Enterprise product marketing management system

Abstract

The invention discloses an enterprise product marketing management system, which comprises a real-time data analysis module, a personalized recommendation module, an integral incentive module, an operation tool integration module and a profit and loss analysis and report module, all interaction behaviors of a user on a platform are captured through a real-time data analysis module, a user portrait is created according to behavior data and preferences of the user through a personalized recommendation module by utilizing deep learning and machine learning technologies, and an integral acquisition rule and a use mode are defined through an integral incentive module. According to the invention, the marketing strategy is optimized, the attraction and incentive effect of the marketing strategy is increased, the operation tool integration module is utilized to provide a background management tool which comprises commodity management, order management and directional pushing functions, and enterprises are helped to optimize the marketing strategy and improve the operation efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to an enterprise product marketing management system. Background Art

[0002] In the current field of enterprise product marketing management, most systems rely on traditional marketing strategies and static data analysis methods, which are unable to respond to market changes and user needs in real time, resulting in low marketing efficiency and insufficient user engagement.

[0003] There is some room for improvement in existing technologies:

[0004] 1. Most existing marketing systems are unable to process and analyze user behavior data in real time, resulting in delayed updates of marketing strategies, lack of intelligent recommendation functions based on user behavior and preferences, and inability to effectively improve user purchase conversion rates. They also lack attractiveness and incentive effects.

[0005] 2. The existing marketing system has a single customer service function and lacks an effective user interaction and feedback mechanism;

[0006] 3. The existing marketing system lacks effective operational tools and profit and loss analysis functions, making it difficult for companies to make accurate marketing decisions. Summary of the invention

[0007] In order to solve the above-mentioned technical problems, the present invention provides an enterprise product marketing management system.

[0008] The technical solution of the present invention is achieved in this way:

[0009] An enterprise product marketing management system, including a real-time data analysis module, a personalized recommendation module, a points incentive module, an operation tool integration module, and a profit and loss analysis and reporting module;

[0010] The real-time data analysis module captures all interactive behaviors of users on the platform, cleans and integrates these data by real-time tracking and recording user behaviors, ensures the accuracy and availability of the data, and uses statistical and machine learning techniques to deeply explore user behavior patterns and preferences, providing the system with accurate user insights and market trend analysis.

[0011] The personalized recommendation module provides users with customized product recommendations by deeply learning the user's behavior data and preferences, analyzing the user's historical behavior in detail, and creating comprehensive user profiles. Using these profiles and advanced algorithms, it intelligently recommends products that users may be interested in, thereby improving user satisfaction and purchase conversion rate;

[0012] The points incentive module provides a way to obtain points through various behaviors, allowing users to use points for discounts, exchange for goods or participate in specific activities, monitor the flow of points, ensure the fairness and transparency of the points system, and continuously optimize the points strategy through data analysis;

[0013] The operation tool integration module provides background management tools to optimize marketing strategies and improve operational efficiency, manage product information; monitor order processes, ensure the accuracy and timeliness of orders, and use the results of user behavior analysis to achieve precision marketing and improve the ROI of marketing activities;

[0014] The profit and loss analysis and reporting module provides real-time financial insights, collects and organizes sales, costs and other key financial indicators, automatically generates profit and loss reports, intuitively displays the impact of marketing activities on the company's financial status, and helps companies make quick data-based decisions.

[0015] Furthermore, the real-time data analysis module includes a data collection unit, a data processing unit and a behavior analysis unit;

[0016] The data collection unit collects user behavior data in real time;

[0017] The data processing unit cleans, classifies and stores the collected data;

[0018] The behavior analysis unit analyzes user behavior patterns and identifies user preferences and needs.

[0019] Furthermore, the personalized recommendation module includes a user portrait construction unit and a recommendation algorithm unit;

[0020] The user portrait construction unit constructs a detailed user portrait based on the user data;

[0021] The recommendation algorithm unit applies a machine learning algorithm to intelligently recommend products based on user portraits.

[0022] Furthermore, the points incentive module includes a points acquisition unit, a points use unit and a points management unit;

[0023] The points acquisition unit defines points acquisition rules;

[0024] The points using unit allows the user to use points to deduct or redeem goods;

[0025] The points management unit manages the issuance, use and expiration of points.

[0026] Furthermore, the operation tool integration module includes a product management unit, an order management unit and a directional push unit;

[0027] The commodity management unit provides the functions of adding, deleting, modifying and checking commodity information;

[0028] The order management unit manages the order status;

[0029] The directional push unit performs directional push of commodities according to user attributes.

[0030] Further, the profit and loss analysis and report module includes a financial data unit and a report generation unit;

[0031] The financial data unit collects and processes financial data;

[0032] The report generation unit automatically generates a profit and loss report and provides marketing effect analysis.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1. The present invention captures all interactive behaviors of users on the platform through a real-time data analysis module, and tracks, records and analyzes these data in real time, providing the function of real-time processing and analysis of user behavior data, so that marketing strategies can quickly respond to changes in the market and user needs;

[0035] Through the personalized recommendation module, deep learning and machine learning technologies are used to create user portraits based on user behavior data and preferences, and intelligently recommend products, which improves the relevance of recommendations and effectively improves the user's purchase conversion rate;

[0036] Through real-time data analysis and personalized recommendations, the system can better understand user needs and provide more personalized services;

[0037] 2. The points incentive module defines the rules for obtaining points and how to use them, which increases the attractiveness and incentive effect of marketing strategies. Users can earn points by shopping, participating in activities or sharing, and use points for discounts or redemption of goods, which increases user participation and loyalty;

[0038] 3. The operation tool integration module provides backend management tools, including product management, order management and targeted push functions, to help companies optimize marketing strategies and improve operational efficiency. The profit and loss analysis and reporting module automatically generates profit and loss reports to provide companies with real-time financial insights and help them make accurate marketing decisions based on data. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a system framework diagram of an enterprise product marketing management system of the present invention;

[0040] Figure 2 It is a structural diagram of the real-time data analysis module in the present invention;

[0041] Figure 3 This is a structural diagram of the personalized recommendation module in the present invention;

[0042] Figure 4 It is a structural schematic diagram of the integral incentive module in the present invention;

[0043] Figure 5 This is a schematic diagram of the structure of the operation tool integration module in the present invention;

[0044] Figure 6 It is a structural diagram of the profit and loss analysis and report module in the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below 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.

[0046] Example 1

[0047] This embodiment aims to provide an enterprise product marketing management system, including a real-time data analysis module, a personalized recommendation module, a points incentive module, an operation tool integration module, and a profit and loss analysis and reporting module;

[0048] The real-time data analysis module captures all interactive behaviors of users on the platform, cleans and integrates these data by real-time tracking and recording user behaviors, ensures the accuracy and availability of data, and uses statistical and machine learning techniques to deeply explore user behavior patterns and preferences, providing the system with accurate user insights and market trend analysis;

[0049] The personalized recommendation module provides users with customized product recommendations by deeply learning the user's behavior data and preferences, analyzing the user's historical behavior in detail, and creating comprehensive user profiles. Using these profiles and advanced algorithms such as collaborative filtering or content recommendation algorithms, it intelligently recommends products that users may be interested in, thereby improving user satisfaction and purchase conversion rate;

[0050] The points incentive module provides ways to obtain points through various behaviors, such as shopping, participating in activities or sharing, allowing users to use points for discounts, redeem goods or participate in specific activities, monitor the flow of points, ensure the fairness and transparency of the points system, and continuously optimize the points strategy through data analysis;

[0051] The operation tool integration module provides background management tools to optimize marketing strategies and improve operational efficiency, manage product information, including listing, delisting and price adjustment; monitor order processes to ensure the accuracy and timeliness of orders, and use the results of user behavior analysis to achieve precision marketing and improve the ROI of marketing activities;

[0052] The profit and loss analysis and reporting module provides real-time financial insights, collects and organizes sales, costs and other key financial indicators, automatically generates profit and loss reports, intuitively displays the impact of marketing activities on the company's financial status, and helps companies make quick data-based decisions.

[0053] Furthermore, the real-time data analysis module includes a data collection unit, a data processing unit and a behavior analysis unit;

[0054] The data collection unit collects user behavior data in real time;

[0055] The data processing unit cleans, classifies and stores the collected data;

[0056] The behavior analysis unit analyzes user behavior patterns and identifies user preferences and needs;

[0057] Specifically, the behavior analysis unit analyzes user behavior patterns and identifies user preferences and needs in the following specific process:

[0058] (1) Data preprocessing

[0059] Remove invalid or erroneous data, such as removing null values ​​or outliers; scale the data to a uniform range, such as between 0 and 1;

[0060] (2) Behavioral data feature extraction

[0061] Identify unique user sessions on the platform; record types of user behavior, such as clicks, views, add to cart, purchases;

[0062] (3) User Behavior Sequence Analysis

[0063] Sequential pattern mining is used to discover frequent patterns in the user's behavior sequence on the platform and collect user behavior data on the platform, such as page visits, product browsing, adding to shopping carts, and purchases;

[0064] Use sequential pattern mining algorithms to find common behavior sequences, for example, which pages users typically browse before making a purchase:

[0065] Sequence support calculation formula:

[0066] Sequence S is the number of times it actually appears in all user behavior data. For example, if we consider the sequence of user purchase behavior, sequence S can be "browse product A, then browse product B, and finally purchase product C." The specific number of times this sequence appears in all recorded user behavior data is the number of times sequence S appears.

[0067] Furthermore, a classification algorithm is introduced to predict user needs:

[0068] ① Use logistic regression to predict user demand for specific product categories based on user behavior;

[0069] ②Prediction formula of logistic regression:

[0070]

[0071] Among them, β is the model parameter and x is the eigenvalue;

[0072] (4) User Preference Identification

[0073] Collaborative filtering: By analyzing the similarity of behaviors between users, recommend products that are liked by similar users, and calculate the similarity between users by calculating cosine similarity:

[0074]

[0075] Among them, r ui and r vi are the ratings of user u and user v on product i respectively; similarity results are used to group users into similar user groups. Users in these groups may have similar preferences and purchasing behaviors. By identifying other users similar to the target user, products can be recommended to the target user based on the preferences of these similar users to achieve personalized recommendations; by analyzing the behavioral similarities between users, products that are liked by similar users can be recommended;

[0076] At the same time, the results of user behavior sequence analysis can be used to enrich user portraits, identify user behavior patterns and preferences, help extract more accurate features when building user portraits, and use these features in recommendation algorithms to generate more personalized recommendations;

[0077] When building user profiles, the results of collaborative filtering can be used directly to identify users’ preferences for products or brands. These preference information is an important part of user profiles and directly affects the relevance of recommendation results.

[0078] Content recommendation: Analyze product features and user behavior, and recommend products that match user preference features:

[0079]

[0080] Convert product descriptions and user review text information into numerical feature vectors, where each dimension represents the TF-IDF weight of a term (keyword); calculate the feature vector of user preferences by analyzing the text content corresponding to the user's historical behavior data (such as browsing, purchasing, and rating); calculate the similarity between the user preference feature vector and the product feature vector to find the product that best matches the user's preference. Through TF-IDF, you can find product features that the user may not have come into contact with but has potential interest in;

[0081] Furthermore, the personalized recommendation module includes a user portrait construction unit and a recommendation algorithm unit;

[0082] The user portrait construction unit constructs a detailed user portrait based on the user data;

[0083] The recommendation algorithm unit applies a machine learning algorithm to intelligently recommend products based on user portraits;

[0084] Furthermore, the points incentive module includes a points acquisition unit, a points use unit and a points management unit;

[0085] The points acquisition unit defines points acquisition rules, such as purchasing goods and participating in activities;

[0086] The points using unit allows the user to use points to deduct or redeem goods;

[0087] The points management unit manages the issuance, use and expiration of points.

[0088] Furthermore, the operation tool integration module includes a product management unit, an order management unit and a directional push unit;

[0089] The commodity management unit provides the functions of adding, deleting, modifying and checking commodity information;

[0090] The order management unit manages the order status, including order placement, payment, and delivery;

[0091] The directional push unit performs directional push of commodities according to user attributes.

[0092] Further, the profit and loss analysis and report module includes a financial data unit and a report generation unit;

[0093] The financial data unit collects and processes financial data, such as sales and costs;

[0094] The report generation unit automatically generates a profit and loss report and provides marketing effect analysis;

[0095] Specifically, the ARIMA model is used for profit and loss analysis and report generation. The specific steps are as follows:

[0096] Step 1: Data preparation, collecting key financial data over a period of time;

[0097] Step 2: Data stationarity test, where the difference is performed on the non-stationary data: ΔY t =Y t -Y t-1 , where Δ represents the first-order difference;

[0098] Step 3: Determine the parameters of the ARIMA model. The parameters of the ARIMA model include:

[0099] p: the order of the autoregressive term (AR term)

[0100] d: The order of difference (to make the data stationary)

[0101] q: order of the moving average term (MA term);

[0102] Step 4: Model fitting: Use the determined parameters to fit the ARIMA model, where the ARIMA model can be expressed as:

[0103] Among them, φ i is the autoregressive coefficient, θ j is the moving average coefficient, L is the lag operator, ∈ t is the error term;

[0104] Use maximum likelihood estimation or other optimization algorithms to estimate the model parameters φ i ,θ j ;

[0105] Step 5: Model prediction, use the fitted model to predict future data;

[0106] Predict future value Y t+h :

[0107]

[0108] in and are the estimated parameters;

[0109] Step 6: Calculate profit and loss. Calculate profit and loss based on the predicted sales and known cost data.

[0110] Assuming that fixed cost is f and variable cost is r% of sales, then: cost = Cf + r × sales, profit = sales - cost;

[0111] Step 7: Report generation, integrating the forecast results and calculated profit and loss into the profit and loss report;

[0112]

[0113] This embodiment realizes the whole process management from data collection to decision support. It captures and processes all interactive behaviors of users on the platform through the real-time data analysis module, and uses statistical and machine learning technologies to deeply explore user behavior patterns and preferences; the personalized recommendation module creates a comprehensive user portrait based on deep learning and user behavior data, and applies collaborative filtering and content recommendation algorithms to provide users with tailored product recommendations, thereby improving user satisfaction and purchase conversion rate; the point incentive module increases user stickiness and participation by defining the rules for obtaining and using points, while ensuring the fairness and transparency of the point system; the operation tool integration module provides background management tools to optimize product and order management and improve operational efficiency; the profit and loss analysis and reporting module automatically generates profit and loss reports by collecting and organizing key financial indicators of sales and costs, helping enterprises to make data-based decisions quickly.

[0114] Example 2

[0115] This embodiment aims to provide an enterprise product marketing management system, including a real-time data analysis module, a personalized recommendation module, a points incentive module, an operation tool integration module, and a profit and loss analysis and reporting module;

[0116] The real-time data analysis module captures all interactive behaviors of users on the platform, cleans and integrates these data by real-time tracking and recording user behaviors, ensures the accuracy and availability of the data, and uses statistical and machine learning techniques to deeply explore user behavior patterns and preferences, providing the system with accurate user insights and market trend analysis.

[0117] The personalized recommendation module provides users with customized product recommendations by deeply learning the user's behavior data and preferences, analyzing the user's historical behavior in detail, and creating comprehensive user profiles. Using these profiles and advanced algorithms, it intelligently recommends products that users may be interested in, thereby improving user satisfaction and purchase conversion rate;

[0118] The points incentive module provides a way to obtain points through various behaviors, allowing users to use points for discounts, exchange for goods or participate in specific activities, monitor the flow of points, ensure the fairness and transparency of the points system, and continuously optimize the points strategy through data analysis;

[0119] The operation tool integration module provides background management tools to optimize marketing strategies and improve operational efficiency, manage product information; monitor order processes, ensure the accuracy and timeliness of orders, and use the results of user behavior analysis to achieve precision marketing and improve the ROI of marketing activities;

[0120] The profit and loss analysis and reporting module provides real-time financial insights, collects and organizes sales, costs and other key financial indicators, automatically generates profit and loss reports, intuitively displays the impact of marketing activities on the company's financial status, and helps companies make quick data-based decisions.

[0121] Furthermore, the real-time data analysis module includes a data collection unit, a data processing unit and a behavior analysis unit;

[0122] The data collection unit collects user behavior data in real time;

[0123] The data processing unit cleans, classifies and stores the collected data;

[0124] The behavior analysis unit analyzes user behavior patterns and identifies user preferences and needs.

[0125] Furthermore, the personalized recommendation module includes a user portrait construction unit and a recommendation algorithm unit;

[0126] The user portrait construction unit constructs a detailed user portrait based on the user data;

[0127] The recommendation algorithm unit applies a machine learning algorithm to intelligently recommend products based on user portraits.

[0128] Furthermore, the points incentive module includes a points acquisition unit, a points use unit and a points management unit;

[0129] The points acquisition unit defines points acquisition rules;

[0130] The points using unit allows the user to use points to deduct or redeem goods;

[0131] The points management unit manages the issuance, use and expiration of points;

[0132] Specific:

[0133] Define the behaviors that users can use to earn points, such as purchasing goods, participating in activities, and user recommendations, and set the point earning rate for each behavior; Points Earning = Consumption Amount × Points Rate

[0134] Furthermore, the personalization of points acquisition rules is introduced:

[0135] Personalize the points earning rules based on the user's segmented groups and preference characteristics;

[0136] Personalized points rate = basic points rate × (user segment weight + user preference weight)

[0137] Allow users to use points according to their preferences, for example, provide more points redemption options for user groups with high preferences; dynamically adjust the points incentive plan based on changes in user behavior and market feedback;

[0138] Dynamic points adjustment = basic points × (1 + market feedback coefficient)

[0139] Set the deduction ratio when points are used to purchase goods, and set the goods or services that points can be redeemed for; deduction amount = points used ÷ deduction ratio

[0140] Record the details of each time a user obtains points, including time, quantity and reason; record the details of each time a user uses points, including time, quantity and usage scenario; set the validity period of points; valid points = total points - expired points

[0141] Provide initial points to new registered users to encourage them to make their first purchase;

[0142] Provide additional points rewards based on the user's consumption frequency and amount;

[0143] Offer point doubling incentives during certain marketing campaigns;

[0144] Analyze user preferences and purchasing patterns using user behavior data provided by the real-time data analysis module; customize personalized points reward plans using user portrait data from the personalized recommendation module;

[0145] Evaluate the impact of the points incentive program on user activity and the contribution of the points incentive program to sales;

[0146]

[0147] Furthermore, the operation tool integration module includes a product management unit, an order management unit and a directional push unit;

[0148] The commodity management unit provides the functions of adding, deleting, modifying and checking commodity information;

[0149] The order management unit manages the order status;

[0150] The directional push unit performs directional push of commodities according to user attributes.

[0151] Further, the profit and loss analysis and report module includes a financial data unit and a report generation unit;

[0152] The financial data unit collects and processes financial data;

[0153] The report generation unit automatically generates a profit and loss report and provides marketing effect analysis. The point incentive scheme provided in this embodiment not only enhances user stickiness and satisfaction, but also improves user participation and purchasing behavior through a personalized reward mechanism. In addition, the scheme improves marketing effectiveness, optimizes inventory management, and improves the market competitiveness of enterprises through data-driven decision-making. At the same time, it also promotes the establishment of long-term cooperative relationships with users, increases brand awareness, and provides flexibility and scalability to adapt to market changes. The implementation of the point scheme also helps enterprises better understand and meet user needs, promotes the business goals and growth of enterprises, and reduces operational risks.

Claims

1. An enterprise product marketing management system, characterized by: Including real-time data analysis module, personalized recommendation module, points incentive module, operation tool integration module and profit and loss analysis and reporting module; The personalized recommendation module provides users with customized product recommendations by deeply learning their behavior data and preferences, analyzes their historical behaviors in detail, and creates a comprehensive user profile. The points incentive module provides a way to obtain points through various behaviors, allowing users to use points for discounts, exchange for goods or participate in specific activities, and monitors the flow of points; The profit and loss analysis and reporting module provides real-time financial insights, collects and organizes sales, costs and other key financial indicators, and automatically generates profit and loss reports.

2. The enterprise product marketing management system according to claim 1, characterized in that: The real-time data analysis module captures all interactive behaviors of users on the platform, cleans and integrates these data by real-time tracking and recording user behaviors, ensures the accuracy and availability of the data, and uses statistical and machine learning techniques to deeply explore user behavior patterns and preferences, providing the system with accurate user insights and market trend analysis.

3. The enterprise product marketing management system according to claim 1, characterized in that: The operation tool integration module provides background management tools to optimize marketing strategies, improve operation efficiency, and manage product information; Monitor the order process to ensure the accuracy and timeliness of orders, use the results of user behavior analysis to achieve precision marketing, and improve the ROI of marketing activities.

4. The enterprise product marketing management system according to claim 2, characterized in that: The real-time data analysis module includes a data collection unit, a data processing unit and a behavior analysis unit; The data collection unit collects user behavior data in real time; The data processing unit cleans, classifies and stores the collected data; The behavior analysis unit analyzes user behavior patterns and identifies user preferences and needs.

5. According to claim 3, the enterprise product marketing management system is characterized in that: The operation tool integration module includes a product management unit, an order management unit and a directional push unit; The commodity management unit provides the functions of adding, deleting, modifying and checking commodity information; The order management unit manages the order status; The directional push unit performs directional push of commodities according to user attributes.

6. The enterprise product marketing management system according to claim 1, characterized in that: The personalized recommendation module includes a user portrait construction unit and a recommendation algorithm unit; The user portrait construction unit constructs a detailed user portrait based on the user data; The recommendation algorithm unit applies a machine learning algorithm to intelligently recommend products based on user portraits; The points incentive module includes a points acquisition unit, a points use unit and a points management unit; The points acquisition unit defines points acquisition rules; The points using unit allows the user to use points to deduct or redeem goods; The points management unit manages the issuance, use and expiration of points; The profit and loss analysis and report module includes a financial data unit and a report generation unit; The financial data unit collects and processes financial data; The report generation unit automatically generates a profit and loss report and provides marketing effect analysis.

7. The enterprise product marketing management system according to claim 6, characterized in that: The specific process of the behavior analysis unit analyzing user behavior patterns and identifying user preferences and needs is as follows: (1) Data preprocessing Remove invalid or erroneous data, remove null values ​​or outliers; scale the data to a uniform range; (2) Behavioral data feature extraction Identify unique user sessions on the Platform; record patterns of user behavior; (3) User Behavior Sequence Analysis Sequential pattern mining is used to discover frequent patterns in the user's behavior sequence on the platform and collect user behavior data on the platform; Use sequential pattern mining algorithms to find common behavior sequences: Sequence support calculation formula: Introduce classification algorithms to predict user needs: ① Use logistic regression to predict user demand for specific product categories based on user behavior; ②Prediction formula of logistic regression: Among them, β is the model parameter and x is the eigenvalue; (4) User Preference Identification Collaborative filtering: By analyzing the similarity of behaviors between users, recommend products that are liked by similar users, and calculate the similarity between users by calculating cosine similarity: Among them, r ui and r vi are the ratings of user u and user v on product i respectively; similarity results are used to group users into similar user groups. Users in these groups may have similar preferences and purchasing behaviors. By identifying other users similar to the target user, products can be recommended to the target user based on the preferences of these similar users to achieve personalized recommendations; by analyzing the behavioral similarities between users, products that are liked by similar users can be recommended; At the same time, the results of user behavior sequence analysis can be used to enrich user portraits, identify user behavior patterns and preferences, help extract more accurate features when building user portraits, and use these features in recommendation algorithms to generate more personalized recommendations; When building user profiles, the results of collaborative filtering can be used directly to identify users’ preferences for products or brands. These preference information is an important part of user profiles and directly affects the relevance of recommendation results. Content recommendation: Analyze product features and user behavior, and recommend products that match user preference features: The product description and user comment text information are converted into numerical feature vectors, where each dimension represents the TF-IDF weight of an entry. By analyzing the text content corresponding to the user's historical behavior data, the feature vector of the user's preference is calculated. The similarity between the user's preference feature vector and the product feature vector is calculated to find the product that best matches the user's preference. Through TF-IDF, the characteristics of products that the user has not been exposed to but has potential interest can be discovered.

8. The enterprise product marketing management system according to claim 6, characterized in that: The profit and loss analysis and report module performs profit and loss analysis and report generation, and the specific steps are as follows: Step 1: Data preparation, collecting key financial data over a period of time; Step 2: Data stationarity test, where the difference is performed on the non-stationary data: ΔY t =Y t -Y t-1 , where Δ represents the first-order difference; Step 3: Determine the parameters of the ARIMA model. The parameters of the ARIMA model include: p: the order of the autoregressive term (AR term) d: The order of difference (to make the data stationary) q: order of the moving average term (MA term); Step 4: Model fitting: Use the determined parameters to fit the ARIMA model, where the ARIMA model can be expressed as: Among them, φ i is the autoregressive coefficient, θ j is the moving average coefficient, L is the lag operator, ∈ t is the error term; Use maximum likelihood estimation or other optimization algorithms to estimate the model parameters φ i ,θ j ; Step 5: Model prediction, use the fitted model to predict future data; Predict future value Y t+h : in and are the estimated parameters; Step 6: Calculate profit and loss. Calculate profit and loss based on the predicted sales and known cost data. Assuming that fixed cost is f and variable cost is r% of sales, then: cost = Cf + r × sales, profit = sales - cost; Step 7: Report generation, integrating the forecast results and calculated profit and loss into the profit and loss report; 9. The enterprise product marketing management system according to claim 6, characterized in that: The points acquisition unit defines the behaviors through which users can acquire points; points acquisition = consumption amount × points rate; Introducing personalized points earning rules: Personalize the points earning rules based on the user's segmented groups and preference characteristics; Personalized points rate = basic points rate × (user segment weight + user preference weight) allows users to use points according to their preferences; Dynamically adjust the points incentive plan based on changes in user behavior and market feedback; Dynamic points adjustment = basic points × (1 + market feedback coefficient); Set the deduction ratio when points are used to purchase goods, and set the goods or services that points can be redeemed for; deduction amount = points used ÷ deduction ratio; Record the details of each time a user obtains points, including the time, quantity and reason; Record the details of each time a user uses points, including the time, quantity and usage scenario; Set the validity period of points; Valid points = total points - expired points.

10. The enterprise product marketing management system according to claim 9, characterized in that: The points acquisition unit evaluates the impact of the points incentive scheme on user activity and the contribution of the points incentive scheme to sales;

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