WO2021042541A1 - Procédé et appareil de guide d'achat dans un nouveau modèle de vente au détail, dispositif et support de stockage - Google Patents

Procédé et appareil de guide d'achat dans un nouveau modèle de vente au détail, dispositif et support de stockage Download PDF

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WO2021042541A1
WO2021042541A1 PCT/CN2019/117714 CN2019117714W WO2021042541A1 WO 2021042541 A1 WO2021042541 A1 WO 2021042541A1 CN 2019117714 W CN2019117714 W CN 2019117714W WO 2021042541 A1 WO2021042541 A1 WO 2021042541A1
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customer
information
customer information
physical store
terminal
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PCT/CN2019/117714
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English (en)
Chinese (zh)
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乐志能
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平安科技（深圳）有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0281—Customer communication at a business location, e.g. providing product or service information, consulting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • G06Q30/0261—Targeted advertisements based on user location
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

Definitions

  • the present invention relates to the field of artificial intelligence technology, and in particular to a method, device, equipment and storage medium for shopping guides in a new retail mode.
  • the current sales method in a physical store is generally that the user enters the physical store, and then the sales consultant judges whether the user is a potential buying user based on experience, and if it is judged to be a potential buying customer, follow up the sales guide.
  • This method relies on the empirical judgment of sales consultants, and requires a high level of ability of sales consultants. When there are many customers, the number of sales consultants is limited, which leads to the loss of potential consumer customers because they are not received by sales consultants, or the sales consultants are just new. At the beginning, if you don't understand any hobbies and characteristics of customers, you need to have too many conversations with customers, which arouses customers' disgust and leads to a lower sales success rate.
  • the main purpose of the present invention is to provide a method, device, equipment and storage medium for shopping guide under the new retail mode, aiming to solve the technical problem of low sales success rate in the prior art.
  • the present invention provides a shopping guide method for commodities under a new retail mode, including:
  • the present invention also provides a commodity shopping guide device under the new retail mode, and the commodity shopping guide device under the new retail mode includes:
  • the location judgment module is used to obtain the location information reported by the customer terminal, and determine whether the customer terminal is located in the target physical store according to the location information;
  • a customer information acquisition module configured to acquire customer information reported by the customer terminal if the customer terminal is located in a target physical store
  • the calculation module is used to calculate the similarity between the customer information and the transactional customer information stored in the historical transaction records to obtain multiple similarity calculation results;
  • the detection module is configured to determine a maximum similarity calculation result from the multiple similarity calculation results, and determine whether the maximum similarity calculation result is less than a preset threshold;
  • a prediction module configured to input the customer information into a preset prediction model if the maximum similarity calculation result is less than a preset threshold, to obtain a product recommendation result for the customer information;
  • the push module is configured to obtain the face image reported by the customer terminal, and send the face image and the product recommendation result to the sales terminal corresponding to the target physical store.
  • the present invention also provides a computer device, the computer device including a memory and a processor;
  • the memory is used to store a computer program
  • the processor is configured to execute the computer program and, when executing the computer program, implement the above-mentioned method for shopping guide under the new retail mode.
  • the present invention also provides a computer-readable storage medium on which is stored a commodity shopping guide program under the new retail mode, and the commodity shopping guide program under the new retail mode is processed When the device is executed, the steps of the commodity shopping guide method under the new retail mode as described above are realized.
  • the product that the customer is interested in is determined according to the customer's information, and the sales staff in the target physical store are notified, so that the sales staff can provide customers with more accurate shopping guide services and increase sales. Efficiency and success rate.
  • FIG. 1 is a schematic diagram of a computer device structure of a hardware operating environment involved in a solution of an embodiment of the present invention
  • FIG. 2 is a schematic flowchart of an embodiment of a merchandise shopping guide method under the new retail mode of the present invention
  • FIG. 3 is a schematic diagram of a scene of an embodiment of a method for shopping guide in a new retail mode according to the present invention
  • FIG. 4 is a schematic diagram of the detailed process of determining that the customer terminal is located in the target physical store in FIG. 2;
  • FIG. 5 is a detailed flowchart of the steps of sending the face image and the product recommendation result to the sales terminal corresponding to the target physical store in FIG. 2;
  • Fig. 6 is a schematic diagram of functional modules of an embodiment of a merchandise shopping guide device in a new retail mode of the present invention.
  • Fig. 1 is a schematic structural diagram of a computer device in a hardware operating environment involved in a solution of an embodiment of the present invention.
  • the computer device in the embodiment of the present invention may be a PC, or a terminal device with data processing capabilities, such as a smart phone or a server.
  • the computer device may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002.
  • the communication bus 1002 is used to implement connection and communication between these components.
  • the user interface 1003 may include a display screen (Display) and an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.
  • the network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).
  • the memory 1005 may be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as a magnetic disk memory.
  • the memory 1005 may also be a storage device independent of the aforementioned processor 1001.
  • FIG. 1 does not constitute a limitation on the computer device, and may include more or less components than those shown in the figure, or a combination of certain components, or different component arrangements.
  • a memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a network operation control application program.
  • the network interface 1004 is mainly used to connect to the back-end server and communicate with the back-end server;
  • the user interface 1003 is mainly used to connect to the client (user side) and communicate with the client; and the processing
  • the device 1001 can be used to call a computer program stored in the memory 1005 and perform the following operations:
  • the processor 1001 may call a computer program stored in the memory 1005, and also perform the following operations:
  • the processor 1001 may call a computer program stored in the memory 1005, and also perform the following operations:
  • the similarity between the customer information and the transaction customer information is calculated by the cosine similarity formula, and multiple similarity calculation results are obtained.
  • the cosine similarity formula is as follows:
  • n(A) represents the number of types of information contained in the customer information
  • n(B) represents the number of types of information contained in the customer information that has been traded
  • n(A ⁇ B) represents that the customer information is the same as that of the customer information that has been traded
  • the amount of information, K is the similarity calculation result.
  • the processor 1001 may call a computer program stored in the memory 1005, and also perform the following operations:
  • the historical transaction records including the product name and the transaction customer information corresponding to the product name;
  • the prediction function formula is as follows:
  • ⁇ i is the weight value of the transaction customer information i
  • x i is the characteristic value corresponding to the transaction customer information i
  • ⁇ T [ ⁇ 1 , ⁇ 2 ,..., ⁇ n ]
  • x [x 1 ,x 2 ,...,x n ]
  • e is a natural constant.
  • the processor 1001 may call a computer program stored in the memory 1005, and also perform the following operations:
  • a target output value greater than or equal to a preset probability value is selected, and a product recommendation result for the customer information is obtained based on the target output value.
  • the processor 1001 may call a computer program stored in the memory 1005, and also perform the following operations:
  • If the product recommendation result exists in the product list execute the acquisition of the face image reported by the customer terminal, and send the face image and the product recommendation result to the target physical store. The steps of the point-of-sale.
  • the processor 1001 may call a computer program stored in the memory 1005, and also perform the following operations:
  • FIG. 2 is a schematic flowchart of an embodiment of a merchandise shopping guide method under the new retail mode of the present invention.
  • the shopping guide methods under the new retail model include:
  • Step S10 Obtain the location information reported by the customer terminal, and determine whether the customer terminal is located in the target physical store according to the location information.
  • the message pushing method is applied to a computer device, and the computer device may specifically be a server.
  • the location information of multiple physical stores is stored on the server.
  • the location information of physical store 1 to physical store 5 are W1, W2, W3, W4, and W5, respectively.
  • the distance between the location information W reported by the customer terminal and W1 to W5 is calculated respectively, and five calculation results are obtained, which are respectively L1 to L5.
  • Step S20 If the customer terminal is located in the target physical store, acquire customer information reported by the customer terminal.
  • the customer needs to pre-register when using the client installed in the customer terminal.
  • some personal information needs to be filled in, such as gender, age, occupation, income status, health status, family medical history, etc. (The type of personal information can be expanded or reduced according to actual needs, and there is no restriction here), and then the customer terminal reports the personal information to the server, and the server stores the personal information in association with the terminal identification of the customer terminal.
  • the personal information that is, customer information
  • the terminal identification of the customer terminal can be obtained from the stored data.
  • Step S30 Calculate the similarity between the customer information and each transaction customer information stored in the historical transaction record to obtain multiple similarity calculation results.
  • step S30 includes:
  • n(A) represents the number of types of information contained in the customer information
  • n(B) represents the number of types of information contained in the customer information that has been traded
  • n(A ⁇ B) represents that the customer information is the same as that of the customer information that has been traded
  • the amount of information, K is the similarity calculation result.
  • Transaction details include: product name, customer information.
  • Table 1 is a data table that stores historical transaction records.
  • Transaction customer information 1 Product name 1
  • Transaction customer information 2 Product name 2
  • Transaction customer information 3 Product name 3
  • Transaction customer information 4 Product name 4
  • the cosine similarity formula is as follows:
  • n(A) represents the type and quantity of information contained in the customer information. If the customer information includes gender, age, occupation, income status, health status, and family medical history, then n(A) is 6; n(B) represents The type and quantity of the information contained in the transaction customer information. If the transaction customer information includes gender, age, occupation, income status, health status, and family medical history, then n(B) is 6; n(A ⁇ B) represents the customer information and The quantity of the same information in the transaction customer information; K represents the calculation result of the similarity between the customer information and the transaction customer information.
  • Step S40 Determine a maximum similarity calculation result from the multiple similarity calculation results, and determine whether the maximum similarity calculation result is less than a preset threshold.
  • the transaction customer information includes transaction customer information 1 to transaction customer information 4
  • four similarity calculation results are calculated, namely K1 to K4, and the largest value is selected from K1 to K4
  • K2 it is determined whether K2 is greater than or equal to the preset threshold.
  • the preset threshold is set according to actual needs, for example, set to 0.8.
  • K2 is greater than or equal to the preset threshold, it means that the personal information of the customer is very similar to that of customer 2 who has already traded.
  • the goods purchased by customer 2 who have already traded are likely to be goods of interest to the customer. Therefore, It is possible to directly use the product purchased by the customer 2 who has already traded as the product to be recommended. For example, if the products purchased by customer 2 include: product 1 and product 3, the product recommendation result is product 1 and product 3.
  • Step S50 If the maximum similarity calculation result is less than a preset threshold, input the customer information into a preset prediction model to obtain a product recommendation result for the customer information.
  • the maximum similarity calculation result is less than the preset threshold, it means that the current customer information is not the same as the stored transactional customer information, and it is impossible to predict the products of interest to the current customer information based on historical transaction records.
  • the customer information is input into the predictive model, and the result of product recommendation based on the customer information is obtained.
  • the characteristic values corresponding to the customer information are input into the three prediction models, and three probability values P1, P2, and P3 are obtained.
  • a preset probability value for example, 0.85
  • Step S60 Obtain the face image reported by the customer terminal, and send the face image and the product recommendation result to the sales terminal corresponding to the target physical store.
  • the face image reported by the customer terminal is further obtained, and the face image and the product recommendation result are generated to the sales terminal corresponding to the target physical store, so that the sales staff of the sales terminal can follow the person
  • the face image finds the customer, and conducts a shopping guide for the customer according to the product recommendation result.
  • a customer registers to use a customer terminal, he will be required to upload a face image.
  • the customer terminal receives the face image, it will report the face image to the server, and the server will associate the face image with the terminal identifier of the customer terminal. Associated storage.
  • FIG. 3 is a schematic diagram of a scene of an embodiment of a method for shopping guide of goods in a new retail mode of the present invention.
  • the server establishes a communication connection with the customer terminal and the sales terminal respectively.
  • the server determines the target physical store where it is located according to the location information reported by the customer terminal, and determines the product recommendation result according to the customer information corresponding to the customer terminal, so that the product
  • the recommendation result and the facial image reported by the customer terminal are pushed to the sales terminal corresponding to the target physical store.
  • the location information reported by the customer terminal is acquired, and based on the location information, it is determined whether the customer terminal is located in the target physical store; if the customer terminal is located in the target physical store, the customer information reported by the customer terminal is acquired; Calculate the similarity between the customer information and the transactional customer information stored in the historical transaction records to obtain multiple similarity calculation results; determine the maximum similarity calculation result from the multiple similarity calculation results, and determine the Whether the maximum similarity calculation result is less than the preset threshold; if the maximum similarity calculation result is less than the preset threshold, the customer information is input into the preset prediction model to obtain the product recommendation result for the customer information; The face image reported by the customer terminal is sent, and the face image and the product recommendation result are sent to the sales terminal corresponding to the target physical store.
  • the product that the customer is interested in is determined according to the customer's information, and the salesperson is notified, so that the salesperson can provide the customer with more accurate shopping guide services, and improve the sales efficiency and success rate.
  • Fig. 4 is a schematic diagram of the detailed process of determining that the customer terminal is located in the target physical store in Fig. 2.
  • step S10 includes:
  • Step S101 Obtain the location information reported by the customer terminal, calculate the distance between the location information and the location information of each physical store, and obtain multiple distance calculation results.
  • the earth is a nearly standard ellipsoid with an equatorial radius of 6378.140 kilometers, a polar radius of 6356.755 kilometers, and an average radius of 6371.04 kilometers. If we assume that the earth is a perfect sphere, then its radius is the average radius of the earth, denoted as R. If the 0 degree longitude line is used as the reference, then the surface distance between these two points can be calculated based on the latitude and longitude of any two points on the surface of the earth (here, the error caused by the topography of the earth surface to the calculation is ignored, and it is only a theoretical estimate).
  • the longitude and latitude of the first point A is (LonA, LatA)
  • the longitude and latitude of the second point B is (LonB, LatB).
  • the east longitude is taken as the positive value (Longitude)
  • the west longitude is taken as the negative value (-Longitude)
  • the north latitude takes 90-latitude value (90-Latitude)
  • the south latitude takes 90+latitude value (90+Latitude)
  • the two points after the above processing are counted as (MLonA,MLatA) and (MLonB, MLatB).
  • the following formula for calculating the distance between two points can be obtained:
  • the location information (the location information reported by the customer terminal and the location information of each physical store pre-stored in the server) may be latitude and longitude. According to the above formula 1 and formula 2, the distance between the customer terminal and each physical store can be obtained.
  • Step S102 It is determined whether there is a target calculation result less than or equal to a preset distance among the multiple distance calculation results.
  • multiple distance calculation results can be obtained according to step S101, and then it is further determined whether there is a target calculation result less than or equal to a preset distance among the multiple distance calculation results.
  • the preset distance is set according to actual needs, for example, set to 50 meters.
  • Step S103 If there is a target calculation result less than or equal to a preset distance among the multiple distance calculation results, it is determined that the customer terminal is located in the target physical store corresponding to the target calculation result.
  • the method before step S10, the method further includes:
  • the historical transaction records including the product name and the transaction customer information corresponding to the product name; calculate the characteristic value corresponding to the transaction customer information, and substitute the characteristic value into the prediction function formula to obtain A prediction function; the multiple prediction functions are iteratively solved to obtain the prediction model corresponding to the product name; the prediction function formula is as follows:
  • ⁇ i is the weight value of the transaction customer information i
  • x i is the characteristic value corresponding to the transaction customer information i
  • ⁇ T [ ⁇ 1 , ⁇ 2 ,..., ⁇ n ]
  • x [x 1 ,x 2 ,...,x n ]
  • e is a natural constant.
  • the transactional customer information corresponding to product 1 in the historical transaction record includes customer information from customer 1 to customer 10; if customer 11 , Customer 12, Customer 13...Customer 20 has purchased product 2, then the transaction customer information corresponding to product 2 in the historical transaction record includes customer information from customer 11 to customer 20; if customer 21, customer 22, customer 23...
  • the customer 30 once purchased the product 3, and the transaction customer information corresponding to the product 3 in the historical transaction record includes the customer information of the customer 21 to the customer 30.
  • customer information includes: gender, age, income, personal health status (the type of information can be expanded or reduced according to actual needs). Calculate the characteristic value corresponding to the customer information according to the preset characteristic value conversion rule.
  • the characteristic value corresponding to the transaction customer information can be obtained. And substitute the eigenvalues into the formula:
  • ⁇ i is the weight value of the transaction customer information i
  • x i is the characteristic value corresponding to the transaction customer information i
  • ⁇ T [ ⁇ 1 , ⁇ 2 ,..., ⁇ n ]
  • x [x 1 ,x 2 ,...,x n ]
  • e is a natural constant.
  • the essence of the prediction model is a specific algorithm.
  • Specific algorithms can include: logistic regression algorithms or neural network algorithms.
  • the prediction model is the LR (Logistic Regression) model.
  • Logistic regression is a classification method, which is mainly used to solve two classification problems (that is, there are only two outputs, representing two classifications respectively).
  • the logistic regression algorithm uses the Logistic function (or called the Sigmoid function), and the curve form of the function is S Type curve, the function form is:
  • ⁇ i is the weight value of the transaction customer information i
  • x i is the characteristic value corresponding to the transaction customer information i
  • ⁇ T [ ⁇ 1 , ⁇ 2 ,..., ⁇ n ]
  • x [x 1 ,x 2 ,...,x n ]
  • e is a natural constant.
  • the prediction model includes: the prediction information is the LR model of product 1, the prediction information is the LR model of product 2, and the prediction information is the LR model of product 3.
  • the way to obtain the prediction information of the LR model of product 1 is: substitute the feature value corresponding to the customer information of customer 1 as sample data 1 into the above formula (3) to obtain the prediction function 1; similarly, the feature corresponding to the customer information of customer 2 The value is substituted into the above formula (3) as the sample data 2 to obtain the prediction function 2, and the characteristic value corresponding to the customer information of the customer 3 to the customer 10 is substituted into the above formula (3) as the sample data 3 to the sample data 10, respectively, to obtain the prediction function 3 To 10, and prediction function 1 to prediction function 10 are all obtained from the information of customers who have purchased product 1, so the output values of prediction function 1 to prediction function 10 are all equal, thereby constructing multiple solution functions, namely prediction function 1.
  • Prediction function 2 Prediction function 2
  • Prediction function 1 Prediction function 3
  • the output value of the LR model is 0 ⁇ 1. The larger the output value, the customer willing The greater the probability of buying product 1.
  • the LR model whose predicted information is product 2 is obtained.
  • the output value of the LR model is 0 to 1. The larger the output value, the greater the probability that the customer corresponding to the input customer information is willing to purchase product 2.
  • the LR model whose prediction information is product 3 the LR model whose prediction information is product 4
  • the LR model whose prediction information is product n can be obtained.
  • the process of quantifying (converting) the transaction customer information (gender, age, income, personal health status) into a numerical value can be based on actual needs and using reasonable rules to convert the transaction customer information (gender, age, Income, personal health status) converted to appropriate values.
  • the characteristic value corresponding to the current customer information is input to the LR model.
  • the output of the LR model is a probability value. The probability value ranges from 0 to 1, and the probability value is higher. Large, indicating that the product corresponding to the LR model is more suitable for the needs of the customer to which the customer information belongs.
  • step S50 includes:
  • the characteristic value corresponding to the customer information is calculated, and the characteristic value is input into the prediction model corresponding to each product to obtain a number of output values; select greater than or equal to the preset probability The value of the target output value is based on the target output value to obtain the product recommendation result for the customer information.
  • multiple training models are obtained by training, for example, the LR model whose prediction information is product 1, the LR model whose prediction information is product 2, and the LR model whose prediction information is product 3. Then, the characteristic values corresponding to the customer information are input into the three prediction models, and three probability values P1, P2, and P3 are obtained. Select a target output value greater than a preset probability value (for example, 0.85) from P1, P2, and P3. If P2 and P3 are greater than or equal to the preset probability value, then P2 and P3 are the target output values, indicating that product 2 and product 3 fit the needs of the customer to whom the current customer information belongs. Therefore, the determined product recommendation result is product 2 and Product 3.
  • a preset probability value for example, 0.85
  • the method before step S60, the method further includes:
  • the product recommendation results are product 2 and product 3. It is necessary to determine whether there is product 2 and product 3 in the target physical store, that is, whether the product recommendation result exists there.
  • step S60 is executed, which avoids waste of sales resources.
  • the out-of-stock reminder can be sent to the customer terminal to remind the customer that the product 2 and product 3 are in the destination physical store. Out of stock status.
  • FIG. 5 is a detailed flowchart of the steps of sending the face image and the product recommendation result to the sales terminal corresponding to the target physical store in FIG. 2.
  • the step of sending the face image and the product recommendation result to the sales terminal corresponding to the target physical store includes:
  • Step S601 Obtain status information of each sales terminal corresponding to the target physical store.
  • each salesperson is equipped with a sales terminal.
  • the status of the sales terminal is adjusted to busy.
  • the salesperson is idle, the status of the sales terminal is adjusted to idle, and the status of the sales terminal is reported To the server.
  • Step S602 Determine a target sales terminal whose status is idle based on the status information.
  • a target sales terminal whose status is idle can be determined from all sales terminals.
  • Step S603 Send the face image and the product recommendation result to the target sales terminal.
  • the target sales terminal in the idle state is first determined, and the face image and the product recommendation result are sent to the target sales terminal in the idle state, so that the target sales terminal belongs to Sales staff can immediately serve customers, realizing reasonable management of sales resources.
  • FIG. 6 is a schematic diagram of functional modules of an embodiment of a merchandise shopping guide device in a new retail mode of the present invention.
  • the commodity shopping guide device in the new retail mode includes:
  • the location judgment module 10 is configured to obtain location information reported by a customer terminal, and determine whether the customer terminal is located in a target physical store according to the location information;
  • the customer information obtaining module 20 is configured to obtain the customer information reported by the customer terminal if the customer terminal is located in the target physical store;
  • the calculation module 30 is used to calculate the similarity between the customer information and the transactional customer information stored in the historical transaction records to obtain multiple similarity calculation results;
  • the detection module 40 is configured to determine a maximum similarity calculation result from the multiple similarity calculation results, and determine whether the maximum similarity calculation result is less than a preset threshold;
  • the prediction module 50 is configured to, if the maximum similarity calculation result is less than a preset threshold, input the customer information into a preset prediction model to obtain a product recommendation result for the customer information;
  • the push module 60 is configured to obtain the face image reported by the customer terminal, and send the face image and the product recommendation result to the sales terminal corresponding to the target physical store.
  • the specific embodiments of the merchandise shopping guide device in the new retail mode of the present invention are basically the same as the various embodiments of the merchandise shopping guide method in the new retail mode described above, and will not be repeated here.
  • the embodiment of the present invention also provides a computer-readable storage medium, the computer-readable storage medium stores a commodity shopping guide program in the new retail mode, and when the commodity shopping guide program in the new retail mode is executed by a processor The steps of each embodiment of the commodity shopping guide method in the above new retail mode are realized.
  • the technical solution of the present invention essentially or the part that contributes to the existing technology can be embodied in the form of a software product, and the computer software product is stored in a storage medium (such as ROM/RAM) as described above. , Magnetic disk, optical disk), including several instructions to make a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) execute the method described in each embodiment of the present invention.
  • a storage medium such as ROM/RAM
  • a terminal device which can be a mobile phone, a computer, a server, or a network device, etc.

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Abstract

La présente invention concerne un procédé et un appareil de guide d'achat dans un nouveau modèle de vente au détail, un dispositif, ainsi qu'un support de stockage. Ledit procédé consiste à : acquérir des informations de client ; calculer une similarité entre des informations de client et des informations concernant un client ayant effectué une transaction, de façon à obtenir une pluralité de similarités ; déterminer une similarité maximale parmi la pluralité de similarités, et lorsque la similarité maximale est inférieure à un seuil prédéfini, entrer les informations de client dans un modèle de prédiction prédéfini afin d'obtenir un résultat de recommandation de produit ; et acquérir une image faciale, et envoyer l'image faciale et le résultat de recommandation de produit à un terminal de vente.
PCT/CN2019/117714 2019-09-06 2019-11-12 Procédé et appareil de guide d'achat dans un nouveau modèle de vente au détail, dispositif et support de stockage WO2021042541A1 (fr)

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