TW202217692A - Customer recommendation list generation method and server end for generating customer recommendation list storing a plurality of customer data, a piece of shopkeeper data and a plurality of shopkeeper consumption data - Google Patents

Abstract

Provided is a server end for generating a customer recommendation list, which stores a plurality of customer data respectively corresponding to a plurality of customers, a piece of shopkeeper data corresponding to a shopkeeper and a plurality of shopkeeper consumption data, wherein each shopkeeper consumption data corresponds to one of the customers; for each customer, the server end acquires, according to the shopkeeper consumption data and the customer data which correspond to the customer, a plurality of consumption parameter values corresponding to a plurality of preset consumption factors, and a consumption result value of a consumption result item relevant to the customer and affected by changes in the consumption factors, and an analysis by means of a variance is preformed to obtain at least one target factor; and for each target factor, a potential total score corresponding to the customer and a recommendation list for indicating a plurality of customers to be recommended are generated according to the consumption parameter value of each customer of the target factor.

Description

Method for generating customer recommendation list and server for generating customer recommendation list

The present invention relates to a data processing method and system suitable for business, in particular to a method and system for generating a customer recommendation list about a store.

In recent years, with the development of information technology, many businesses in the consumer sector, such as convenience stores, restaurants, or stores in tourist attractions, have evolved from the previous way of conducting various marketing promotions in stores to stimulate consumers to consume, to marketing information. It is transmitted to the consumer's mobile device to attract consumers to consume. For example, a store has a marketing terminal corresponding to the store, wherein the marketing terminal communicates with a plurality of mobile devices corresponding to a plurality of consumers who have visited the store for consumption. , when the store wants to carry out marketing promotion, it will send the marketing message to the mobile device of each consumer who has visited the store for consumption through the marketing terminal. When these consumers see the marketing message through the mobile device, they can get Know the current marketing activities of the store. Although the above-mentioned publicity method can inform all consumers who have visited the store to know the marketing message, there are still some drawbacks. In the modern society that emphasizes precision marketing, marketing activities emphasize the provision of marketing activities to specific potential target audiences, such as the main source of customers for stores. Inefficiency also wastes a lot of money and time cost. Therefore, industry players in related fields have invested a lot of effort to develop methods for how to know the main source of customers.

Therefore, the purpose of the present invention is to provide a method for generating a customer recommendation list for knowing the main customer source of a store.

Furthermore, another object of the present invention is to provide a server for learning the main customer sources of a store.

Therefore, the method for generating a customer recommendation list of the present invention is implemented by a server, which stores a plurality of customer data corresponding to multiple customers, a store data corresponding to a store, and a plurality of store consumption data, wherein Each customer data includes a plurality of customer attributes of the corresponding customer, each store consumption data corresponds to one of the corresponding customers and includes a consumption record of the corresponding customer's consumption at the store in a previous time interval. The method for generating a customer recommendation list includes a step A, a step B, a step C, a step D, a step E, and a step F.

In step A, for each customer, according to the store consumption data and customer data corresponding to the customer, a plurality of consumption parameter values related to the customer and corresponding to a plurality of predetermined consumption factors are obtained.

In the step B, for each customer, according to the store consumption data corresponding to the customer, obtain a corresponding consumption result item related to the customer and affected by the change of at least one of the consumption factors. Consumption result value.

In the step C, at least one target factor is obtained from the consumption factors by means of variance analysis using the consumption result item as a dependent variable.

In the step D, for each target factor, an average value and a standard deviation corresponding to each target factor are calculated according to the consumption parameter value of each customer of the target factor.

In this step E, for each customer, a total potential score of a corresponding customer is calculated according to the consumption parameter value of the customer corresponding to each target factor, the average value and the standard deviation corresponding to each target factor.

In the step F, according to the total potential score of each customer, a plurality of customers to be recommended are obtained from the customers, and a recommendation list indicating the customers to be recommended is generated.

Furthermore, the present invention is used for generating a customer recommendation list server, which includes a storage module and a processing module electrically connected to the storage module.

The storage module stores multiple pieces of customer data corresponding to multiple customers, one store data corresponding to one store, and multiple store consumption data, wherein each customer data includes multiple customer attributes of the corresponding customer, each The store's consumption data corresponds to one of the waiting customers and includes a consumption record of the corresponding customer's consumption at the store in a previous time interval.

Wherein, for each customer, the processing module obtains a plurality of consumption parameter values related to the customer and corresponding to a plurality of preset consumption factors according to the store consumption data and customer data corresponding to the customer, and obtains a plurality of consumption parameter values related to the customer. And a consumption result value corresponding to a consumption result item that is affected by the change of at least one of the corresponding consumption factors, and using the consumption result item as the dependent variable to obtain at least one consumption result from the consumption factors by using variance analysis. target factor, and for each target factor, according to the consumption parameter value of each customer of the target factor, calculate a mean value and a standard deviation corresponding to each target factor, and for each customer, according to the corresponding customer The consumption parameter value of each target factor, the average value and the standard deviation corresponding to each target factor are used to calculate the potential total score of a pair of corresponding customers, and according to the potential total score of each customer, obtain from the customers A plurality of customers to be recommended are generated, and a recommendation list indicating the customers to be recommended is generated.

The effect of the present invention is: the server generates the consumption parameter values and the consumption result value of the customer in the consumption factors according to the consumption data of the store and the customer data, and uses the variance analysis to obtain The at least one target factor, and then according to the customer's consumption parameter value corresponding to each target factor, the average value and the standard deviation corresponding to each target factor, the potential total score corresponding to the customer is calculated to obtain the If you want to recommend customers, you can know the main source of customers of the store.

Before the present invention is described in detail, it should be noted that in the following description, similar elements are designated by the same reference numerals.

Referring to FIGS. 1 and 2, a first embodiment of the method for generating a customer recommendation list of the present invention is implemented by a system 9 for generating a customer recommendation list as shown in FIG. A server terminal 91 for generating a customer recommendation list, a sales terminal 92 and a sales terminal 93 communicatively connected to the server terminal 91 via a communication network 900 .

The server 91 includes a storage module 911, a communication module 913 connected to the communication network 900, and a processing module 912 electrically connected to the storage module 911 and the communication module 913. The storage module 911 One store information corresponding to one store, multiple customer data corresponding to multiple customers, and multiple store consumption data are stored. The store information includes a store location, a store business hours, a store contact information and other information related to the store, and each customer information includes a plurality of customer attributes related to the customer, such as gender, age, living location, work Attributes such as location, each store consumption data corresponds to one of the corresponding customers, and includes a consumption record of the corresponding customer in the store, and the consumption record includes a consumption amount, a consumption time, a consumption item, etc. material. Here, the server 91 is, for example, a cloud server, a supercomputer, a personal computer, or any of other similar devices.

The sales terminal 92 corresponds to the store, and generates consumption data of the store when one of the customers makes purchases at the store. Here, the sales terminal 92 is, for example, a cash register, a point of service (POS), or any of other similar devices.

The marketing terminal 93 also corresponds to the store and is held by an operator of the store. Here, the marketing terminal 93 is, for example, any one of a personal computer, a notebook computer, a tablet computer, a smart phone, or other similar devices.

Referring to FIG. 1 and FIG. 2, the first embodiment of the method for generating a customer recommendation list of the present invention includes a step 1, a step 2, a step 3, a step 4, a step 5, a step 6, a step 7, and A step 8 describes how the server 91 generates a recommendation list indicating a plurality of customers to be recommended.

In step 1, when one of the customers goes to the store for consumption, the sales terminal 92 generates a target store consumption data including a target consumption record corresponding to the current consumption, and then the target store consumption data sent to the server 91 . The sales terminal 92 may send the consumption data of the target store to the server terminal 91 immediately after generating the consumption data of the target store, or may store a plurality of consumption data of the target store within a previous time interval (for example, a week ago), and then store the consumption data of the target store at one time. The target store consumer data is sent to the server 91 .

In step 2, after the server 91 receives the consumption data of the target store through the communication network 900 through the communication module 913, the processing module 912 of the server 91 stores the consumption data of the target store in a The storage module 911 is used as one of the store consumer data.

Referring to FIG. 3 , in step 3, for each customer, the processing module 912 obtains, in the previous time interval, the data related to the customer and corresponding to the customer according to the store consumption data and customer data corresponding to the customer. Multiple consumption parameter values for a preset consumption factor. Specifically, the consumption factors include living distance items, working distance items, consumption frequency items, average consumption amount items, etc. When performing step 3, for each customer, the processing module 912 consumes according to each store's consumption For the consumption time of the consumption record in the data, obtain the store consumption data and customer data corresponding to the customer in the previous time interval (for example, a week), and obtain a plurality of data related to the customer and corresponding consumption factors Consumption parameter value. The step 3 includes a sub-step 31, a sub-step 32, a sub-step 33, and a sub-step 34, explaining how to obtain the consumption parameter values.

In this sub-step 31 , for each customer, the processing module 912 takes the number of transactions of the customer's store consumption data as a consumption parameter value related to the customer and corresponding to the item of consumption times. For example, in the previous week, the customer visited the store for five times and generated five store consumption data. The consumption amounts were 200 yuan, 150 yuan, 300 yuan, 250 yuan, and 400 yuan respectively. 912 takes the number of transactions corresponding to the store's consumption data of the customer in the previous week, that is, five transactions, as the consumption parameter value of the item related to the customer and corresponding to the number of consumptions.

In this sub-step 32, for each customer, the processing module 912 calculates the distance between the residential location and the store location according to the residential location in the customer data corresponding to the customer and the store location in the store data to obtain As the consumption parameter value related to the customer and corresponding to the living distance item.

In this sub-step 33, for each customer, the processing module 912 calculates the distance between the work location and the store location according to the work location in the customer data corresponding to the customer and the store location in the store data to obtain As the consumption parameter value related to the customer and corresponding to the working distance item.

In the sub-step 34, for each customer, the processing module 912 averages the consumption amount in the store consumption data of the customer as a consumption parameter value related to the customer and corresponding to the average consumption amount item. Continuing the above example, the processing module 912 takes the average value of 200 yuan, 150 yuan, 300 yuan, 250 yuan, and 400 yuan to obtain a value of 260 yuan as the consumption parameter value of the item related to the customer and corresponding to the average consumption amount.

It is worth mentioning that, in the first embodiment, the consumption factors include the living distance item, the working distance item, the consumption frequency item, and the average consumption amount item, but in other embodiments, the consumption factors can also be Including, for example, age item or consumption time item. The consumption parameter value corresponding to the age item can be obtained from the age attribute in each customer data, and the consumption parameter value corresponding to the consumption time item can be related to the store corresponding to the customer through statistics. According to the consumption data, for example, in the consumption data of the five stores related to the customer, the consumption time is 10:00 am, 7:00 pm, 8:00 pm, 6:30 pm, and 90:00 pm, respectively, the processing module 912 Then the consumption parameter values related to the customer and corresponding to the consumption time item are 0:0 to 6:00, 1 to 12:00, 12:00 to 18:00, and 18:00 to 24:4 Second-rate.

In step 4, for each customer, the processing module 912 obtains a consumption result related to the customer and affected by the change of at least one of the consumption factors according to the consumption data of the store corresponding to the customer A consumption result value corresponding to the item. In detail, the consumption result item is a total consumption amount item, and the processing module 912 sums up the consumption amount in the store consumption data of the corresponding customer as the item related to the customer and corresponding to the total consumption amount For the consumption result value, for example, following the previous example, the processing module 912 adds up 200 yuan, 150 yuan, 300 yuan, 250 yuan, and 400 yuan to obtain 1300 yuan, and regards 1300 yuan as related to the customer and corresponding consumption The consumption result value of the total amount item.

In step 5, the processing module 912 uses the consumption result item as a dependent variable to obtain at least one target factor from the consumption factors by analysis of variance (ANOVA). Specifically, the consumption factors As the independent variable in the variance analysis, the at least one target factor represents that the difference in the target factor has a significant impact on the consumption result item, for example, when the consumption frequency item is one of the at least one target factor In the case of the above, it means that the number of times the customers visit the store for consumption in the previous time interval will affect the total consumption item of each customer in the previous time interval.

In step 6, for each target factor, the processing module 912 calculates an average value and a standard deviation corresponding to each target factor according to the consumption parameter value of each customer of the target factor.

In step 7, for each customer, the processing module 912 calculates a pair of corresponding customers according to the customer's consumption parameter value corresponding to each target factor, the average value and the standard deviation corresponding to each target factor The potential total score of , in detail, for each customer, the processing module 912 corresponds to the potential total score Z of the customer according to the following formula:

為第i個目標因子的該平均值，

為該客戶對應第i個目標因子的該消費參數值，

為第i個目標因子的該標準差，

為第i個目標因子的一權重，代表第i個目標因子在該潛力總分Z中的重要性，例如多個目標因子中包括該消費次數項目及該居住距離項目，而該消費次數項目的該權重及該居住距離項目的該權重分別為2與1時，代表在該潛力總分Z中，該消費次數項目的影響比該居住距離項目的影響來的重要。 where n is the number of the at least one target factor,

is the average of the ith target factor,

is the consumption parameter value of the customer corresponding to the i-th target factor,

is the standard deviation of the ith target factor,

is a weight of the i-th target factor, representing the importance of the i-th target factor in the potential total score Z, for example, multiple target factors include the consumption frequency item and the living distance item, and the consumption frequency item of When the weight and the weight of the living distance item are respectively 2 and 1, it means that in the total potential score Z, the influence of the consumption frequency item is more important than the influence of the living distance item.

In step 8, the processing module 912 obtains the customers to be recommended from the customers according to the potential total score of each customer, and generates the recommendation list indicating the customers to be recommended, and sends the recommendation list through the communication module. The group 913 transmits the recommendation list to the marketing terminal 93 through the communication network 900. Specifically, for each customer, the processing module 912 determines whether the total potential score of the customer is greater than a threshold. When the potential total score is greater than the threshold, the processing module 912 regards the customer as a customer to be recommended. After judging all customers, the processing module 912 generates the recommendation list indicating the customers to be recommended, wherein the recommendation list also includes an analysis result related to the at least one target factor and the consumption result item, such as The main customer sources of the store, that is, the customers corresponding to the higher total consumption amount, have more consumption times in the previous time interval. In this way, when the operator obtains the recommendation list through the input operation of the marketing terminal 93, he can know which ethnic group the main customer source of the store belongs to, such as customers who visit the store more frequently, Customers who live close to the store, or customers whose work area is closer to the store, etc., can then carry out marketing promotions for these customers who belong to the main source of customers, so as to achieve the goal of precise marketing.

Referring to FIG. 2 and FIG. 4 , further, a second embodiment of the method for generating a customer recommendation list of the present invention is implemented by another customer recommendation list generating system 9 as shown in FIG. 4 . The other customer recommendation list generates The system 9 is similar to the customer recommendation list generation system 9 shown in FIG. 2 , the difference is that the server 91 also communicates with a plurality of corresponding customers through the communication module 913 via the communication network 900 . Client 94.

Referring to FIGS. 4 and 5 , the second embodiment of the method for generating a customer recommendation list of the present invention is substantially a variation of the first embodiment, and includes a step 11 , a step 12 , a step 13 , a step 14 , and a step 15, a step 16, a step 17, a step 18, a step 19, and a step 20 of which the step 11, the step 12, the step 13, the step 14, the step 15, the step 16, and The step 17 is respectively similar to the step 1 , the step 2 , the step 3 , the step 4 , the step 5 , the step 6 , and the step 7 in the first embodiment. The differences between the second embodiment and the first embodiment will be described below.

Referring to FIG. 4 and FIG. 5, in step 18, the processing module 912 obtains the customers to be recommended from the customers according to the total potential score of each customer, and generates information about the customers to be recommended and includes multiple each of the recommendation lists corresponding to the client codes of the customers to be recommended, and the recommendation list is transmitted to the marketing terminal 93 through the communication network 900 through the communication module 913, wherein each client code corresponds to the corresponding client codes to be recommended Customer profile of the customer.

In step 19 , the marketing terminal 93 generates and transmits a marketing request including a marketing message to the server terminal 91 through the input operation of the operator.

In step 20, when the server 91 receives the marketing request from the marketing terminal 93 through the communication network 900 via the communication module 913, the processing module 912 of the server 91 according to the marketing request, The client codes in the recommendation list, and the client information corresponding to each client code, transmit the marketing message to a plurality of target clients 941 in the clients 94 through the communication module 913 through the communication network 900 , wherein the target clients 941 respectively correspond to the clients to be recommended.

In detail, in the second embodiment, the recommendation list generated by the processing module 912 is a de-identified list, that is, only the client codes can be known from the recommendation list, but not directly Or indirectly identify who the customers to recommend are, at this time, the marketing terminal 93 cannot directly transmit the marketing message to the target clients 941 corresponding to the customers to be recommended, but must transmit the marketing message including the marketing message request to the server 91, the processing module 912 of the server 91 can obtain the contact information of the customers to be recommended according to the customer codes and the customer information corresponding to each customer code, and through the communication module The group 913 transmits the marketing message to the target clients 941 through the communication network 900, thereby effectively protecting the privacy of the personal data of the recommending clients when transmitting and displaying the recommendation list, so as to avoid causing personal information risk of leakage.

To sum up, in the method for generating a customer recommendation list of the present invention, the server 91 obtains a plurality of customers to be recommended by using variance analysis according to the store information, the store consumption data, and the customer data, and generates an instruction list. The recommendation list of the customers to be recommended, whereby the operator can know that the main source of customers of the store is the customers to be recommended through the recommendation list, and then conduct accurate marketing for the customers to be recommended. In addition, When the recommendation list is a de-identified list, it can also effectively protect the privacy rights of the personal data of the customers who want to recommend, and avoid the concern of infringing personal privacy when transmitting the recommendation list, so the purpose of the present invention can indeed be achieved. .

However, the above are only examples of the present invention, and should not limit the scope of implementation of the present invention. Any simple equivalent changes and modifications made according to the scope of the patent application of the present invention and the contents of the patent specification are still included in the scope of the present invention. within the scope of the invention patent.

1~8: Steps 31~34: Substeps 11~20: Steps 9: Customer recommendation list generation system 900: Communication Network 91: Servo end 911: Storage Module 912: Processing Modules 913: Communication module 92: Sales side 93: Marketing side 94: Client 941: target client

Other features and effects of the present invention will be clearly presented in the embodiments with reference to the drawings, wherein: 1 is a flowchart illustrating a first embodiment of a method for generating a customer recommendation list of the present invention; FIG. 2 is a block diagram illustrating a customer recommendation list generation system implementing the first embodiment of the customer recommendation list generation method of the present invention; 3 is a flow chart illustrating the sub-steps of a step 3 in the first embodiment; 4 is a block diagram illustrating another customer recommendation list generation system implementing a second embodiment of the customer recommendation list generation method of the present invention; and FIG. 5 is a flow chart illustrating the second embodiment of the method for generating a customer recommendation list of the present invention.

1~8: Steps

Claims (10)

A method for generating a customer recommendation list is implemented by a server. The server stores multiple pieces of customer data corresponding to multiple customers, one store information corresponding to one store, and multiple store consumption data, wherein each customer The data includes a plurality of customer attributes of the corresponding customer, the consumption data of each store corresponds to one of the corresponding customers and includes a consumption record of the corresponding customer in the store in a previous time interval, the customer recommendation list The generation method includes the following steps: (A) for each customer, according to the store consumption data and customer data corresponding to the customer, obtain a plurality of consumption parameter values related to the customer and corresponding to a plurality of preset consumption factors; (B) For each customer, obtain a consumption result value corresponding to a consumption result item related to the customer and affected by the change of at least one of the consumption factors according to the consumption data of the store corresponding to the customer ; (C) obtaining at least one target factor from the consumption factors by using the variance analysis with the consumption result item as the dependent variable; (D) for each target factor, calculate a mean value and a standard deviation corresponding to each target factor according to the consumption parameter value of each customer of the target factor; (E) For each customer, calculate a potential total score for the corresponding customer according to the consumption parameter value of the customer corresponding to each target factor, the average value and the standard deviation corresponding to each target factor; and (F) According to the potential total score of each customer, obtain a plurality of customers to be recommended from the customers, and generate a recommendation list indicating the customers to be recommended. According to the method for generating a customer recommendation list according to claim 1, the store information includes a pair of store locations corresponding to the store, and the customer attributes in each customer data include a residence location and a work location of the corresponding customer. The consumption record in the consumption data of a store includes a consumption amount consumed by the corresponding customer at the store, wherein, in step (A), the consumption factors include a living distance item, a work distance item, a consumption frequency item, and Average consumption amount item, and this step (A) includes the following sub-steps: (A-1) For each customer, take the number of transactions of the customer's store consumption data as the consumption parameter value related to the customer and corresponding to the consumption frequency item; (A-2) For each customer, calculate the distance between the residence and the store location based on the residence location in the customer profile corresponding to the customer and the store location in the store profile as a reference to the customer and for the store location. The consumption parameter value of the project that should live in distance; (A-3) For each customer, according to the work location in the customer data corresponding to the customer and the store location in the store data, calculate the distance between the work location and the store location as a reference to the customer and to the store location. The value of the consumption parameter for the distance item that should be worked; and (A-4) For each customer, the consumption amount in the store's consumption data of the corresponding customer is averaged as the consumption parameter value related to the customer and corresponding to the average consumption amount item. According to the method for generating a customer recommendation list according to claim 1, the consumption record in the consumption data of each store includes a consumption amount of the corresponding customer in the store, wherein, in the step (B), the consumption result The item is an item of total consumption amount. For each customer, the consumption amount in the store consumption data of the corresponding customer is summed up as the consumption result value related to the customer and corresponding to the total consumption amount item. The method for generating a customer recommendation list according to claim 1, wherein, in the step (E), for each customer, the total potential score Z corresponding to the customer is calculated according to the following formula:

where n is the number of the at least one target factor,

is the average of the ith target factor,

is the consumption parameter value of the customer corresponding to the i-th target factor,

is the standard deviation of the ith target factor,

is a weight of the i-th target factor. According to the method for generating a customer recommendation list as described in claim 1, the server terminal is further connected to a marketing terminal of a corresponding store, and a plurality of clients corresponding to the corresponding customers, wherein, after the step (F), further Contains the following steps: (G) When the server receives a marketing request including a marketing message from the marketing, the server sends the marketing message to a plurality of targets in the clients according to the marketing request and the recommendation list Clients, wherein the target clients respectively correspond to the clients who want to recommend. A server for generating customer recommendation lists, including: a storage module, storing multiple pieces of customer data corresponding to multiple customers, one store data corresponding to one store, and multiple store consumption data, wherein each customer data includes multiple customer attributes of the corresponding customer, each A store's consumption data corresponds to one of the corresponding customers and includes a consumption record of the corresponding customer's consumption at the store in a previous time interval; and a processing module, electrically connected to the storage module; Wherein, for each customer, the processing module obtains a plurality of consumption parameter values related to the customer and corresponding to a plurality of preset consumption factors according to the store consumption data and customer data corresponding to the customer, and obtains a plurality of consumption parameter values related to the customer. And a consumption result value corresponding to a consumption result item that is affected by the change of at least one of the corresponding consumption factors, and using the consumption result item as the dependent variable to obtain at least one consumption result from the consumption factors by using variance analysis. target factor, and for each target factor, according to the consumption parameter value of each customer of the target factor, calculate a mean value and a standard deviation corresponding to each target factor, and for each customer, according to the corresponding customer The consumption parameter value of each target factor, the average value and the standard deviation corresponding to each target factor are used to calculate the potential total score of a pair of corresponding customers, and according to the potential total score of each customer, obtain from the customers A plurality of customers to be recommended are generated, and a recommendation list indicating the customers to be recommended is generated. The server for generating a customer recommendation list according to claim 6, wherein the store data includes a store location of a corresponding store, and the customer attributes in each customer data include a residence location of the corresponding customer and a work location, the consumption record in the consumption data of each store includes a consumption amount consumed by the corresponding customer at the store, wherein the consumption factors include the living distance item, the work distance item, the consumption frequency item, and the average Consumption amount item, for each customer, the processing module takes the number of transactions of the customer's store consumption data as the consumption parameter value in the item related to the customer and the corresponding consumption times, and according to the customer data corresponding to the customer. The living location and the store location in the store information, calculate the distance between the residence location and the store location as the consumption parameter value related to the customer and corresponding to the living distance item, and according to the customer data corresponding to the customer. The work location and the store location in the store information, calculate the distance between the work location and the store location as a consumption parameter value related to the customer and corresponding to the work distance item, and will consume the store for the customer. The consumption amount in the data is averaged as the consumption parameter value related to the customer and corresponding to the average consumption amount item. The server for generating a customer recommendation list according to claim 6, wherein the consumption record in each store's consumption data includes a consumption amount of the corresponding customer's consumption at the store, wherein the consumption result item is A consumption total amount item. For each customer, the processing module adds the consumption amount in the store consumption data of the corresponding customer as the consumption result value related to the customer and corresponding to the consumption total amount item. The server for generating a customer recommendation list according to claim 6, wherein, for each customer, the processing module calculates the potential total score Z corresponding to the customer according to the following formula:

where n is the number of the at least one target factor,

is the average of the ith target factor,

is the consumption parameter value of the customer corresponding to the i-th target factor,

is the standard deviation of the ith target factor,

is a weight of the i-th target factor. The server for generating a customer recommendation list according to claim 6 is connected to a marketing terminal of a corresponding store and a plurality of clients corresponding to the corresponding customers via a communication network, and the server further includes a connection to In the communication module of the communication network, when the processing module receives a marketing request including a marketing message from the marketing terminal via the communication module, the processing module sends a message to the processing module through the marketing request and the recommendation list according to the marketing request and the recommendation list. The communication module transmits the marketing message to a plurality of target clients among the clients, wherein the target clients respectively correspond to the clients to be recommended.

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