Marketing scene process management system and method based on artificial intelligence
Through the marketing scenario process management system based on artificial intelligence, using data collection and analysis modules, user interests and analysis models are established, and marketing processes are optimized. The problem of traditional marketing relying on manual experience is solved, and accurate and efficient marketing management is achieved to adapt to complexity. Market environment and customer needs.
Patent Information
- Application Number
- CN202411136080.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-19
- Publication Date
- 2024-12-10
AI Technical Summary
Traditional marketing processes rely on manual experience and intuition, and lack in-depth analysis of large amounts of data, resulting in insufficient marketing efficiency and accuracy, making it difficult to cope with complex market environments and customer needs.
Adopt a marketing scenario process management system based on artificial intelligence, and through data collection, analysis and optimization modules, user interest models and analysis models are established, marketing processes are optimized, and accurate and efficient marketing management is achieved.
Through accurate analysis and classification of consumer data, precise marketing strategies for different user groups are formulated to improve the efficiency and accuracy of marketing management, reduce manual intervention, and improve marketing effectiveness.
Smart Images
Figure CN119107107A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of intelligent information management, and in particular to a marketing scenario process management system and method based on artificial intelligence. Background Art
[0002] The planning of marketing activities is the most popular way of product publicity and promotion. Its business characteristics are usually to combine multiple businesses, formulate preferential promotion content, and formulate sales rules to achieve the purpose of marketing. With the increasing number of large-scale domestic activities, the traditional large-scale event model based on human management has been greatly impacted: the traditional way is to judge the attractiveness of goods to consumers through manual experience, grasp the needs of consumers, and then carry out corresponding marketing methods for the goods. However, since the process mostly relies on manual labor, it is time-consuming and labor-intensive, and there may be subjective influencing factors such as inconsistent judgment standards, which is not conducive to improving marketing efficiency and effectiveness.
[0003] In the current marketing field, enterprises are facing increasingly complex market environments and customer demands, and traditional marketing process orchestration methods often rely on manual experience and intuition, lacking in-depth analysis and utilization of large amounts of data. Therefore, in order to reduce manual intervention and improve work efficiency, a management system that can realize automation and intelligence of marketing scenario processes is needed. The present invention solves this technical problem. Summary of the invention
[0004] The present invention provides an artificial intelligence-based marketing scenario process management system and method, which can collect and analyze marketing-related data, making it convenient for staff to formulate corresponding marketing processes based on user interests, thereby achieving more accurate and efficient marketing management.
[0005] A marketing scenario process management system based on artificial intelligence, including
[0006] Data collection module, used to collect data related to marketing activities, including consumer information and order information;
[0007] The data storage module is used to store the collected data and process the data;
[0008] Marketing analysis module, used to analyze the collected data, create user profiles, and analyze users' personal preferences;
[0009] The process optimization module is used to generate optimization plans for the marketing process based on the analysis results and the existing marketing process.
[0010] Furthermore, the data collection module includes an e-commerce platform, a shopping app, and a WeChat public account.
[0011] Furthermore, the process optimization module includes a business logic evaluation unit, a process addition and deletion unit, a process modification unit and a process detection unit.
[0012] A marketing scenario process management method based on artificial intelligence, based on the marketing scenario process management system based on artificial intelligence as described above, comprises the following steps:
[0013] S1. Collect data including consumer information, order information and previous marketing process information;
[0014] S2. Save the collected document data and standardize the data;
[0015] S3. Establish user interest model through neural network model to realize user portrait and facilitate consumer classification;
[0016] S4. Build an analysis model based on the basic information of different types of consumers to analyze their impact on product purchases, facilitating precision marketing;
[0017] S5. Adjust the original marketing process plan according to different user types and different products to optimize the marketing process.
[0018] Furthermore, the step S2 includes the following steps:
[0019] S21. Convert the collected semi-structured and unstructured data into structured data;
[0020] S22, processing the data and deleting the repetitive data and the data in irregular format;
[0021] S33. The processed data is saved for subsequent use.
[0022] Furthermore, the step S3 includes the following steps:
[0023] S31. Count the user's interest in different aspects of the product and express it in the following ways:
[0024] U={(w1, v1), (w2, v2),...(w m , v m )}
[0025] Among them, w represents the consumer's interest keyword for the product, and v represents the weight value corresponding to the interest keyword. The higher the weight value, the more interested the user is in this interest keyword.
[0026] S32. Calculate the value of v using the TF-IDF algorithm. The calculation method is as follows:
[0027]
[0028] Among them, TN(l i , m) is the keyword l i The number of times in document m, n represents the total number of documents, DN(l i ) means it contains l i The number of documents;
[0029] S33, dividing the above-obtained data into a training set and a test set, and establishing a user interest model through a neural network algorithm.
[0030] Further, the step S4 includes the following steps:
[0031] S41. Establish a regression model for binary variables, expressed by the following expression:
[0032]
[0033] The dependent variable y is a binary variable with values of 1 and 0, x is the risk factor that affects y, and p = p(y = 1 | x) represents the probability that y = 1 under the condition x;
[0034] S42. The multivariate logistic regression model is expressed as follows:
[0035]
[0036] In this case, whether the consumer buys the product is set as the dependent variable y, x i is the independent variable, indicating the basic information of consumers;
[0037] S43. Based on the above model, the weight of each factor on the purchase of goods is calculated, and the impact of various basic information of consumers on the purchase of goods is learned, so as to formulate precise marketing strategies for consumer groups.
[0038] Furthermore, the step S5 includes the following steps:
[0039] S51. Perform logic detection and analysis on the marketing process through the business logic evaluation unit;
[0040] S52. Based on the information obtained from the user interest model and the analysis model, the staff modifies the original marketing process by adding or deleting process units and changing process units to optimize the marketing process.
[0041] S53. Use the detection unit to detect the optimized marketing process to ensure the normal operation of the marketing process.
[0042] The technical effects of the present invention are as follows:
[0043] (1) The present invention acquires basic information and order information of consumers by collecting data, and then analyzes the data, creates user portraits based on user interest models, classifies users, and then analyzes which factors of users of different types will affect the purchase of goods through analysis models, so that accurate marketing scenario processes can be formulated based on this information, thereby achieving more accurate and efficient marketing management;
[0044] (2) Traditional marketing methods are usually based on the staff's own experience, without data analysis, which makes marketing less accurate. This solution divides users into different types of groups through user interest models, making it easier to develop different marketing methods for different types of groups. In addition, since it combines the collected basic information of consumers, order information and other data, the model can classify users more accurately.
[0045] (3) The analytical model can analyze which factors of consumers will have an impact on the purchase of goods, so that these factors can be focused on in subsequent marketing planning, making marketing more accurate and efficient. BRIEF DESCRIPTION OF THE DRAWINGS
[0046] Figure 1 It is a flowchart of the present invention. DETAILED DESCRIPTION
[0047] The technical means of the present invention will be clearly and completely described below in conjunction with specific embodiments and drawings.
[0048] A marketing scenario process management system based on artificial intelligence, including
[0049] Data collection module, used to collect data related to marketing activities, including consumer information and order information;
[0050] The data storage module is used to store the collected data and process the data;
[0051] Marketing analysis module, used to analyze the collected data, create user profiles, and analyze users' personal preferences;
[0052] The process optimization module is used to generate optimization plans for the marketing process based on the analysis results and the existing marketing process.
[0053] Through data collection, we can collect basic information, behavioral attribute information, etc. of users, integrate the collected information to form a big data center, and then call the relevant methods in the data storage module to normalize and store the collected information. After storage is completed, it can provide data for the processing of user portrait engine business. With this data, we can build user portraits. After building the user portrait model, we can carry out precise marketing of products based on the results of portrait analysis.
[0054] Furthermore, the data collection module includes an e-commerce platform, a shopping app, and a WeChat public account.
[0055] Furthermore, the process optimization module includes a business logic evaluation unit, a process addition and deletion unit, a process modification unit and a process detection unit.
[0056] See also Figure 1 , a marketing scenario process management method based on artificial intelligence, based on the marketing scenario process management system based on artificial intelligence as mentioned above, comprising the following steps:
[0057] S1. Collect data including consumer information, order information and previous marketing process information;
[0058] S2. Save the collected document data and standardize the data;
[0059] S3. Establish user interest model through neural network model to realize user portrait and facilitate consumer classification;
[0060] S4. Build an analysis model based on the basic information of different types of consumers to analyze their impact on product purchases, facilitating precision marketing;
[0061] S5. Adjust the original marketing process plan according to different user types and different products to optimize the marketing process.
[0062] This embodiment mainly uses a data collector to collect and process external data. Corresponding interfaces are set for different application systems, APP applications, or WeChat public accounts. Data administrators can flexibly define, maintain and modify the interfaces, which facilitates the expansion and upgrade of the system. The collected information includes but is not limited to the consumer's basic information, order information, types and frequency of purchased goods, types, frequency and reasons for returns, etc.
[0063] Furthermore, the step S2 includes the following steps:
[0064] S21. Convert the collected semi-structured and unstructured data into structured data;
[0065] S22, processing the data and deleting the repetitive data and the data in irregular format;
[0066] S33. The processed data is saved for subsequent use.
[0067] This system can continuously collect data from data channels according to the data collection settings. The collection process is automatic. The data saving function in the system after collection can also be automatically performed according to the setting specifications. Specifically, the automatic saving of data content can be achieved through the service interface constructed by the program. The message content received during the data saving process, as well as the saving status and saved content can be presented in the data saving log, which is convenient for troubleshooting after problems occur during the data saving process.
[0068] Furthermore, the step S3 includes the following steps:
[0069] S31. Count the user's interest in different aspects of the product and express it in the following ways:
[0070] U={(w1, v1), (w2, v2),...(w m ,v m )}
[0071] Among them, w represents the consumer's interest keyword for the product, and v represents the weight value corresponding to the interest keyword. The higher the weight value, the more interested the user is in this interest keyword.
[0072] S32. Calculate the value of v using the TF-IDF algorithm. The calculation method is as follows:
[0073]
[0074] Among them, TN(l i , m) is the keyword l i The number of times in document m, n represents the total number of documents, DN(l i ) means it contains l i The number of documents;
[0075] S33, dividing the above-obtained data into a training set and a test set, and establishing a user interest model through a neural network algorithm.
[0076] Whether it is an e-commerce platform, a shopping app, or a WeChat public account, the core of data collection is how to find the consumer group that is suitable for participation. In essence, it is a process of screening consumer portraits using big data technology. By calculating individual data such as consumer gender and age, as well as consumer behavior data such as purchase time, product type, and order information, a multi-dimensional portrait of the consumer is achieved, which facilitates the analysis and study of the consumer group and market demand trends of a certain product.
[0077] Further, the step S4 includes the following steps:
[0078] S41. Establish a regression model for binary variables, expressed by the following expression:
[0079]
[0080] The dependent variable y is a binary variable with values of 1 and 0, x is the risk factor that affects y, and p = p(y = 1 | x) represents the probability that y = 1 under the condition x;
[0081] S42. The multivariate Logistic regression model is expressed as follows:
[0082]
[0083] In this case, whether the consumer buys the product is set as the dependent variable y, x i is the independent variable, indicating the basic information of consumers;
[0084] S43. Based on the above model, the weight of each factor on the purchase of goods is calculated, and the impact of various basic information of consumers on the purchase of goods is learned, so as to formulate precise marketing strategies for consumer groups.
[0085] Taking a certain style of goods on the Internet as an example, we conduct data analysis to investigate the impact of users' basic behavioral characteristics on the purchase of goods. First, we quantify the data in the above part, and then establish an analysis model to obtain the main factors that affect users' purchase of goods. Then, we establish multiple Logistic regression models to process the data and calculate the weight of each factor on the purchase of goods. This is used to study how each piece of information affects whether to buy goods, thereby realizing the study of the impact of consumers' basic attributes and personal preferences on the purchase of goods, which is convenient for the subsequent development of precise marketing strategies;
[0086] Through the above method, the relationship between the interests and hobbies of user groups and the types and styles of goods can be analyzed. At the same time, the product promotion department can adjust the marketing methods and means for users with different purchase probabilities, including factors such as the type of goods and aspects of consumers' interests. That is to say, the greatest advertising intensity, the longest time, the largest number of advertisements, etc. can be adopted for the time period of network stay and the shopping platform where users with extremely high purchase probability stay; for users with higher purchase probability, the above-mentioned intensity can be reduced, the time of delivery can be shortened, and the number of delivery can be reduced; for users with relatively low purchase probability, the lowest advertising intensity, the shortest time, and the least amount of platform delivery can be adopted, so as to achieve targeted marketing for different users.
[0087] The user's information includes but is not limited to age, gender, education, occupation, etc.
[0088] Furthermore, the step S5 includes the following steps:
[0089] S51. Perform logic detection and analysis on the marketing process through the business logic evaluation unit;
[0090] S52. Based on the information obtained from the user interest model and the analysis model, the staff modifies the original marketing process by adding or deleting process units and changing process units to optimize the marketing process.
[0091] S53. Use the detection unit to detect the optimized marketing process to ensure the normal operation of the marketing process.
[0092] Since there is less data analysis in the original traditional marketing scenario process, it is difficult to carry out precision marketing for different types of consumers. Therefore, through the content recorded in this plan, the data is analyzed to obtain different user portraits and the impact of user interests on the purchase of goods. Then, the previous marketing process can be changed through the process optimization module. It can be deleted, added, etc. according to the actual situation, so as to achieve precision marketing.
[0093] The communication objects of precision marketing are no longer all customers but target customers after segmentation. Precision marketing is not only a marketing tool based on the Internet, but also no longer relies solely on mass media. Instead, it mainly communicates through social platforms, search engines and other niche media. Through efficient interaction, the audience's contact is more intuitive, so that the implementation effect can be measured and more accurate. The premise of precision marketing is accurate positioning. Only by using accurate advertising based on user behavior can we accurately reflect the user's hobbies and provide users with accurate and personalized services. To achieve precision marketing, we must have accurate marketing concepts, accurately analyze customers, accurately position products in the market, accurately place products in different regions, improve customer experience, and finally accurately manage the brand.
[0094] The above embodiments are only preferred embodiments of the present invention. The above embodiments are merely exemplary embodiments used to illustrate the principles of the present invention and are not intended to limit the scope of the present invention. For those skilled in the art, various modifications and improvements can be made on the basis of the present invention without departing from the spirit and essence of the present invention. These modifications and improvements are also regarded as the protection scope of the present invention. Therefore, this application protects not only the above embodiments, but also the scope consistent with the principles and features of the present invention.
Claims
1. A marketing scenario process management system based on artificial intelligence, characterized in that: include Data collection module, used to collect data related to marketing activities, including consumer information and order information; The data storage module is used to store the collected data and process the data; Marketing analysis module, used to analyze the collected data, create user profiles, and analyze users' personal preferences; The process optimization module is used to generate optimization plans for the marketing process based on the analysis results and the existing marketing process.
2. The marketing scenario process management system based on artificial intelligence according to claim 1 is characterized in that: The data collection module includes e-commerce platforms, shopping apps, and WeChat public accounts.
3. The marketing scenario process management system based on artificial intelligence according to claim 1 is characterized in that: The process optimization module includes a business logic evaluation unit, a process addition and deletion unit, a process modification unit and a process detection unit.
4. A marketing scenario process management method based on artificial intelligence, based on the marketing scenario process management system based on artificial intelligence as claimed in claim 1, characterized in that: The following steps are involved: S1. Collect data including consumer information, order information and previous marketing process information; S2. Save the collected document data and standardize the data; S3. Establish user interest model through neural network model to realize user portrait and facilitate consumer classification; S4. Build an analysis model based on the basic information of different types of consumers to analyze their impact on product purchases, facilitating precision marketing; S5. Adjust the original marketing process plan according to different user types and different products to optimize the marketing process.
5. The marketing scenario process management system based on artificial intelligence according to claim 4 is characterized in that: The step S2 comprises the following steps: S21. Convert the collected semi-structured and unstructured data into structured data; S22, processing the data and deleting the repetitive data and the data in irregular format; S33. The processed data is saved for subsequent use.
6. The marketing scenario process management system based on artificial intelligence according to claim 4 is characterized in that: The step S3 comprises the following steps: S31. Count the user's interest in different aspects of the product and express it in the following ways: U={(w1,v1),(w2,v2),……,(w m ,v m )} Among them, w represents the consumer's interest keyword for the product, and v represents the weight value corresponding to the interest keyword. The higher the weight value, the more interested the user is in this interest keyword. S32. Calculate the value of v using the TF-IDF algorithm. The calculation method is as follows: Among them, TN(l i , m) is the keyword l i The number of times in document m, n represents the total number of documents, DN(l i ) means it contains l i The number of documents; S33, dividing the above-obtained data into a training set and a test set, and establishing a user interest model through a neural network algorithm.
7. The marketing scenario process management system based on artificial intelligence according to claim 4 is characterized in that: The step S4 comprises the following steps: S41. Establish a regression model for binary variables, expressed by the following expression: The dependent variable y is a binary variable with values of 1 and 0, x is the risk factor that affects y, and p = p(y = 1 | x) represents the probability that y = 1 under the condition x; S42. The multivariate logistic regression model is expressed as follows: In this case, whether the consumer buys the product is set as the dependent variable y, x i is the independent variable, indicating the basic information of consumers; S43. Based on the above model, the weight of each factor on the purchase of goods is calculated, and the impact of various basic information of consumers on the purchase of goods is learned, so as to formulate precise marketing strategies for consumer groups.
8. The marketing scenario process management system based on artificial intelligence according to claim 4 is characterized in that: The step S5 comprises the following steps: S51. Perform logic detection and analysis on the marketing process through the business logic evaluation unit; S52. Based on the information obtained from the user interest model and the analysis model, the staff modifies the original marketing process by adding or deleting process units and changing process units to optimize the marketing process. S53. Use the detection unit to detect the optimized marketing process to ensure the normal operation of the marketing process.
Citation Information
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