Marketing platform optimization method and system based on big data driving and medium

Abstract

The invention relates to the technical field of big data, and discloses a marketing table optimization method and system based on big data driving and a medium, and the method comprises the steps: generating a marketing strategy assembly and a marketing execution assembly of a marketing table according to a business process line of the marketing table and business data of a marketing business; generating a historical marketing interface of the marketing platform according to the marketing historical data, and generating a real-time marketing interface of the marketing platform according to the real-time behavior data; calling a marketing strategy component through a historical marketing interface and a real-time marketing interface, and updating component data in the called marketing strategy component; the marketing execution component is updated according to the marketing strategy updating component, and the marketing strategy in the marketing strategy updating component is executed according to the updated marketing execution component; and determining a marketing index of the marketing platform according to the execution result, and optimizing component parameters in the marketing strategy component in the marketing platform through the marketing index. According to the invention, the accuracy of marketing table optimization can be improved.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and particularly to an optimization method, system and medium for a marketing middle platform driven by big data. Background Art

[0002] With the popularization of technologies such as the Internet and the Internet of Things, the amount of data that enterprises can obtain has increased exponentially. And in the market, products and services are highly homogeneous. In order to stand out, enterprises need to accurately position the target customer group and provide personalized marketing, which prompts enterprises to use the marketing middle platform to integrate and analyze marketing data, mine potential value, so as to formulate more accurate and effective marketing strategies.

[0003] The existing marketing middle platform optimization technology optimizes the marketing middle platform through rules. For example, corresponding marketing rules are set according to certain specific attributes of customers (such as age, region, purchase history, etc.). When customers meet these rules, specific marketing activities or strategy adjustments are triggered. In practical applications, once the rules are set, it is relatively difficult to modify and adjust them, and it is difficult to quickly respond to the dynamic changes in the market and the diversity of customer needs, resulting in a relatively low accuracy when optimizing the marketing middle platform. Summary of the Invention

[0004] The present invention provides an optimization method, system and medium for a marketing middle platform driven by big data, and its main purpose is to solve the problem of relatively low accuracy when optimizing the marketing middle platform.

[0005] To achieve the above purpose, an optimization method for a marketing middle platform driven by big data provided by the present invention includes:

[0006] Generating a business process line based on the marketing business of the marketing middle platform, and generating a marketing strategy component and a marketing execution component of the marketing middle platform according to the business process line and the business data of the marketing business;

[0007] Obtaining the marketing historical data and real-time behavior data of a preset group of customers, generating a historical marketing interface of the marketing middle platform according to the marketing historical data, and generating a real-time marketing interface of the marketing middle platform according to the real-time behavior data;

[0008] Invoking the marketing strategy component through the historical marketing interface and the real-time marketing interface, and updating the component data in the invoked marketing strategy component to obtain a marketing strategy update component;

[0009] Updating the marketing execution component according to the marketing strategy update component, and executing the marketing strategy in the marketing strategy update component according to the updated marketing execution component to obtain an execution result;

[0010] Determine the marketing metrics of the marketing middle platform based on the execution result, and optimize the component parameters in the marketing strategy components in the marketing middle platform through the marketing metrics.

[0011] Optionally, generating a business process line based on the marketing business of the marketing middle platform includes:

[0012] Identify marketing products based on the marketing business of the marketing middle platform, and determine product positioning process points according to the marketing products;

[0013] Identify the marketing attributes in the marketing business, extract the key attributes in the marketing attributes, and determine the key marketing process points of the marketing business according to the key attributes;

[0014] Generate a business process line according to the process sequence corresponding to the product positioning process points and the key marketing process points.

[0015] Optionally, generating the marketing strategy components and marketing execution components of the marketing middle platform according to the business process line and the business data of the marketing business includes:

[0016] Divide the business process line into a product positioning process, a marketing mix strategy process, a strategy execution process, and a strategy evaluation process;

[0017] Extract the product elements in the product positioning process, and determine the strategy coordination elements in the marketing mix strategy process according to the product elements;

[0018] Package the product elements and the strategy coordination elements into the marketing strategy components of the marketing middle platform;

[0019] Extract the execution elements in the strategy execution process and the evaluation elements in the strategy evaluation process;

[0020] Package the execution elements and the evaluation elements into the marketing execution components of the marketing middle platform.

[0021] Optionally, generating the historical marketing interface of the marketing middle platform according to the marketing historical data includes:

[0022] Extract the data fields in the marketing historical data;

[0023] Generate interface request parameters according to the user identification field and the query time field in the data fields;

[0024] Determine the historical marketing function body of the marketing middle platform according to the interface request parameters;

[0025] Package the data fields into the historical marketing function body to obtain the historical marketing interface;

[0026] Associate the historical marketing interface with the data repository corresponding to the marketing historical data to obtain the historical marketing interface of the associated marketing middleware platform.

[0027] Optionally, the invoking of the marketing strategy component through the historical marketing interface and the real-time marketing interface includes:

[0028] Identify the historical marketing features corresponding to the historical marketing interface and the real-time marketing features corresponding to the real-time marketing interface;

[0029] Associate the historical marketing features and the real-time marketing features with the component elements in the marketing strategy component respectively to obtain a historical marketing adaptation matrix and a real-time marketing adaptation matrix;

[0030] Match the preset marketing business requirements with the historical marketing adaptation matrix and the real-time marketing adaptation matrix respectively to obtain a matching matrix;

[0031] Generate a calling factor for the marketing strategy component according to the matching matrix, and determine a calling trigger condition for the marketing strategy component according to the calling factor;

[0032] Invoke the marketing strategy component using the calling trigger condition.

[0033] Optionally, the updating of the component data in the invoked marketing strategy component to obtain a marketing strategy update component includes:

[0034] Extract the data timeliness type corresponding to the component data in the invoked marketing strategy component, and extract the marketing data corresponding to the data timeliness type;

[0035] Determine an update factor for the marketing strategy component according to the data timeliness type;

[0036] When the update factor is greater than or equal to a preset target update factor, update the component elements in the marketing strategy component according to the marketing data.

[0037] Optionally, the updating of the marketing execution component according to the marketing strategy update component includes:

[0038] Perform a quantization operation on the data timeliness type to obtain a type factor;

[0039] Identify the update status in the marketing strategy update component according to the type factor;

[0040] Calculate an update index for the marketing execution component according to the update status, where the update index calculation formula is:

[0041]

[0042] Wherein, D is the update index, and W is the type factor. is the policy weight after the t-th update corresponding to the i-th update factor in the marketing strategy update component, and C it is the index quantization value after the t-th update corresponding to the i-th update factor in the marketing strategy update component, and B it-1 is the index quantization value of the (t - 1)-th time corresponding to the i-th update factor;

[0043] Update the execution elements in the marketing execution component according to the update index.

[0044] Optionally, determining the marketing metrics of the marketing middle platform according to the execution result includes:

[0045] Extract the user metric data and market metric data in the execution result;

[0046] Calculate the marketing metrics of the marketing middle platform according to the user metric data and the market metric data:

[0047]

[0048] Wherein, M is the marketing metric, A is the market share change rate in the market metric data, I is the industry growth rate in the market metric data, X u is the metric value of the u-th metric in the user metric data, X v is the metric value of the v-th metric in the market metric data, U is the number of metrics in the user metric data, and V is the number of metrics in the market metric data.

[0049] To solve the above problems, the present invention also provides a marketing middle platform optimization system driven by big data, and the system includes:

[0050] A marketing component generation module, configured to generate a business process line based on the marketing business of the marketing middle platform, and generate the marketing strategy component and the marketing execution component of the marketing middle platform according to the business process line and the business data of the marketing business;

[0051] A marketing interface generation module, configured to obtain the marketing historical data and real-time behavior data of a preset group of customers, generate a historical marketing interface of the marketing middle platform according to the marketing historical data, and generate a real-time marketing interface of the marketing middle platform according to the real-time behavior data;

[0052] A marketing strategy component update module, configured to call the marketing strategy component through the historical marketing interface and the real-time marketing interface, and update the component data in the called marketing strategy component to obtain a marketing strategy update component;

[0053] A marketing execution component execution module is used to update the component according to the marketing strategy, update the marketing execution component, and execute the marketing strategy in the updated marketing execution component to obtain an execution result.

[0054] A marketing middle platform optimization module is used to determine the marketing metrics of the marketing middle platform according to the execution result, and optimize the component parameters in the marketing strategy component in the marketing middle platform through the marketing metrics.

[0055] To solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one computer program is stored, and the at least one computer program is executed by a processor in an electronic device to implement the above-mentioned marketing middle platform optimization method driven by big data.

[0056] In the embodiment of the present invention, the generated business process line can present complex marketing operations in a visual and orderly manner; the marketing strategy component and the marketing execution component are constructed based on the business process line and business data, so that the components closely fit the actual marketing business needs of the enterprise; the generated historical marketing interface and real-time marketing interface greatly improve the efficiency of data call, and the enterprise can quickly and accurately obtain the required information from a large amount of data through these interfaces without performing cumbersome query operations in a complex data storage system; by calling the marketing strategy component through the historical marketing interface and the real-time marketing interface and updating the component data, the marketing strategy can be adjusted in a timely manner as the market changes and the dynamic evolution of customer needs; the updated marketing strategy component incorporates the latest customer data and market information, making the formulated marketing strategy more accurate and effective; updating the marketing execution component according to the marketing strategy update component ensures a high degree of consistency between marketing execution and the latest marketing strategy; determining the marketing metrics through the execution result presents the effectiveness of the marketing activity in a quantitative manner; optimizing the component parameters in the marketing strategy component according to the marketing metrics realizes the continuous improvement of the marketing middle platform. Therefore, the marketing middle platform optimization method, system and medium proposed by the present invention can solve the problem of low accuracy in optimizing the marketing middle platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a flowchart of the marketing middle platform optimization method driven by big data provided by an embodiment of the present invention;

[0058] Figure 2 It is a functional module diagram of the marketing middle platform optimization system driven by big data provided by an embodiment of the present invention.

[0059] The realization, functional features and advantages of the object of the present invention will be further described in conjunction with the embodiments with reference to the drawings. Detailed implementation manners

[0060] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0061] An embodiment of the present application provides an optimization method for a marketing middle platform driven by big data. The execution subject of the optimization method for the marketing middle platform driven by big data includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the optimization method for the marketing middle platform driven by big data can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, Content Delivery Network (CDN), and big data and artificial intelligence platforms.

[0062] Referring to Figure 1 As shown, it is a schematic flowchart of an optimization method for a marketing middle platform driven by big data provided by an embodiment of the present invention. In this embodiment, the optimization method for the marketing middle platform driven by big data includes:

[0063] S1. Generate a business process line based on the marketing business of the marketing middle platform, and generate a marketing strategy component and a marketing execution component of the marketing middle platform according to the business process line and the business data of the marketing business.

[0064] In an embodiment of the present invention, the business process line refers to connecting product positioning process points and key marketing process points in a reasonable order to form a complete and coherent business operation process diagram, which shows the whole process from product positioning to the realization of marketing goals through the implementation of various key marketing activities and strategies.

[0065] In an embodiment of the present invention, generating a business process line based on the marketing business of the marketing middle platform includes:

[0066] Identify marketing products according to the marketing business of the marketing middle platform, and determine product positioning process points according to the marketing products;

[0067] Identify marketing attributes in the marketing business, extract key attributes in the marketing attributes, and determine key marketing process points of the marketing business according to the key attributes;

[0068] Generate a business process line according to the process sequence corresponding to the product positioning process points and the key marketing process points.

[0069] Specifically, the marketing operations involved in the marketing center usually revolve around a series of products or services. Identifying marketing products means clarifying the specific products or services that an enterprise promotes and sells in a specific marketing operation. For example, for an intelligent fitness equipment enterprise, its marketing operations may involve multiple products such as intelligent bracelets and intelligent treadmills. Product positioning refers to determining the position of a product in the target market, clarifying the target customer group for the product, that is, analyzing factors such as the needs, preferences, and consumption capabilities of target customers, as well as studying the characteristics and market shares of competitors' products. A product positioning process point refers to a process step of a marketing product in the business process line. Then, determine the product positioning process points based on the marketing products.

[0070] Specifically, marketing operations have multiple attributes that describe various aspects of marketing operations. For example, marketing channels (online e-commerce platforms, offline stores, etc.), types of marketing activities (promotion activities, new product launches, etc.), characteristics of target customers (age, gender, region, etc.), marketing budgets, etc. Among these numerous marketing attributes, not all attributes play an equally important role in the success of marketing operations. Key attributes are those that have a key impact on the implementation and effectiveness of marketing operations. Then, extract the key attributes and determine the corresponding key marketing process points. The key attributes include marketing mix strategies, marketing execution strategies, and marketing evaluation results. Then, use the marketing mix strategies, marketing execution strategies, and marketing evaluation results as process steps in the business process line respectively. The business process line is product positioning, marketing mix strategies, marketing execution strategies, and marketing evaluation results, that is, determine the marketing results of the product in the order of product positioning, marketing mix strategies, marketing execution strategies, and marketing evaluation results.

[0071] Furthermore, the business process line clearly presents the complete steps and logical sequence of marketing operations from start to end, providing a guarantee for the optimized implementation of marketing strategies. Therefore, it is necessary to optimize the marketing strategies of the marketing platform based on the business process line to ensure that the optimized marketing strategies can be effectively implemented in each marketing link, thereby achieving the continuous improvement and optimization of marketing operations.

[0072] In the embodiments of the present invention, the business data of the marketing business refers to various data information generated and collected during the development of the marketing business, including but not limited to customer data, market data, marketing channel data, and sales data; the marketing strategy component is the core part of the marketing middle platform, which integrates the product positioning and a series of marketing mix strategy elements formulated around the product positioning. It is a systematic marketing strategic framework, including product elements (such as product functions, features, target customers, etc.) and the coordinated marketing mix strategy elements (such as product, price, channel, promotion strategy, etc.), which can accurately position the product in the market, formulate effective marketing strategies to attract target customers, and achieve marketing goals; the marketing execution component is responsible for transforming the marketing strategy into actual actions, monitoring and evaluating the execution effect, including various resources and actions required for implementing the marketing strategy, such as execution elements like human resources, financial resources, channel operation actions, promotion activity execution, etc., and evaluation elements for measuring the execution effect, such as data indicators like sales volume, customer satisfaction, market share, etc., which can efficiently execute the marketing strategy and timely adjust the execution strategy according to the evaluation feedback to ensure the smooth development of marketing activities and the achievement of marketing goals.

[0073] In the embodiments of the present invention, generating the marketing strategy component and the marketing execution component of the marketing middle platform according to the business process line and the business data of the marketing business includes:

[0074] Dividing the business process line into a product positioning process, a marketing mix strategy process, a strategy execution process, and a strategy evaluation process;

[0075] Extracting the product elements in the product positioning process and determining the strategy coordination elements in the marketing mix strategy process according to the product elements;

[0076] Encapsulating the product elements and the strategy coordination elements into the marketing strategy component of the marketing middle platform;

[0077] Extracting the execution elements in the strategy execution process and the evaluation elements in the strategy evaluation process;

[0078] Encapsulating the execution elements and the evaluation elements into the marketing execution component of the marketing middle platform.

[0079] Specifically, the product positioning process is the initial and crucial stage of marketing business operations. It analyzes the needs of customers of different ages, genders, and exercise habits regarding the functions, appearance, price, etc. of fitness equipment, and at the same time studies the characteristics and competitive situation of similar products in the market, so as to determine the unique positioning of its own products; the marketing mix strategy process is based on product positioning. This process formulates a series of marketing strategy combinations, including product strategies (such as product function improvement, packaging design), price strategies (pricing, discount strategies), channel strategies (online e-commerce platforms, offline specialty stores, etc.), and promotion strategies (advertising placement, promotional activities), etc. These strategies cooperate with each other to jointly promote the promotion and sales of products; the strategy execution process is the process of transforming the marketing mix strategy into actual actions. For example, according to the selected channel strategy, put products on the e-commerce platform, arrange the store, and carry out online advertising placement and hold limited-time discount activities according to the promotion strategy to ensure the effective implementation of various marketing strategies; the strategy evaluation process is to monitor and evaluate the effect of strategy execution. By collecting and analyzing relevant data, such as product sales volume, customer satisfaction, market share, etc., judge whether the marketing strategy has achieved the expected goal.

[0080] Specifically, the product elements in the product positioning process cover all aspects of the product, including product functions (such as the exercise monitoring function and data synchronization function of intelligent fitness equipment), product characteristics (such as material, size, battery life), characteristics of the target customer group (age range, consumption ability, hobbies, etc.), product price range, etc. Then the product elements are the core content of product positioning and determine the unique value of the product in the market; according to the extracted product elements, determine the strategy coordination elements in the marketing mix strategy process. The strategy coordination elements include product strategy, price strategy, channel strategy, and promotion strategy. Furthermore, integrate and encapsulate the product elements and strategy coordination elements into the marketing strategy components of the marketing middle platform. The marketing strategy components are an aggregate that includes product positioning and a series of coordinated marketing strategies formulated around this positioning. For example, the product elements (high-end materials, advanced functions, high-consumption target customers, etc.) and strategy coordination elements (high-end product strategy, high pricing, high-end channels, high-end promotion) of high-end intelligent fitness equipment together constitute the marketing strategy components, and enterprises can plan and execute marketing activities based on this component.

[0081] Furthermore, the execution elements in the strategy execution process include the actions and resources for specifically implementing the marketing strategy. Thus, the execution elements include online channel execution elements, offline channel execution elements, and promotional activity execution elements. The evaluation elements in the strategy evaluation process are mainly the data and metrics used to measure the execution effect of the marketing strategy. For example, product sales data can be used to evaluate the market acceptance of the product by comparing the sales volumes in different time periods and regions. The results of customer satisfaction surveys can be used to understand customers' satisfaction with products and services and identify existing problems. The changes in market share can be used to judge the changes in the enterprise's position in the market competition. Therefore, the evaluation elements provide feedback information to the enterprise, helping it understand the execution effect of the marketing strategy. Subsequently, the execution elements and evaluation elements are integrated and encapsulated into the marketing execution component of the marketing middle platform. This component contains various resources and actions required for implementing the marketing strategy, as well as the criteria and data for evaluating the execution effect, ensuring that the marketing strategy can be smoothly implemented and continuously optimized through evaluation feedback. For example, the marketing execution component of an intelligent fitness equipment enterprise contains execution elements such as personnel and funds for online and offline channel operations, as well as evaluation elements such as sales volume and customer satisfaction. The enterprise can carry out the actual operation of marketing activities based on this component and adjust the execution strategy according to the evaluation results.

[0082] Furthermore, based on the constructed marketing strategy component and marketing execution component, the marketing middle platform can be continuously optimized. Before optimization, it is necessary to analyze the marketing data of group customers.

[0083] S2. Obtain the historical marketing data and real-time behavior data of preset group customers, generate the historical marketing interface of the marketing middle platform according to the historical marketing data, and generate the real-time marketing interface of the marketing middle platform according to the real-time behavior data.

[0084] In the embodiment of the present invention, the historical marketing data refers to various data records related to marketing activities of preset group customers in the past period, including but not limited to customers' purchase records, browsing histories, participation in promotional activities, customer complaints and feedback. The real-time behavior data refers to the behavior data related to marketing that group customers are currently performing, such as the product pages that customers are currently browsing, the real-time operation trajectories in websites or applications, the interactive situations of online activities they are participating in, real-time location information (if authorized to obtain), etc., which can reflect customers' current behavior states and interest points in real time, enabling enterprises to promptly capture customers' immediate needs for making real-time marketing decisions and responses.

[0085] Specifically, the marketing historical data of group customers can be obtained from a pre-stored storage area through computer statements with data scraping functions (such as Java statements, Python statements, etc.), where the storage area includes but is not limited to databases and blockchains; and the real-time behavior data of users is collected by embedding analysis code in the enterprise's website or mobile application.

[0086] Furthermore, marketing historical data is often stored in different data sources, such as customer relationship management systems, sales databases, e-commerce platform backends, etc. These data have different formats and storage methods, and it is difficult to directly obtain and use them. Therefore, the data needs to be unified and integrated to improve the data query efficiency.

[0087] In the embodiment of the present invention, the historical marketing interface is an important part of the marketing middle platform. It is a program interface used to obtain specific marketing historical data from the marketing historical data repository. This interface defines specific request parameters (such as user identification and query time) and combines the corresponding function body to implement the query and acquisition operations of the data.

[0088] In the embodiment of the present invention, generating the historical marketing interface of the marketing middle platform according to the marketing historical data includes:

[0089] Extracting the data fields from the marketing historical data;

[0090] Generating interface request parameters according to the user identification field and query time field in the data fields;

[0091] Determining the historical marketing function body of the marketing middle platform according to the interface request parameters;

[0092] Encapsulating the data fields into the historical marketing function body to obtain the historical marketing interface;

[0093] Associating the historical marketing interface with the data repository corresponding to the marketing historical data to obtain the associated historical marketing interface of the marketing middle platform.

[0094] Specifically, marketing historical data contains a lot of information related to marketing activities, which is stored in a structured or semi-structured form. Data fields are the basic units that make up this data. For example, basic customer information (name, age, gender, etc.), purchase records (purchased product name, purchase time, purchase quantity, purchase amount, etc.), and information on participating in promotional activities (activity name, participation time, obtained discounts, etc.). Extracting data fields means separating each specific information item from the marketing historical data. The user identification field in the data field is the field used to uniquely identify each customer. Common ones include customer ID, mobile phone number, email, etc. Through the user identification field, a specific customer and their related marketing historical data can be accurately located. For example, using the customer ID as the user identification field, when querying the marketing history of a certain customer, the corresponding data can be obtained based on this unique identifier; the query time field is used to specify the time range for querying data, such as the start time and end time. By setting the query time field, marketing historical data within a specific time period can be obtained. For example, if an enterprise wants to know the purchase records of a certain customer in the past month, it can set the query time field to the start and end times of the past month. Furthermore, by combining the user identification field and the query time field, interface request parameters are formed. These parameters are the key information for requesting specific marketing historical data from the system, and subsequently, the system will determine the data range and object to be queried based on these parameters.

[0095] Specifically, the historical marketing function body is a piece of program code that defines how to obtain corresponding marketing historical data from the data repository according to the interface request parameters. The function body contains the logic for interacting with the data repository. For example, database query statements (such as SQL statements) are used to retrieve relevant data from the database based on the user identification and query time. According to the interface request parameters, specific query conditions and operation steps are determined in the function body. For example, if the interface request parameters are that the customer ID is "12345" and the query time is from January 1, 2023, to December 31, 2023, then the historical marketing function body will contain a database query statement for this customer ID and time range to obtain the customer's marketing historical data within the specified time, and the data fields are integrated into the historical marketing function body, so that the function body not only contains the logic for obtaining data but also specific data content. The encapsulation process is to associate the data fields with the function body to ensure that the function body can correctly process and return these data when executed. Through encapsulation, a historical marketing interface, a functional unit that can be called by other systems or modules, is finally formed to obtain specific marketing historical data. The data repository is where the marketing historical data is stored, such as a database, a data warehouse, etc. Associating the historical marketing interface with the data repository enables the interface to establish a connection with the actual data storage location. Then, when other systems or modules call the historical marketing interface, the interface can accurately obtain the required marketing historical data from the corresponding repository. For example, associating the historical marketing interface with the enterprise's customer relationship management database, when the interface is called to query the customer's marketing history, the interface can retrieve relevant data from the database and return it to the caller.

[0096] In the embodiment of the present invention, the steps of generating the real-time marketing interface of the marketing middle platform according to the real-time behavior data are the same as those of generating the historical marketing interface of the marketing middle platform according to the marketing historical data, so they will not be elaborated again here. The real-time marketing interface is a bridge between the marketing middle platform, the real-time behavior data, and other marketing-related systems, and can obtain the customer's behavior data from the data source in real time, timely reflecting the customer's immediate needs and interest changes.

[0097] Furthermore, the historical marketing interface can obtain a large amount of marketing historical data, and the real-time marketing interface can obtain the customer's behavior data in real time. Based on the historical marketing interface and the real-time marketing interface, comprehensive marketing data can be obtained, ensuring the accuracy during the subsequent optimization of the marketing middle platform.

[0098] S3. Call the marketing strategy component through the historical marketing interface and the real-time marketing interface, and update the component data in the called marketing strategy component to obtain a marketing strategy update component.

[0099] In an embodiment of the present invention, invoking a marketing strategy component refers to the process of activating and executing a marketing strategy component from a component library of a marketing middleware according to certain conditions and rules.

[0100] In an embodiment of the present invention, invoking the marketing strategy component through the historical marketing interface and the real-time marketing interface includes:

[0101] Identifying the historical marketing features corresponding to the historical marketing interface and the real-time marketing features corresponding to the real-time marketing interface;

[0102] Associating the historical marketing features and the real-time marketing features with the component elements in the marketing strategy component respectively to obtain a historical marketing adaptation matrix and a real-time marketing adaptation matrix;

[0103] Matching the preset marketing business requirements with the historical marketing adaptation matrix and the real-time marketing adaptation matrix respectively to obtain a matching matrix;

[0104] Generating a call factor for the marketing strategy component according to the matching matrix, and determining a call trigger condition for the marketing strategy component according to the call factor;

[0105] Invoking the marketing strategy component by using the call trigger condition.

[0106] Specifically, the marketing historical data obtained through the historical marketing interface contains rich information. For example, from the historical sales data, features such as the sales trend of products in different time periods, the purchase frequency and preferences of different customer groups, and the input-output ratio of various marketing activities can be identified; information such as customer behavior data obtained in real time through the real-time marketing interface can also extract real-time marketing features, such as the content of the page that the customer is currently browsing, the product link clicked, the search keyword, and the real-time location information.

[0107] Specifically, the marketing strategy component includes component elements such as product positioning, pricing strategy, channel strategy, promotion strategy, etc. Then, the historical marketing features are associated with the component elements, that is, the relationship between each historical marketing feature and each component element is analyzed. If the historical marketing feature shows that a certain region had a high price sensitivity to a certain type of product in the past, then this feature can be associated with the price strategy element in the marketing strategy component. Similarly, the real-time marketing features are associated with the component elements to judge the impact of real-time customer behavior on each component element. The historical marketing adaptation matrix and the real-time marketing adaptation matrix represent the relationship between historical marketing features, real-time marketing features and each component element in the marketing strategy component in a quantitative or qualitative manner. The elements in the matrix can be correlation scores, influence degree levels, etc., which are used to intuitively display the adaptation situation between features and elements. For example, an element in the historical marketing adaptation matrix may indicate that the influence degree of a certain historical sales trend feature on the product positioning component element is high, medium or low. For example, high is the value 3, medium is the value 2, and low is the value 1.

[0108] Exemplarily, in the historical marketing adaptation matrix, the marketing strategy component elements are product positioning, pricing strategy, channel strategy, and promotion strategy. The sales trend, customer group preference, and marketing activity effect in the historical marketing matrix are corresponding to the marketing strategy component elements. For example, the relationship between the sales trend and product positioning is in line with the positioning of young and fashionable people. In the fourth quarter, the publicity of personalized appearance design can be strengthened to attract more customers, and the adaptation degree is high. Then, the relationship between the sales trend and product positioning is determined as 3.

[0109] Furthermore, the marketing business requirements include the time dimension, the goal - orientation dimension, and the marketing precision dimension. In the time dimension, it includes short - term decision - making and long - term planning. For example, when the marketing business requirements focus on the current market response and immediate marketing decisions, attracting new customers or activating dormant customers in the goal - orientation dimension, and conducting marketing activities with high precision requirements in the marketing precision dimension, the real - time marketing adaptation matrix is matched. When the business requirements involve long - term marketing strategy formulation, product planning or market trend analysis, business requirements for improving customer retention rate and loyalty in the goal - orientation dimension, and analyzing the overall market situation in the marketing precision dimension, the historical marketing adaptation matrix is matched. If the matching matrix is the historical marketing adaptation matrix, the historical marketing data call factor for the product element and the collaborative strategy in the marketing strategy component is 1, and the real - time marketing data call factor is 0. When the matching matrix is the real - time marketing adaptation matrix, the call factor for the historical marketing data in the marketing strategy component is 0, and the call factor for the real - time marketing data is 1. When the call factor for the historical marketing data is 1, the call trigger condition for the marketing strategy component is to trigger the historical marketing interface. When the call factor for the real - time marketing data is 0, the call trigger condition for the marketing strategy component is to trigger the real - time marketing interface. In addition, the historical marketing interface and the real - time marketing interface need to be combined to determine the accurate marketing strategy. At this time, the call factor for the historical marketing data in the marketing strategy component is 1, and the call factor for the real - time marketing data is 1, and its trigger condition is to trigger the real - time marketing interface and the historical marketing interface, so as to call the marketing strategy component according to the call trigger condition.

[0110] Furthermore, the market environment is dynamically changing. Consumers' needs, preferences, purchase behaviors, and competitors' strategies are all evolving continuously. By updating component data in a timely manner, enterprises can adjust the marketing focus and direction, keep the marketing strategy in sync with market changes, and better adapt to market dynamics.

[0111] In the embodiment of the present invention, the marketing strategy update component is a new component obtained by updating and optimizing the called marketing strategy component.

[0112] In the embodiment of the present invention, updating the component data in the called marketing strategy component to obtain the marketing strategy update component includes:

[0113] Extracting the data timeliness type corresponding to the component data in the called marketing strategy component, and extracting the marketing data corresponding to the data timeliness type;

[0114] Determining the update factor of the marketing strategy component according to the data timeliness type;

[0115] When the update factor is greater than or equal to a preset target update factor, update the component elements in the marketing strategy component according to the marketing data.

[0116] Specifically, the data timeliness combination types are historical data, real-time data, and historical data + real-time data. Historical data indicates that the relevant component data in the marketing strategy component is sourced from the marketing data accumulated over a past period; real-time data indicates that the component data is based on the currently generated real-time marketing-related data; historical data + real-time data combines historical marketing data and real-time marketing data. For the historical data type, relevant data is obtained from the enterprise's historical database; for the real-time data type, data is obtained through a real-time data acquisition system; for the historical data + real-time data type, corresponding data needs to be extracted from the historical database and the real-time data acquisition system respectively.

[0117] Specifically, the update factor is an indicator used to measure whether the marketing strategy component needs to be updated and the degree of update. Since historical data has a long time span and relatively stable data with an unobvious change trend, the update factor is 0.1, and the update factor range is 0 - 1. The larger the value, the higher the necessity of update. Since real-time data reflects the current market and customer immediate situation, the update factor is 0.9. The determination of the update factor should consider both the stability and trend of historical data and the market change speed reflected by real-time data. Then the update factor is 0.5, and the target update factor is 0.5. If the update factor determined by the data timeliness type reaches or exceeds the preset target update factor, the component elements in the marketing strategy component need to be updated. Suppose the update factor determined by the historical data + real-time data timeliness type is 0.5, which is equal to the preset target update factor of 0.5. At this time, if the marketing data shows that the recent customer demand for product personalization has increased significantly (reflected by real-time data), and the historical data also shows an increasing trend of customer preference for personalized products, then the component elements of the product positioning need to be updated to emphasize the personalized features of the product; in terms of the price strategy, a differentiated price strategy may be formulated for personalized customized products; in terms of the channel strategy, increase the investment in online platforms that can display personalized products; in terms of the promotion strategy, exclusive promotion activities for personalized customized products may be launched, such as discounts for customized products and giving customized gifts.

[0118] Furthermore, after the marketing strategy component is updated, the marketing execution component will verify the marketing effect in the marketing strategy component. Therefore, the marketing execution component needs to be analyzed.

[0119] S4. Update the marketing execution component according to the marketing strategy update component, and execute the marketing strategy in the marketing strategy update component according to the updated marketing execution component to obtain an execution result.

[0120] In the embodiments of the present invention, the marketing strategy update component bears the latest market insights, changes in customer needs, and the adjustment direction of the enterprise's marketing strategy. The core task of the marketing execution component is to transform the marketing strategy into actual actions. By updating the marketing execution component based on the marketing strategy update component, it can ensure that the marketing execution link closely follows the changes in the marketing strategy.

[0121] In the embodiments of the present invention, updating the marketing execution component according to the marketing strategy update component includes:

[0122] Performing a quantification operation on the data timeliness type to obtain a type factor;

[0123] Identifying the update status in the marketing strategy update component according to the type factor;

[0124] Calculating the update index of the marketing execution component according to the update status, where the update index calculation formula is:

[0125]

[0126] where D is the update index, W is the type factor, is the strategy weight after the t-th update corresponding to the i-th update factor in the marketing strategy update component, C it is the index quantization value after the t-th update corresponding to the i-th update factor in the marketing strategy update component, B it-1 is the index quantization value of the (t - 1)-th time corresponding to the i-th update factor;

[0127] Updating the execution elements in the marketing execution component according to the update index.

[0128] Specifically, the type factor corresponding to the historical data in the data timeliness type is determined to be 1, the type factor corresponding to the real-time data in the data timeliness type is determined to be 3, and the type factor corresponding to the combination of historical data and real-time data in the data timeliness type is determined to be 2. When the type factor is small, the update status is relatively stable; when the type factor is large, it indicates that the marketing strategy update component is greatly affected by real-time data and may have been updated more frequently and significantly, and the update status is relatively active. Through the type factor, the update degree and direction of the marketing strategy update component can be initially determined.

[0129] Specifically, the update metric is a comprehensive value used to measure the degree to which the marketing execution component needs to be updated. It takes into account the data timeliness type and the changes in each update factor in the marketing strategy update component. The marketing strategy update component may include multiple update factors, such as product positioning, pricing strategy, channel strategy, etc. (denoted by i for different update factors). Each factor has a corresponding strategy weight after each update (denoted by t for the number of updates). This weight represents the importance of this factor in the current marketing strategy. For example, after a certain update, the strategy weight of the product positioning factor is 0.4, indicating that product positioning occupies an important position in the current marketing strategy. For each update factor, there is a quantified metric value after the t-th update. For example, after the product positioning is updated, metrics such as market share and target customer group satisfaction are used to quantify its effect. Then, by calculating (C it -B it-1 ) / C it the change ratio of this factor in this update can be determined. Furthermore, based on the value of the update metric, decisions can be made on how to update the execution elements in the marketing execution component. The execution elements include the specific operation steps, personnel arrangements, resource allocations, etc. of the marketing activities. If the update metric D is large, it indicates that the changes in the marketing strategy update component are significant, and the marketing execution component needs to be updated substantially, such as adjusting the work focus of the sales team, reallocating the advertising budget, changing the product promotion channels, etc. If the update metric D is small, then the marketing execution component may only need to make some minor adjustments or optimizations, such as fine-tuning the details of the promotional activities, optimizing the customer service process, etc. In this way, the marketing execution component can be kept consistent with the marketing strategy update component to ensure the effective execution of marketing activities.

[0130] Furthermore, execute the marketing strategy in the marketing strategy update component according to the updated marketing execution component to obtain the execution result. That is, according to the updated marketing execution component, put the marketing strategy in the marketing strategy update component into practice, which means that in actual operation, relevant personnel carry out various marketing activities in an orderly manner based on the personnel arrangements, resource allocations, time plans, and operation processes specified in the marketing execution component. For example, the social media operation team, according to the time plan, produces and publishes relevant promotional content within the specified time period based on the new product strategy and promotional strategy; the market promotion specialist organizes and implements offline promotional activities according to the resource allocation plan, etc. By executing the marketing strategy in the marketing strategy update component according to the updated marketing execution component, the execution result is obtained, and the execution result includes user metric data and market metric data.

[0131] Even further, the execution result intuitively reflects the actual performance of the marketing strategy in the market. By converting it into specific marketing metrics, such as sales volume, sales quantity, market share, etc., it can accurately measure whether the marketing activities have achieved the expected goals, thereby judging the effectiveness of the marketing activities.

[0132] S5. Determine the marketing metrics of the marketing middleware based on the execution result, and optimize the component parameters in the marketing strategy components in the marketing middleware through the marketing metrics.

[0133] In the embodiment of the present invention, the marketing metric is a quantitative value, which synthesizes user metric data and market metric data, and simultaneously considers factors such as the market share change rate and the industry growth rate, and is used to measure the effect of the marketing activities of the marketing middleware.

[0134] In the embodiment of the present invention, the determining the marketing metrics of the marketing middleware according to the execution result includes:

[0135] Extract the user metric data and market metric data in the execution result;

[0136] Calculate the marketing metrics of the marketing middleware according to the user metric data and the market metric data:

[0137]

[0138] Wherein, M is the marketing metric, A is the market share change rate in the market metric data, I is the industry growth rate in the market metric data, X u is the metric value of the u-th metric in the user metric data, X v is the metric value of the v-th metric in the market metric data, U is the number of metrics in the user metric data, and V is the number of metrics in the market metric data.

[0139] Specifically, the average values of the user metrics and the market metrics are calculated respectively and normalized, and then the square root of the sum of their squares is taken to obtain a basic comprehensive index, and then multiplied by a factor considering the impact of the competitor's market share change. The greater the change in the competitor's market share, the greater the impact on this index. Finally, it is multiplied by a factor reflecting the impact of industry growth. The faster the industry grows, the higher the index.

[0140] Specifically, calculate the average values of the user metrics and the market metrics respectively and perform normalization processing, then take the square root of the sum of their squares to obtain a basic comprehensive index, and then multiply it by a factor considering the impact of the competitor's market share change. The greater the change in the competitor's market share, the greater the impact on this index. Finally, multiply it by a factor reflecting the impact of industry growth. The faster the industry grows, the higher the index.

[0141] Furthermore, marketing metrics are a quantitative reflection of the overall effectiveness of marketing activities. By analyzing marketing metrics, it is possible to clarify the actual performance of marketing strategies in the market, understand whether marketing activities have achieved the expected goals, and identify areas where there are still deficiencies. For example, if the marketing metrics show that the market share is growing slowly, it may mean that the current marketing strategy is not effective in expanding the market, and adjustments need to be made to the relevant component parameters.

[0142] In the embodiments of the present invention, optimizing the component parameters in the marketing strategy components in the marketing middle platform through the marketing metrics includes:

[0143] When the marketing metric is greater than or equal to a preset marketing threshold, the component parameters in the marketing strategy components in the marketing middle platform are used as the optimized component parameters, and the marketing strategy components in the marketing middle platform are generated according to the optimized component parameters.

[0144] When the marketing metric is less than the preset marketing threshold, the marketing impact parameters corresponding to the marketing metric are identified, and the target component parameters of the marketing strategy components are identified according to the marketing impact parameters;

[0145] Optimize the marketing strategy corresponding to the target component parameters, and update the marketing strategy components in the marketing middle platform according to the optimized impact strategy.

[0146] Specifically, a marketing threshold is pre-customized. This threshold is determined comprehensively based on factors such as the enterprise's marketing goals, market conditions, and past marketing experience, and represents a standard value of the marketing effect that the enterprise expects to achieve. For example, the enterprise sets a marketing threshold for market share growth of 10%, a sales growth threshold of 20%, etc. When the calculated marketing metric is greater than or equal to the preset marketing threshold, it indicates that the marketing strategy corresponding to the component parameters in the current marketing middle platform has achieved good results in the market and reached or exceeded the enterprise's expected goals. In this case, the existing component parameters are directly recognized as the optimized component parameters, and new marketing strategy components in the marketing middle platform are generated according to the determined optimized component parameters. The new components will continue to guide subsequent marketing activities because their effectiveness has been verified.

[0147] Specifically, when the marketing metric is less than the preset marketing threshold, it indicates that the current marketing strategy has not achieved the expected results of the enterprise and needs to be optimized. Identify the marketing impact parameters related to the marketing metric. The marketing impact parameter refers to the relevant data of various factors that affect the marketing metric. For example, if the marketing metric is market share, then the marketing impact parameters may include the competitiveness of the product (such as product functions, quality, etc.), price advantage, channel coverage, attractiveness of promotional activities, etc. By analyzing these parameters, the reasons for the unsatisfactory marketing metric can be found. For example, after analysis, it is found that the product price is too high, resulting in slow growth of market share. Then the product price is an important marketing impact parameter, and the target component parameter refers to those parameters in the marketing strategy component that are directly related to the marketing impact parameter. For example, if it is identified that the product price is the key marketing impact parameter affecting market share, then in the marketing strategy component, the parameters related to the price strategy (such as pricing level, price adjustment mechanism, etc.) are the target component parameters.

[0148] Furthermore, for the determined target component parameters, formulate specific optimization plans. For example, for the target component parameters related to the price strategy, if it is identified that the product price is too high and affects the market share, then strategies such as considering reducing the product price or launching some price discount packages can be considered; the price adjustment mechanism can also be optimized to make it more flexible to adapt to market changes; according to the optimized impact strategy, update the marketing strategy component of the marketing middle platform, and integrate the optimized parameters and strategies into the marketing strategy component so that it can better guide subsequent marketing activities to improve the marketing metric and achieve the enterprise's marketing goal. For example, update the adjusted price strategy parameters to the marketing strategy component, and at the same time, the relevant component parameters such as the promotion strategy and channel strategy need to be adjusted accordingly to ensure the coordination and effectiveness of the entire marketing strategy.

[0149] As Figure 2 shown, it is a functional module diagram of the marketing middle platform optimization system driven by big data according to an embodiment of the present invention.

[0150] The marketing middle platform optimization system 100 driven by big data according to the present invention can be installed in an electronic device. According to the functions implemented, the marketing middle platform optimization system 100 can include a marketing component generation module 101, a marketing interface generation module 102, a marketing strategy component update module 103, a marketing execution component execution module 104, and a marketing middle platform optimization module 105. The modules of the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by the processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0151] In this embodiment, the functions of each module / unit are as follows:

[0152] The marketing component generation module 101 is configured to generate a business process line based on the marketing business of the marketing middle platform, and generate a marketing strategy component and a marketing execution component of the marketing middle platform according to the business process line and the business data of the marketing business;

[0153] The marketing interface generation module 102 is configured to obtain the marketing historical data and real-time behavior data of a preset group of customers, generate a historical marketing interface of the marketing middle platform according to the marketing historical data, and generate a real-time marketing interface of the marketing middle platform according to the real-time behavior data;

[0154] The marketing strategy component update module 103 is configured to call the marketing strategy component through the historical marketing interface and the real-time marketing interface, and update the component data in the called marketing strategy component to obtain a marketing strategy update component;

[0155] The marketing execution component execution module 104 is configured to update the marketing execution component according to the marketing strategy update component, and execute the marketing strategy in the marketing strategy update component according to the updated marketing execution component to obtain an execution result;

[0156] The marketing middle platform optimization module 105 is configured to determine the marketing metrics of the marketing middle platform according to the execution result, and optimize the component parameters in the marketing strategy component in the marketing middle platform through the marketing metrics.

[0157] Specifically, each module in the marketing middle platform optimization system 100 based on big data driving described in the embodiments of the present invention adopts the same technical means as those in the above Figure 1 The marketing middle platform optimization method based on big data driving described, and can produce the same technical effects, which will not be elaborated here.

[0158] The present invention also provides a computer-readable storage medium, where the readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, it can implement:

[0159] Generate a business process line based on the marketing business of the marketing middle platform, and generate a marketing strategy component and a marketing execution component of the marketing middle platform according to the business process line and the business data of the marketing business;

[0160] Obtain the marketing historical data and real-time behavior data of a preset group of customers, generate a historical marketing interface of the marketing middle platform according to the marketing historical data, and generate a real-time marketing interface of the marketing middle platform according to the real-time behavior data;

[0161] Calling the marketing strategy component through the historical marketing interface and the real-time marketing interface, and updating component data in the called marketing strategy component to obtain a marketing strategy update component;

[0162] The marketing execution component is updated according to the marketing strategy update component, and the marketing strategy in the marketing strategy update component is executed according to the updated marketing execution component to obtain an execution result;

[0163] The marketing indicators of the marketing center are determined according to the execution results, and the component parameters in the marketing strategy components in the marketing center are optimized through the marketing indicators.

[0164] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0165] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0166] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0167] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0168] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is not limited only according to the above description, so it is intended to include all changes within the meaning and scope of the equivalent elements of the claims in the present invention.

[0169] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is a theory, method, technology, and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use the knowledge to obtain the best results.

[0170] In addition, it is obvious that the word "including" does not exclude other units or steps, and the singular does not exclude the plural. The multiple units or systems stated in the system claims can also be implemented by one unit or system through software or hardware. The terms such as first and second are used to denote names and do not denote any particular order.

[0171] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing a marketing middle platform driven by big data, characterized in that, The method includes: Generating a business process line based on the marketing business of the marketing middle platform, and generating a marketing strategy component and a marketing execution component of the marketing middle platform according to the business process line and the business data of the marketing business; Obtaining the marketing historical data and real-time behavior data of a preset group of customers, generating a historical marketing interface of the marketing middle platform according to the marketing historical data, and generating a real-time marketing interface of the marketing middle platform according to the real-time behavior data; Invoking the marketing strategy component through the historical marketing interface and the real-time marketing interface, and updating the component data in the invoked marketing strategy component to obtain a marketing strategy update component; Updating the marketing execution component according to the marketing strategy update component, and executing the marketing strategy in the marketing strategy update component according to the updated marketing execution component to obtain an execution result; Determining the marketing metrics of the marketing middle platform according to the execution result, and optimizing the component parameters in the marketing strategy component in the marketing middle platform through the marketing metrics.

2. The optimization method of the marketing middle platform driven by big data as claimed in claim 1, wherein The generating a business process line based on the marketing business of the marketing middle platform includes: Identifying marketing products according to the marketing business of the marketing middle platform, and determining product positioning process points according to the marketing products; Identifying the marketing attributes in the marketing business, extracting the key attributes in the marketing attributes, and determining the key marketing process points of the marketing business according to the key attributes; Generating a business process line according to the process sequence corresponding to the product positioning process point and the key marketing process point.

3. The optimization method of the marketing middleware based on big data drive according to claim 1, characterized in that, The generating the marketing strategy component and the marketing execution component of the marketing middle platform according to the business process line and the business data of the marketing business includes: Dividing the business process line into a product positioning process, a marketing mix strategy process, a strategy execution process, and a strategy evaluation process; Extracting product elements in the product positioning process, and determining strategy coordination elements in the marketing mix strategy process according to the product elements; Encapsulating the product elements and the strategy coordination elements into a marketing strategy component of the marketing middle platform; Extracting execution elements in the strategy execution process and evaluation elements in the strategy evaluation process; Encapsulating the execution elements and the evaluation elements into a marketing execution component of the marketing middle platform.

4. The optimization method of the marketing middleware platform driven by big data as claimed in claim 1, wherein, The generating the historical marketing interface of the marketing middle platform according to the marketing historical data includes: Extracting data fields in the marketing historical data; Generating interface request parameters according to the user identification field and the query time field in the data fields; Determining a historical marketing function body of the marketing middle platform according to the interface request parameters; Encapsulating the data fields into the historical marketing function body to obtain a historical marketing interface; Associating the historical marketing interface with a data repository corresponding to the marketing historical data to obtain an associated historical marketing interface of the marketing middle platform.

5. The optimization method of the marketing middle platform based on big data drive according to claim 1, characterized in that The invoking the marketing strategy component through the historical marketing interface and the real-time marketing interface includes: Identifying the historical marketing features corresponding to the historical marketing interface and the real-time marketing features corresponding to the real-time marketing interface; Associate the historical marketing features and the real-time marketing features with the component elements in the marketing strategy components respectively to obtain a historical marketing adaptation matrix and a real-time marketing adaptation matrix; Match the preset marketing business requirements with the historical marketing adaptation matrix and the real-time marketing adaptation matrix respectively to obtain a matching matrix; Generate a call factor for the marketing strategy components according to the matching matrix, and determine the call trigger condition for the marketing strategy components according to the call factor; Invoke the marketing strategy components using the call trigger condition.

6. The optimization method of the marketing middle platform driven by big data according to claim 1, characterized in that Updating the component data in the invoked marketing strategy components to obtain a marketing strategy update component, including: Extract the data timeliness type corresponding to the component data in the invoked marketing strategy components, and extract the marketing data corresponding to the data timeliness type; Determine the update factor of the marketing strategy components according to the data timeliness type; When the update factor is greater than or equal to a preset target update factor, update the component elements in the marketing strategy components according to the marketing data.

7. The optimization method of the marketing middle platform driven by big data according to claim 6, characterized in that Updating the marketing execution components according to the marketing strategy update components, including: Perform a quantization operation on the data timeliness type to obtain a type factor; Identify the update status in the marketing strategy update components according to the type factor; Calculate the update index of the marketing execution components according to the update status, where the update index calculation formula is: Among them, D is the update index, and W is the type factor. is the policy weight after the t-th update corresponding to the i-th update factor in the marketing strategy update component, C it is the index quantization value after the t-th update corresponding to the i-th update factor in the marketing strategy update component, B it-1 is the index quantization value of the (t - 1)-th time corresponding to the i-th update factor; Update the execution elements in the marketing execution components according to the update index.

8. The optimization method of the marketing middle platform based on big data drive according to claim 1, characterized in that Determining the marketing indicators of the marketing middle platform according to the execution result, including: Extract the user indicator data and market indicator data in the execution result; Calculate the marketing indicators of the marketing middle platform according to the user indicator data and the market indicator data: Among them, M is the marketing indicator, A is the market share change rate in the market indicator data, I is the industry growth rate in the market indicator data, X u is the indicator value of the u-th indicator in the user indicator data, X v is the indicator value of the v-th indicator in the market indicator data, U is the number of indicators in the user indicator data, and V is the number of indicators in the market indicator data.

9. An abnormal user behavior processing system based on cloud computing services, characterized in that, For executing the optimization method of the marketing middle platform driven by big data as described in any one of claims 1-8, the system includes: A marketing component generation module, configured to generate a business process line based on the marketing business of the marketing middle platform, and generate the marketing strategy components and marketing execution components of the marketing middle platform according to the business process line and the business data of the marketing business; A marketing interface generation module, configured to obtain the marketing historical data and real-time behavior data of a preset group of customers, generate a historical marketing interface of the marketing middle platform according to the marketing historical data, and generate a real-time marketing interface of the marketing middle platform according to the real-time behavior data; A marketing strategy component update module, configured to invoke the marketing strategy components through the historical marketing interface and the real-time marketing interface, and update the component data in the invoked marketing strategy components to obtain a marketing strategy update component; A marketing execution component execution module, configured to update the marketing execution components according to the marketing strategy update components, and execute the marketing strategies in the marketing strategy update components according to the updated marketing execution components to obtain an execution result; A marketing middle platform optimization module, configured to determine the marketing indicators of the marketing middle platform according to the execution result, and optimize the component parameters in the marketing strategy components in the marketing middle platform through the marketing indicators.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the optimization method of the marketing middle platform driven by big data as described in any one of claims 1 to 8.

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