A system for client complex health management strategy analysis and application

Abstract

The present application relates to the field of customer compound health management strategy analysis and application technology, and in particular to a customer compound health management system for the beauty industry, the main functions of which are embodied in strategy analysis algorithms and application algorithms, and the system is distributed in the following module units: a merchant APP module, a merchant management module, an information management unit, an order management unit, an after-sales service unit and a prediction unit, the information management unit, the order management unit, the prediction unit and the after-sales service unit are all located inside the merchant management module, and the merchant APP module is used for customers to place orders and understand the tracking of the orders. The present application can predict the next cycle time of the customer health management project, prompt the user to make a reasonable appointment time before and after the project purchased by the customer reaches the next cycle, place an order and make an appointment, quickly understand the progress of the order, facilitate the management of the merchant to the customer, also facilitate the customer to make an appointment for the project, and obtain the expected result.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of customer management, and specifically relates to a system for customer compound health management strategy analysis and application. BACKGROUND

[0002] The beauty industry, commonly referred to as the beauty industry, focuses on improving people's appearance, boosting self-confidence, and meeting people's pursuit of beauty.

[0003] The beauty industry includes: plastic surgery, which involves surgical and non-surgical methods. Surgical methods include face lifting, eye reshaping, nose reshaping, skin beautification, and injection beauty, etc.; non-surgical methods mainly involve beauty skin care, which focuses on skin care, including cleaning, moisturizing, anti-aging, and whitening, improving skin quality and appearance through various products and technologies such as masks, massages, exfoliation, and light therapy, beauty and hairdressing, which focuses on hairstyle and hair color design, including haircut, hair dyeing, hair perming, hair care, and styling, using various tools and technologies such as scissors, electric curling irons, chemical dyes, and styling products, to create beautiful hairstyles and hair colors, beauty nutrition, which focuses on the relationship between diet and nutrition and beauty, providing diet plans, nutritional supplements, and dietary guidance, etc. services, by improving physical health and eating habits, and then improving appearance.

[0004] In the skin beautification industry, regular management and maintenance of the skin are usually done by old customers, while new customers are usually attracted through activities. Therefore, old customers need to be managed and maintained.

[0005] The existing technology usually only manages old customers by making regular follow-ups to manage customer satisfaction, maintains old customers, so that old customers can consume again. However, the time when old customers want to place an order for skin beautification again is difficult to predict; when old customers place an order, the instant consumption demand of old customers is difficult to meet due to the presence of customers who are undergoing the same business; and after making a reservation, customers cannot understand the progress of their reservation status. SUMMARY

[0006] In view of the deficiencies of the prior art, the application provides a system for customer compound health management strategy analysis and application to solve the above technical problems.

[0007] To achieve the above purpose, the application provides the following technical scheme: a system for customer compound health management strategy analysis and application, comprising a merchant APP module, a merchant management module, an information management unit, an order management unit, an after-sales service unit, and a prediction unit. The information management unit, the order management unit, the after-sales service unit, and the prediction unit are located inside the merchant management module.

[0008] The merchant APP module is used for customers to place orders and understand the tracking of the orders;

[0009] The merchant management module and the merchant APP module are connected through a network to obtain the order information of the customers and timely process the orders of the customers;

[0010] The information management unit is used to manage the information of the customers;

[0011] The order management unit is used to feedback the order information of the customers;

[0012] The after-sales service unit is used to visit the customers after the service;

[0013] The prediction unit is used to predict the situation and cycle of the customers to place orders again to support the decision-making of the merchants.

[0014] Preferably, the merchant APP module enters the information when a new customer enters the store for service, and makes the new customer download and install the merchant APP. The entered information includes the name of the customer, the order item, the order time and date, and the order number.

[0015] Preferably, the information management unit obtains the information of the new customers through the merchant APP module and stores and saves the information in the information management unit. The information of the old customers is called out in real time through the saved items in the information management unit to understand the information of the old customers. The information of the old customers includes the number of orders, the order time, and the order item. The information management unit establishes a quick search function for the information of the customers by establishing a keyword matching and indexing method. The merchant can obtain the information of the customers by inputting the keywords.

[0016] Preferably, the order management unit obtains the order of the customers after the customers place orders through the merchant management module, determines and receives the order information, checks the order information, determines the equipment and supplies required by the merchant, and ensures the sufficiency of the materials.

[0017] The equipment includes a handheld beauty instrument. The handheld beauty instrument is used to help the customers beautify and remotely transmit the skin state of the customers to the intelligent center of the beauty salon. The intelligent center of the beauty salon automatically recommends the beauty items required by the current customers according to the uploaded skin state. The recommended beauty items can be displayed on the merchant APP and / or the handheld beauty instrument.

[0018] Preferably, the after-sales service unit delivers a feedback questionnaire through the merchant APP module. The merchant obtains the satisfaction degree of the customers for the service through the feedback questionnaire, collects the data, uploads the data to the prediction unit, and recommends related items for consultation for the customers through big data to attract the customers to further consume the service.

[0019] Preferably, a prediction model is established in the prediction unit, and the prediction model construction step collects period data of each item ordered by the merchant customer, uses a linear regression model as a model algorithm basis, substitutes the data set into the prediction model, trains the model using historical data, optimizes the model, trains the model using the gradient descent method, and evaluates the model by mean square error.

[0020] Preferably, the linear regression model is a multiple linear regression involving multiple independent variables and one dependent variable, the independent variables are different item conditions ordered by the user, and the dependent variable is the current item reordering condition. The formula is:

[0021] y = B0 + B1 * X1 + B2 * X2 + … Bn * Xn + e

[0022] Where n is the number of independent variables, y is the dependent variable, X1, X2, … Xn are independent variables, B0, B1, … Bn are regression coefficients, and e is an error term.

[0023] The gradient descent method finds the optimal parameter value of the model training by iteration. The iteration step selects an initial parameter as a starting point, calculates the gradient value in the initial parameter, updates the parameter according to the calculated gradient, and performs repeated iteration operations until the optimal training value is found. The gradient value calculation formula is:

[0024] Gr = ax (Au / Ax) + ay (Au / Ay) + az (Au / Az)

[0025] Where Gr is the gradient vector value of function r, ax, ay, and az are unit vectors of point (x, y, z), and Au / Ax, Au / Ay, and Au / Az are partial derivatives of point (x, y, z), i.e. the rate of change of a point on the coordinate axis.

[0026] Preferably, the mean square error formula is:

[0027] MSE = ∑ (c-a) ∧2 / n

[0028] Where ∑ represents summation, c is the predicted value, a is the true value, and n is the sample size. The formula measures the prediction error size index of the model.

[0029] Preferably, the data obtained by the information management unit and the order management unit is substituted into the prediction unit to predict the next period of the beauty project of the user. Through the predicted time value, the merchant pre-reserves the project time and makes thoughtful prompts to the user through the merchant APP module, prompting the user that the service project is about to reach the next period. The user independently orders or cancels the order according to the satisfaction of the inner service, and after the user orders, the order management unit processes it.

[0030] Preferably, after the merchant obtains the user order, the order progress is fed back in real time through the order management unit, and the order progress is displayed in real time through the merchant APP module, so that the order progress is quickly understood by opening the merchant APP module, and the order items are processed within a specified time.

[0031] Compared with the prior art, the application has the following beneficial effects:

[0032] The customer management system developed by the application can more conveniently and quickly manage customers, without the need for telephone reservation, and can predict the next cycle time of customer service items, prompt the user after the items purchased by the customer reach the next cycle, and perform order reservation processing, quickly understand the progress of the order, conveniently manage the user for the merchant, and also conveniently perform reservation processing of the items for the user, achieving a win-win situation. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 The customer management system block diagram of the application. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0035] The application provides a technical solution: a system for customer complex health management strategy analysis and application, comprising a merchant APP module, a merchant management module, an information management unit, an order management unit, an after-sales service unit and a prediction unit, the information management unit, the order management unit, the after-sales service unit and the prediction unit being located inside the merchant management module.

[0036] The merchant APP module is used for customers to place orders and understand the tracking of the orders.

[0037] The merchant management module and the merchant APP module are connected through a network to obtain the order information of the customers and timely process the orders of the customers.

[0038] The information management unit is used for managing the information of the customers.

[0039] The order management unit is used for feedback processing of the order information of the customers.

[0040] The after-sales service unit is used for follow-up processing of the customers after the service.

[0041] The prediction unit is used to predict the case and cycle of customer reordering, and to make decision support for the merchant;

[0042] The inconvenience of telephone reservation ordering includes:

[0043] Complicated process: The steps of reservation ordering may be too complex, requiring users to fill in a large amount of information or go through multiple page jumps; users may need to repeatedly enter the same information such as name, address, etc., resulting in a poor experience;

[0044] System instability: The reservation system may malfunction or crash, causing users to fail to successfully reserve or order, and network problems may cause the reservation process to be interrupted or fail;

[0045] Poor interface design: The design of the reservation interface may not be intuitive or easy to use, making it difficult for users to find the desired options or functions;

[0046] Limited time selection: The available time period may be limited, unable to meet the specific needs of users, and the adjustment of reservation time may not be flexible enough, resulting in users needing to try multiple times to find a suitable time;

[0047] Lack of real-time feedback: During the reservation or ordering process, the system may not provide real-time feedback or status updates, making it difficult for users to understand the progress of the reservation, and users may need to query the reservation status through other means, increasing the additional communication cost;

[0048] The customer management system developed by the present application can more conveniently and quickly manage customers, without the need for telephone reservation, and can predict the next cycle time of customer service projects. When the customer's purchased project reaches the next cycle, the user can be prompted to make a reservation and handle the order, quickly understand the progress of their order, facilitate the management of users by the merchant, and also facilitate the reservation and handling of projects by the user.

[0049] Further, the merchant APP module enters information when a new customer enters the store for service, and downloads and installs the merchant APP for the new customer. The entered information includes the customer's name, ordered project, order time and date, and order number.

[0050] Further, the information management unit obtains the information of the new customer through the merchant APP module and stores it, and the information of the old customer is retrieved in real time from the stored project in the information management unit to understand the information of the old customer, including the number of orders, order time, and ordered project. The information management unit establishes a quick search function for the customer's information by establishing a keyword matching and indexing method, and the merchant can obtain the customer's information by inputting a keyword;

[0051] The information management unit classifies, archives, organizes and stores the collected information to facilitate subsequent search and use, and stores the information in an electronic manner, such as cloud storage, a database, etc.

[0052] Further, the order management unit obtains the order placed by the customer through the merchant management module after the customer places an order, determines and receives the information of the order, checks the order information, determines the equipment and supplies required by the merchant, and ensures the sufficiency of the materials;

[0053] The equipment includes a handheld beauty instrument. The handheld beauty instrument not only helps the customer to beautify, but also remotely transmits the skin state of the customer to the intelligent center of the beauty salon, and automatically recommends the beauty project required by the current customer according to the uploaded skin state by the intelligent center. The recommended beauty project can be displayed on the merchant APP and / or the handheld beauty instrument.

[0054] Further, the after-sales service unit delivers a feedback questionnaire through the merchant APP module. The merchant obtains the satisfaction degree of the customer on the service through the feedback questionnaire, collects the data, uploads the data to the prediction unit, and recommends relevant project consultation for the customer through big data to attract the customer to further consume the service;

[0055] Further, a prediction model is established in the prediction unit. The prediction model construction step collects the cycle data of the customer of the merchant under each project order, takes a linear regression model as a model algorithm basis, substitutes the data set into the prediction model, trains the model by using the historical data, adjusts and optimizes the model, trains the model by using the gradient descent method, and evaluates the model by using the mean square error.

[0056] Further, the linear regression model is a multiple linear regression, which involves multiple independent variables and one dependent variable. The independent variables are different project conditions ordered by the user, and the dependent variable is the condition of reordering in the current project. The formula is:

[0057] y = B0 + B1 * X1 + B2 * X2 + … Bn * Xn + e

[0058] Where n is the number of independent variables, y is the dependent variable, X1, X2, …, Xn are independent variables, B0, B1, …, Bn are regression coefficients, and e is an error term.

[0059] Independent variable selection: selecting appropriate independent variables is one of the prerequisites for multiple regression prediction. The correlation matrix between variables is used for selection.

[0060] Data preprocessing: since the units of each independent variable may be different, the independent variables and the dependent variable need to be converted to a unified standard score for comparison of the size of the regression coefficient.

[0061] The multiple linear regression model assumes that there is a linear relationship between the independent variables and the dependent variable, and that the error term satisfies the conditions of normal distribution, zero mean and homoscedasticity.

[0062] The gradient descent method finds the optimal parameter value of the model training through iteration. The iteration step selects the initial parameter as the starting point, calculates the gradient value in the initial parameter, updates the parameter according to the calculated gradient, and performs repeated iteration operations until the optimal training value is found. The gradient value calculation formula is:

[0063] Gr=ax(Au / Ax)+ay(Au / Ay)+az(Au / Az)

[0064] Where Gr is the gradient vector value of function r, ax, ay and az are unit vectors of point (x, y, z), and Au / Ax, Au / Ay and Au / Az are partial derivatives of point (x, y, z), that is, the rate of change of a point on the coordinate axis.

[0065] Further, the mean square error formula is:

[0066] MSE=∑(c-a)∧2 / n

[0067] Where ∑ represents summation, c is the predicted value, a is the true value, and n is the sample size. The formula measures the prediction error size index of the model;

[0068] Through big data analysis technology, the purchase history and behavior data of customers are analyzed in depth, and through analyzing the customer's purchase mode, purchase frequency, purchase amount and other indicators, the customer's future purchase behavior and order possibility are predicted;

[0069] Establish a personalized recommendation system, recommend beauty products or services that may be interested to customers according to their historical purchase records, browsing behavior and preferences. This recommendation system can improve the purchase conversion rate, thereby predicting the customer's order behavior.

[0070] Further, through the data obtained by the information management unit and the order management unit, the data set is substituted into the prediction unit to predict the next cycle of beauty project for the user. Through the predicted time value, the merchant pre-reserves the project time and makes a thoughtful prompt to the user through the merchant APP module, prompting the user that the service project is about to reach the next cycle. The user makes an order or cancels the order according to the satisfaction of the service in the heart, and after the user places an order, the order management unit processes it.

[0071] Further, after the merchant acquires the user order, the order progress is fed back in real time through the order management unit, and the order progress is displayed in real time through the merchant APP module, so as to quickly understand the order progress by opening the merchant APP module, and the order items are processed within a specified time;

[0072] The items purchased by the user are a plurality of small items in a large item, and there is periodicity between the small items. The user only needs to purchase a large item, and after the current item is completed, the next item service is continued. At this time, the user needs to separately place an order for the item category, and can also choose to do different items in different merchants. The period predicted in the present application is the latter.

[0073] It should be noted that, in this document, the terms such as first and second are used merely to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment.

[0074] Although the embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A system for client complex health management policy analysis and application, characterized by, The system comprises a merchant APP module, a merchant management module, an information management unit, an order management unit, an after-sales service unit and a prediction unit, the information management unit, the order management unit, the after-sales service unit and the prediction unit are located in the merchant management module; The merchant APP module is used for customers to place orders and understand the tracking of the orders; the items purchased by the user are a plurality of small items in a large item, and there is periodicity between the small items; the user needs to place an order for each small item of the item category and select different beauty items in different merchants; The merchant management module and the merchant APP module are connected through a network to obtain the order situation of the customers and timely process the orders of the customers; The information management unit is used for managing the information of the customers; after each merchant obtains the order of the user, the order progress is fed back in real time through the order management unit, the order progress is displayed in real time through the merchant APP module, so that the order progress is quickly obtained by opening the merchant APP module, and the current beauty item order is processed within a specified time; The order management unit is used for feedback processing of the order information of the customers; after the customers place an order, the order taken by the customers is obtained through the merchant management module, the information of the order is determined and received, the order information is checked to determine the equipment and supplies required by the merchant, and the sufficiency of the materials is ensured; The after-sales service unit is used for follow-up processing of the customers after the service; The prediction unit is used for predicting the situation and period of the customers placing orders again to support the decision-making of the merchant; The data obtained through the information management unit and the order management unit is substituted into the prediction unit to predict the beauty item of the next period of the user, the merchant pre-reserves the item time through the predicted time value, and makes a thoughtful prompt to the user through the merchant APP module, prompting the user that the service item is about to reach the next period, the user independently places an order or cancels the order according to the satisfaction of the service in the heart, and after the user places an order, the order management unit processes it; A prediction model is established in the prediction unit, the prediction model is constructed by collecting the period data of each item order of the customers of the merchant, a linear regression model is used as the model algorithm basis, the data set is substituted into the prediction model, the model is trained by using the historical data to adjust and optimize the model, the gradient descent method is used to train the model, and the model is evaluated by the mean square error; The linear regression model is a multiple linear regression, which involves multiple independent variables and one dependent variable, the independent variables are different item conditions of the user placing an order, the dependent variable is the condition of placing an order again in the current item, and the formula is: Wherein n is the number of independent variables, y is the dependent variable, X1, X2……Xn are independent variables, B0, B1……Bn are regression coefficients, and e is an error term; The gradient descent method finds the optimal parameter value of the model by iteration, and the iteration step is to select an initial parameter as the starting point, calculate the gradient value in the initial parameter, update the parameter according to the calculated gradient, and repeat the iteration operation until the optimal training value is found, wherein the gradient value calculation formula is: Wherein Gr is the gradient vector value of the function r, ax, ay and az are unit vectors of the point (x, y, z), and Au / Ax, Au / Ay and Au / Az are partial derivatives of the point (x, y, z), that is, the change rate of a point on the coordinate axis.

2. The system for client complex health management policy analysis and application according to claim 1, characterized in that: The merchant APP module enters information when a new customer enters the store for service, and makes the new customer download and install the merchant APP. The entered information includes the customer's name, the ordered item, the order date and time, and the order number.

3. The system for client complex health management policy analysis and application according to claim 1, wherein: The information management unit obtains the information of the new customer through the merchant APP module and stores it. The information of the old customer is called out in real time through the saved items in the information management unit to understand the information of the old customer. The information of the old customer includes the number of orders, the order time and the order item. The information management unit establishes a quick search function for the customer's information by establishing a keyword matching and indexing method. The merchant can obtain the customer's information by inputting the keyword.

4. The system for client complex health management policy analysis and application according to claim 1, wherein: The equipment includes a handheld beauty instrument, which is used to help customers beautify and remotely transmit the skin state of the customers to the beauty salon intelligent center. The beauty salon intelligent center automatically recommends the current customer's beauty project according to the uploaded skin state.

5. The system for client complex health management policy analysis and application according to claim 1, wherein: The after-sales service unit delivers feedback questionnaires through the merchant APP module. The merchant obtains the customer's satisfaction with the service through the feedback questionnaire and collects the data and uploads it to the prediction unit. The after-sales service unit also recommends related project consultation for the customer through big data to attract the customer to further consume the service. The recommended related project is displayed on the merchant APP module for the customer to select and watch.

6. The system for client complex health management policy analysis and application according to claim 1, wherein, The mean square error formula is: wherein is expressed as a sum, c is the predicted value, a is the true value, n is the number of samples, and the evaluation of the prediction error size indicator of the model is measured by the formula

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