 points; Whether the login IP has registered other accounts. If the login IP has registered other accounts, P7 will be 3 points; if the login IP has not registered other accounts, P7 will be 0 points; Whether the customer service staff has been contacted. If so, P8 is 2 points; if not, P8 is 0 points; Whether the mobile phone number has been cancelled. If the mobile phone number has been cancelled, X1 is 5 points; if the mobile phone number has not been cancelled, X1 is 0 points; Whether it has been more than 7 days since the last login. If it has been more than 7 days, X2 is 2 points; if it has been less than 7 days, X2 is 0 points; The customer rating is calculated based on the customer behavior model according to the account information; the account information includes: the number of purchase page openings within a set time, the number of logins within a set time, the last login client type, whether the novice tutorial is clicked, whether the novice benefit coupon is clicked to use immediately, whether the login IP has logged in to other accounts, whether the login IP has registered other accounts, whether the customer service staff has been contacted, whether the mobile phone number has been cancelled, and whether it has been more than 7 days since the last login; First-level rating from customers: If 30≤F1, the customer’s first level rating is S1; If 20≤F1<30, the customer’s first level rating is A1; If 10≤F1<20, the customer’s first level rating is B1; If F1<10, the customer's first level assessment is C1.

[0027] In this embodiment, preferably, the sales level module is specifically: Sales behavior model: F2 = (Y1 / Y2) × 100 + (N1 + N2) × 0.5; where Y1 is the number of orders completed by the sales team last month, Y2 is the number of orders allocated last month, N1 is the average of all customers’ final ratings of the sales team; N2 is the average of all customers’ ratings of the sales team’s timely resolution of customer concerns; If 30≤F2, the sales level is S2; If 20≤F2<30, the sales level is A2; If 10≤F2<20, the sales level is B2; If F2 is less than 10, the sales level is C2; by conducting sales ratings once a month, sales personnel can be given opportunities for promotion and become more competitive.

[0028] In this embodiment, preferably, the allocation module is specifically as follows: within a set time, obtain the first level of all customers, and then allocate the customers to corresponding sales according to the allocation strategy; the allocation strategy is to evenly allocate the first set percentage value among customers of the same level to sales of the corresponding level, and evenly allocate the second set percentage value among customers of this level to sales of the highest level among unallocated ones; evenly allocate the third set percentage value among customers of this level to sales of the highest level among unallocated ones; evenly allocate the fourth set percentage value among customers of this level to unallocated sales; the sum of the first set percentage value, the second set percentage value, the third set percentage value and the fourth set percentage value is 1.

[0029] For example, divide the customers with the first level of S1 into 50%, 25%, 15% and 10%; then distribute 50% of the customers with the first level of S1 evenly to the sales with the sales level of S2, distribute 25% of the customers with the first level of S1 evenly to the sales with the sales level of A2, distribute 15% of the customers with the first level of S1 evenly to the sales with the sales level of B2, distribute 10% of the customers with the first level of S1 evenly to the sales with the sales level of C2. Through the above distribution method, sales can get more reasonable opportunities, capable sales can get opportunities, improve their sales level, and greatly increase the enthusiasm of sales.

[0030] Since the device introduced in the second embodiment of the present invention is a device used to implement the method of the first embodiment of the present invention, those skilled in the art can understand the specific structure and deformation of the device based on the method introduced in the first embodiment of the present invention, so it is not described here in detail. All devices used in the method of the first embodiment of the present invention belong to the scope of protection of the present invention.

[0031] Although the specific implementation modes of the present invention are described above, those skilled in the art should understand that the specific implementation modes described are only illustrative and are not intended to limit the scope of the present invention. Equivalent modifications and changes made by those skilled in the art in accordance with the spirit of the present invention should be included in the scope of protection of the claims of the present invention.

Claims

1. A network customer allocation method based on a customer management system, characterized in that: The steps include: Step 1: Customer behavior model: F1=(n1×P1+n2×P2+P3+P4+P5+P6+P7+P8)-(X1+X2); n1: the number of times the purchase page is opened within a set time, P1: 2 points; n2: number of logins within the set time period, P2: 1 minute; Determine the login client type. If the login client type is a computer, P3 is 2 points; if the login client type is a mobile terminal, P3 is 1 point; Whether to click the novice tutorial. If you click the novice tutorial, P4 is 2; if you do not click the novice tutorial, P4 is 0; Whether to click on the novice welfare coupon to use it immediately. If you click on the novice welfare coupon to use it immediately, P5 is 5 points; if you do not click on the novice welfare coupon to use it immediately, P5 is 0 points; Whether the login IP has logged in to other accounts. If the login IP has logged in to other accounts, P6 is 2 points; if the login IP has not logged in to other accounts, P6 is 0 points; Whether the login IP has registered other accounts. If the login IP has registered other accounts, P7 will be 3 points; if the login IP has not registered other accounts, P7 will be 0 points; Whether the customer service staff has been contacted. If so, P8 is 2 points; If you have not contacted customer service, P8 will be 0 points; Whether the mobile phone number has been cancelled. If the mobile phone number has been cancelled, X1 is 5 points; if the mobile phone number has not been cancelled, X1 is 0 points; Whether it has been more than 7 days since the last login. If it has been more than 7 days, X2 is 2 points; if it has been less than 7 days, X2 is 0 points; The customer rating is calculated based on the customer behavior model according to the account information; the account information includes: the number of purchase page openings within a set time, the number of logins within a set time, the last login client type, whether the novice tutorial is clicked, whether the novice benefit coupon is clicked to use immediately, whether the login IP has logged in to other accounts, whether the login IP has registered other accounts, whether the customer service staff has been contacted, whether the mobile phone number has been cancelled, and whether it has been more than 7 days since the last login; First-level rating from customers: If 30≤F1, the customer’s first level rating is S1; If 20≤F1<30, the customer’s first level rating is A1; If 10≤F1<20, the customer’s first level rating is B1; If F1 < 10, the customer's first level assessment is C1; Step 2: Establish a sales behavior model, calculate the sales information through the sales behavior model to obtain a sales score; and obtain the second level of the sales according to the sales score corresponding to the preset sales level table; Step 3: Get the first level of all customers within the set time, and then assign the customers to the corresponding sales according to the allocation strategy; Step 4: Encrypt the account and the account information, and store them on the server.

2. A method for allocating network customers based on a customer management system according to claim 1, characterized in that: The encryption method is as follows: extracting unique identification information from the server's hardware, including the number of CPU cores, memory, hard disk capacity and MAC address of the network card, with the memory and hard disk capacity both in GB; combining the acquired unique identification information in a set order to generate a unique machine code string; then generating 128-bit random characters; combining the machine code string with the 128-bit random characters as an encryption seed; performing a hash operation on the encryption seed through the SHA256 hash function to generate a 256-bit AES symmetric encryption key, and using the AES symmetric encryption key for encryption.

3. A method for allocating network customers based on a customer management system according to claim 1, characterized in that: The step 2 is specifically as follows: Sales behavior model: F2 = (Y1 / Y2) × 100 + (N1 + N2) × 0.5; where Y1 is the number of orders completed by the sales team last month, Y2 is the number of orders allocated last month, N1 is the average of all customers’ final ratings of the sales team; N2 is the average of all customers’ ratings of the sales team’s timely resolution of customer concerns; If 30≤F2, the sales level is S2; If 20≤F2<30, the sales level is A2; If 10≤F2<20, the sales level is B2; If F2<10, the sales level is C2.

4. A method for allocating network customers based on a customer management system according to claim 1, characterized in that: The step 3 is specifically as follows: within a set time, obtain the first level of all customers, and then allocate the customers to corresponding sales according to the allocation strategy; the allocation strategy is to evenly allocate the first set percentage value among customers of the same level to sales of the corresponding level, and evenly allocate the second set percentage value among customers of this level to sales of the highest level among unallocated ones; evenly allocate the third set percentage value among customers of this level to sales of the highest level among unallocated ones; evenly allocate the fourth set percentage value among customers of this level to unallocated sales; the sum of the first set percentage value, the second set percentage value, the third set percentage value and the fourth set percentage value is 1.

5. A network customer allocation device based on a customer management system, characterized in that: include: Customer level module, customer behavior model: F1=(n1×P1+n2×P2+P3+P4+P5+P6+P7+P8)-(X1+X2); n1: the number of times the purchase page is opened within a set time, P1: 2 points; n2: number of logins within the set time period, P2: 1 minute; Determine the login client type. If the login client type is a computer, P3 is 2 points; if the login client type is a mobile terminal, P3 is 1 point; Whether to click the novice tutorial. If you click the novice tutorial, P4 is 2; if you do not click the novice tutorial, P4 is 0; Whether to click on the novice welfare coupon to use it immediately. If you click on the novice welfare coupon to use it immediately, P5 is 5 points; if you do not click on the novice welfare coupon to use it immediately, P5 is 0 points; Whether the login IP has logged in to other accounts. If the login IP has logged in to other accounts, P6 is 2 points; if the login IP has not logged in to other accounts, P6 is 0 points; Whether the login IP has registered other accounts. If the login IP has registered other accounts, P7 will be 3 points; if the login IP has not registered other accounts, P7 will be 0 points; Whether the customer service staff has been contacted. If so, P8 is 2 points; If you have not contacted customer service, P8 will be 0 points; Whether the mobile phone number has been cancelled. If the mobile phone number has been cancelled, X1 is 5 points; if the mobile phone number has not been cancelled, X1 is 0 points; Whether it has been more than 7 days since the last login. If it has been more than 7 days, X2 is 2 points; if it has been less than 7 days, X2 is 0 points; The customer rating is calculated based on the customer behavior model according to the account information; the account information includes: the number of purchase page openings within a set time, the number of logins within a set time, the last login client type, whether the novice tutorial is clicked, whether the novice benefit coupon is clicked to use immediately, whether the login IP has logged in to other accounts, whether the login IP has registered other accounts, whether the customer service staff has been contacted, whether the mobile phone number has been cancelled, and whether it has been more than 7 days since the last login; First-level rating from customers: If 30≤F1, the customer’s first level rating is S1; If 20≤F1<30, the customer’s first level rating is A1; If 10≤F1<20, the customer’s first level rating is B1; If F1 < 10, the customer's first level assessment is C1; The sales grade module establishes a sales behavior model, calculates the sales information through the sales behavior model to obtain a sales score; and obtains the second grade of the sales according to the sales score corresponding to the preset sales grade table; The allocation module obtains the first level of all customers within a set time, and then allocates customers to corresponding sales according to the allocation strategy; The storage module encrypts the account and the account information and stores them in the server.

6. A network client allocation device based on a client management system according to claim 5, characterized in that: The encryption method is as follows: extracting unique identification information from the server's hardware, including the number of CPU cores, memory, hard disk capacity and MAC address of the network card, with the memory and hard disk capacity both in GB; combining the acquired unique identification information in a set order to generate a unique machine code string; then generating 128-bit random characters; combining the machine code string with the 128-bit random characters as an encryption seed; performing a hash operation on the encryption seed through the SHA256 hash function to generate a 256-bit AES symmetric encryption key, and using the AES symmetric encryption key for encryption.

7. A network client allocation device based on a client management system according to claim 5, characterized in that: The sales level module is specifically: Sales behavior model: F2 = (Y1 / Y2) × 100 + (N1 + N2) × 0.5; where Y1 is the number of orders completed by the sales team last month, Y2 is the number of orders allocated last month, N1 is the average of all customers’ final ratings of the sales team; N2 is the average of all customers’ ratings of the sales team’s timely resolution of customer concerns; If 30≤F2, the sales level is S2; If 20≤F2<30, the sales level is A2; If 10≤F2<20, the sales level is B2; If F2<10, the sales level is C2.

8. A network client allocation device based on a client management system according to claim 5, characterized in that: The allocation module is specifically as follows: within a set time, the first level of all customers is obtained, and then the customers are allocated to corresponding sales according to the allocation strategy; the allocation strategy is to evenly allocate the first set percentage value among customers of the same level to sales of the corresponding level, and evenly allocate the second set percentage value among customers of this level to sales of the highest level among unallocated ones; evenly allocate the third set percentage value among customers of this level to sales of the highest level among unallocated ones; evenly allocate the fourth set percentage value among customers of this level to unallocated sales; the sum of the first set percentage value, the second set percentage value, the third set percentage value and the fourth set percentage value is 1.

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