E-commerce order management system

Abstract

The e-commerce order management system belongs to the field of e-commerce management technology, including a classification unit, a query unit, a data receiving unit, a prompt unit, a bad review analysis unit, a statistical unit, a client receiving unit, and a management receiving unit; the classification unit, the query unit, the data receiving unit, the prompt unit, the bad review analysis unit, the statistical unit, and the management receiving unit are application software installed on the customer service side, and the client receiving unit is an application software installed on the client. The present invention can classify customers and warn risky customers; it has a function of reminding the quantity of inventory goods, and can remind customers when there are discounts or new products on the market; if the customer service staff is not online and a customer gives a bad review of the product, the customer service staff can handle it in time, and the e-commerce management staff can supervise the service quality of the online customer service staff; it can push sales data text messages to relevant management staff of the e-commerce. The present invention brings a better shopping experience to customers and is conducive to increasing the sales volume of goods.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet sales, and in particular to an e-commerce order management system. Background Art

[0002] With the development of science and technology and the change of people's shopping concepts, online shopping has penetrated into people's lives, enabling people to shop without leaving home, bringing great convenience to people and effectively promoting the circulation of goods. At present, the number of e-commerce platforms is increasing (such as Taobao, JD.com, Pinduoduo, Suning.com, etc.). In order to achieve good sales performance and increase the flow of goods, merchants use various data methods to provide customers with a better shopping experience.

[0003] With the continuous advancement of technology, e-commerce order management technology has also been further developed. The authorized patent of my country with patent number "201711366105.9" and patent name "An e-commerce order management system" records that "the structure of the present invention is simple, data information can be encrypted, data security is guaranteed, and the operating efficiency of the e-commerce platform is improved". As can be seen from the above, although the comparative patent has achieved the technical effect of the invention described in it to a certain extent, it is subject to technical limitations. Like other technologies in this field, it still has the following technical disadvantages. First, it cannot classify customers within a unit of time. In other words, whether it is a high-quality old customer or a new customer, the priority of shipment is the same, which cannot bring a better shopping experience to high-quality old customers. Second, it cannot warn risky customers (such as customers who have bad reviews on the product) when a customer purchases an item online. In other words, the online customer service staff cannot understand the specific situation. In this way, it is possible that risky customers will deliberately find fault after purchasing the product for various reasons, which will have an adverse impact on the sales of the product and the credibility of the store. Third, it does not have the function of reminding the quantity of inventory goods. When the relevant management personnel do not check the replenishment in time, it is possible that the customer's purchase quantity requirements cannot be met when the sales volume is large, which will have an adverse impact on both customers and merchants. Fourth, after delivery, customers usually need to log in to the relevant website to check whether the goods have been delivered, etc. It does not have the function of SMS reminding customers according to the preferences of the corresponding customers when there are discounts or new products are launched. Correspondingly, it cannot provide customers with a better shopping experience, and it is not conducive to the effective sales of goods. Fifth, when the customer service staff is not online, if a customer gives a bad review of the product, because the customer service staff cannot contact the customer in time for corresponding processing, other customers will learn about the bad review, which will have a greater adverse impact on the sales of the product. Sixth, it cannot push sales data SMS to the mobile phones of relevant e-commerce managers. Managers need to log in to the relevant website to understand inventory and sales data, etc., which will bring inconvenience to the management work of relevant managers and is not conducive to the sales of goods. In summary, there is still room for improvement in the existing technology. It is particularly necessary to provide an e-commerce order management system that can solve the above problems. Summary of the invention

[0004] In order to overcome the drawbacks of e-commerce platforms as described in the background technology due to technical limitations, the present invention provides an e-commerce order management system which, under the joint action of relevant software, can classify customers within a unit time, can warn risky customers when a customer purchases items online, has the function of reminding the quantity of inventory goods, can send text messages to customers when there are discounts or new products are on the market, can promptly handle if the customer service staff is not online and a customer gives a bad review of the product, and can push sales data text messages to the mobile phones of relevant e-commerce managers, thereby bringing customers a better shopping experience and helping to increase the sales volume of e-commerce products.

[0005] The technical solution adopted by the present invention to solve its technical problem is:

[0006] An e-commerce order management system includes a classification unit, a query unit, a data receiving unit, a prompt unit, a bad review analysis unit, a statistics unit, a client receiving unit, and a management receiving unit; the classification unit, query unit, data receiving unit, prompt unit, bad review analysis unit, and statistics unit are application software installed in the customer service end of the e-commerce Internet device, the client receiving unit is an application software installed in the customer Internet device, and the management receiving unit is an application software installed in the Internet devices of e-commerce management personnel and customer service personnel; the classification unit can analyze the shopping behavior of all customers in daily store product sales in combination with the bad review analysis unit and the data in the database, and classify the orders within a unit time, ship according to customer priority, provide different gifts for customers of different levels according to sales strategy requirements, and can notify customers by SMS via the client receiving unit after shipment; the query unit can automatically combine the corresponding product quantity listed by the store to and the actual number of goods stored in the warehouse received by the data receiving unit, and based on recent commodity sales data, when the number of goods is less than a threshold, the prompt unit can prompt the customer service personnel online and send data to the management receiving unit, and the management receiving unit actively prompts the customer service personnel and management personnel to replenish the stock through text messages; the negative review analysis unit can collect and analyze the customer's negative review data in real time, and can send data to the management receiving unit through the prompt unit when the customer service personnel is not online. The management receiving unit actively prompts the customer service personnel and management personnel to go online in time to solve the negative review problem through text messages, so as to prevent the adverse effects on commodity sales and store reputation caused by the failure to handle the negative reviews in time; the statistical unit can remind customers through text messages according to customer preferences, discounts or new products on the market, so as to increase the sales volume of the goods, and the statistical unit can also generate a commodity sales table at intervals and send it to the management receiving unit, so that e-commerce managers can understand the various data generated by sales in real time.

[0007] Furthermore, in the daily store commodity sales, the classification unit collects statistics on the commodity sales data within a unit time at regular intervals and classifies the various data respectively.

[0008] Furthermore, the negative review analysis unit has a database sub-unit, which can count and sort the commodity purchase volume data of the corresponding customers in the store's all commodity sales history time, as well as the number of negative review data given by the corresponding customers. When the negative review analysis unit performs data analysis in the classification unit, it can sort the data from high to low according to the number of commodities purchased by different normal customers, and at the same time, sort the number of negative reviews of customers who gave negative reviews and the number of commodities purchased by them from high to low.

[0009] Furthermore, the normal customers refer to customers who have not given bad reviews to all the products in the store. The classification unit delivers goods according to customer priority levels, or provides different gifts for customers of different levels. Priority is given to normal customers who purchase a large number of goods and customers who give the most bad reviews per unit time. Customers can be reminded by text messages after delivery to improve the shopping experience of old customers and prevent customers with bad reviews from making dissatisfied purchases again and giving bad reviews, which will have an adverse impact on the sales of goods. Specifically, the act of giving gifts is mainly aimed at normal customers who purchase a large number of goods per unit time.

[0010] Furthermore, in the query unit, the corresponding quantity of goods in the warehouse can be analyzed based on artificial intelligence image recognition analysis in combination with the actual warehouse. Specifically, the data on the quantity of goods in the actual warehouse is transmitted to the data receiving unit wirelessly. At the same time, the data on the quantity of goods in the actual warehouse can also be entered manually, and the method of obtaining data is more flexible.

[0011] Furthermore, the query unit has a product warning subunit, which stores minimum threshold data on the quantity of multiple products sold in the store. When the query unit finds that the quantity of the corresponding product is less than the threshold and there is a situation that affects sales, it can output a trigger signal to the prompt unit to output prompt instruction data.

[0012] Furthermore, the negative review analysis unit can automatically collect facial data of customer service personnel through a camera while collecting and analyzing negative review data of customers in real time. When the facial data of the customer service personnel cannot be collected and there has been no operation on the PC keyboard, mouse, or mobile phone virtual keyboard for a certain period of time, it is determined that the customer service personnel is not online.

[0013] Furthermore, when the negative review analysis unit sends data to the management receiving unit via the prompt unit, the text message content includes the name, quantity, and time of the goods purchased by the customer who gave the negative review, as well as the content of the negative review and the customer's telephone contact data. The customer service staff can contact the customer in time to negotiate and cancel the negative review data as much as possible. At the same time, the management staff can prompt the customer service staff to contact the customer who gave the negative review as soon as possible to prevent the customer service staff from ignoring the text message information and failing to process the product data in time.

[0014] Furthermore, the negative review analysis unit can also automatically analyze the online customer service's online time and chat records with buyers. When the customer service is not up to standard or there are other factors that affect the sales of goods, the e-commerce management personnel will be notified through the prompt unit through the management end receiving unit.

[0015] Furthermore, the statistical unit can analyze the customer's preference data through the types and quantity data of goods purchased by normal customers, comment data, etc. counted by the database sub-unit, and actively remind the customer via SMS when the store puts the goods that the customer is interested in, and provide data such as the name, function and price of the corresponding goods.

[0016] Furthermore, the statistical unit can also automatically collect data on various commodities sold in the store, and make a chart format according to the name, time, sales volume per unit time, and sales amount, and then send it to the management end receiving unit of the manager.

[0017] The beneficial effects of the present invention are as follows: under the joint action of the classification unit, query unit, data receiving unit, prompt unit, bad review analysis unit, statistical unit, client receiving unit, management end receiving unit, etc., the present invention can classify customers within a unit time, and when a customer purchases items online, it can warn risky customers; it has a function of reminding the quantity of goods in stock, and can send SMS reminders to customers when there are discounts or new products are on the market; if the customer service staff is not online and a customer gives a bad review of the product, the customer service staff can handle it in time, and the e-commerce management staff (such as the store owner) can supervise the service quality of the online customer service staff; it can push sales data SMS to the mobile phones of relevant management staff of the e-commerce. The present invention can bring a better shopping experience to customers and help increase the sales volume of goods of e-commerce. Based on the above, the present invention has good application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a software architecture block diagram of the present invention. DETAILED DESCRIPTION

[0019] Figure 1 As shown in, the e-commerce order management system includes a classification unit, a query unit, a data receiving unit, a prompt unit, a bad review analysis unit, a statistical unit, a client receiving unit, and a management-end receiving unit; the classification unit, query unit, data receiving unit, prompt unit, bad review analysis unit, and statistical unit are application software installed in an e-commerce PC or a smart phone, a tablet computer, etc., the client receiving unit is an application software installed in a customer's e-commerce PC or a smart phone, a tablet computer, etc. shopping APP, and the management-end receiving unit is an application software installed in an e-commerce manager or customer service staff's e-commerce PC or a smart phone, a tablet computer, etc.

[0020] Figure 1As shown in , in the classification unit application, in the daily store commodity sales, the bad review analysis unit can retrieve the data in the database to analyze the shopping behavior of all customers, classify the orders within a unit time, and ship according to the customer priority level, or provide different gift small commodities for customers of different levels. After the shipment, the client receiving unit can send a text message to the customer. Specifically, in the daily store commodity sales, the classification unit counts the commodity sales data within a unit time at a certain interval (for example, every hour), guarantees the timeliness of shipment as much as possible, and classifies various data separately. The bad review analysis unit has a database subunit, which can count and sort the commodity purchase data of the corresponding customers in the historical time of all commodity sales of the store, as well as the number of bad review data given by the corresponding customers, so that the data is more comprehensive and provides more data for the classification unit; when the bad review analysis unit performs data analysis in the classification unit, it can sort the number of commodities purchased by different normal customers from high to low, and at the same time sort the number of bad reviews of customers who give bad reviews and the number of commodities purchased from high to low (customers with more bad reviews are ranked first, and customers with fewer bad reviews are ranked last). Normal customers specifically refer to customers who have not given bad reviews in all the products in the store. The classification unit ships goods according to customer priority levels, or provides different free small commodities for customers of different levels. In a unit of time, normal customers who purchase a large number of goods and customers who give the most bad reviews are given priority for delivery, which improves the shopping experience of old customers and prevents customers with bad reviews from being dissatisfied with their purchases again and giving bad reviews again, which has an adverse impact on the sales of goods. Specifically, the act of giving away small commodities is mainly aimed at normal customers who purchase a large number of goods per unit time, customers who purchase a large number of goods and have good reputation, and have not given bad reviews.

[0021] Figure 1As shown in , the query unit can automatically combine the corresponding number of goods listed by the store and the number of goods stored in the actual warehouse received by the data receiving unit, and according to the recent sales data of goods, when the number of goods is less than the threshold, the prompt unit can prompt the customer service staff online and send data to the management receiving unit. The management receiving unit actively prompts the customer service staff and management personnel (such as the store owner) to replenish the goods through SMS, so that the prompting method is diversified. In the application of the query unit, the corresponding number of goods in the warehouse can be analyzed based on artificial intelligence image recognition analysis in combination with the actual warehouse (the camera collects the image data of the goods at the relevant position in the warehouse, and then inputs it into the PC for analysis, and the corresponding number of goods in the warehouse is obtained according to the height and width data of all goods, as well as the height and width data of each single product). Specifically, the actual number of goods in the warehouse is transmitted to the data receiving unit by wireless via the 4G wireless module. At the same time, the actual number of goods in the warehouse can also be entered manually, and the data acquisition method is more flexible. The query unit has a product warning subunit, which stores the minimum threshold data of various products sold in the store. When the query unit finds that the number of corresponding products is less than the threshold and there is a situation that affects sales, it can output a trigger signal to the prompt unit to output prompt instruction data.

[0022] Figure 1 As shown in , the negative review analysis unit can collect and analyze the negative review data of customers in real time. When the customer service staff is not online, the prompt unit can send data to the management receiving unit. The management receiving unit actively prompts the customer service staff and management personnel to go online in time to solve the negative review problem through SMS, so as to prevent the customer service staff from being online and not handling the negative review in time, which will have an adverse effect on the sales of goods and the reputation of the store. The negative review analysis unit collects and analyzes the negative review data of customers in real time, and can automatically collect the facial data of the customer service staff through the camera. When the facial data of the customer service staff cannot be collected and there has been no operation on the PC keyboard, mouse, or mobile phone virtual keyboard for a certain period of time, it is judged that the customer service staff is not online. When the negative review analysis unit sends data to the management-end receiving unit via the prompt unit, the text message content includes the name, quantity, and time of the goods purchased by the customer with the negative review, as well as the content of the negative review and the customer's telephone contact data. The customer service staff can contact the customer in time to negotiate and cancel the negative review data as much as possible. At the same time, the management staff can prompt the customer service staff to contact the customer with the negative review as soon as possible to prevent the customer service staff from ignoring the text message information and failing to process the product data in time. Specifically, the negative review analysis unit can also automatically analyze the online customer service time and the chat records with the buyer. When the customer service is not up to standard or there are other factors that affect the sales of the goods, the prompt unit will be used to prompt the e-commerce management staff through the management-end receiving unit.

[0023] Figure 1As shown in , the statistical unit can analyze the customer's preference data information through the types and quantity data of goods purchased by normal customers, comment data, etc. collected by the database subunit, and actively remind the customer by SMS when the store puts the goods that the customer is interested in, and give the name, function and price of the corresponding goods, so as to increase the sales volume of the goods. The statistical unit can also automatically collect the data of various goods sold in the store, and make a chart format according to the name, time, sales volume per unit time, and sales amount, and then send it to the mobile client of the manager. Specifically, the statistical unit generates a commodity sales table at each interval and sends it to the mobile client, so that the e-commerce manager can understand the data in real time.

[0024] Figure 1 As shown in , the present invention can classify customers within a unit time under the joint action of the classification unit, query unit, data receiving unit, prompt unit, bad review analysis unit, statistical unit, client receiving unit, and management receiving unit, and can warn risky customers when customers purchase items online; it has a function of reminding the number of goods in stock, and can send SMS reminders to customers when there are discounts or new products are on the market; if the customer service staff is not online and a customer gives a bad review of the product, the customer service staff can handle it in time, and the e-commerce management staff (such as the store owner) can supervise the service quality of the online customer service staff; it can push sales data SMS to the mobile phones of relevant management staff of the e-commerce. The present invention can bring better shopping experience to customers and help increase the sales volume of goods of e-commerce.

[0025] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and scope of the equivalent elements of the claims are included in the present invention.

[0026] In addition, it should be understood that although the present specification is described according to the implementation mode, the implementation mode does not only include an independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in the embodiments can also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. E-commerce order management system, characterized by: It includes a classification unit, a query unit, a data receiving unit, a prompt unit, a bad review analysis unit, a statistics unit, a client receiving unit, and a management receiving unit; the classification unit, query unit, data receiving unit, prompt unit, bad review analysis unit, and statistics unit are application software installed in the customer service end of the e-commerce Internet device, the client receiving unit is an application software installed in the customer Internet device, and the management receiving unit is an application software installed in the Internet devices of e-commerce management personnel and customer service personnel; the classification unit can analyze the shopping behavior of all customers in daily store product sales in combination with the bad review analysis unit and the data in the database, and classify the orders within a unit time, ship according to customer priority, provide different gifts for customers of different levels according to sales strategy needs, and notify customers by SMS via the client receiving unit after shipment; the query unit can automatically combine the corresponding product quantity listed by the store and the data The receiving unit receives the actual number of goods stored in the warehouse, and based on the recent commodity sales data, when the number of goods is less than the threshold, the prompt unit can prompt the customer service personnel online and send data to the management receiving unit. The management receiving unit actively prompts the customer service personnel and management personnel to replenish the stock through text messages; the negative review analysis unit can collect and analyze the customer's negative review data in real time, and can send data to the management receiving unit through the prompt unit when the customer service personnel is not online. The management receiving unit actively prompts the customer service personnel and management personnel to go online in time to solve the negative review problem through text messages, so as to prevent the adverse effects on commodity sales and store reputation caused by the failure to handle the negative reviews in time; the statistical unit can remind customers through text messages according to customer preferences, discounts or new products on the market, so as to increase the sales volume of the goods, and the statistical unit can also generate a commodity sales table at intervals and send it to the management receiving unit, so that e-commerce managers can understand the various data generated by sales in real time.

2. The e-commerce order management system according to claim 1, characterized in that: In the daily store merchandise sales, the classification unit counts the merchandise sales data within a unit time at certain intervals and classifies various types of data.

3. The e-commerce order management system according to claim 1, characterized in that: The negative review analysis unit has a database sub-unit, which can count and sort the commodity purchase volume data of the corresponding customers in the store's all commodity sales history, as well as the number of negative review data given by the corresponding customers. When the negative review analysis unit performs data analysis in the classification unit, it can sort the data from high to low according to the number of commodities purchased by different normal customers, and at the same time, sort the number of negative reviews of customers who gave negative reviews and the number of commodities purchased by customers from high to low.

4. The e-commerce order management system according to claim 3, characterized in that: Normal customers refer to customers who have not given bad reviews to all the products in the store. The classification unit delivers goods according to customer priority levels, or provides different gifts for customers of different levels. Priority is given to normal customers who purchase a large number of goods and customers who give the most bad reviews in a unit of time. Customers can be reminded by text messages after delivery to improve the shopping experience of old customers and prevent customers with bad reviews from making dissatisfied purchases again and giving bad reviews, which will have an adverse impact on the sales of goods. Specifically, the act of giving gifts is mainly aimed at normal customers who purchase a large number of goods in a unit of time.

5. The e-commerce order management system according to claim 1, characterized in that: In the query unit, the actual warehouse can be combined with the corresponding commodity quantity in the warehouse based on artificial intelligence image recognition analysis. Specifically, the actual commodity quantity data in the warehouse is transmitted to the data receiving unit wirelessly. At the same time, the actual commodity quantity data in the warehouse can also be entered manually, and the data acquisition method is more flexible.

6. The e-commerce order management system according to claim 1, characterized in that: The query unit has a product warning subunit, which stores minimum threshold data on the quantity of various products sold in the store. When the query unit finds that the quantity of the corresponding product is less than the threshold and there is a situation that affects sales, it can output a trigger signal to the prompt unit to output prompt instruction data.

7. The e-commerce order management system according to claim 1, characterized in that: The negative review analysis unit collects and analyzes the negative review data of customers in real time, and can automatically collect the facial data of customer service personnel through the camera. When the facial data of the customer service personnel cannot be collected and there has been no operation on the PC keyboard, mouse, or mobile phone virtual keyboard for a certain period of time, it is determined that the customer service personnel is not online.

8. The e-commerce order management system according to claim 1, characterized in that: When the negative review analysis unit sends data to the management receiving unit via the prompt unit, the text message content includes the name, quantity, and time of the goods purchased by the customer who gave the negative review, as well as the content of the negative review and the customer's telephone contact data. The customer service staff can contact the customer in time to negotiate and cancel the negative review data as much as possible. At the same time, the management staff can prompt the customer service staff to contact the customer who gave the negative review as soon as possible to prevent the customer service staff from ignoring the text message information and failing to process the product data in time.

9. The e-commerce order management system according to claim 1, characterized in that: The negative review analysis unit can also automatically analyze the online customer service's online time and chat records with buyers. When the customer service is not up to standard or there are other issues that affect product sales, the e-commerce management personnel will be notified through the prompt unit and the management receiving unit.

10. The e-commerce order management system according to claim 1, characterized in that: The statistical unit can analyze the customer's preference data through the data of the types and quantity of goods purchased by normal customers and the comment data collected by the database subunit, and actively remind the customer by SMS when the store puts the goods that the customer is interested in, and give the name, function and price of the corresponding goods; The statistical unit can also automatically collect data on various commodities sold in the store, and make charts according to the name, time, sales volume per unit time, and sales amount, and then send them to the management receiving unit of the manager.

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