Customer profitability evaluation method based on shop management system

Abstract

The invention discloses a customer profitability evaluation method based on a shop management system. The method comprises the following steps: S1, demand analysis and index determination; according to the method, cigarette operation profit guidance is converted into full-shop profit guidance, diversified profit guidance requirements of customers need to be met, higher requirements are put forward for profit guidance ability of customer specialists, the customer profit guidance strategy library focuses on two aspects of customer information data construction and profit guidance strategy selection, and the customer profit guidance strategy library can be used for profit guidance of customers. Aiming at the key, urgent and common problems that customer information data is not comprehensive, customer tags are not established, profit guidance strategies are not rich, content measures are simple, strategy selection is not accurate, customer requirements are not met and the like, customer operation data are analyzed to accurately find customer requirements, and a profit guidance service strategy is intelligently matched; the effects of intelligent decision making, accurate decision making and human-based decision making are achieved on the customer profit guidance service, the customer profit guidance work quality and efficiency can be improved, and the customer satisfaction degree is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of store management systems, and in particular to a method for evaluating customer profitability based on a store management system. Background Art

[0002] Under the monopoly system, tobacco products, as special fast-moving consumer goods, require short and wide sales channels. Therefore, the tobacco industry needs a large number of retail terminals to achieve consumption. As a key channel connecting tobacco product manufacturers and retail terminals, tobacco companies directly affect the market circulation of tobacco products. They must maintain a good customer relationship and firmly control the retail channels. This is related to the survival space of tobacco companies and should not be underestimated. At present, the retail business channels of the tobacco industry are mainly divided into three types: direct retail terminals, retail chain terminals and individual business terminals, most of which are individual business terminals. At present, retail terminals such as individual business terminals are suffering from the combined impact of multiple factors such as the epidemic and rising rents, and their operating difficulties are becoming increasingly prominent.

[0003] However, due to the small increase in cigarette supply, the incremental demand for cigarettes cannot be effectively met for a long time, the growth rate of cigarette gross profit has slowed down, and customer profitability faces major challenges. The tight supply of cigarettes has led to the failure to effectively exert the drainage effect of cigarettes. At the same time, there are prominent problems such as "supply does not meet demand and oversupply" in the short term, which has led to a continuous decline in customer satisfaction with cigarette supply. In addition, with the continuous impact of e-commerce companies, retail chain companies and non-tobacco products, the tobacco industry's control over retail channels is declining. Judging from the results of the 2021 customer satisfaction survey, retail customers are less satisfied with the profit guidance services provided by tobacco companies. The goal of customer profit guidance services is to meet the actual needs of customers' operations and help customers achieve profitability. However, in actual operations, the effect of customer profit guidance services is poor. This situation is more common among rural power grid customers and low-end customers. There is not much change before and after customer specialists carry out profit guidance services, and customer needs are not met.

[0004] In summary, it is necessary to propose an evaluation method of customer profitability based on store management system to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a method for evaluating customer profitability based on a store management system to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for evaluating customer profitability based on a store management system, the method being implemented based on the store management system and comprising the following steps:

[0008] S1. Demand analysis and indicator determination;

[0009] S2. Data collection and collation: collect customer-related data through the store management system, including order information, product sales, and customer consumption records;

[0010] S3. Data analysis and indicator calculation: calculate customer sales, profit contribution, purchase frequency, average order value and other indicators according to demand;

[0011] S4. Profitability evaluation model establishment: Based on the calculated indicators, a customer profitability evaluation model is established;

[0012] S5. Model verification and adjustment: verify the established profitability evaluation model and check the rationality and accuracy of the model;

[0013] S6. Result analysis and visualization: Analyze the evaluation results, identify the customer's profitability and its influencing factors, and present the evaluation results in a visual form;

[0014] S7. Develop improvement measures and strategies. According to the evaluation results, develop improvement measures and marketing strategies for different customers;

[0015] S8. Continuously monitor and optimize, and adjust and optimize store management strategies in a timely manner based on monitoring results.

[0016] Based on the above method, the present invention also proposes a store management system, which is based on the VUE front-end framework and uses HTML5, CSS, and JS Web technologies to build a user interface for interacting with users, displaying data, and receiving user input. It also uses the Spring back-end framework, which is composed of Spring MVC and Spring Boot components to process business logic and data persistence operations. The system is characterized in that it includes a demand analysis module, an environment preparation module, a project creation module, a back-end development module, a front-end development module, a configuration test module, a build deployment module, and an operation and debugging module.

[0017] Preferably, the requirement analysis module is used to determine the specific functions that the system needs to implement, including interface design, function implementation methods, and create a detailed requirement map to provide guidance for subsequent development.

[0018] Preferably, the environment preparation module is used to install and configure JDK, Web server and IDE, set up Spring framework, including modules such as Spring Boot, Spring MVC and Spring Data, and prepare for the development environment of the project.

[0019] Preferably, the project creation module generates the basic structure of the SpringBoot project using tools provided by Spring Initializr or IDE, and adds necessary dependency libraries to establish a project skeleton.

[0020] Preferably, the back-end development module is used to write controller classes to process HTTP requests, implement business logic service classes, ensure the readability, maintainability and scalability of the code, and follow best practices and design patterns.

[0021] Preferably, the front-end development module is used to create a VUE project, use HTML5, CSS, and JS to build the basic structure and style of the user interface, and build an intuitive, easy-to-use and beautiful user interface.

[0022] Preferably, the configuration test module is used to configure the properties of the application according to project requirements, write and run unit tests and integration tests to verify the correctness of the code, and ensure the collaborative work between different components.

[0023] Preferably, the building and deployment module uses Maven or Gradle building tools to build the project, generate executable WAR or JAR files, and deploy these files to a Web server to configure the server to run the application.

[0024] Preferably, the operation and debugging module accesses the application through a browser to perform functional testing and debugging, pays attention to user feedback and performance indicators, and promptly discovers and solves potential problems to ensure the stability and reliability of the system.

[0025] Compared with the prior art, the beneficial effects of the present invention are: the present invention shifts from profit guidance for cigarette business to profit guidance for the entire store, which needs to meet the diverse profit guidance needs of customers and puts forward higher requirements on the profit guidance capabilities of customer specialists. The customer profit guidance strategy library focuses on two aspects: customer information data construction and profit guidance strategy selection. It targets key, urgent and common problems such as incomplete customer information data, unestablished customer tags, insufficient profit guidance strategies, simple content measures, inaccurate strategy selection, and unmet customer needs. By analyzing customer business data to identify customer needs, and intelligently matching profit guidance service strategies, the present invention achieves smart decision-making, accurate decision-making, and decision-making based on people in customer profit guidance services, which helps to improve the quality and efficiency of customer profit guidance work and improve customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 Shows a topological diagram of the store management system of the present invention;

[0027] Figure 2 A flow chart of the method for evaluating customer profitability of the present invention is shown;

[0028] Figure 3 The development process of the JavaScript-based front-end framework of the present invention is shown;

[0029] Figure 4 The weight calculation process of the hierarchical analysis method using matlab is shown in the present invention;

[0030] Figure 5 The data security algorithm design diagram of the present invention is shown;

[0031] Figure 6 The indicator system diagram of the "SW-six-dimensional capability analysis model" of the present invention is shown;

[0032] Figure 7 The profit guidance service strategy library architecture diagram of the present invention is shown. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] Example 1

[0035] See also Figure 1 The present invention proposes a store management system, which is based on the VUE front-end framework and uses HTML5, CSS, and JS Web technologies to build a user interface for interacting with users, displaying data, and receiving user input. It also uses the Spring back-end framework, which is composed of Spring MVC and Spring Boot components to process business logic and data persistence operations. The system is characterized by including a demand analysis module, an environment preparation module, a project creation module, a back-end development module, a front-end development module, a configuration test module, a build deployment module, and an operation and debugging module.

[0036] In this embodiment, it should also be noted that the requirement analysis module is used to determine the specific functions that the system needs to implement, including interface design, function implementation methods, and create a detailed requirement map to provide guidance for subsequent development.

[0037] In this embodiment, it should also be noted that the environment preparation module is used to install and configure JDK, Web server and IDE, set up the Spring framework, including modules such as Spring Boot, Spring MVC and Spring Data, and prepare for the project development environment.

[0038] In this embodiment, it should also be noted that the project creation module uses the tools provided by Spring Initializr or IDE to generate the basic structure of the Spring Boot project and add necessary dependency libraries to establish the project skeleton.

[0039] In this embodiment, it should also be noted that the backend development module is used to write controller classes to process HTTP requests, implement business logic service classes, ensure the readability, maintainability and scalability of the code, and follow best practices and design patterns.

[0040] In this embodiment, it should also be noted that the front-end development module is used to create a VUE project, use HTML5, CSS, and JS to build the basic structure and style of the user interface, and build an intuitive, easy-to-use and beautiful user interface.

[0041] In this embodiment, it should also be noted that the configuration test module is used to configure the properties of the application according to project requirements, write and run unit tests and integration tests to verify the correctness of the code, and ensure the collaborative work between different components.

[0042] In this embodiment, it should also be noted that the build and deployment module uses Maven or Gradle build tools to build the project, generate executable WAR or JAR files, and deploy these files to the Web server to configure the server to run the application.

[0043] In this embodiment, it should also be noted that the running debugging module accesses the application through a browser to perform functional testing and debugging, pays attention to user feedback and performance indicators, and promptly discovers and solves potential problems to ensure the stability and reliability of the system.

[0044] Example 2

[0045] See also Figure 2 ,Based on the above system, the customer profitability evaluation method includes the following steps:

[0046] S1. Demand analysis and indicator determination;

[0047] S2. Data collection and collation: collect customer-related data through the store management system, including order information, product sales, and customer consumption records;

[0048] S3. Data analysis and indicator calculation: calculate customer sales, profit contribution, purchase frequency, average order value and other indicators according to demand;

[0049] S4. Profitability evaluation model establishment: Based on the calculated indicators, a customer profitability evaluation model is established;

[0050] S5. Model verification and adjustment: verify the established profitability evaluation model and check the rationality and accuracy of the model;

[0051] S6. Result analysis and visualization: Analyze the evaluation results, identify the customer's profitability and its influencing factors, and present the evaluation results in a visual form;

[0052] S7. Develop improvement measures and strategies. According to the evaluation results, develop improvement measures and marketing strategies for different customers;

[0053] S8. Continuously monitor and optimize, and adjust and optimize store management strategies in a timely manner based on monitoring results.

[0054] Example 3

[0055] See also Figures 3 to 5 ,In the actual application of customer profitability evaluation, the specific implementation process of the above system is:

[0056] 1. Architecture process design:

[0057] See also Figure 3 ,The store management system of the present invention adopts a Web+Spring-based architecture, and the front-end adopts a VUE framework, including HTML5, CSS, JS Web technologies, which are responsible for interacting with users, displaying data and receiving user input;

[0058] The backend uses the Spring framework, including Spring MVC, Spring Boot and other components, which is responsible for processing business logic, data persistence and other operations;

[0059] The front and back ends communicate through the HTTPS protocol to achieve data transmission and exchange;

[0060] In order to build an efficient, stable and scalable system based on the Web and Spring framework, a series of orderly and interrelated development steps were taken;

[0061] The development process is as follows;

[0062] (1) Requirements analysis: clarify the specific functions that the web program needs to achieve, such as interface design and function implementation methods, and create a detailed requirements map;

[0063] (2) Prepare the environment, install and configure JDK, web server, and IDE, and set up the Spring framework, including SpringBoot, Spring MVC, and Spring Data modules;

[0064] (3) Create a Spring project and use the tools provided by Spring Initializr or IDE to generate the basic structure of the Spring Boot project. Add necessary dependency libraries such as Spring Web and database connection driver according to project requirements to establish a stable and scalable project skeleton.

[0065] (4) Back-end development: Based on the readability, maintainability and scalability of the code, while following best practices and design patterns, write controller classes to handle HTTP requests and implement business logic service classes according to the results of demand analysis;

[0066] (5) Front-end development, creating VUE projects, using HTML5, CSS, and JS to build the basic structure and style of the user interface, and building an intuitive, easy-to-use, and beautiful user interface;

[0067] (6) Configuration and testing: configure application properties according to project requirements, such as database connection information, server port, etc., write and run unit tests to verify the correctness of the code, and perform integration tests to ensure the collaboration between different components;

[0068] (7) Build and deploy: Use build tools such as Maven or Gradle to build the project and generate executable WAR or JAR files;

[0069] We will then deploy these files to a web server and configure the server to run our application.

[0070] (8) Operation and debugging: access the application through a browser and perform functional testing and debugging, pay close attention to user feedback and performance indicators, and promptly identify and resolve potential problems;

[0071] 2. Data processing method:

[0072] 2.1. Construction of OLAP database based on MPP architecture

[0073] To ensure the efficiency, accuracy and scalability of data processing, this system uses DBMS (database management system) to build an OLAP (online analytical processing) database based on MPP (massive parallel processing) architecture;

[0074] The MPP-based OLAP database can efficiently store, manage, and query massive amounts of structured data with its high-performance parallel processing capabilities and data warehouse functions.

[0075] By distributing data on multiple nodes and using parallel processing technology, complex data analysis queries can be quickly executed, providing multi-dimensional analysis and aggregation operations on data;

[0076] This system then introduces Python, a flexible and feature-rich programming language with numerous data processing and analysis libraries, including NumPy or Pandas, which can easily perform data cleaning, conversion, feature extraction and other operations to achieve mutual optimization with the database. The steps are as follows:

[0077] (1) Data export: First, export the data files to be processed in CSV, JSON, etc. formats from the OLAP database;

[0078] (2) Data loading: Then, the exported data is loaded into the Python computing engine, and the data is loaded, converted, and processed using libraries such as Pandas;

[0079] This process can be flexibly adjusted according to specific data processing requirements, including data cleaning, missing value filling and other operations;

[0080] (3) Data processing and analysis: In the Python computing engine, various algorithms and models are used to conduct in-depth statistical analysis of data;

[0081] (4) Result writing: Finally, the processed result data is written back to the OLAP database for further data analysis and visualization;

[0082] 2.2. Application of Analytical Hierarchy Process (AHP)

[0083] See also Figure 4 , the analytic hierarchy process is to decompose the decision problem into different hierarchical structures in the order of the overall goal, each level of sub-goals, evaluation criteria and specific alternative plans, and then use the method of solving the eigenvector of the judgment matrix to obtain the priority weight of each element of each level to an element of the previous level, and finally recursively merge the final weight of each alternative plan to the overall goal by the weighted sum method. The one with the largest final weight is the optimal plan;

[0084] When using the analytic hierarchy process to process data, this study strictly followed its principles and application steps;

[0085] First, the goals, criteria, and solutions of the decision-making problem were clarified, and a hierarchical model was constructed;

[0086] Subsequently, the judgment matrix was efficiently processed using Matlab software, and the rationality and reliability of the data were ensured through consistency checks;

[0087] On this basis, the weights of the profit evaluation model indicators are accurately calculated, providing a scientific basis for decision-making;

[0088] In the whole process, the application of Matlab software greatly improved the accuracy and efficiency of calculation, and the hierarchical analysis method has great practical value;

[0089] 3. Data security design:

[0090] See also Figure 5 In terms of application integration, this system forms REST API by abstracting and encapsulating business logic, providing service capabilities for internal and external applications of the system, and uniformly managing services through the service management platform to improve the flexibility of service provision, and improve the security of third-party calls through key distribution;

[0091] The data synchronization process uses https to prevent data from being stolen or changed during transmission, ensuring data integrity.

[0092] Example 4

[0093] See also Figure 6 and Figure 7 ,In the actual application of customer profitability evaluation, the specific implementation process of the above method is as follows:

[0094] 1.SW-Six-Dimensional Capability Analysis Model

[0095] 1.1. Indicator content setting

[0096] Set "people, goods, and venues" as the first-level indicators, and slice the dimensions according to the internal and external elements of the customer's business. Focus on the customer themselves and consumer management to subdivide the "people" indicator into basic development capabilities and data operation capabilities. Focus on the types of goods to subdivide the "goods" indicator into cigarette sales capabilities and non-tobacco sales capabilities. Focus on store conditions and surrounding environments to refine the "venue" indicator into terminal development capabilities and business district development capabilities. The six-dimensional capability indicators of customers are obtained and set as second-level indicators;

[0097] According to the composition of factors and historical data, the six secondary indicators are further sliced ​​into dimensions, and 34 tertiary indicators such as age, business format, and store investment are set to build the "SW-six-dimensional capability analysis model" indicator system (see Figure 6 );

[0098] Based on whether the indicators can be changed through guidance services, they are divided into 25 variable indicators and 9 immutable indicators;

[0099] The three indicators constitute the customer six-dimensional capability indicator library (see Table 1). The three-level indicator relationship presents a logical tree structure. The third-level indicators are the basic indicators and data fields of the "SW-six-dimensional capability analysis model". The customer information database is built around the third-level indicators for framework construction and data collection, providing data sources for the analysis model.

[0100] Table 1 Index library of "SW-six-dimensional capability analysis model"

[0101]

[0102]

[0103] 1.2. Index score weight

[0104] In order to comprehensively and scientifically analyze the client's operating capabilities, the "SW-Six-Dimension Capability Analysis Model" takes the client itself and its position into consideration, and uses the client's secondary indicator score and city ranking as the main quantitative conclusions, so it is necessary to establish specific scoring rules;

[0105] The third-level indicators are scored on a 10-point scale, with fixed situations and value ranges set on the 10 score values. The indicators are assigned to the corresponding specific data in the customer information database and the scores are determined;

[0106] Secondary indicators are also scored on a 10-point scale. The actual scores of the third-level indicators are calculated separately according to their respective weights, and the corresponding secondary indicator scores are summarized. The six secondary indicators of the customer are ranked citywide.

[0107] In this way, we can obtain the customer's scores of various third-level indicators, second-level indicators and city rankings, and thus fully understand the customer's actual business situation and its position in the city;

[0108] The focus of this set of scoring rules is on the weighting of the three-level indicators;

[0109] The weight factor analysis method is used to assign the weights of the three-level indicators. An expert evaluation team is formed to conduct a one-to-one comparison of the three-level indicators to assign the indicator weights.

[0110] The first step is to form an evaluation team, which includes six experts from inside and outside the industry, including an account specialist with more than 10 years of work experience, a marketing manager, a marketing department head, a cigarette retail customer with more than 15 years of operating experience, and a university business school professor;

[0111] The second step is for the experts to fill in the weight factor judgment table (see Table 2), compare and score the row and column indicators based on their relative importance, and there are a total of C(n,m)=A(n,m) / A(m,m)=n! / m!×(nm)! comparison combinations, and the maximum score difference is 4 points, and the total score is 4*C(n,m);

[0112] For example, if indicator 1 is compared with indicator 2, indicator 1 is very important and gets 4 points, relatively important and gets 3 points, and so on until it gets 0 points;

[0113] The third step is to count the results filled in by the experts, fill in the weight factor calculation statistics table (see Table 3), and convert the results into the weight of each indicator;

[0114] In this way, we can get the weights of all the third-level indicators in the corresponding second-level indicators;

[0115] Table 2 Weight factor judgment table (taking the secondary indicator "terminal development power" as an example)

[0116]

[0117] Table 3 Weight factor calculation statistics (taking the third-level indicator "terminal development power" as an example)

[0118]

[0119]

[0120] 1.3. Analysis results presentation

[0121] After the indicator content and indicator score weights are clarified, the customer's various indicator conditions will be quantified, but only the quantitative results of each customer indicator will be obtained, not the final analysis results;

[0122] The analysis results should meet the purpose of the analysis, that is, to find the strengths and weaknesses of the customer, and provide a basis for the subsequent formulation of the strategy library and the promotion of "SW" type strategies;

[0123] Therefore, the analysis results of the "SW-Six-Dimension Capability Analysis Model" should present the customer's strengths and weaknesses indicators (secondary indicators);

[0124] Therefore, it is necessary to clarify the criteria for judging the advantages and disadvantages of the customer's secondary indicators;

[0125] The customer disadvantage secondary indicator is generated by the internal and external comparison method, that is, the secondary indicator is ranked by comparing the customers in the whole city according to the secondary indicator score, and then the ranking results of each secondary indicator are compared with each other, and the second-level indicator with the lowest ranking is selected as the customer's disadvantage secondary indicator. Since the three-level indicators under the secondary indicator "business district development power" are all immutable indicators and have no operability, this indicator is not considered in the disadvantage secondary indicator;

[0126] Considering operability and indicator value, we selected the top 50% secondary indicators in the city as the customer's advantage indicators;

[0127] Since customer disadvantage indicators are the main focus of customer profit guidance and are also the explicit needs of customers, they must be detailed, specific, operational, and clear in direction. If guidance services are only based on customer secondary indicators, problems such as insufficient demand exploration, unfocused service, and inaccurate strategy content will arise.

[0128] Therefore, the customer's disadvantage indicators should be lowered from secondary indicators to tertiary indicators to facilitate the subsequent development of profit guidance services;

[0129] The customer disadvantage third-level indicator is generated by the indicator association method and the exhaustive method, that is, the third-level indicator with the smallest score and the most variable is selected from the subordinate third-level indicators of the disadvantage second-level indicator as the preferred disadvantage third-level indicator; the third-level indicator with the smallest score and the most variable is selected from the remaining third-level indicators (not under the disadvantage second-level indicator) as the alternative disadvantage third-level indicator;

[0130] In terms of presentation, radar charts are used to present the scores of secondary indicators, line charts are used to present the rankings, and the disadvantaged secondary indicators are marked; charts are used to present the scores of tertiary indicators, with the rankings at the top and the preferred and alternative disadvantaged tertiary indicators marked;

[0131] This presentation method not only makes the disadvantage indicators clear at a glance, but also makes it easy to check the scores of other indicators of customers, quickly grasp the customer's optimal explicit needs, and find the right direction for profit guidance services;

[0132] 2. Secondary deformation strategy formulation rules:

[0133] By analyzing the 34 third-level indicator data of customers through the "SW-Six-Dimensional Capability Analysis Model", 34 types of third-level indicators of disadvantages can be obtained in theory, of which 25 types of third-level indicators of disadvantages are variable;

[0134] These 25 variable types of disadvantage level 3 indicators are the 25 explicit needs of current customers. The profit guidance service strategy should focus on these 25 explicit needs. In theory, 25 service strategies can be generated, and each service strategy corresponds to a type of disadvantage level 3 indicator. However, this correspondence is only based on a simple "one-to-one" association, and does not take into account the degree of difference in customers' disadvantage level 3 indicators and whether they have advantage indicators, which cannot meet customers' differentiated service needs.

[0135] For example, the disadvantage level 3 indicators of customers A and B are both "level" indicators, and their explicit needs are to improve the level, but the scores of the level 3 indicators of the two are different. Customer A scored 6 points for level 17, and customer B scored 2 points for level 6. Moreover, customer A's cigarette sales force indicator ranks in the top 50% of the region, and has a cigarette sales advantage. If the same service strategy is used for both, the service effects may be very different.

[0136] Therefore, we should fully consider the differences in the degree of the third-level disadvantage indicators of customers and their own business advantages, combine the customer SW double factors, and transform and expand the 25 service strategies that originally directly correspond to the third-level disadvantage indicators, tailor-make service strategies for different customers, form a profit guidance service strategy library, and effectively improve the richness and diversity of the strategy content;

[0137] Therefore, the strategy library adopts the "secondary deformation formulation rule", that is, after formulating the basic service strategy content according to the customer's third-level disadvantage indicator type, it is transformed once in combination with the customer's associated advantage indicator (which has a direct correlation impact on the third-level disadvantage indicator), focusing on giving full play to the customer's business advantages to improve the third-level disadvantage indicator; then, it is transformed twice in terms of service time in combination with the score of the third-level disadvantage indicator to form an advanced service strategy, and tailor different strategy contents for customers;

[0138] The scores of the three-level disadvantage indicators are divided into three levels: 1-3 points are priority, 4-7 points are secondary priority, and 8-10 points are general level. The corresponding strategic service duration is 3, 2, and 1 months;

[0139] The basic service strategy content undergoes secondary transformation, and different strategy contents will be generated for different customers with the same three-level disadvantage indicators. Then, service measures and suggestions will be formulated based on the actual work situation. A knowledge base with video or text instructions as the main content will be built to guide the implementation, thus building a customer profit guidance strategy library with high systematization, strong guidance and good operability (see the strategy library architecture for details). Figure 7 ), effectively meet the differentiated explicit needs of customers based on their own business advantages and disadvantages, solve problems such as simplification of customer service and homogeneity of guidance, and realize "one thousand faces for one thousand people";

[0140] 3.SW dual-factor matching logic:

[0141] In order to reduce the experience judgment of customer specialists, the customer profit guidance strategy library should have an intelligent matching function, that is, automatically matching profit guidance strategies according to the results of the customer's six-dimensional ability analysis;

[0142] Since the customer profit guidance strategy library is established based on the customer's strengths and weaknesses, the customer's strengths and weaknesses should also be considered in strategy matching. The "SW double-factor matching logic" based on conditional judgment logic is used to push "SW" type service strategies to customers, that is, "disadvantage factor (W) + advantage factor (S) = corresponding service strategy (SW)" such as "SB-WD" type strategies;

[0143] After a comprehensive analysis of customer data, the "SW-six-dimensional capability analysis model" obtains the analysis results of the customer's first choice, alternative disadvantage three-level indicators and advantage two-level indicators. Based on the analysis results, the system uses the "SW dual-factor matching logic" to perform strategic matching on the first choice, alternative disadvantage three-level indicators respectively;

[0144] That is, first determine and locate the corresponding basic service strategy based on the customer's third-level disadvantage indicators, and then determine and match the corresponding strategy content in the strategy library based on the customer's various related advantage second-level indicators and third-level indicators;

[0145] The profit guidance service strategy selected through the "SW dual-factor matching logic" completely gets rid of the subjective experience judgment of the customer specialists. The data analysis results and intelligent matching logic are used as the only basis for strategy selection, realizing "data decision-making" and "determining strategies based on numbers", effectively solving problems such as insufficient experience of customer specialists, incomplete services, and ineffective guidance, and providing customers with more accurate and intelligent profit guidance services;

[0146] In addition, considering that some customers may have too many advantage indicators, the system may match multiple strategies for the same third-level disadvantage indicator. In order to reduce the single workload of account specialists, the profit guidance service is divided into phased work, and the most urgent needs of customers can be accurately met with limited energy. Customers can be given the right to choose independently, and the service strategy can be finally determined by the "customer-I collaborative decision-making" approach, that is, the combination of multiple strategy contents selected by the system is pushed to the customer, and the customer chooses one of the strategy contents according to his or her own needs and wishes. Finally, the account specialist will carry out profit guidance work in accordance with the service measures corresponding to the strategy content selected by the customer, supplemented by the guidance of the knowledge base, so as to focus on needs, provide clear guidance, and implement standardized procedures.

[0147] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating customer profitability based on a store management system, the method being implemented based on the store management system, characterized in that: The following steps are involved: S1. Demand analysis and indicator determination; S2. Data collection and collation: collect customer-related data through the store management system, including order information, product sales, and customer consumption records; S3. Data analysis and indicator calculation: calculate the customer's sales, profit contribution, purchase frequency, and average order value according to demand; S4. Profitability evaluation model establishment: Based on the calculated indicators, a customer profitability evaluation model is established; S5. Model verification and adjustment: verify the established profitability evaluation model and check the rationality and accuracy of the model; S6. Result analysis and visualization: Analyze the evaluation results, identify the customer's profitability and its influencing factors, and present the evaluation results in a visual form; S7. Develop improvement measures and strategies. According to the evaluation results, develop improvement measures and marketing strategies for different customers; S8. Continuously monitor and optimize, and adjust and optimize store management strategies in a timely manner based on monitoring results.

2. The store management system is based on the VUE front-end framework and uses HTML5, CSS, and JS Web technologies to build a user interface for interacting with users, displaying data, and receiving user input. It also uses the Spring back-end framework, which is composed of Spring MVC and Spring Boot components to process business logic and data persistence operations. It is characterized by: It includes requirement analysis module, environment preparation module, project creation module, back-end development module, front-end development module, configuration and testing module, build and deployment module, and run and debug module.

3. The store management system according to claim 2, characterized in that: The requirement analysis module is used to determine the specific functions that the system needs to implement, including interface design, function implementation methods, and create a detailed requirement map to provide guidance for subsequent development.

4. The store management system according to claim 3, characterized in that: The environment preparation module is used to install and configure JDK, Web server and IDE, set up the Spring framework, including Spring Boot, Spring MVC and Spring Data modules, and prepare for the project development environment.

5. The store management system according to claim 4, characterized in that: The project creation module uses tools provided by SpringInitializr or IDE to generate the basic structure of the Spring Boot project and add necessary dependency libraries to establish the project skeleton.

6. The store management system according to claim 5, characterized in that: The backend development module is used to write controller classes to process HTTP requests, implement business logic service classes, ensure code readability, maintainability and scalability, and follow best practices and design patterns.

7. The store management system according to claim 6, characterized in that: The front-end development module is used to create a VUE project, use HTML5, CSS, and JS to build the basic structure and style of the user interface, and build an intuitive, easy-to-use, and beautiful user interface.

8. The store management system according to claim 7, characterized in that: The configuration test module is used to configure the properties of the application according to project requirements, write and run unit tests and integration tests to verify the correctness of the code, and ensure the collaboration between different components.

9. The store management system according to claim 8, characterized in that: The build and deploy module uses Maven or Gradle build tools to build the project, generates executable WAR or JAR files, and deploys these files to a Web server to configure the server to run the application.

10. The store management system according to claim 9, characterized in that: The operation and debugging module accesses the application through a browser to perform functional testing and debugging, pays attention to user feedback and performance indicators, and promptly discovers and solves potential problems to ensure the stability and reliability of the system.

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