Shop intelligent analysis operation method and device based on big data, and storage medium

Abstract

The invention discloses a big data-based store intelligent analysis and operation method and device and a storage medium. The method comprises the following steps: obtaining user information entering a store page, screening a plurality of to-be-pushed products from a system, and pushing promotion link images of the plurality of to-be-pushed products to a user; when the user enters the page of the corresponding to-be-pushed product through the promotion link image, obtaining the product information of the corresponding to-be-pushed product, further obtaining the promotion copywriting of the corresponding to-be-pushed product from the system in a matching manner, and playing the promotion copywriting of the current product to the user in a popup manner; and when the user enters the live broadcast room through the product page of the corresponding to-be-pushed product, obtaining the corresponding to-be-pushed product and judging whether the corresponding to-be-pushed product is matched with the live broadcast product or not, and if not, replacing the live broadcast product with the corresponding to-be-pushed product so as to live broadcast the corresponding to-be-pushed product. According to the invention, intelligent and personalized pushing can be realized for the user, and the intelligence of shop operation is improved.

Description

Technical Field

[0001] This invention relates to store operations, and more particularly to a method, apparatus, and storage medium for intelligent store analysis and operation based on big data. Background Technology

[0002] Currently, store operations are mainly managed manually to achieve routine management tasks such as inventory management and daily maintenance. However, when it comes to product promotion, the focus is primarily on the subjective experience of operations personnel to analyze and promote the store's performance. This makes it impossible to achieve precise targeting of different users, timely and automatic delivery of various promotional notifications, or intelligent push notifications, resulting in issues such as low customer satisfaction and repurchase rates. Furthermore, relying on operations personnel for management also leads to a heavy workload and a high risk of errors. Summary of the Invention

[0003] In order to overcome the shortcomings of the existing technology, one of the objectives of this invention is to provide a store intelligent analysis and operation method based on big data, which can solve the problems of stores being unable to achieve intelligent and accurate push notifications in the existing technology.

[0004] The second objective of this invention is to provide a store intelligent analysis and operation device based on big data, which can solve the problems in the existing technology that stores cannot achieve intelligent and accurate push notifications.

[0005] The third objective of this invention is to provide a computer-readable storage medium that can solve the problems in the prior art where stores cannot achieve intelligent and accurate push notifications.

[0006] One of the objectives of this invention is achieved through the following technical solution:

[0007] A big data-based intelligent analysis and operation method for stores, comprising:

[0008] Product push steps: Obtain user information entering the store page, filter multiple products to be pushed from the system based on user information, and then overlay the promotional link images of multiple products to be pushed in sequence and display them to the user in a pop-up window;

[0009] Copywriting push steps: When a user enters the corresponding product page through the promotional link image, the product information of the corresponding product is obtained, and the promotional copy of the corresponding product is matched from the system and played to the user through a pop-up window.

[0010] Live Stream Push Steps: When a user enters the live stream room through the product page of the product to be pushed, the corresponding product to be pushed is retrieved and it is determined whether the product to be pushed matches the product currently being live-streamed. If not, the product currently being live-streamed is replaced with the corresponding product to be pushed in order to live stream the product to be pushed.

[0011] Furthermore, the personalized push step also includes: obtaining user information entering the store page and determining whether the user is a new user based on the user information; if so, obtaining the user's behavior data in the store in real time and constructing user profile information based on the user information; if not, matching user profile information from the system based on the user information and filtering multiple products to be pushed to the user based on the user profile information.

[0012] Furthermore, it also includes: New product push notification steps: When a user enters the store homepage, the store retrieves new products within a preset time period and displays the product page link of each new product to the user through a pop-up window based on the promotional copy of each new product.

[0013] Furthermore, the copywriting push step also includes: when the user browses the corresponding product to be pushed for a preset time, generating exclusive coupons and recommended pairing suggestions based on the user profile information and the corresponding product to be pushed, and pushing them to the user in the form of a pop-up window.

[0014] Furthermore, it also includes: the recall notification step: by statistically analyzing the time when each user last entered the store and when that time reaches a preset time, multiple recall products and discount information for each recall product are matched based on the user profile information, and a recall notification is generated, and the recall notification is pushed to the user's mobile terminal via SMS.

[0015] Furthermore, in the copywriting push step, when obtaining the product information of the corresponding product to be pushed and then matching the promotional copy of the corresponding product to be pushed from the system, the promotional copy of the corresponding product to be pushed is also modified according to user profile information and AI big model to generate a promotional copy that matches the user and the new promotional copy is played to the user through a pop-up window.

[0016] Furthermore, it also includes: statistical steps: by statistically analyzing all users within the system and classifying users according to their source, the correlation between the promotion platform and user traffic can be obtained based on user type, and then promotion suggestions can be provided based on the correlation between the promotion platform and user traffic.

[0017] The second objective of this invention is achieved by the following technical solution:

[0018] The big data-based intelligent store analysis and operation device includes a memory and a processor. The memory stores a store intelligent analysis and operation program that runs on the processor. The store intelligent analysis and operation program is a computer program. When the processor executes the store intelligent analysis and operation program, it implements the steps of the big data-based intelligent store analysis and operation method as one of the objectives of this invention.

[0019] The third objective of this invention is achieved by the following technical solution:

[0020] A computer-readable storage medium storing a store intelligent analysis and operation program thereon, the store intelligent analysis and operation program being a computer program, which, when executed by a processor, implements the steps of big data-based store intelligent analysis and operation as one of the objectives of this invention.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0022] This invention uses user profile information to provide timely personalized push notifications to users, including product recommendations and promotional copy, thereby improving user experience, increasing user satisfaction and product repurchase rate, achieving intelligent management of stores, and making store operations more efficient. Attached Figure Description

[0023] Figure 1 The flowchart of the intelligent store analysis and operation method based on big data provided by this invention. Detailed Implementation

[0024] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments. It should be noted that, without conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0025] Example 1

[0026] This invention enables intelligent management of stores and intelligent push notifications to users, thereby improving user satisfaction and repurchase rates. Specifically, this invention provides a preferred embodiment of a store intelligent analysis and operation method based on big data, such as... Figure 1 As shown, it includes:

[0027] Step S1: Obtain user information entering the store page, filter out multiple products to be pushed from the system based on the user information, and overlay the promotional link images of multiple products to be pushed in sequence and display them to the user in a pop-up window.

[0028] Specifically, when a user enters any page of the store, the system can filter out multiple products to be pushed to the user based on the user's information, and then overlay the promotional links of these products in a pop-up window. When the user clicks on the corresponding promotional link, they can be redirected to the relevant product page to view the product.

[0029] This invention combines user information to push product pages that users are interested in when entering a store, enabling users to quickly view products that interest them, achieving intelligent personalized recommendations, and improving user satisfaction and repurchase rate.

[0030] More specifically, when a user enters the store as a new user, their behavior data within the store is acquired in real time to construct a user profile, enabling intelligent recommendations for subsequent purchases. When a user enters the store as a existing user, user profile information is generated from the system based on the user's information, and then multiple products are selected for recommendation based on this profile. The user profile is a virtual representation built based on user data, used to describe the typical characteristics of a certain type of user. This invention constructs a user profile for each user by acquiring basic user information and integrating data based on store behavior characteristics, enabling personalized product recommendations. The basic user information includes fundamental dimensions such as age, occupation, gender, and region. User behavior characteristics include purchasing habits, types of products browsed, frequency of product browsing, duration of product browsing, time intervals between repeat purchases, purchase frequency, consumption preferences, and user reviews of purchased products. By integrating the aforementioned data and combining it with AI models and algorithms to analyze user information, a unique user ID is generated and stored in the system. When a user enters a store, their user profile is generated based on their information, and then corresponding products are matched and pushed to the user based on this profile. This invention achieves precise targeted product delivery through multi-dimensional tags based on user profiles, improving user satisfaction and repurchase rates. It also enhances user experience by providing personalized content based on user preferences.

[0031] Step S2: When a user enters the corresponding product page through the promotional link image, the system obtains the product information of the corresponding product to be promoted, then matches and obtains the promotional copy of the corresponding product to be promoted, and plays the promotional copy of the current product to the user through a pop-up window.

[0032] Specifically, when a user enters the corresponding product page through a promotional link image, the system retrieves the product information of the product to obtain the promotional copy for the product and pushes it to the user. This promotional copy includes promotional videos and images.

[0033] Furthermore, this invention can also match personalized promotional plans for users based on user profile information. Specifically, when obtaining the promotional copy for a product to be pushed, the invention further modifies the promotional copy for the corresponding product based on user profile information and an AI model to generate a promotional copy that matches the user, and then displays the new promotional copy to the user via a pop-up window. By combining user profile information to push promotional copy to users, this invention can meet user needs and achieve personalized push notifications. For example, some users prefer image-based promotions, while others prefer video-based promotions. By achieving personalized push notifications, user satisfaction is improved, and precise targeting is achieved.

[0034] Step S3: When a user enters the live broadcast room through the product page of the product to be pushed, obtain the product to be pushed and determine whether the product to be pushed matches the product currently being broadcast. If not, replace the product currently being broadcast with the product to be pushed to broadcast the product to be pushed.

[0035] Specifically, when a user enters the live stream based on the product page of the product to be promoted, the corresponding live stream will also be launched based on that product. Currently, store live streams generally have fixed times for product broadcasts, or some are automated 24 hours a day, but the products being broadcast are usually pre-set. When a user enters the live stream, the product being broadcast is not the one the user was browsing. The user then has to wait for the broadcast product to change, which often leads to the user quickly leaving the live stream.

[0036] Therefore, when this invention detects that a user enters the live stream room through a corresponding product to be pushed, it switches the live stream product according to the currently selected product to be pushed, thereby enabling a live stream of that product and stimulating the user's interest. This invention achieves personalized live streaming for users by switching live streams based on the products they are interested in; specifically, this can be achieved through pre-recorded live stream videos or by activating a live stream robot.

[0037] Preferably, when new products are launched in the store, recommendations for these new products are also implemented to users. That is, when a user enters the store's homepage, the store retrieves new products launched within a preset time period and displays a link to the product page of each new product via a pop-up window, based on the promotional copy for each product. This pushes new product information to users who enter the store's homepage.

[0038] Preferably, step S2 further includes: when the user browses the corresponding product to be pushed for a preset time, generating exclusive coupons and recommended pairing suggestions based on the user profile information and the corresponding product to be pushed, and pushing them to the user in the form of a pop-up window.

[0039] Specifically, this invention also increases user purchase rates by pushing exclusive coupons to different users. It also generates recommended product pairing suggestions based on the products being pushed to. For example, if a user is currently browsing children's calcium supplements, a vitamin D pairing suggestion can be pushed.

[0040] More preferably, the present invention further includes: a recall notification step: by statistically analyzing the time of each user's last visit to the store, and when that time reaches a preset time, matching multiple recall products and their respective discount information based on user profile information to generate a recall notification, and then pushing the recall notification to the user's mobile terminal via SMS. By generating recall notifications for users who have not shopped or visited the store for an extended period, and pushing products of interest to the user and the latest discount information to the user, the present invention aims to recall the user.

[0041] More preferably, this invention further classifies users by statistically analyzing all users within the system and categorizing them according to their source. Then, it mines the correlation between promotional platforms and user traffic based on user type, providing promotional suggestions based on this correlation. For example, if a user views a product's promotional page through a corresponding promotional platform, such as Douyin or Kuaishou, and then opens the corresponding store's app to view the product, the mobile terminal can identify which platform the user entered through. This allows for the classification of the user's source, and by mining the user source, the correlation between promotional platforms and user traffic can be extracted. This data can then be used as supporting data for store marketing promotions, enabling the development of better marketing strategies.

[0042] Example 2

[0043] A big data-based intelligent store analysis and operation device includes a memory and a processor. The memory stores an intelligent store analysis and operation program that runs on the processor. The intelligent store analysis and operation program is a computer program. When the processor executes the intelligent store analysis and operation program, it performs the following steps:

[0044] Product push steps: Obtain user information entering the store page, filter multiple products to be pushed from the system based on user information, and then overlay the promotional link images of multiple products to be pushed in sequence and display them to the user in a pop-up window;

[0045] Copywriting push steps: When a user enters the corresponding product page through the promotional link image, the product information of the corresponding product is obtained, and the corresponding promotional copy of the product is matched from the system and played to the user through a pop-up window.

[0046] Live Stream Push Steps: When a user enters the live stream room through the product page of the product to be pushed, the corresponding product to be pushed is retrieved and it is determined whether the product to be pushed matches the product currently being live-streamed. If not, the product currently being live-streamed is replaced with the corresponding product to be pushed in order to live stream the product to be pushed.

[0047] Furthermore, the personalized push step also includes: obtaining user information entering the store page and determining whether the user is a new user based on the user information; if so, obtaining the user's behavior data in the store in real time and constructing user profile information based on the user information; if not, matching user profile information from the system based on the user information and filtering multiple products to be pushed to the user based on the user profile information.

[0048] Furthermore, it also includes: New product push notification steps: When a user enters the store homepage, the store retrieves new products within a preset time period and displays the product page link of each new product to the user through a pop-up window based on the promotional copy of each new product.

[0049] Furthermore, the copywriting push step also includes: when the user browses the corresponding product to be pushed for a preset time, generating exclusive coupons and recommended pairing suggestions based on the user profile information and the corresponding product to be pushed, and pushing them to the user in the form of a pop-up window.

[0050] Furthermore, it also includes: the recall notification step: by statistically analyzing the time when each user last entered the store and when that time reaches a preset time, multiple recall products and discount information for each recall product are matched based on the user profile information, and a recall notification is generated, and the recall notification is pushed to the user's mobile terminal via SMS.

[0051] Furthermore, in the copywriting push step, when obtaining the product information of the corresponding product to be pushed and then matching the promotional copy of the corresponding product to be pushed from the system, the promotional copy of the corresponding product to be pushed is also modified according to user profile information and AI big model to generate a promotional copy that matches the user and the new promotional copy is played to the user through a pop-up window.

[0052] Furthermore, it also includes: statistical steps: by statistically analyzing all users within the system and classifying users according to their source, the correlation between the promotion platform and user traffic can be obtained based on user type, and then promotion suggestions can be provided based on the correlation between the promotion platform and user traffic.

[0053] Example 3

[0054] A computer-readable storage medium storing a store intelligent analysis and operation program thereon, the store intelligent analysis and operation program being a computer program, which, when executed by a processor, performs the following steps:

[0055] Product push steps: Obtain user information entering the store page, filter multiple products to be pushed from the system based on user information, and then overlay the promotional link images of multiple products to be pushed in sequence and display them to the user in a pop-up window;

[0056] Copywriting push steps: When a user enters the corresponding product page through the promotional link image, the product information of the corresponding product is obtained, and the corresponding promotional copy of the product is matched from the system and played to the user through a pop-up window.

[0057] Live Stream Push Steps: When a user enters the live stream room through the product page of the product to be pushed, the corresponding product to be pushed is retrieved and it is determined whether the product to be pushed matches the product currently being live-streamed. If not, the product currently being live-streamed is replaced with the corresponding product to be pushed in order to live stream the product to be pushed.

[0058] Furthermore, the personalized push step also includes: obtaining user information entering the store page and determining whether the user is a new user based on the user information; if so, obtaining the user's behavior data in the store in real time and constructing user profile information based on the user information; if not, matching user profile information from the system based on the user information and filtering multiple products to be pushed to the user based on the user profile information.

[0059] Furthermore, it also includes: New product push notification steps: When a user enters the store homepage, the store retrieves new products within a preset time period and displays the product page link of each new product to the user through a pop-up window based on the promotional copy of each new product.

[0060] Furthermore, the copywriting push step also includes: when the user browses the corresponding product to be pushed for a preset time, generating exclusive coupons and recommended pairing suggestions based on the user profile information and the corresponding product to be pushed, and pushing them to the user in the form of a pop-up window.

[0061] Furthermore, it also includes: the recall notification step: by statistically analyzing the time when each user last entered the store and when that time reaches a preset time, multiple recall products and discount information for each recall product are matched based on the user profile information, and a recall notification is generated, and the recall notification is pushed to the user's mobile terminal via SMS.

[0062] Furthermore, in the copywriting push step, when obtaining the product information of the corresponding product to be pushed and then matching the promotional copy of the corresponding product to be pushed from the system, the promotional copy of the corresponding product to be pushed is also modified according to user profile information and AI big model to generate a promotional copy that matches the user and the new promotional copy is played to the user through a pop-up window.

[0063] Furthermore, it also includes: statistical steps: by statistically analyzing all users within the system and classifying users according to their source, the correlation between the promotion platform and user traffic can be obtained based on user type, and then promotion suggestions can be provided based on the correlation between the promotion platform and user traffic.

[0064] The above embodiments are merely preferred embodiments of the present invention and should not be construed as limiting the scope of protection of the present invention. Any non-substantial changes and substitutions made by those skilled in the art based on the present invention shall fall within the scope of protection claimed by the present invention.

Claims

1. A shop intelligent analysis operation method based on big data, characterized in that, The shop intelligent analysis operation method comprises: A product pushing step: obtaining user information entering a shop page and screening a plurality of to-be-pushed products from the system according to the user information, and sequentially superimposing promotion link images of the plurality of to-be-pushed products and displaying the promotion link images to the user in the form of a pop-up window; A copy pushing step: when the user enters a corresponding to-be-pushed product page through the promotion link image, obtaining product information of the corresponding to-be-pushed product, and then matching promotion copy of the corresponding to-be-pushed product from the system and playing the promotion copy of the current product to the user in the form of a pop-up window; A live broadcast pushing step: when the user enters a live broadcast room through the product page of the corresponding to-be-pushed product, obtaining the corresponding to-be-pushed product and judging whether the corresponding to-be-pushed product matches a product being live broadcasted, if not, replacing the product being live broadcasted with the corresponding to-be-pushed product to live broadcast the corresponding to-be-pushed product. 2.The big data-based store intelligence analysis operation method of claim 1, wherein, The personalized pushing step further comprises: obtaining user information entering a shop page and judging whether the user is a new user according to the user information, if yes, obtaining behavior data of the user in the shop in real time and constructing user portrait information according to the user information; if not, matching user portrait information from the system according to the user information and screening a plurality of to-be-pushed products for the user according to the user portrait information. 3.The big data-based store intelligence analysis operation method of claim 1, wherein, Further comprising: A new product pushing step: when the user enters a shop home page, obtaining new products in a shop within a preset time period and showing product page links of each new product to the user in the form of a pop-up window according to promotion copy of each new product. 4.The big data-based store intelligence analysis operation method of claim 1, wherein, The copy pushing step further comprises: when the user browses the corresponding to-be-pushed product for a time reaching a preset time, generating an exclusive coupon and a recommended matching suggestion according to the user portrait information and the corresponding to-be-pushed product and pushing them to the user in the form of a pop-up window. 5.The big data-based store intelligence analysis operation method of claim 1, wherein, Further comprising: A recall notification step: by counting a time of the last time each user enters a shop and when the time reaches a preset time, matching a plurality of recall products and discount information of each recall product according to user portrait information to generate a recall notification, and pushing the recall notification to a mobile terminal of the user in the form of a short message. 6.The big data-based store intelligence analysis operation method of claim 1, wherein, In the copy pushing step, when obtaining product information of the corresponding to-be-pushed product and then matching promotion copy of the corresponding to-be-pushed product from the system, the promotion copy of the corresponding to-be-pushed product is also corrected according to the user portrait information and an AI large model to generate promotion copy matched with the user and play the new promotion copy to the user in the form of a pop-up window. 7.The big data-based store intelligence analysis operation method of claim 1, wherein, Further comprising: A statistical step: by counting all users in the system and classifying the users according to user sources, then mining an association relationship between a promotion platform and user flow according to the user types, and then providing promotion suggestions for marketing promotion according to the association relationship between the promotion platform and the user flow. 8.A shop intelligent analysis operation device based on big data, comprising a memory and a processor, characterized in that, The memory has stored thereon a shop intelligent analysis operation program running on the processor, the shop intelligent analysis operation program is a computer program, and the processor implements the steps of the shop intelligent analysis operation method based on big data as claimed in any one of claims 1-7 when executing the shop intelligent analysis operation program.

9. A computer-readable storage medium having stored thereon a store intelligence analysis operation program, characterized by, The shop intelligent analysis operation program is a computer program, and the shop intelligent analysis operation program realizes the steps of the shop intelligent analysis operation based on big data when the processor executes the shop intelligent analysis operation program.

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