Intention-driven marketing activity automatic generation method and system

Abstract

The invention provides an intent-driven marketing activity automatic generation method and system, and the method comprises the steps: obtaining intra-bank business data and a tag library, carrying out the processing of customer data through a big data portrait and a tag algorithm model, and generating and outputting enhanced customer group data and a customer 360 portrait; based on the generated customer group data and customer 360 portraits, classifying and layering customers, establishing and managing a high-quality marketing material library through a management background, tracking and analyzing use data of materials, and outputting user demands and behavior analysis results; issuing an online marketing task to the mobile terminal of the employee, automatically tracking and collecting task completion data, and generating a precision marketing strategy for different clients; meanwhile, using data of the materials are tracked and analyzed, user requirements and behavior analysis results are output, marketing strategies are changed according to behaviors of clients, market changes can be quickly responded, and marketing efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of digital marketing, in particular to an intent-driven marketing activity automatic generation method and system. BACKGROUND

[0002] In the current financial industry competition, customer relationship management and precision marketing have become one of the core competencies, and the traditional marketing mode has many pain points: first, the marketing strategy depends on the personal experience of the customer manager, lacks data support, and is difficult to scale; second, customer data is scattered in various business systems, forming a data island, and cannot form a unified customer view; third, the planning, execution and tracking process of marketing activities is complicated, the response to market changes is slow, and the efficiency is low; finally, the marketing effect is difficult to quantify and evaluate, and cannot form an effective closed-loop optimization.

[0003] In the prior art, although there are some customer relationship management systems and marketing automation tools, they mostly have the following shortcomings: the customer portrait dimension is single, and the behavior data and business data cannot be deeply integrated; the generation of marketing strategies still requires a lot of manual intervention, and the automation and intelligence degree is insufficient; the coupling degree between system modules is low, and the end-to-end automation process from customer insight to strategy generation, task execution and effect evaluation cannot be realized; there is a lack of effective employee empowerment and performance linkage mechanism, which makes it difficult to implement good strategies. SUMMARY

[0004] The technical problem to be solved by the present application is to overcome the defects in the prior art, and the present application provides an intent-driven marketing activity automatic generation method and system.

[0005] To solve the above technical problems, the present application provides the following technical solutions: In a first aspect, the present application provides an intent-driven marketing activity automatic generation method, comprising: Obtaining in-line business data and a label library, processing customer data using a big data portrait and label algorithm model, generating and outputting enhanced customer group data and a customer 360 portrait; Based on the generated customer group data and customer 360 portrait, classifying and layering customers, and generating and outputting personalized precision marketing strategies for different customer layer groups; Building and managing a high-quality marketing material library through a management background, tracking and analyzing material usage data, and outputting user demand and behavior analysis results; Distributing online marketing tasks to the mobile terminals of employees, and automatically tracking and collecting task completion data to output task completion reports; Processing data, generating and outputting data reports including data dashboards, hero lists and lost customer analysis.

[0006] Preferably, the inventory data is converted into inventory asset data, and output is used as a basis for decision-making to reduce the cost of innovation and marketing cost.

[0007] Preferably, through the mobile terminal platform, the in-line product, activity and information are displayed to the customer terminal, and the fission marketing activity is triggered and executed based on the interactive behavior of the customer.

[0008] Preferably, the customer 360 portrait includes the basic information of the customer, the asset level, the business handled, the added staff information, the group information to which the customer belongs, and the dynamic information of the customer.

[0009] Preferably, the high-quality marketing material library includes popular activities, marketing tactics, promotional posters and product materials, and the material sources include management background configuration and external real-time information automatically captured through the interface.

[0010] Preferably, the tracking and analysis of the use data of the material includes monitoring the embedded content through the intelligent radar module, generating and sending a notification reminder to the customer manager according to the dynamic behavior of the customer, so as to perfect the customer portrait and evaluate the marketing effect.

[0011] Preferably, the performance data for formulating the staff reward and punishment system is generated according to the task completion report.

[0012] In the second aspect, the application provides an intention-driven marketing activity automatic generation system, including the intention-driven marketing activity automatic generation method, The server combines the in-line business data and the tag library, processes the customer data by using the big data portrait and the tag algorithm model, generates and outputs the enhanced customer group data and the customer 360 portrait; The customer analysis module classifies and layers the customers based on the generated customer group data and the customer 360 portrait, and generates and outputs the individualized precision marketing strategy for different customer layered groups; The material management module builds and manages the high-quality marketing material library through the management background, and tracks and analyzes the use data of the material, and outputs the user demand and behavior analysis result; The task management module issues the online marketing task to the mobile terminal of the staff, and automatically tracks and collects the task completion data, and outputs the task completion report; The data report module processes the data, generates and outputs the data report including the data board, the hero list and the lost customer analysis.

[0013] In the third aspect, the application provides an electronic device, including: The memory and the processor; The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, which implement the steps of the intent-driven marketing activity automatic generation method when executed by the processor.

[0014] In a fourth aspect, the present application provides a computer readable storage medium storing computer executable instructions, which implement the steps of the intent-driven marketing activity automatic generation method when executed by a processor.

[0015] Compared with the prior art, the beneficial effects of the present application include: expanding customer group data and generating customer 360 portraits through big data portrait and label algorithm model, classifying and layering customers, and generating precise marketing strategies for different customers; at the same time, tracking and analyzing the use data of the materials, outputting the user demand and behavior analysis results, changing the marketing strategy according to the customer behavior, and quickly responding to market changes to speed up the marketing efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0016] The disclosure of the present application will be described with reference to the accompanying drawings. It should be understood that the drawings are only for illustrative purposes, and are not intended to limit the scope of protection of the present application. In the drawings, the same reference numerals are used to refer to the same parts. Among them: Figure 1 The flowchart of the intent-driven marketing activity automatic generation method according to one embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0017] It is easy to understand that, according to the technical solution of the present application, those skilled in the art can propose a plurality of structure modes and implementation modes which can be replaced with each other without changing the essential spirit of the present application. Therefore, the following specific embodiments and drawings are only exemplary descriptions of the technical solution of the present application, and should not be regarded as the whole or as a limitation or restriction of the technical solution of the present application.

[0018] Embodiment 1, refer to Figure 1 For one embodiment of the present application, an intent-driven marketing activity automatic generation method is provided, which comprises: S100: Multi-source data fusion and dynamic customer portrait construction The original data is converted into customer insights with commercial significance and operability.

[0019] S101: Data acquisition and preprocessing The system server acquires multi-dimensional data in real time or in batches from various business systems in the industry through a data bus, such as a core system, a credit system, an online banking system, and a mobile banking APP. These data include: Customer attribute data: static information such as age, gender, occupation, education, and geographic location.

[0020] Asset and transaction data: deposit balance, financial product holdings, AUM, recent half-year transaction records, loan records, credit card bills, etc.

[0021] Behavioral data: APP login frequency, page dwell time, function click heat map, search keywords, customer service call recording, etc.

[0022] External data: credit data, social security data, and consumption preference data introduced through authorized and compliant channels.

[0023] The system performs ETL (Extraction, Transformation, and Loading) processing on the obtained raw data, including data cleaning (de-duplication and de-noising), data standardization (unit unification and format specification), and data correlation (linking data from different sources through customer ID). In this process, the system establishes a unified customer data model (Unified Customer Profile Model) and creates a unique master record for each customer.

[0024] S102: Tagging and Profile Enhancement The system uses a pre-set label algorithm model library to calculate the pre-processed data and assign rich labels to customers. These labels are multi-level: Factual labels: directly from data, such as "logged into the APP 3 times in the past week" and "holds XX financial product."

[0025] Model labels: calculated through machine learning models, such as: Use clustering models (e.g., K-Means, DBSCAN) to group customers and find similar behavior patterns.

[0026] Use classification models (e.g., logistic regression, random forest) to predict customer "churn risk probability" and "high-yield product purchase inclination."

[0027] Use association rule analysis, such as the Apriori algorithm, to find the association between products, such as customers who have purchased product A are likely to need service B.

[0028] Predictive labels: based on time series models such as ARIMA and LSTM, predict future asset changes and life cycle value (LTV).

[0029] The final generated customer 360 profile is a dynamic and three-dimensional view presented in the form of visual dashboards in the management background. It includes but is not limited to: Basic information panel: displays the core identity information of the customer.

[0030] Asset Panorama: Show the distribution and trend of the customer's assets in a ring chart and trend chart.

[0031] Product Ownership Map: Show the relationship between the customer and the products in the industry in a relationship chart.

[0032] Behavior Timeline: Record the key interaction behaviors of the customer in chronological order (e.g. "On October 27, 2023, browse the mortgage product detail page for more than 3 minutes").

[0033] Association Network: Show the customer's relationship manager (staff) information, the customer group they belong to (e.g. "Wealth Management VIP Group"), and their social relationship chain (subject to compliance).

[0034] Intention Signal Zone: Real-time display of the latest customer dynamics captured by the intelligent radar module, such as "recently frequently querying information related to overseas consumption", which is considered as a strong "potential demand for overseas services" intention signal.

[0035] S200: Customer segmentation and strategy generation based on machine learning, responsible for converting customer insights into executable business strategies.

[0036] S201: Customer segmentation and intention recognition Instead of simply performing demographic segmentation, the system uses a multi-dimensional, dynamic hybrid segmentation model. For example, by combining the customer's "current value", "potential value" and "risk of loss" dimensions, a three-dimensional matrix is constructed to divide customers into eight customer groups such as "high-value stable type", "high-potential growth type", "risk warning type", and "general maintenance type".

[0037] More importantly, the system monitors the behavior data stream generated in S100 through a real-time intention recognition engine. For example, when a customer continuously browses multiple financial product pages related to studying abroad and searches for the "foreign exchange" keyword in a short period of time, the engine will immediately recognize the "financial needs for children studying abroad" and temporarily classify the customer into the "high-priority-studying abroad financial intention customer group".

[0038] S202: Individualized strategy automatic generation The system has a built-in marketing strategy rule engine. The operation personnel can pre-configure a series of "IF-THEN" rules, for example: IF the customer belongs to "high-value stable type" AND the intention signal contains "large amount of money inflow" THEN trigger the "private bank exclusive financial manager connection" strategy.

[0039] IF Customer belongs to "high risk of attrition" AND no interaction in the last 30 days THEN trigger the "push high attractive coupon and inform the account manager" strategy.

[0040] In more advanced implementations, the system introduces a reinforcement learning mechanism, where the system treats each marketing touch (such as pushing a product or using a script) as an "action" and positive customer feedback (such as clicks or purchases) as "rewards". Through continuous trial and error, the system can automatically optimize the best marketing actions for different customers in different situations, enabling the strategy to evolve autonomously without relying entirely on human rules.

[0041] The generated strategy will explicitly specify: target customer group, recommended product / activity, recommended channel (APP / Push / short message / account manager), recommended material, and optimal touch time.

[0042] S300: Intelligent material management and content adaptation S301: Construction and empowerment of material library Through the management background, the operation personnel can upload, classify, label and manage a large number of marketing material library. This library not only includes traditional popular activities, marketing scripts, promotional posters, product materials, but also extends to H5 interactive pages, short video scripts, live broadcast topics and other rich media content.

[0043] Material sources are diversified: Internal creation: produced by the head office / branch marketing team.

[0044] External scraping: automatically scrape financial news and hot events through API interface and generate marketing script points associated with them through NLP technology. For example, when the central bank announces a rate cut, the system can automatically scrape the news and suggest that the account manager use "rate cut is good for the capital market, your asset allocation can consider..." and other related scripts.

[0045] Integrate AIGC (artificial intelligence generated content) capabilities, input key product information and target customer characteristics, and the system can automatically generate multiple versions of advertising copy, email drafts, and even poster design suggestions.

[0046] S302: Material effect analysis and intelligent matching The system labels each material with rich metadata tags (such as "target customer group: young white-collar workers", "product type: credit", "style: humorous"), and at the same time, through the burying point, it comprehensively tracks the exposure, click rate, conversion rate, sharing rate, and dwell time of the material. Effect indicators.

[0047] When the strategy generated by S200 needs to call materials, the content adaptation engine will intelligently match the most effective materials from the material library according to the customer characteristics and marketing goals defined in the strategy. For example, for the promotion of credit cards for "young white-collar" customers, the engine will prefer to select posters with fashionable design, lively language, and the highest click rate in historical delivery.

[0048] S400: Task automation distribution and employee empowerment This step is the "last mile" of strategy implementation, and the key is to empower front-line employees rather than simply assigning tasks.

[0049] S401: Task assignment and scenario-based empowerment The system converts the strategy generated in S200 into specific to-do tasks and pushes them to the employee mobile APP through the message queue. The task content is not just "recommend product B to customer A", but a "scenario-based empowerment workbench". When the customer manager clicks on the task, he can see: Customer portrait snapshot: key information, recent dynamics, and identified intentions of the customer.

[0050] Recommended scripts: the optimal scripts provided by S300, which can be copied and sent by the customer manager with one click.

[0051] Supporting materials: related posters, product links.

[0052] Best contact time suggestion: the best contact time period analyzed by the system based on the customer's historical behavior.

[0053] Similar case reference: show other customer managers' successful marketing cases for similar customers.

[0054] S402: Execution tracking and performance linkage The system automatically tracks the entire life cycle of the task: from assignment, customer manager reading, execution (such as making a phone call, sending a WeChat message), to customer feedback (such as clicking on a link, replying to information), all these interaction data will be recorded.

[0055] Based on the task completion data, the system automatically generates multi-dimensional task completion reports and employee performance data. These data are not only used to calculate performance bonuses, but also to generate "hero list" and display on team data dashboard, creating a positive competitive atmosphere. At the same time, the system can also identify employees who need training and help, achieving fine-grained management.

[0056] S500: Full-link monitoring, analysis and feedback optimization This step realizes the closed loop and drives the continuous evolution of the system.

[0057] S501: Panoramic data dashboard and attribution analysis The system provides a manager-oriented panoramic data dashboard that presents real-time core indicators of marketing activities, such as the size of customer touch, conversion rate, input-output ratio, and changes in customer satisfaction.

[0058] The system supports marketing attribution analysis, which can analyze which marketing touch or touches contributed to a successful conversion (such as purchasing a financial product) (was it the customer manager's call or the previous APP push?), thereby helping to optimize the allocation of marketing resources.

[0059] S502: Churned customer analysis and stock asset value-added The system has a special churned customer analysis module that performs retrospective analysis on the historical data of churned customers, constructs a "churned customer portrait", and identifies key early warning signals before churn (such as "transaction frequency has decreased for three consecutive months" and "APP login frequency has dropped sharply"). These insights are used to optimize the churn risk prediction model in S200 and develop proactive customer retention strategies.

[0060] Finally, the entire system converts the dormant stock data in the industry into stock asset data that can drive business growth through the above processes. For example, through analysis, it is found that certain customers have a high demand for high-end services after their assets reach a certain threshold. Based on this, precise customer upgrade plans can be developed to effectively reduce the high cost of acquiring new customers from outside.

[0061] Embodiment 2, the above is a schematic scheme of the intent-driven marketing activity automation generation method. It should be noted that the technical scheme of the intent-driven marketing activity automation generation system belongs to the same concept as the technical scheme of the intent-driven marketing activity automation generation method described above. The technical scheme of the intent-driven marketing activity automation generation system in this embodiment is not described in detail. The details can be referred to the description of the technical scheme of the intent-driven marketing activity automation generation method.

[0062] This embodiment also provides an intent-driven marketing activity automation generation system, which includes: S601: Data acquisition and preprocessing layer Data interface gateway: responsible for interfacing with internal and external data source systems to ensure data security and stable flow.

[0063] Stream-batch integrated data processing engine: uses Apache Flink or Spark framework to realize the unification of real-time data stream processing and batch historical data processing.

[0064] Data warehouse: stores raw and processed data to support upper-layer analysis.

[0065] S602: AI capability platform layer Algorithm Model Library: Encapsulates various machine learning models for customer profiling, prediction, and segmentation, and provides standard API call services.

[0066] Natural Language Processing Engine: Used to analyze customer service recordings and customer feedback texts to extract sentiment and key intents.

[0067] AIGC Engine: Provides intelligent content generation capabilities.

[0068] Real-time intent calculation engine: responsible for processing behavioral data streams and identifying customer intent signals in real time.

[0069] S603: Business Application Mid-Level Layer Customer Profile Service Center: Implements S100 functions and provides a unified customer profile query service to external parties.

[0070] Strategy generation and decision engine: Implements S200 functions and is the control center of the system.

[0071] Material and Content Management Center: Enables S300 functionality.

[0072] Task Scheduling and Tracking Center: Implements S400 functionality and manages the entire lifecycle of tasks.

[0073] Intelligent Radar Module: As an independent subsystem, it is responsible for monitoring embedded points, discovering key customer behaviors, and triggering real-time alerts.

[0074] Analysis and Reporting Service Center: Implements S500 functions, assembles and provides various data reports.

[0075] S604: Front-end application layer Employee mobile app: A work platform for account managers, where they can receive tasks, view customer profiles, and interact with customers.

[0076] Management backend (web interface): A platform used by operations and management personnel to configure strategies, manage materials, and view data dashboards.

[0077] Customer touchpoint layer: including in-house apps, mini-programs, official websites, etc., which are the final display interfaces for marketing content and the main collection points for customer behavior data.

[0078] As one can imagine, data and profiles: Mr. Zhang, a customer, repeatedly viewed the details page of a large-denomination certificate of deposit and a certain annuity insurance product on his mobile banking app. The intelligent radar module captured this behavior, updated his profile in real time, and added a signal indicating a "strong financial management need".

[0079] Layering and strategy: According to Mr. Zhang's asset level and this intention, the strategy engine classifies him into the "wealth management potential customer group" and automatically generates the strategy of "combination recommendation of large time deposit and annuity insurance, which is interpreted by customer manager Li through telephone".

[0080] Material matching: The content adaptation engine selects the latest and clearest introduction long picture of two products and a set of standard dialogues for "stable investors" from the library and binds them to the task.

[0081] Task assignment and empowerment: Li's APP immediately receives the task push, and after he clicks it, he fully understands Mr. Zhang's needs and the system's recommended solution, and one-key dials the phone.

[0082] Feedback and optimization: After the phone, Li records "customer is interested, and appointment for detailed discussion on the weekend" on the APP, and the system records this touch as "effective communication". Mr. Zhang comes to the store to buy on the weekend, and the transaction data is captured by the system, marking this task as "successful conversion". The successful strategy and material combination will be recorded by the reinforcement learning model for subsequent optimization.

[0083] Through the system, real-time marketing response, intelligent strategy development, empowered execution process, and precise effect evaluation are realized, which ultimately significantly improves marketing efficiency, customer experience, and employee productivity.

[0084] The embodiment also provides an electronic device, comprising: a memory and a processor; the memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions, and the computer executable instructions realize the steps of the intent-driven marketing activity automatic generation method when executed by the processor.

[0085] The embodiment also provides a computer readable storage medium, which stores computer executable instructions, and the computer executable instructions realize the steps of the intent-driven marketing activity automatic generation method when executed by the processor.

[0086] The storage medium provided in the embodiment belongs to the same inventive concept as the intent-driven marketing activity automatic generation method provided in the above embodiment, and the technical details not described in detail in the embodiment can be referred to the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0087] Those skilled in the art can clearly understand the present application by the above description of the embodiments, and the present application can be realized by software and necessary general hardware, and of course, can also be realized by hardware. Based on such understanding, the technical solutions of the present application or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH, a hard disk, or an optical disc, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the method of various embodiments of the present application.

[0088] The technical scope of the present application is not limited to the above description, and those skilled in the art can make various modifications and changes to the above embodiments without departing from the technical idea of the present application, and these modifications and changes should all be within the protection scope of the present application.

Claims

1. A method for automatically generating intent-driven marketing campaigns, characterized in that, include: Acquire industry business data and tag library, use big data profiling and tag algorithm models to process customer data, generate and output enhanced customer group data and 360-degree customer profiles; Based on the generated customer group data and customer 360 profile, customers are classified and segmented, and personalized precision marketing strategies are generated and output for different customer segments. The management backend is used to build and manage a high-quality marketing material library, track and analyze material usage data, and output user needs and behavior analysis results. Send online marketing tasks to employees' mobile devices, automatically track and collect task completion data, and output task completion reports; Process data to generate and output data reports that include data dashboards, leaderboards, and churn analysis.

2. The method for automatically generating intent-driven marketing campaigns according to claim 1, characterized in that, Transform existing data into existing asset data and output decision-making basis for reducing customer acquisition costs and marketing costs.

3. The method for automatically generating intent-driven marketing campaigns according to claim 1, characterized in that, Through mobile platforms, we showcase industry products, activities, and information to customers, and trigger and execute viral marketing campaigns based on customer interactions.

4. The method for automatically generating intent-driven marketing campaigns according to claim 1, characterized in that, The 360-degree customer profile includes the customer's basic information, asset level, transactions completed, information on added bank staff, information on affiliated groups, and customer activity information.

5. The method for automatically generating intent-driven marketing campaigns according to claim 1, characterized in that, The high-quality marketing material library includes popular events, marketing scripts, promotional posters, and product information. The materials are sourced from management backend configurations and external real-time information automatically captured through APIs.

6. The method for automatically generating intent-driven marketing campaigns according to claim 1, characterized in that, Tracking and analyzing the usage data of creative materials includes monitoring the embedded content through a smart radar module, generating and sending notifications to account managers based on the dynamic behavior of customers, in order to improve customer profiles and evaluate marketing effectiveness.

7. The method for automatically generating intent-driven marketing campaigns according to claim 1, characterized in that, Based on the output task completion report, generate performance data for developing employee reward and punishment systems.

8. An intent-driven marketing campaign automated generation system, comprising the intent-driven marketing campaign automated generation method according to any one of claims 1-7, characterized in that, The server, combining industry business data and tag library, uses big data profiling and tag algorithm models to process customer data, generate and output enhanced customer group data and customer 360 profiles. The customer analysis module classifies and stratifies customers based on the generated customer group data and customer 360 profiles, and generates and outputs personalized precision marketing strategies for different customer groups. The creative management module allows users to build and manage a high-quality marketing creative library through the management backend, track and analyze creative usage data, and output user needs and behavior analysis results. The task management module issues online marketing tasks to employees' mobile devices, automatically tracks and collects task completion data, and outputs task completion reports. The data reporting module processes data and generates and outputs data reports including data dashboards, leaderboards, and churn analysis.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the intention-driven marketing campaign automated generation method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the intention-driven marketing campaign automated generation method according to any one of claims 1 to 7.

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