TWI778568B - Systems and methods for generating recommendation list - Google Patents

Abstract

Systems and methods for generating recommendation list is 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 customer, a historical recommendation client list and a historical client tracking list which are corresponding to the salesperson from the list module based on the customer data of the salesperson. 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

Recommendation list production system and method

This case is about a recommendation list production system and method for recommending contactable customer lists for salespersons, especially a system and method for screening and recommending customers based on the interaction between salespersons and customers.

When conducting business development, financial industry salesmen will basically have first-hand contact with customers through phone calls, gradually establish relationships with customers, and finally complete related services. In the past, salespersons managed customer information by themselves and decided the customers to contact based on their feelings. However, now salespersons interact with customers through many channels and methods, such as face-to-face interviews, telephone interviews, questionnaires, and status inspections. The number of customers who are salespersons To a certain extent, the traditional manual management method often misses some customers, and the salesman may also have personal factors and seldom contact some customers, thus missing the opportunity to provide services. In addition, many customers may have opinions on the service of the salesman after the salesman provides services, but due to many factors, they do not respond to the salesman. If the salesperson does not follow up immediately, it may lead to a decrease in customer satisfaction and affect the follow-up. opportunity to provide services.

Therefore, the inventor has come up with a system and method for producing a recommendation list. Based on the interaction history between the salesperson and the customer, a recommendation list is produced to the salesperson, so as to assist the salesperson to contact the customer at an appropriate frequency and promote the interaction between the salesperson and the customer.

In view of this, the present invention proposes a recommendation list production system, which is used for an electronic device with storage and calculation functions, including a salesperson database, storing a plurality of salesperson data, and the salesperson data includes the salesperson name; a The login module is connected to the clerk database to receive a clerk's login data, and compares the clerk's login data with the clerk's data, and provides the clerk's data to a List generation module; a list module, including a customer list database, a recommendation list database and a tracking list database, wherein the customer list database stores a plurality of customer data, the customer data includes customer names, customer birthdays , customer phone number, tracking label and label 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 stores a plurality of customer interaction data, The customer interaction data includes the salesperson name, customer name and interaction date; the list generation module connects the login module, the list module and the first customer interaction record database, and requests the list module according to the salesperson 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 according to a sorting condition to generate a list to be recommended. The number of transactions is combined with the customer list of birthdays in the predetermined period to generate a daily recommendation list; and a data output module is used to display the daily recommendation list.

A method for producing a recommended list, using a login module to receive a salesperson's login data, and comparing the salesperson's login data with the salesperson's data in a salesperson database. A list generation module; the list generation module requests a list module for a customer list, a historical recommendation list and a historical tracking list corresponding to the salesperson according to the salesperson data; the list generation module determines each customer list according to the number of customers in the customer list The number of daily recommendations; the list generation module subtracts the list of customers with birthdays within a predetermined period, the historical recommendation list and the historical tracking list from the customer list, and sorts them according to a sorting condition to generate a list to be recommended; the list generation module The group selects customers in sequence from the list to be recommended until the number of daily recommendations is met, and merges with the list of customers with birthdays within the predetermined period to generate a daily recommendation list; the list generation module provides the daily recommendation list to a data output module; and the data output module outputs the daily recommendation list to a display device for display.

In order to make the technical content, purpose and advantages of the present invention easier to understand, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings. However, this description is for exemplary embodiments and is not intended to limit the present invention. category.

Referring to FIG. 1 , a schematic diagram of the system architecture of an embodiment of the recommended list production system of the present invention 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 The customer interaction record database 109 and a data output module 110 .

Referring to FIG. 2 , a schematic flowchart of an embodiment of a method for producing a recommendation list according to the present invention, the method is applied to the system for producing a recommendation list 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, the fifth The customer interaction record database 109 and the list module 104 may be a storage device, such as a memory or a hard disk, or an electronic device having a storage device, such as a personal computer or a server. The salesperson database 102 can store and provide a plurality of salesperson data, and the salesperson data can include the salesperson's name, employee number, telephone number, address, ID number, and the unit to which they belong. 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 the salesperson visited, etc., and the second customer interaction record database 106 stores the information filled in by the customer. Completed questionnaires and questionnaire records. The questionnaire records include customer name, customer information, questionnaire form (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 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, and salesperson. The fifth customer interaction record database 109 stores a product insurance policy acceptance record, and the product insurance policy acceptance record includes the customer name, customer information, acceptance date, and salesperson. Those with ordinary knowledge in the technical field should understand that the data and quantity stored in the interactive record database of the present invention can identify the salesperson and its corresponding customers, and the data stored in the interaction between salespersons and customers changes with needs and situations. , 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 interaction date between the client and the company or between the client and the salesperson.

In this embodiment, the list module 104 includes a client list database 1041, a recommendation list database 1042, and a tracking list database 1043. The client list database 1041 stores a plurality of client names and client information, and the client information includes the client's birthday, phone number , address and ID number, etc. The recommendation list database 1042 is used for storing recommendation lists, wherein any recommendation list corresponds to a salesperson. The tracking list database 1043 is used for storing tracking lists, any of which corresponds to a salesperson. For a salesperson using the recommendation list production system of the present invention 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. For those who have interacted with customers, the tracking list database 1043 should have a corresponding tracking list; for salespersons who are not using the recommended list production system of the present invention for the first time, the recommendation list database 1042 stores a recommendation 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 executing step 201 . In step 201, the login module 101 guides the salesman 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 logs in to the module 101 as the salesperson The login data is searched for the salesperson database 102 and compared, if the salesperson login data is consistent with any salesperson data in the salesperson database 102 , it is determined that the comparison is passed, and the process proceeds to step 202 .

In step 202, the login module 101 provides the salesperson information to a list generation module 103, and the list generation module 103 requests a list of clients corresponding to the salesperson and a historical recommendation list from the list module 104 according to the salesperson data 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, for example, the processor is a central processing unit (CPU), or other programmable 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) , a graphics processing unit (GPU) or other similar elements or a combination of the above elements.

In step 203, the list generation module 103 determines the number of daily recommendations according to the customer list of the salesperson. After obtaining the customer list of the salesperson in step 202, the list generation module 103 calculates the total number of customers on the customer list, and calculates the number of customers on the customer list according to the customer list. The total interval determines the number of recommended transactions 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, and 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 this recommendation list production system is 170, it falls into the level of 151-200 customers, and the corresponding number of daily recommendation There are 10 transactions. Accordingly, the list generation module 103 can determine the number of recommended transactions per day according to the customer list of the salesman.

In order to improve customer satisfaction, the salesperson should contact the customer before the customer's birthday. Therefore, in step 204 of the recommendation list production system of the present invention, the list generation module 103 obtains the salesperson's customer list and the customer's birthday in step 202, And according to the login date, the customers with birthdays within a predetermined period are inquired backward. In this embodiment, the list generation module 103 selects the list of customers with birthdays within three days after the login date from the customer list. The day is January 1, and the list generation module 103 selects the list of customers with birthdays within three days from the 170 customers, namely, the list of customers with birthdays on January 2, January 3, and January 4 in total, as priority. A list of clients recommended to the salesman.

In addition, in order to avoid the disparity in the frequency of customers being contacted when the salesperson is interacting with customers, the list of customers with birthdays within three days generated in step 203 is the priority recommendation. Therefore, this recommendation list production system is in step 205. , the list generation module 103 takes the customer list minus the customer list with 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 far to near, and generate a waiting list Recommended list. In this embodiment, the total number of customers corresponding to the logged-in salesperson is 170. After subtracting a total of 3 customers with birthdays within three days, 10 on the previous recommendation list and 10 on the previous tracking list, the remaining 147 The customers are sorted according to the interaction date and the login date, and the customers who have not interacted for a long time will be ranked first, so that they can be recommended to the salesman as soon as possible.

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

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. 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.

As can be seen from the above, the recommended list production system and method of the present invention can determine the number of daily recommendations according to the number of customers in the customer list, and then generate a daily recommendation list according to the customer's birthday, the historical recommendation list and the historical tracking list. Then, the data output module 110 outputs the recommendation result to a display device for the salesman to use.

Although the present invention has been disclosed by the above embodiments, it is not intended to limit the present invention. Anyone with ordinary knowledge in the technical field can make some changes and modifications without departing from the spirit and scope of the present invention. , shall be the scope of the rights claimed by this patent, so the protection scope of this patent shall be determined by the scope of the appended patent application.

101: Login module 102: Salesperson Database 103: List generation module 104: List Mods 1041: Client List Database 1042: Referral List Database 1043: Tracklist Database 105: The first customer interaction record database 106:Second customer interaction record database 107: The third customer interaction record database 108: Fourth customer interaction record database 109: The fifth customer interaction record database 110: Data output module 201 to 208: Steps

FIG. 1 is a schematic diagram of an embodiment of a recommendation list production system according to the present invention. FIG. 2 is a schematic flowchart of an embodiment of a method for producing a recommendation list according to the present invention.

101: Login module

102: Salesperson Database

103: List generation module

104: List Mods

1041: Client List Database

1042: Referral List Database

1043: Tracklist Database

105: The first customer interaction record database

106:Second customer interaction record database

107: The third customer interaction record database

108: Fourth customer interaction record database

109: The fifth customer interaction record database

110: Data output module

Claims (13)

A recommendation list production system is used for an electronic device with storage and calculation functions, comprising: a salesperson database, storing a plurality of salesperson data, the salesperson data including the salesperson name; a login module, and the salesperson The salesperson database connection is used to receive a salesperson's login data, and compare the salesperson's login data with the salesperson's data. If the comparison is passed, the salesperson information is provided to a list generation module; a List module, including a customer list database, a recommendation list database and a tracking list database, wherein the customer list database stores a plurality of customer data, the customer data includes customer name, customer birthday, customer phone number, tracking label 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 stores a plurality of customer interaction data, the customer interaction data includes business The name of the customer, 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 for the data corresponding to the salesperson according to the salesperson's data. A 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 customer list with a birthday within a predetermined period from the customer list, and the historical recommendation The list and the historical tracking list are sorted according to a sorting condition to generate a to-be-recommended list. The list-generating module selects customers in sequence from the to-be-recommended list until the daily recommendation number is satisfied, and matches the The customer lists with birthdays within the predetermined period are combined to generate a daily recommendation list; 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 date the salesman logs in to the customer's birthday. The recommendation list production system according to claim 1, wherein the sorting condition is arranged in order from far to near by interaction date. The recommendation list production system according to claim 1, wherein the sorting 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. The recommendation list production system according to claim 1, wherein the list generation module is further used for requesting the current customer activity record and customer name corresponding to the salesperson from the first activity record database, and according to the customer name in the The customer list of the list module is affixed with the tracking label and the label date. The recommendation list production system according to claim 5, wherein the list generation module further selects ten customer names closest to the label date and the login date according to the login date of the salesman to generate the tracking list. A method for producing a recommendation list, comprising: using a login module to receive a salesperson's login data, and comparing the salesperson's login data with salesperson data in a salesperson database, if the comparison is made, the business The employee data is provided to a list generation module; the list generation module requests a list module from a list module for a customer list, a historical recommendation list and a historical tracking list corresponding to the salesperson according to the salesperson information; the list generation module The number of daily referrals is determined according to the number of clients in the client list; The list generation module generates a list to be recommended by subtracting the list of customers with birthdays within a predetermined period, the historical recommendation list and the historical tracking list from the customer list, and sorts them according to a sorting condition to generate a list to be recommended; the list generation module Select customers in sequence from the to-be-recommended list until the daily recommendation number is met, and combine with the list of customers with birthdays within the predetermined period to generate a daily recommendation list; the list generation module provides the daily recommendation list to a data output module; and the data output module outputs the daily recommendation list to a display device for display. The method for producing a recommendation list according to claim 7, wherein when the list generation module determines the number of recommendations per day, it further determines the customer number level distance according to the number of customers in the customer list, and then obtains the corresponding customer number level distance according to the customer number level distance. Recommended number of pens per day. The method for producing a recommendation list as described in claim 7, wherein the predetermined period refers to the period from the date when the salesman logs in to the customer's birthday. The method for producing a recommendation list according to claim 7, wherein the sorting condition is arranged in order of interaction date from far to near. The method for producing a recommended list according to claim 7, wherein the sorting 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. The method for producing a recommendation list according to claim 7, further comprising a labeling step, wherein the labeling step is to request the current-day customer activity record corresponding to the salesperson from the first activity record database by the list generation module and customer name, and affix a tracking label and a label date to the customer list of the list module according to the customer name. The method for producing a recommendation list according to claim 7, further comprising a step of producing a tracking list, and the step of producing a tracking list is to select a label date and a logon date by the list generating module according to the date when the salesman logs in Create the tracking list with the closest ten customer names.

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