A blockchain-based enterprise marketing planning system

Abstract

The present invention discloses a blockchain-based enterprise marketing planning system, which relates to the field of Internet technology, including designing smart contracts, defining dynamic pricing rules and trigger conditions, and deploying smart contracts to a blockchain platform; monitoring market data in real time and analyzing them through smart contracts on the blockchain platform, judging whether prices need to be adjusted and generating information that trigger conditions are satisfied; based on the information that trigger conditions are satisfied, the smart contract automatically performs price adjustment operations and generates price adjustment records; based on the price adjustment records, predicts consumers' future purchasing behaviors and preferences, and generates personalized marketing suggestions; based on the personalized marketing suggestions, performs effect evaluation and analysis, and optimizes marketing strategies; based on the optimized marketing strategies, updates the rules and conditions in the smart contract. Through the information based on the satisfaction of the trigger conditions, the automation and traceability of price adjustments are achieved, and the trust of consumers and partners is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of Internet technology, and in particular to an enterprise marketing planning system based on blockchain. Background Art

[0002] In recent years, with the rapid development of the Internet and big data technology, the field of corporate marketing planning has ushered in unprecedented changes. Traditional marketing strategies often rely on manual experience and limited data analysis, making it difficult to respond to market changes and consumer needs in real time. However, with the popularity of e-commerce, social media and search engines, companies can obtain a large amount of market data. In corporate marketing planning, the application of blockchain technology can not only ensure the security and reliability of data, but also realize automated price adjustments and marketing strategy optimization through smart contracts, thereby improving the market responsiveness and competitiveness of enterprises.

[0003] Although the existing enterprise marketing planning system has made some progress, there are still many shortcomings in real-time data processing, automated decision-making and personalized recommendations. First, traditional data processing methods often require manual intervention, and the process of data cleaning and standardization is cumbersome and error-prone, which cannot meet real-time requirements. Secondly, the existing dynamic pricing systems mostly rely on static rules and simple models, lack the ability to respond to changes in market supply and demand and prices of competing products in real time, resulting in delayed price adjustments and difficulty in effectively responding to market fluctuations. In addition, most of the existing personalized recommendation systems are based on a single data source and simple algorithms, which cannot fully capture the multi-dimensional behavioral characteristics of consumers, resulting in poor recommendation results. Finally, the existing marketing strategy optimization methods mostly rely on post-analysis, lack real-time feedback and dynamic adjustment mechanisms, and cannot correct deviations in marketing activities in a timely manner. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a blockchain-based enterprise marketing planning system to solve the problems of non-real-time data processing, delayed dynamic pricing and inaccurate personalized recommendations in existing enterprise marketing planning systems.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a blockchain-based enterprise marketing planning system, which includes a collection and preprocessing module that collects and preprocesses market data in real time to generate a standardized data set;

[0008] Smart contract design and deployment module: based on standardized data sets, design smart contracts, define dynamic pricing rules and trigger conditions, and deploy smart contracts to the blockchain platform;

[0009] The market data monitoring and analysis module monitors and analyzes market data in real time through smart contracts on the blockchain platform, determines whether prices need to be adjusted, and generates information on whether trigger conditions are met;

[0010] Price adjustment execution module: based on the information that the trigger conditions are met, the smart contract automatically executes the price adjustment operation and generates a price adjustment record;

[0011] The consumer behavior prediction module predicts consumers’ future purchasing behavior and preferences based on price adjustment records and generates personalized marketing recommendations;

[0012] The marketing effect evaluation and optimization module evaluates and analyzes the effect based on personalized marketing suggestions and optimizes the marketing strategy;

[0013] The smart contract rule update module updates the rules and conditions in the smart contract according to the optimized marketing strategy.

[0014] As a preferred solution of the enterprise marketing planning system based on blockchain described in the present invention, real-time collection of market data and preprocessing to generate a standardized data set includes the following steps:

[0015] The market data includes consumer behavior data, competitive product price data, and market supply and demand data;

[0016] Clean the collected market data and convert it into a unified format;

[0017] Integrate data collected from different platforms into a unified dataset;

[0018] The cleaned, transformed and integrated data are used to generate standardized data sets.

[0019] As a preferred solution of the enterprise marketing planning system based on blockchain described in the present invention, wherein: based on the standardized data set, designing a smart contract, defining the rules and trigger conditions for dynamic pricing, and deploying the smart contract to the blockchain platform includes the following steps:

[0020] Design smart contracts based on standardized data sets;

[0021] Define dynamic pricing rules for market supply and demand and competitive product prices, as well as trigger conditions for specific times and events;

[0022] Use Solidity language to write smart contract code, define product structure and dynamic pricing logic, and provide methods to update product data and adjust prices;

[0023] Select a blockchain platform, use the Truffle framework and Ganache test network to deploy and test smart contracts, generate confirmation information of successful deployment and store it in the database.

[0024] As a preferred solution of the enterprise marketing planning system based on blockchain described in the present invention, wherein: real-time monitoring and analysis of market data through smart contracts on the blockchain platform, determining whether prices need to be adjusted and generating information that triggers conditions are met include the following steps:

[0025] Read the contract address and confirmation information of successful transaction from the database;

[0026] Collect purchase history, search history, browsing history and interaction history from e-commerce platforms, social media and search engines in real time;

[0027] Collect product prices and price change records from competing platforms in real time;

[0028] Collect market demand and market supply data in real time from the market data platform;

[0029] Use big data analysis tools to extract key indicators of market supply and demand, competitive product prices, and consumer behavior;

[0030] Based on the impact of market supply and demand, competitive product prices, and consumer behavior, a dynamic pricing formula is defined, expressed as:

[0031] ;

[0032] in, For the new price, is the current price of the product, is the market demand at the current time point, is the market demand at the previous point in time, is the supply at the current time point, is the average price of competing products at the current time point, is the average price of competing products at the previous point in time, is the adjustment coefficient for the impact of changes in market demand on prices, The adjustment coefficient for the impact of price changes of competing products on prices;

[0033] Determine whether the price needs to be adjusted based on the dynamic pricing results;

[0034] According to the judgment result, the judgment conditions of the specific time and the specific event are triggered, and the information that the triggering conditions are met is generated and stored in the database.

[0035] As a preferred solution of the enterprise marketing planning system based on blockchain described in the present invention, wherein: based on the information that the trigger condition is satisfied, the smart contract automatically performs the price adjustment operation, and the generation of the price adjustment record includes the following steps:

[0036] Read from the database whether the triggering conditions and the new price are met;

[0037] Based on the trigger conditions and new prices, the product update method of the smart contract is called to update the product data and automatically perform price adjustments;

[0038] The details of the price adjustment are recorded in a smart contract and stored on the blockchain.

[0039] As a preferred solution of the enterprise marketing planning system based on blockchain described in the present invention, wherein: predicting the future purchasing behavior and preferences of consumers based on price adjustment records and generating personalized marketing suggestions include the following steps:

[0040] Read price adjustment records and related consumer behavior data from the database;

[0041] Extract key features based on price adjustment records and consumer behavior data;

[0042] Select the random forest model and use the extracted features and historical purchase records to train the random forest model;

[0043] Using the trained random forest model, the comprehensive score of each consumer is calculated based on the current consumer behavior data and price adjustment records. The expression is:

[0044] ;

[0045] in, For the The overall rating of consumers, For the The purchase frequency of a consumer, For the The purchase amount of each consumer, For the The change in purchase quantity of a consumer before and after the price adjustment is, For the The frequency of keywords searched by consumers, For the The browsing time of each consumer, For the The number of interactions per consumer, Index for consumers, is the price sensitivity coefficient, is the adjustment coefficient for the impact of changes in market supply and demand and competitive product prices on consumer behavior, is the time decay factor, is the number of days from the current time;

[0046] Sort the combined scores of all consumers to identify high-value and low-value consumer groups;

[0047] Push relevant product recommendations and advertisements based on consumers’ preferred product categories.

[0048] As a preferred solution of the enterprise marketing planning system based on blockchain described in the present invention, the following steps are included: based on personalized marketing suggestions, effect evaluation and analysis are performed to optimize the marketing strategy:

[0049] Read the generated personalized marketing suggestions from the database;

[0050] Execute specific marketing activities based on the generated marketing suggestions;

[0051] Set up a data collection mechanism to collect and integrate various feedback data and store them in the database;

[0052] Use statistical analysis and visualization tools to evaluate the collected feedback data;

[0053] Dynamically adjust marketing strategies based on evaluation results.

[0054] As a preferred solution of the enterprise marketing planning system based on blockchain described in the present invention, wherein: according to the optimized marketing strategy, updating the rules and conditions in the smart contract includes the following steps:

[0055] Read the marketing strategies optimized based on effect evaluation and analysis from the database. These strategies include new promotion methods, adjusted product portfolios, and optimized personalized recommendation rules.

[0056] Adjust the pricing rules in the smart contract based on the optimized marketing strategy;

[0057] Update pricing rules and trigger conditions in smart contracts and test the updated smart contracts;

[0058] After the test passes, a confirmation message of successful deployment is generated and stored in the database.

[0059] The beneficial effects of the present invention are as follows: based on a standardized data set, a smart contract is designed to define the rules and trigger conditions for dynamic pricing, and the smart contract is deployed on a blockchain platform; the market data is monitored and analyzed in real time through the smart contract on the blockchain platform to determine whether the price needs to be adjusted and to generate information that the trigger conditions are satisfied; based on the information that the trigger conditions are satisfied, the smart contract automatically performs the price adjustment operation and generates a price adjustment record; based on the price adjustment record, the future purchasing behavior and preferences of consumers are predicted and personalized marketing suggestions are generated; based on the personalized marketing suggestions, the effect is evaluated and analyzed and the marketing strategy is optimized; based on the optimized marketing strategy, the rules and conditions in the smart contract are updated. By using the information based on the satisfaction of the trigger conditions, the automation and traceability of price adjustment are achieved, and the trust of consumers and partners is enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0061] Figure 1 This is a flow chart of the enterprise marketing planning system based on blockchain in Example 1.

[0062] Figure 2 This is a flowchart for generating trigger conditions in Example 1. DETAILED DESCRIPTION

[0063] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0064] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0065] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0066] Example 1, reference Figure 1 and Figure 2, which is the first embodiment of the present invention, and provides a blockchain-based enterprise marketing planning system, comprising the following steps:

[0067] S1. Collect market data in real time and preprocess it to generate a standardized data set, including the following steps:

[0068] Market data includes consumer behavior data, competitor price data, and market supply and demand data; clean the collected market data and convert it into a unified format; integrate the data collected from different platforms into a unified data set; generate a standardized data set from the cleaned, converted, and integrated data.

[0069] Furthermore, consumer behavior data include users' purchase history on e-commerce platforms, including purchased product ID, quantity, amount, purchase time, etc., users' search history on e-commerce platforms or search engines, including search keywords, search time, etc., users' browsing history on e-commerce platforms, including browsed product ID, page dwell time, etc., users' interaction records on social media, including comments, likes, shares, etc.; competitor price data include the current price of competitor products, including product ID, price, price update time, etc., historical change records of competitor product prices, including price before and after price change, change time, etc.; market supply and demand data include the demand for specific products in the market, including product ID, demand, demand time, etc., the supply of specific products in the market, including product ID, supply, supply time, etc., and the company's own inventory information, including product ID, inventory, update time, etc.

[0070] S2. Based on the standardized data set, design the smart contract, define the rules and trigger conditions for dynamic pricing, and deploy the smart contract to the blockchain platform, including the following steps:

[0071] Design smart contracts based on standardized data sets; define dynamic pricing rules for market supply and demand and competitive product prices, and trigger conditions for specific times and events; use Solidity language to write smart contract code, define product structure and dynamic pricing logic, and provide methods for updating product data and adjusting prices; select a blockchain platform, use the Truffle framework and Ganache test network to deploy and test smart contracts, generate confirmation information for successful deployment and store it in the database.

[0072] Going further, the rules for dynamic pricing are as follows:

[0073] When market supply increases, lower prices appropriately; when demand increases, increase prices appropriately; when competing products lower prices, adjust prices appropriately to maintain competitiveness.

[0074] S3, real-time monitoring and analysis of market data through smart contracts on the blockchain platform, determining whether prices need to be adjusted and generating information on trigger conditions, including the following steps:

[0075] Read the contract address and confirmation information of successful transaction from the database; collect purchase records, search records, browsing records and interaction records from e-commerce platforms, social media and search engines in real time; collect product prices and price change records from competing platforms in real time; collect market demand and market supply data from market data platforms in real time; use big data analysis tools to extract key indicators of market supply and demand, competing product prices and consumer behavior; define dynamic pricing formula based on the influence of market supply and demand, competing product prices and consumer behavior, expressed as:

[0076] ;

[0077] in, For the new price, is the current price of the product, is the market demand at the current time point, is the market demand at the previous point in time, is the supply at the current time point, is the average price of competing products at the current time point, is the average price of competing products at the previous point in time, is the adjustment coefficient for the impact of changes in market demand on prices, The adjustment coefficient for the impact of price changes of competing products on prices;

[0078] Determine whether the price needs to be adjusted based on the dynamic pricing results;

[0079] According to the judgment result, the judgment conditions of the specific time and the specific event are triggered, and the information that the triggering conditions are met is generated and stored in the database.

[0080] Furthermore, the specific operations of the trigger conditions are:

[0081] When the market supply exceeds 120% of the average supply, a price reduction is triggered; when the market demand exceeds 120% of the average demand, a price increase is triggered; during holidays and promotional seasons, real-time adjustments are made based on the price adjustment strategy; the current and historical prices of competing products are obtained, and when the price of competing products drops by more than 10%, a price reduction is triggered.

[0082] S4. Based on the information that the triggering conditions are met, the smart contract automatically performs the price adjustment operation, and the generation of the price adjustment record includes the following steps:

[0083] Read information from the database about whether the triggering conditions of the new price are met; based on the triggering conditions and the new price, call the product update method of the smart contract to update the product data and automatically perform price adjustments; record the detailed information of the price adjustment in the smart contract and store it on the blockchain.

[0084] S5. Based on the price adjustment records, predicting the consumer's future purchasing behavior and preferences and generating personalized marketing suggestions include the following steps:

[0085] Read price adjustment records and related consumer behavior data from the database; extract key features based on price adjustment records and consumer behavior data; select a random forest model and use the extracted features and historical purchase records to train the random forest model; use the trained random forest model to calculate the comprehensive score of each consumer based on the current consumer behavior data and price adjustment records, expressed as:

[0086] ;

[0087] in, For the The overall rating of consumers, For the The purchase frequency of a consumer, For the The purchase amount of each consumer, For the The change in purchase quantity of a consumer before and after the price adjustment is, For the The frequency of keywords searched by consumers, For the The browsing time of each consumer, For the The number of interactions per consumer, Index for consumers, is the price sensitivity coefficient, is the adjustment coefficient for the impact of changes in market supply and demand and competitive product prices on consumer behavior, is the time decay factor, is the number of days from the current time;

[0088] Sort the comprehensive scores of all consumers to identify high-value and low-value consumer groups; push relevant product recommendations and advertisements based on consumers' preferred product categories.

[0089] S6. Based on personalized marketing suggestions, conduct effect evaluation and analysis, and optimize marketing strategies, including the following steps:

[0090] Read the generated personalized marketing suggestions from the database; execute specific marketing activities based on the generated marketing suggestions; set up a data collection mechanism to collect and integrate various feedback data and store them in the database; use statistical analysis and visualization tools to evaluate the collected feedback data; dynamically adjust the marketing strategy based on the evaluation results.

[0091] Going further, the specific steps of the marketing campaign are as follows:

[0092] High-value consumers: Design and send exclusive discounts, membership privileges and other advanced promotions. For example, send exclusive discount codes to high-value consumers via email or SMS, and provide privileges such as free trials or double points.

[0093] Low-value consumers: Provide basic preferential information to encourage them to increase the frequency and amount of purchases. For example, push basic promotional information such as limited-time discounts and full-reduction activities to low-value consumers through social media or advertising platforms.

[0094] Product recommendations: Push relevant product recommendations and advertisements based on consumers’ preferred product categories. For example, use a recommendation system to generate a personalized product list and display it to consumers through the homepage of the e-commerce platform, recommendation slots, or email marketing.

[0095] The specific data of the data collection mechanism include: the total sales brought by each promotion activity, customer satisfaction feedback collected through questionnaires, online reviews, etc., the conversion rate from clicking on promotion information to actual purchase, and the repurchase rate of customers after the promotion activity.

[0096] The specific steps for effect evaluation are as follows:

[0097] Sales analysis: Calculate the total sales brought by each promotion, compare the effects of different promotions, and find out the best performing types of activities.

[0098] Customer satisfaction analysis: Analyze customer satisfaction feedback to understand which promotions and product recommendations are most popular with customers.

[0099] Conversion rate analysis: Calculate the conversion rate from clicking on promotional information to actual purchase, and evaluate the attractiveness and effectiveness of promotional activities.

[0100] Customer retention rate analysis: Analyze the customer repurchase rate after promotional activities and evaluate the impact of promotional activities on customer loyalty.

[0101] The specific steps to adjust your marketing strategy are as follows:

[0102] Optimize promotion methods: Based on the analysis results of sales and conversion rates, optimize the form and content of promotion activities. For example, if a certain type of promotion has a high conversion rate but low sales, you can consider increasing the discount or extending the event time.

[0103] Adjust product mix: Adjust product mix based on the analysis results of customer satisfaction and repurchase rate. For example, if certain products are highly rated by customers but have a low repurchase rate, consider increasing promotions for related products or launching new products.

[0104] Personalized recommendation optimization: Optimize the personalized recommendation algorithm based on customer preferences and purchasing behavior. For example, if the click-through rate of some recommended products is low, the parameters of the recommendation algorithm can be adjusted to improve the accuracy of the recommendation.

[0105] S7. Based on the optimized marketing strategy, updating the rules and conditions in the smart contract includes the following steps:

[0106] Read marketing strategies optimized based on effect evaluation and analysis from the database. These strategies include new promotion methods, adjusted product portfolios, and optimized personalized recommendation rules. Adjust the pricing rules in the smart contract based on the optimized marketing strategies. Update pricing rules and trigger conditions in the smart contract, and test the updated smart contract. After the test passes, generate confirmation information of successful deployment and store it in the database.

[0107] In summary, the present invention is as follows: based on a standardized data set, a smart contract is designed, the rules and trigger conditions of dynamic pricing are defined, and the smart contract is deployed on a blockchain platform; the market data is monitored and analyzed in real time through the smart contract on the blockchain platform, it is determined whether the price needs to be adjusted and the information of the trigger conditions being satisfied is generated; based on the information of the trigger conditions being satisfied, the smart contract automatically performs the price adjustment operation and generates a price adjustment record; based on the price adjustment record, the future purchase behavior and preferences of consumers are predicted, and personalized marketing suggestions are generated; based on the personalized marketing suggestions, the effect is evaluated and analyzed, and the marketing strategy is optimized; based on the optimized marketing strategy, the rules and conditions in the smart contract are updated. By realizing the automation and traceability of price adjustment based on the information of the trigger conditions being satisfied, the trust of consumers and partners is enhanced.

[0108] Example 2, referring to Table 1, is the second example of the present invention. To further verify the technical solution of the present invention, experimental simulation data of a blockchain-based enterprise marketing planning system is provided.

[0109] An e-commerce company was selected as the experimental subject. The company mainly sells electronic products, including smartphones, tablets, and laptops. The experimental period is three months. During the experiment, the system collects market data in real time, including consumer behavior data, competitive product price data, and market supply and demand data, and performs preprocessing to generate a standardized data set. Subsequently, a smart contract is designed based on the standardized data set, dynamic pricing rules and trigger conditions are defined, and the smart contract is deployed to the blockchain platform. The system monitors market data in real time through smart contracts on the blockchain platform, analyzes it, determines whether prices need to be adjusted, and generates information that the trigger conditions are met. Based on the information that the trigger conditions are met, the smart contract automatically executes the price adjustment operation and generates a price adjustment record. Finally, based on the price adjustment record, the future purchasing behavior and preferences of consumers are predicted, personalized marketing suggestions are generated, and the effect evaluation and analysis are performed to optimize the marketing strategy.

[0110] The details are shown in Table 1 below:

[0111] Table 1 Experimental data record table

[0112]

[0113] By comparing the data of the control group and the experimental group, it can be clearly seen that the enterprise marketing planning system based on blockchain proposed in the present invention has shown significant advantages in responding to market changes. When the market demand increases or the price of competing products increases, the experimental group can adjust the price in time, thereby effectively increasing sales. For example, when the market demand increases from 1,000 to 1,200, the new price of the experimental group is increased from 200 yuan to 240 yuan, resulting in a 50% increase in sales, while the control group did not adopt a dynamic pricing strategy, and sales did not change.

[0114] On the contrary, when the market supply is greater than the demand or the price of competing products drops, the experimental group can also respond quickly and appropriately lower the price to maintain competitiveness. For example, when the supply is 1,000, the demand is 800, and the price of competing products is 130 yuan, the experimental group will reduce the price from 200 yuan to 160 yuan. Although the sales volume has decreased by 40%, compared with the control group, this strategy helps to maintain market share and avoid customer loss due to excessively high prices.

[0115] In summary, the enterprise marketing planning system based on blockchain not only improves the enterprise's response speed to market changes, but also enhances its survival and profitability in a highly competitive market environment. By dynamically adjusting prices, enterprises can better match market demand and optimize inventory management, while improving customer satisfaction and promoting long-term development. In addition, the system also has a high degree of transparency and security, which helps to establish the good reputation of the enterprise and further consolidate its market position.

[0116] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A blockchain-based enterprise marketing planning system, characterized by: include, The collection and preprocessing module collects and preprocesses market data in real time to generate standardized data sets; Smart contract design and deployment module: based on standardized data sets, design smart contracts, define dynamic pricing rules and trigger conditions, and deploy smart contracts to the blockchain platform; The market data monitoring and analysis module monitors and analyzes market data in real time through smart contracts on the blockchain platform, determines whether prices need to be adjusted, and generates information on whether trigger conditions are met; Price adjustment execution module: based on the information that the trigger conditions are met, the smart contract automatically executes the price adjustment operation and generates a price adjustment record; The consumer behavior prediction module predicts consumers’ future purchasing behavior and preferences based on price adjustment records and generates personalized marketing recommendations; The marketing effect evaluation and optimization module evaluates and analyzes the effect based on personalized marketing suggestions and optimizes the marketing strategy; Smart contract rule update module, which updates the rules and conditions in the smart contract according to the optimized marketing strategy; The smart contracts on the blockchain platform monitor market data in real time and analyze it to determine whether prices need to be adjusted and generate information that triggers the conditions to be met. The following steps are included: Read the contract address and transaction hash from the database to confirm that the deployment was successful; Collect purchase history, search history, browsing history and interaction history from e-commerce platforms, social media and search engines in real time; Collect product prices and price change records from competing platforms in real time; Collect market demand and market supply data in real time from the market data platform; Use big data analysis tools to extract key indicators of market supply and demand, competitive product prices, and consumer behavior; Based on the impact of market supply and demand, competitive product prices, and consumer behavior, a dynamic pricing formula is defined, expressed as: ； in, For the new price, is the current price of the product, is the market demand at the current time point, is the market demand at the previous point in time, is the supply at the current time point, is the average price of competing products at the current time point, is the average price of competing products at the previous point in time, is the adjustment coefficient for the impact of changes in market demand on prices, The adjustment coefficient for the impact of price changes of competing products on prices; Determine whether the price needs to be adjusted based on the dynamic pricing results; According to the judgment result, the judgment conditions of specific time and specific event are triggered, and the information that the triggering conditions are met is generated and stored in the database; Based on the information that the trigger conditions are met, the smart contract automatically performs the price adjustment operation. Generating the price adjustment record includes the following steps: Read from the database whether the triggering conditions and the new price are met; Based on the trigger conditions and new prices, the product update method of the smart contract is called to update the product data and automatically perform price adjustments; Record the details of the price adjustment in a smart contract and store it on the blockchain; Based on the price adjustment records, predicting consumers' future purchasing behavior and preferences and generating personalized marketing recommendations include the following steps: Read price adjustment records and related consumer behavior data from the database; Extract key features based on price adjustment records and consumer behavior data; Select the random forest model and use the extracted features and historical purchase records to train the random forest model; Using the trained random forest model, the comprehensive score of each consumer is calculated based on the current consumer behavior data and price adjustment records. The expression is: ； in, For the The overall rating of consumers, For the The purchase frequency of a consumer, For the The purchase amount of each consumer, For the The change in purchase quantity of a consumer before and after the price adjustment is, For the The frequency of keywords searched by consumers, For the The browsing time of each consumer, For the The number of interactions per consumer, Index for consumers, is the price sensitivity coefficient, is the adjustment coefficient for the impact of changes in market supply and demand and competitive product prices on consumer behavior, is the time decay factor, is the number of days from the current time; Sort the combined scores of all consumers to identify high-value and low-value consumer groups; Push relevant product recommendations and advertisements based on consumers’ preferred product categories.

2. The enterprise marketing planning system based on blockchain as claimed in claim 1, characterized in that: Collecting market data in real time and preprocessing it to generate a standardized data set includes the following steps: The market data includes consumer behavior data, competitive product price data, and market supply and demand data; Clean the collected market data and convert it into a unified format; Integrate data collected from different platforms into a unified dataset; The cleaned, transformed and integrated data are used to generate standardized data sets.

3. The enterprise marketing planning system based on blockchain as claimed in claim 2, characterized in that: Based on the standardized data set, designing smart contracts, defining the rules and trigger conditions for dynamic pricing, and deploying smart contracts to the blockchain platform include the following steps: Design smart contracts based on standardized data sets; Define dynamic pricing rules for market supply and demand and competitive product prices, as well as trigger conditions for specific times and events; Use Solidity language to write smart contract code, define product structure and dynamic pricing logic, and provide methods to update product data and adjust prices; Ethereum is selected as the blockchain platform, and the Truffle framework and Ganache test network are used to deploy and test smart contracts. Confirmation information of successful deployment is generated and stored in the database.

4. The enterprise marketing planning system based on blockchain as claimed in claim 3, characterized in that: Based on personalized marketing suggestions, we evaluate and analyze the effects and optimize the marketing strategy, including the following steps: Read the generated personalized marketing suggestions from the database; Execute specific marketing activities based on the generated marketing suggestions; Set up a data collection mechanism to collect and integrate various feedback data and store them in the database; Use statistical analysis and visualization tools to evaluate the collected feedback data; Dynamically adjust marketing strategies based on evaluation results.

5. The enterprise marketing planning system based on blockchain as claimed in claim 4, characterized in that: According to the optimized marketing strategy, updating the rules and conditions in the smart contract includes the following steps: Read the marketing strategy optimized based on effect evaluation and analysis from the database; Adjust the pricing rules in the smart contract based on the optimized marketing strategy; Update pricing rules and trigger conditions in smart contracts and test the updated smart contracts; After the test passes, a confirmation message of successful deployment is generated and stored in the database.

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