TWM565841U - Appointed merchant recommendation system - Google Patents

Abstract

An appointed merchant recommendation system comprises a client database, a merchant database, an analytic module, a map module and an internet publishing module. The client database is configured to store a plurality of client information, each client information comprises a plurality of geographic locations. The merchant database is configured to store a plurality of merchant information, each merchant information comprises a merchant location. The analytic module retrieves one from the plurality of client information, calculates a plurality of weights corresponding to the plurality of geographic locations according to a current time and a current date, and calculates a plurality of search radii according to a default distance. The map module calculates a plurality of search regions and a search range according to the plurality of geographic locations with the plurality of radii and generates a recommendation list selected from the merchant database. The internet publishing module sends a marketing message according to the recommendation list.

Description

Special merchant recommendation system

This creation is about a financial business promotion system, especially a system and method for recommending special merchants.

The popularity of the Internet and smart phones has spawned a variety of new forms of applications and services. In terms of the purchasing behavior of daily life, people can now use their mobile devices to directly checkout, the so-called action payment. Different from the traditional use of cash or credit cards, the use of mobile devices to pay for various services or goods costs to bring more convenience to consumers.

However, in the development phase of mobile payment, customers often do not know which merchants can pay for direct consumption through actions. Therefore, the customer may have to go to the official website of the mobile payment to check the address of the special merchant, and then query the map with this address to obtain the route to the nearby merchants. Such complicated steps bring a lot of inconvenience to the consumer. In addition, the various offers released by the special store also lack direct and immediate channels to inform consumers. On the whole, there is currently a lack of a system and method for customers and special merchants who are suitable for payment.

In view of this, the present invention is to provide a special merchant to pay for the customer's customized recommendation action, thereby increasing the number of customers joining the mobile payment platform and expanding the coverage of the special merchant.

The special merchant recommendation system according to an embodiment of the present invention includes: a customer database, a merchant database, an analysis module, a map module, and a network publishing module. The customer database is used to store a plurality of customer information, each of which includes location information and consumer information. Location information includes a number of geographic locations that are home addresses, company addresses, GPS location addresses, branch addresses, and ATM addresses. Consumer information includes the type of consumption and the amount of consumption. The merchant database is used to store a plurality of merchant information, and each merchant information includes a merchant location and a preferential scheme, and the preferential scheme includes a plurality of preferential strategies. The analysis module is electrically connected to the customer database, and the analysis module is configured to obtain a customer information from the customer database, and calculate a weight corresponding to each geographic location in the customer information according to the current date and the current time, and according to a pre- Set the distance and multiple weights to calculate one search radius for each geographic location. The map module is electrically connected to the customer database, the merchant database and the analysis module, and the map module is configured to calculate a plurality of search areas according to multiple geographical locations in the customer information and multiple search radii corresponding to the geographic locations, according to the The search area calculates a search range, and selects a recommendation list from a plurality of business locations according to the search range and the consumption information, and the recommended list includes a plurality of recommended businesses. The map module further includes a task module, and the task module is configured to select one of the plurality of preferential strategies from the preferential solutions corresponding to the plurality of recommended merchants according to the current date and the current time. The network publishing module is electrically connected to the map module and is configured to send a sales message according to the recommended list, and the marketing message includes multiple preferential policies selected by the task module. Marketing messages are sent to email addresses, websites or mobile devices in the form of newsletters, web banners or pushes.

The method for recommending a merchant according to an embodiment of the present invention includes: storing a plurality of customer information in a customer database, each customer information including location information and consumption information; storing a plurality of merchant information in a merchant database, each The business information includes the location and the preferential plan of the merchant, the preferential scheme includes a plurality of preferential strategies; obtaining a customer information from the customer database; and calculating one of the plurality of geographic locations in the extracted customer information according to the current date and the current time Weighting; calculating a search radius corresponding to each of the plurality of geographic locations according to a preset distance and a plurality of weights; calculating a plurality of search areas according to the plurality of geographic locations in the customer information and the plurality of search radii corresponding to the geographic locations; The search area calculates a search range; the recommended list is selected from the plurality of business locations according to the search scope and the consumption information, and the recommended list includes a plurality of recommended merchants, and the plurality of recommended merchants are selected according to the current date and the current time respectively. One of the preferential strategies; According to the recommended list to send a marketing message, marketing message includes multiple Businesses are each selected discount policy.

According to the above content, a special merchant recommendation system and method thereof disclosed by the present invention analyzes the collected customer information and business information, and finds out the recommended merchants whose individual customers have a tendency to consume in their living circles and the recommended merchants. Providing a preferential strategy to instantly send a marketing message including a recommended merchant and a preferential strategy to the customer's mobile communication device or email address in the form of a newsletter, a customized web banner or a push; the recommended offer Merchants and offers are adjusted according to the current date and time, so that you can find the special merchants that best meet the customer's current needs, achieve the effect of matching merchants and mobile payment customers, and improve the use of mobile payments.

The above description of the disclosure and the following description of the embodiments are intended to illustrate and explain the spirit and principles of the present invention, and to provide further explanation of the scope of the patent application of the present invention.

The detailed features and advantages of the present invention are described in detail below in the embodiments, which are sufficient to enable any skilled artisan to understand the technical contents of the present invention and implement it according to the contents, the scope of the patent application and the drawings. Anyone familiar with the relevant art can easily understand the purpose and advantages of this creation. The following examples are intended to further illustrate the scope of this creation, but do not limit the scope of the creation in any way.

Please refer to FIG. 1 , which is a structural diagram of a special merchant recommendation system according to an embodiment of the present invention, including: a customer database 12 , a merchant database 14 , an analysis module 3 , a map module 5 , and a network . Release module 7. The function of each component in the system will be described in detail below.

The customer database 12 stores a plurality of customer information, each of which includes location information and consumption information. Specifically, the location information includes multiple geographic locations, such as a home address, a company address, a Global Positioning System (GPS) location address, a branch address, and an Automated Teller Machine (ATM) address. Consumer information includes the type of consumption and the amount of consumption. In practice, the financial institution that provides the action payment can obtain the above location information from the personal information and the cabinet record retained by the customer, and obtain the GPS location address from the customer's mobile communication device, and each time the customer uses the credit card or the financial card. Obtain the above consumption information in the consumption record of the transaction behavior.

The merchant database 14 stores merchant information of a plurality of special merchants, which refers to a merchant that has reached an agreement with a financial institution to provide payment by the consumer using the action. Each business information includes a business location and a discount plan, and the preferential plan includes a plurality of preferential policies, such as discounts or consumer credit feedback, which are determined by the special merchants and financial institutions, and recorded. In the merchant database 14.

Please refer to Figure 1. The analysis module 3 is electrically connected to the customer database 12. In practice, the analysis module 3 is, for example, an application running on a server of a financial institution. The analysis module 3 obtains a customer information from the customer database 12, and calculates a weight corresponding to each of the plurality of geographic locations in the customer information according to the current date and the current time, and then calculates the weight according to a preset distance and multiple weights. Each geographic location corresponds to one of the search radii.

For example, for a commuter working on weekdays, the location of each day of the day includes the home, the home to the company, the company and the company, as shown in the first and second columns of the table below. The analysis module 3 can sort out the "time-location" relationship table of the same type of customers according to the personal information retained by all customers in the financial institution through the big data analysis and summary, and in the third to fifth columns of the table below. Set the weight values for each location at different times. These weight values may be adjusted as the customer information collected by the customer database increases. It should be noted that in order to facilitate understanding and explanation, the following table only distinguishes the weight combinations of different states by “time”; in practice, in order to make a more accurate assessment of the scope of activities of different types of customers at different times, the analysis module 3 can be added to the date or special holiday classification conditions, so more types of weight combination settings are predefined.  <TABLE border="1" borderColor="#000000" width="85%"><TBODY><tr><td> Status</td><td> Commuting</td><td> Going to work</td> <td> Lunch</td><td> Go to work</td><td> Commuter</td><td> Get off work</td></tr><tr><td> Start time</td><td > 00 0 0 0 0 0 0 0 0 0 0 0 0 0 Td></tr><tr><td> weight 1 (company) </td><td> 0.6 </td><td> 0.9 </td><td> 0.6 </td><td> 0.9 < /td><td> 0.3 </td><td> 0.3 </td></tr><tr><td> Weight 2 (GPS) </td><td> 0.9 </td><td> 0.6 </td><td> 0.9 </td><td> 0.6 </td><td> 0.9 </td><td> 0.6 </td></tr><tr><td> weight 3 (home) </td><td> 0.3 </td><td> 0.3 </td><td> 0.3 </td><td> 0.3 </td><td> 0.6 </td><td> 0.9 < /td></tr></TBODY></TABLE>

Based on the previous examples, the following describes how the analysis module 3 operates. The analysis module 3 searches the upper table according to the current time (for example, 3:00 pm, 1500), obtains the weight 1 of the company address as 0.9, the weight 2 of the GPS location address is 0.6, and the weight 3 of the home address is 0.3, wherein the GPS location address The location information of the customer's last location can be periodically returned by the customer's smart phone or other mobile communication device. The analysis module 3 further includes setting a preset distance, for example, 1000 meters. Therefore, the analysis module 3 can calculate corresponding three sets of search radii according to the preset distance and three weights: 900 meters, 600 meters, and 300 meters. It should be noted that the preset distance can also adaptively adjust the range of values according to whether the customer has a vehicle, and therefore the same preset distance is not applicable to all customers in the customer database 12.

The map module 5 is electrically connected to the customer database 12, the merchant database 14 and the analysis module 3. The map module 5 itself has a map information, and a plurality of geographical locations in a customer information selected according to the analysis module 3. And the search radius calculated by the analysis module 3 calculates a plurality of search areas on the map. Please refer to FIG. 1 and FIG. 2 together. Specifically, the map module 5 is centered on the company address a1, the GPS address a2, and the home address a3, and the three sets of search radii calculated by the analysis module 3 are on the map. Calculate three circular search ranges, as shown in Figure 2: the search range corresponding to the company address a1 and the search radius of 900 meters is S1; the search range corresponding to the GPS address a2 and the search radius of 600 meters is S2 And the search range corresponding to the home address a3 and the search radius of 300 meters is S3. The map module 5 further calculates a search range S _A according to the search areas S1 S S3. In practice, the map module 5 performs a set operation to calculate the search range S _A , for example, taking any two of the three search areas S1 S S3 . The intersection of the three search areas S1~S3. In FIG. 2, the search range S _A calculated by the map module 5 is taken as a union.

On the other hand, the map module 5 pre-marks the merchant location of each merchant information in the merchant database 14 on the map. Therefore, each time the local map module 5 calculates a search range S _A of the customer, the recommended merchants in the search range S _A can be found, for example, m1~m6 indicated in FIG. 2, and the selected ones are filtered out. The merchant information of the merchant m1~m6 is recommended as a recommendation list and sent to the network publishing module 7. Based on the above screening method, it can be ensured that the selected recommended merchants m1~m6 are within the living circle of the customer's frequent activities, and the recommended merchants m1~m6 in the recommended list are instantly adjusted based on the GPS positioning information of the current time and the customer's return. list of.

Please review Figure 1. The network publishing module 7 is electrically connected to the map module 5, and the network publishing module 7 is configured to send a line of pin messages according to the recommended list. Marketing messages are sent to the customer's email address, the financial institution's website, or the customer's mobile communication device in the form of a newsletter, web banner, or push. In this way, for the average consumer, it is possible to immediately know the current merchants who can use the action payment in the vicinity when viewing the email, browsing the bank website or viewing the mobile phone push, and then going to use the action payment for the consumer behavior. For the special merchant, the special merchant recommendation system disclosed by an embodiment of the present invention can be included in the recommendation list by the map module 5 when the customer is located near the merchant location, thereby improving the visibility of the merchant. For financial institutions, the special merchant recommendation system disclosed in this creation can increase the willingness of customers to use mobile payment and attract more potential customer groups to join the action payment.

In another embodiment of the present creation, the map module 5 selects the recommendation list from the plurality of merchant locations according to the search scope S _A , and the map module 5 further includes the previous screening according to the consumption information in the customer database 12 . The list of recommendations is further screened. In this way, it can be ensured that the recommended merchants in the selected recommendation list are more likely to attract the customer's high interest in addition to the easy arrival of the incentives.

In another embodiment of the present invention, the map module 5 further includes a task module (not shown). The task module is configured to select one of the plurality of preferential policies from the offer provided by the recommended merchant according to the current date and the current time, and the marketing message distributed by the network publishing module 7 further includes a task module. Select the respective preferential strategies from multiple merchants. For example, the task module may, for example, set an order from an item provided by each of the plurality of special merchants, and notify the customer through the marketing message that if the transaction of the goods is completed in an orderly manner in the specified order, the return can be returned. A certain amount of consumer feedback, or a more favorable discount on the next purchase to these special merchants. The mission module can combine the concepts of Geocatching and Geomarketing to customize the marketing schemes of each merchant to the marketing information applicable to each customer by means of big data analysis. When the customer obtains the marketing message sent by the network publishing module 7, the customer is willing to go to the recommended merchant and use the action to pay for the consumption, thereby increasing the popularity of the action payment.

Please refer to FIG. 3 , which is a flow chart of a method for recommending a merchant according to an embodiment of the present invention. Please refer to step P1: store customer information. Specifically, the financial institution stores a plurality of customer information in the customer database 12, and each customer information includes a location information and a consumption information.

Please refer to step P2: store business information. In detail, after the financial institution has agreed with the special merchants, the information of each merchant, including the location of the special merchant and the preferential scheme offered by the financial institution, includes multiple preferential strategies in the preferential scheme, and stores the merchant information in the merchant database. 14 in.

Please refer to step P3: Take out a customer information. Specifically, the analysis module 3 obtains one of the plurality of customer information stored in the customer database 12. In practice, the special merchant recommendation system disclosed in the present application may include multiple analysis modules 3 to perform this step P3 in parallel and repeatedly to publish the most immediate recommendation list to the respective smart phones of different customers.

Please refer to step P4: Calculate the weight and search radius corresponding to multiple geographical locations. According to the example described above, the analysis module 3 queries the weight table above to obtain the weight values corresponding to the company address a1, the GPS address a2, and the home address a3 according to the current time when the execution module itself executes, and according to the analysis mode. The preset preset distances and the above-mentioned weight values in the group 3 respectively calculate the search radii corresponding to the respective geographic locations.

Please refer to step P5: Calculate the search area and search range. In detail, the map module 5 calculates a plurality of search areas according to a plurality of geographical locations in the customer information and a search radius corresponding to the geographic locations, such as the search areas S1 to S3 as shown in FIG. 2, and then the map module 5 A search range S _{A is} calculated based on these search areas S1 to S3.

Please refer to step P6: Filter the recommendation list according to the consumption information and search scope. In detail, the map module 5 selects a recommendation list from the plurality of merchant locations stored in the merchant database 14 according to the search range, and the recommendation list includes a plurality of recommended merchants, and the recommended merchants are all located in the search range calculated in step P5. Within S _A.

In another embodiment of the present creation, the recommendation list screened in step P6 further includes filtering by the map module 5 according to the consumption information stored in the customer database 12. Thereby, the map module 5 can use the above mechanism to find a plurality of special merchants that best meet the customer's consumption needs and are within the scope of the customer's activities.

In step P6 of another embodiment of the present invention, the map module 5 further includes a task module. The task module selects a preferential strategy from each of the preferential offers provided by the plurality of recommended merchants according to the current date and the current time. In other words. The list of recommendations selected by the map module 5 includes, in addition to the list of merchants suitable for the customer to use the action payment, and the preferential policies provided by each of the recommended merchants on the list. In addition, the task module can further provide a consumption task mode, for example, integrating the preferential policies of the plurality of merchants, and after the customer goes to the plurality of recommended merchants on the recommendation list in the order specified by the task module, using the action payment payment, providing Customers spend feedback or get a better discount. The design of the task module can be adjusted on an instant basis based on the marketing strategy to increase the consumer's willingness to consume after learning the marketing message.

Please refer to step P7: Send marketing messages. In particular, the web publishing module 7 sends marketing messages based on the recommended list. In another embodiment of the present invention, the sent marketing message further includes a plurality of preferential policies selected by the task module. Through the cooperation of marketing messages and task modules, it is expected to enhance the physical promotion activities of customers participating in mobile payment special merchants, instantly increase revenue, deepen customer impressions and create a good relationship between customers and special merchants.

Based on the above, the special merchant recommendation system and method disclosed by the present invention find out the individual customers in the company by analyzing the customer information collected by the financial institution and the business information of the special merchants that cooperate with the agreement, combined with the method of big data operation. Recommended merchants with a tendency to consume in the life circle and the preferential strategies provided by these recommended merchants to instantly send marketing messages including recommended merchants and preferential strategies to customers in the form of newsletters, customized web banners or pushes. Mobile communication device or email address. The recommended merchants and their offers are adjusted according to the current date and the current time, so that the special merchants that best meet the current needs of the customers can be found, the functions of the special merchants and the mobile payment customers can be achieved, and the action payment can be improved. Usage.

Although the present invention has been disclosed above in the foregoing embodiments, it is not intended to limit the present invention. The changes and refinements that are made without departing from the spirit and scope of this creation are within the scope of patent protection of this creation. Please refer to the attached patent application scope for the scope of protection defined by this creation.

12‧‧‧Customer Database
14‧‧‧Business database
3‧‧‧Analysis module
5‧‧‧Map module
7‧‧‧Web Publishing Module
A1‧‧‧ company address
A2‧‧‧GPS address
A3‧‧‧Home address
S1~S3‧‧‧Search area
S _A ‧‧‧Search range
M1~m6‧‧‧Recommended merchants
P1~P7‧‧‧ steps

FIG. 1 is a structural diagram of a special merchant recommendation system according to an embodiment of the present invention. FIG. 2 is a schematic diagram of a search area and a search range according to an embodiment of the present invention. FIG. 3 is a flow chart showing a method for recommending a merchant according to an embodiment of the present invention.

Claims (6)

A special merchant recommendation system includes: a customer database for storing a plurality of customer information, each of the customer information including a location information, the location information includes a plurality of geographic locations; and a merchant database for storing the plurality of merchants Business information, each of the business information includes a merchant location; an analysis module electrically connected to the customer database, the analysis module is configured to obtain one of the customer information from the customer database, according to Calculating, by a current date and a current time, a weight corresponding to each of the geographic locations in the customer information, and calculating a search radius corresponding to each of the geographic locations according to a preset distance and the weights; a map module, electricity Linking the customer database, the merchant database and the analysis module, the map module is configured to calculate a plurality of search areas according to the geographic locations and the search radius in the customer information, and calculate according to the search areas a search range, and screening a list of recommendations from the merchant locations based on the search range, the recommendation list including a plurality of pushes Businesses; and a web publishing module is electrically connected to the map module, the web publishing module to send a marketing message based on the recommended list. The special offer system of claim 1, wherein the geographic locations include a home address, a company address, a global positioning system location address, a branch address, and an automated teller machine address. The customer recommendation system of claim 1 , wherein each of the customer information further includes a consumption information, the consumption information includes a consumption type and a consumption amount; and the map module further includes filtering out the consumption information according to the consumption information. The list of recommendations. The special merchant recommendation system of claim 1, wherein the marketing message is sent to an email address, a website or a mobile communication device in the form of a newsletter, a web banner or a push. The special merchant recommendation system of claim 1, wherein each of the merchant information of the merchant database further comprises a preferential scheme, wherein the preferential scheme includes a plurality of preferential strategies; and the map module further comprises a task module The task module is configured to select one of the preferential policies from the preferential solutions corresponding to the recommended merchants according to the current date and the current time, and the marketing message further includes the task module selected by the task module. These preferential strategies. The special offer recommendation system of claim 1, wherein the map module performs a set operation according to the search areas to calculate the search range.