TWM614130U - Systems for generating recommendation list - Google Patents

Abstract

Systems for generating recommendation list are disclosed. The system includes a salesperson database, a login module, a list module, a first client interaction record database, a list generation module, and a data output module. The login module receives the login data of a salesperson, and then the list module requests the list of customers, a historical recommendation list and a historical tracking list which are corresponding to the salesperson from the list module based on the salesperson data. The list generation module decides based on the number of customers in the customer list. The number of daily recommendations, and then calculate a daily recommendation list based on the customer’s birthday, historical recommendation list and historical tracking list and then output the recommendation result to a display device with the data output module.

Description

Recommended list production system

This case is about a recommendation list production system that recommends contact lists for salespersons, especially a system that screens and recommends customers based on the interaction between salespersons and customers.

When financial industry salespersons conduct business development, they will basically make first-hand contact with customers through telephone visits, gradually establish relationships with customers and finally complete related services. In the past, salespersons managed customer information on their own and decided which customers to contact based on their feelings. However, now there are quite a few channels and methods for salespersons to interact with customers, such as face-to-face interviews, telephone interviews, questionnaires, current status inspections, etc. The number of customers who are salespersons When it reaches a certain level, the traditional manual management method often misses some customers, and the salesperson may have personal factors and rarely contact some customers, thus missing the opportunity to provide services. In addition, many customers may have opinions on the salesman’s service after the salesman provides services, but they do not respond to the salesman due to many factors. If the salesman does not follow up immediately, it may lead to a decrease in customer satisfaction and affect the follow-up Opportunities to provide services.

Therefore, the creator came up with a recommendation list production system, based on the interaction history between the salesperson and the customer, produced the recommendation list to the salesperson, in order to help the salesperson contact customers at an appropriate frequency and promote the interaction between the salesperson and the customer.

In view of this, this creation proposes a recommendation list production system, which is used for electronic devices with storage and calculation functions, including a salesperson database, storing multiple salesperson data, the salesperson information includes the name of the salesperson; The login module is connected to the clerk database to receive a clerk’s login data and compare the clerk’s login data with the clerk’s data. If the comparison is made, the clerk’s data will be provided to a clerk. List generation module; a list module, including a customer list database, a recommendation list database, and a tracking list database. The customer list database stores multiple customer data, including the customer’s name and the customer’s birthday , Customer phone number, tracking tag and tag date, the recommendation list database stores at least one historical recommendation list, the tracking list database stores at least one historical tracking list; a first customer interaction record database, which stores multiple customer interaction data, The customer interaction data includes the salesperson’s name, customer’s name and the date of interaction; the list generation module connects the login module, the list module and the first customer interaction record database, and requests the list module based on the salesperson’s data Corresponding to the salesperson’s customer list, a historical recommendation list, and a historical tracking list, the list generation module determines the number of daily recommendations based on the number of customers in the customer list, and then subtracts the number of birthdays within a predetermined period from the customer list The customer list, the historical recommendation list, and the historical tracking list are sorted by a sorting condition to generate a list to be recommended. The list generation module selects customers from the list to be recommended in order until the daily recommendation is satisfied The number of transactions is combined with the customer list of birthdays within the predetermined period to generate a daily recommendation list; and a data output module is used to display the daily recommendation list.

In order to make the technical content, purpose and advantages of this creation easier to understand, the implementation of this creation will be further described in detail below in conjunction with the accompanying drawings. However, this description is a description of exemplary embodiments and is not intended to limit this creation. The category.

1 is a schematic diagram of the system architecture of an embodiment of the recommended list production system of the present creation, which includes a recommended list production system, including a salesperson database 102, a login module 101, a list module 104, A list generation module 103, a first customer interaction record database 105, a second customer interaction record database 106, a third customer interaction record database 107, a fourth customer interaction record database 108, a fifth Customer interaction record database 109 and a data output module 110.

Refer to FIG. 2, which is a schematic diagram of the operation process of an embodiment of the recommended list production system of the present creation, which is applied to the recommended list production system shown in FIG. 1.

In this embodiment, the salesperson database 102, the first customer interaction record database 105, the second customer interaction record database 106, the third customer interaction record database 107, the fourth customer interaction record database 108, and the fifth The customer interaction record database 109 and the list module 104 can be a storage device, such as a memory or a hard disk, or an electronic device with a storage device, such as a personal computer or a server. The salesperson database 102 can store and provide multiple salesperson information, and the salesperson information can include the salesperson's name, employee number, telephone number, address, identity card number, and affiliated unit, etc. The first customer interaction record database 105 stores the history of customer interview records, including the interview channel, interview date, interview time, customer name, and salesperson visited, and the second customer interaction record database 106 stores customer input The completed questionnaire and questionnaire record. The questionnaire record includes the customer name, customer information, questionnaire format (paper or electronic), questionnaire channel (official website, external website, oral questionnaire), questionnaire filling date, questionnaire filling time and salesperson, etc. The third customer interaction record database 107 stores customer insurance policy health check records. The customer policy health check records include customer name, customer information, report, report completion date, and salesperson who established the health check. The fourth customer interaction record database 108 stores life insurance policy acceptance records. The life insurance policy acceptance records include customer name, customer information, life insurance policy, acceptance date, salesperson, and so on. The fifth customer interaction record database 109 stores product insurance policy acceptance records. The product insurance policy acceptance records include customer name, customer information, acceptance date, salesperson, and so on. Those with general knowledge in the technical field should understand that the data and quantity stored in the interactive record database of this creation can be changed according to the needs and circumstances under the identifiable salesperson and the corresponding customer and the interaction between the salesperson and the customer. , Not limited to the aforementioned information. The interview date, interview time, questionnaire filling date, questionnaire filling time, report completion date and acceptance date stored in the aforementioned database are the date of interaction between the customer and the company or the customer and the salesperson.

In this embodiment, the list module 104 includes a customer list database 1041, a recommendation list database 1042, and a tracking list database 1043. The customer list database 1041 stores multiple customer names and customer information, and the customer information includes customer birthdays and phone numbers. , Address and ID number, etc. The recommended list database 1042 is used to store recommended lists, and any one of the recommended lists corresponds to a salesperson. The tracking list database 1043 is used to store tracking lists, and any tracking list corresponds to a salesperson. For the salesperson using the recommended list production system created for the first time, the recommendation list database 1042 does not have a corresponding recommendation list. If the salesperson has not interacted with the customer, the tracking list database 1043 does not have a corresponding tracking list, such as a salesperson You have already interacted with the customer, and the tracking list database 1043 should have a corresponding tracking list; for the salesperson who is not using the recommended list production system of this creation for the first time, the recommended list database 1042 stores the recommended list corresponding to the salesperson , The tracking list database 1043 stores the tracking list corresponding to the salesperson.

The login module 101 includes a data receiving interface, and the data receiving interface is output in the form of a screen and displayed on a computer device for performing step 201. In step 201, the login module 101 guides the salesperson to input the salesperson's login information through the data receiving interface, such as the salesperson's name, employee number, phone number or account number and other information required for system login, and then the login module 101 uses the salesperson The login data is searched in the salesperson database 102 and compared. If the salesperson login data matches any salesperson data in the salesperson database 102, it is judged that the comparison is passed, and step 202 is continued.

In step 202, the login module 101 provides the salesperson data to a list generation module 103, and the list generation module 103 requests the list module 104 according to the salesperson data for a list of customers corresponding to the salesperson and a historical recommendation list. With a history tracking list. The list generation module 103 can run on an electronic device with a processor, such as a personal computer or a server with a processor. The processor is, for example, a central processing unit (CPU), or other programmable devices. General purpose or special purpose micro control unit (micro control unit, MCU), microprocessor (microprocessor), digital signal processor (digital signal processor, DSP), special application integrated circuit (application specific integrated circuit, ASIC) , Graphics processing unit (GPU) or other similar components or a combination of the above components.

In step 203, the list generation module 103 determines the number of daily recommendations based on the salesperson’s customer list. The list generation module 103 calculates the total number of customers on the customer list after obtaining the salesperson’s customer list in step 202, and calculates the total number of customers on the customer list based on the customer list. The total level determines the number of recommendations per day. The total number of customers in this embodiment includes five levels: 0-50 customers, 51-100 customers, 101-150 customers, 151-200 customers, and 201 customers. The number of daily recommendations is 3 respectively. , 5, 8, 10, and 15, for example, when the total number of customers who log in to the recommended list production system is 170, it falls into the range of 151-200 customers, corresponding to the number of daily recommendations According to this, the list generation module 103 can determine the number of daily recommendations based on the salesperson’s customer list.

In order to improve customer satisfaction, the salesperson should contact the customer before the customer’s birthday. Therefore, in the recommended list production system of this creation, in step 204, the list generation module 103 obtains the salesperson’s customer list and the customer’s birthday in step 202. And according to the log-in date, the customer whose birthday is within a predetermined period is searched backward. In this embodiment, the list generation module 103 filters out the customer list from the customer list and selects the customer list with the birthday within three days after the log-in date. For example, a salesperson logs into the recommended list production system The day is January 1, and the list generation module 103 selects a list of customers with birthdays within three days from 170 customers, that is, a list of customers with birthdays on January 2, January 3, and January 4 as priority. The list of customers recommended to the salesperson.

In addition, in order to avoid the excessive disparity in the frequency of customers being contacted by the salesperson during customer visits and other interactions, the customer list of birthdays within three days generated in step 203 is a priority recommendation. Therefore, the recommendation list production system is in step 205 , The list generation module 103 takes the customer list minus the customer list of birthdays within three days, the previous recommendation list, and the previous tracking list, and then uses the interaction date as the sorting condition to arrange the customers in order from the farthest to the most recent to generate a waiting list. Recommended list. In this embodiment, the total number of customers corresponding to the logged-in salesperson is 170. After subtracting 3 customers with birthdays within three days, 10 from the previous recommendation list and 10 from the previous tracking list, there will be 147 remaining customers. Customers are sorted according to the interaction date and the login date. The customers who haven't interacted for a long time will be ranked first to recommend to the salesperson as soon as possible.

In step 206, the list generation module 103 sequentially selects customers from the list generated in step 205 until the number of daily recommendations is satisfied. In this embodiment, since the number of daily recommendations is 10, the list generation module Group 103 selects the top 10 customers from 147 customers, and combines 3 customers with birthdays within three days to generate a daily recommendation list. Therefore, the daily recommendation list of this embodiment has 13 customers in total.

In step 207, the list generation module 103 provides the daily recommendation list to the data output module 110. In step 208, the data output module 110 outputs the daily recommendation list to a screen for display. The output mode can be wired cable or wired Network or wireless network transmission. Wired network or wireless network transmission includes at least one network communication protocol for the recommendation list production system to establish a network connection with the device where the screen is located to transmit the daily recommendation list.

From the above, the recommended list production system of this creation can determine the number of daily recommendations based on the number of customers in the customer list, and then generate a daily recommendation list based on the customer’s birthday, historical recommendation list and historical tracking list. The data output module outputs the recommendation result to a display device for the salesperson to use.

Although the above embodiments have been disclosed in the examples of this creation, they are not used to limit the creation. Anyone with ordinary knowledge in the technical field can make some changes and modifications without departing from the spirit and scope of this creation. , Should be the scope of rights claimed in this patent, so the scope of protection of this patent shall be subject to the scope of the appended patent application.

101: login module 102: salesman database 103: List generation module 104: List Module 1041: Customer List Database 1042: Recommended List Database 1043: Tracking List Database 105: The first customer interaction record database 106: The second customer interaction record database 107: The third customer interaction record database 108: The fourth customer interaction record database 109: The fifth customer interaction record database 110: Data output module

Figure 1 is a schematic diagram of an embodiment of a recommended list production system created by the author. Fig. 2 is the intention of the operation process of an embodiment of a recommended list production system method created by the author.

101: login module

102: salesman database

103: List generation module

104: List Module

1041: Customer List Database

1042: Recommended List Database

1043: Tracking List Database

105: The first customer interaction record database

106: The second customer interaction record database

107: The third customer interaction record database

108: The fourth customer interaction record database

109: The fifth customer interaction record database

110: Data output module

Claims (6)

A recommendation list production system for electronic devices with storage and calculation functions, including: A clerk database, storing multiple clerk data, the clerk information includes the name of the clerk; A login module is connected to the salesperson database to receive a salesperson login data, and compare the salesperson login data with the salesperson data. If the comparison is made, the salesperson data is provided to A list generation module; A list module, including a customer list database, a recommendation list database, and a tracking list database. The customer list database stores multiple customer data, including customer name, customer birthday, customer phone number, and tracking Label and label date, the recommendation list database stores at least one historical recommendation list, and the tracking list database stores at least one historical tracking list; A first customer interaction record database, storing a plurality of customer interaction data, the customer interaction data including the name of the salesperson, the name of the customer, and the date of interaction; The list generation module connects the login module, the list module and the first customer interaction record database, and requests the list module corresponding to the salesperson’s customer list, a historical recommendation list, and a list of customers based on the salesperson’s data. Historical tracking list. The list generation module determines the number of daily recommendations based on the number of customers in the customer list, and then subtracts the customer list from the customer list for the birthday within a predetermined period, the historical recommendation list and the historical tracking list, and A sorting condition sorts them to generate a list to be recommended. The list generation module selects customers from the list to be recommended until the number of recommendations per day is met, and merges with the list of customers with birthdays within the predetermined period. Generate a list of daily recommendations; and A data output module is used to display the daily recommendation list. The recommendation list production system as described in claim 1, wherein the predetermined period refers to the period from the day when the salesperson logs in to the birthday of the customer. According to the recommendation list production system described in claim 1, wherein the sorting condition is arranged in order of the interaction date from farthest to most recent. The recommendation list production system according to claim 1, wherein the ranking condition further includes a priority ranking condition, and the priority ranking condition is a tracking tag but the interaction date is greater than a tracking period. For example, the recommended list production system described in claim 1, wherein the list generation module is further used to request from the first activity record database the customer activity record and customer name corresponding to the salesperson on the day, and based on the customer name The tracking label and the date of the label are affixed to the customer list of the salesperson list module. According to the recommendation list production system of claim 5, the list generation module further selects ten customer names that are closest to the previous date of the tag date and the login date according to the login date of the salesperson, and produces the tracking list.

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