A method and system for regular maintenance of customer information

Abstract

The present invention provides a method and system for periodic maintenance of customer information, the method comprising the following steps: integrating customer data from multiple channels and preprocessing the data; performing customer segmentation on the preprocessed customer data and identifying the behavioral characteristics and preferences of each customer group; labeling each type of customer group according to the identification results; creating a user profile of the current customer group according to the customer label; drawing a customer journey map based on the user profile to identify the key contact points from awareness to purchase; determining a message push scheme for each customer group according to the customer journey map and the user profile. The present invention is used to provide users with a better experience and personalized services. The present invention is used to improve customer relationship management and sales conversion efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for periodically maintaining customer information. Background Art

[0002] A customer information regular maintenance system is a solution designed to ensure that companies can continuously update and optimize their customer data. Such a system is essential to maintaining the effectiveness and competitiveness of customer relationship management (CRM). Disadvantages of the existing system: Data is not updated in a timely manner: After customer information changes, it is not reflected in the system in a timely manner. Low data accuracy: Inaccurate data due to input errors or outdated information. Lack of automation: Reliance on manual input and updates, which is inefficient. Insufficient security: Customer information may be leaked due to failure to take appropriate security measures. Poor user experience: The interface is unfriendly and difficult to operate and understand. Lack of personalized services: The system fails to provide personalized services or recommendations based on customer information.

[0003] In this regard, the present application provides a method and system for regular maintenance of customer information. Summary of the invention

[0004] The present invention provides a method and system for regular maintenance of customer information, which not only improves customer relationship management and sales conversion efficiency through user portraits and journey maps, but also provides customers with personalized services or recommendations.

[0005] In one aspect, the present invention provides a method for periodically maintaining customer information, comprising the following steps:

[0006] Integrate and pre-process customer data from multiple channels;

[0007] Perform customer segmentation on the pre-processed customer data and identify the behavioral characteristics and preferences of each customer group;

[0008] Based on the identification results, label each customer group; based on the customer labels, create user profiles for the current customer group;

[0009] Based on user portraits, draw customer journey maps and identify key touchpoints from awareness to purchase;

[0010] Determine the message push plan for each customer group based on the customer journey map and user portrait.

[0011] In an practicable manner, the step of performing customer segmentation on the pre-processed customer data and identifying the behavioral characteristics and preferences of each customer group includes:

[0012] Select key features from pre-processed customer data; and perform exploratory analysis on customer data to identify basic features and distribution of customer data;

[0013] Based on key features and exploratory analysis results, multiple customers are divided into groups with similar behaviors and preferences using clustering algorithms;

[0014] For each cluster group, analyze its behavior patterns and preferences on key features.

[0015] In an practicable manner, the step of creating a user profile of the current customer group based on the customer tags specifically includes:

[0016] Determine the elements that need to be included in the customer profile;

[0017] Obtain relevant data based on the determined portrait elements;

[0018] Create a preliminary draft portrait for each customer group based on relevant data;

[0019] Add a personalized description for each portrait and create one or more typical cases for each portrait to obtain a user portrait.

[0020] In an practicable manner, the steps of drawing a customer journey map based on user portraits and identifying key touch points from awareness to purchase specifically include:

[0021] Integrate user portrait information;

[0022] Based on user personas, identify the key stages of the customer journey;

[0023] Based on user profiles and customer behavior data, identify the touchpoints that customers may encounter at each stage of the journey;

[0024] Map the touchpoints on a journey map and describe the customer’s experience and feelings at each touchpoint;

[0025] Create a visual journey map based on the stages and touchpoints of the journey.

[0026] In an practicable manner, the step of determining a message push solution for each customer group based on the customer journey map and user portraits specifically includes:

[0027] Analyze the customer journey map to determine when customers are most likely to accept information successfully;

[0028] Determine the timing of message push based on user portraits, journey maps, and the time when customers are most likely to accept information;

[0029] Develop personalized message content;

[0030] Generate and execute message push plans based on the timing, channels and frequency of message push;

[0031] According to the monitoring and feedback results, the effectiveness of message push is analyzed. When the effectiveness is lower than the preset threshold, the message push plan is adjusted.

[0032] In an implementable manner, a user is allowed to perform customer monetization and group monetization operations on potential customers;

[0033] Customer monetization operations include:

[0034] Create a push notification plan for users;

[0035] Receive basic information filled in by users and create rules based on the basic information;

[0036] When receiving a customer's request to add a new customer, add a corresponding tag to the current customer based on the channel code and set rules;

[0037] Determine current customer tags and needs, and generate personal SOPs based on customer tags and needs;

[0038] When the time to add a potential customer exceeds the preset threshold, the user is reminded to send a push message to the customer.

[0039] On the other hand, the present invention also provides a customer information regular maintenance system, the system includes an integration module, an identification module, a user portrait creation module, a journey map drawing module and a push plan determination module, wherein:

[0040] The integration module is used to integrate customer data from multiple channels and pre-process the data;

[0041] The identification module is used to segment the pre-processed customer data and identify the behavioral characteristics and preferences of each customer group;

[0042] The user portrait creation module is used to label each customer group according to the identification results; and to create a user portrait of the current customer group according to the customer label;

[0043] The journey mapping module is used to draw a customer journey map based on the user portrait and identify key touch points from awareness to purchase;

[0044] The push scheme determination module is used to determine the message push scheme for each customer group based on the customer journey map and user portrait.

[0045] In an practicable manner, the identification module includes a feature extraction submodule, an analysis submodule, a group division submodule, and a behavior pattern and preference determination submodule; wherein:

[0046] The feature extraction submodule is used to select key features from the pre-processed customer data;

[0047] The analysis submodule is used to perform exploratory analysis on customer data to identify basic characteristics and distribution of customer data;

[0048] The group segmentation submodule is used to segment multiple customers into groups with similar behaviors and preferences based on key features and exploratory analysis results and using a clustering algorithm;

[0049] The behavior pattern and preference determination submodule is used to analyze the behavior pattern and preference of each cluster group on key features.

[0050] In an implementable manner, the user portrait creation module includes an element determination submodule, a data acquisition submodule, a draft creation submodule and a case creation submodule; wherein,

[0051] The element determination submodule is used to determine the elements that need to be included in the customer group portrait;

[0052] The data acquisition submodule is used to acquire relevant data according to the determined portrait elements;

[0053] The draft creation submodule is used to create a preliminary draft portrait for each customer group based on relevant data;

[0054] The case creation submodule is used to add a personalized description to each portrait, and to create one or more typical cases for each portrait to obtain a user portrait.

[0055] In an practicable manner, the journey map drawing module includes a stage determination submodule, a contact point identification submodule, a description submodule and a map creation submodule; wherein:

[0056] The stage determination submodule is used to integrate user profile information; based on the user profile, determine the key stages of the customer journey;

[0057] The contact point identification submodule is used to identify the contact points that customers may encounter at each journey stage based on user portraits and customer behavior data;

[0058] The description submodule is used to map the contact points on the journey map and describe the customer's experience and feelings at each contact point;

[0059] The map creation submodule is used to create a visual journey map according to various stages and contact points of the journey.

[0060] The beneficial effects that can be achieved by the present invention are as follows: The maintenance method of the present application helps to better understand and serve customers, improve customer satisfaction and business results. Through customer segmentation and portrait creation, it is possible to have a deeper understanding of customer behavior patterns and preferences, thereby better meeting customer needs and expectations. Based on the refined user portraits and journey maps, a personalized message push plan can be formulated to improve the relevance and attractiveness of communication, thereby improving customer engagement and satisfaction.

[0061] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0062] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0064] Figure 1 This is a flow chart of a method for periodically maintaining customer information in Embodiment 1 of the present invention;

[0065] Figure 2 This is a flow chart of the steps of identifying the behavioral characteristics and preferences of each customer group in Example 1 of the present invention;

[0066] Figure 3 This is a flowchart of the steps of creating a user portrait of the current customer group in Example 1 of the present invention. DETAILED DESCRIPTION

[0067] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0068] This embodiment 1 provides a method for periodically maintaining customer information, including the following steps:

[0069] Integrate and pre-process customer data from multiple channels;

[0070] Perform customer segmentation on the pre-processed customer data and identify the behavioral characteristics and preferences of each customer group;

[0071] Based on the identification results, label each customer group; based on the customer labels, create user profiles for the current customer group;

[0072] Based on user portraits, draw customer journey maps and identify key touchpoints from awareness to purchase;

[0073] Determine the message push plan for each customer group based on the customer journey map and user portrait.

[0074] Working principle of the above technical solution: see Figure 1 , this application first integrates customer data from multiple channels, including but not limited to sales records, customer service logs, social media interactions, website visit data, etc. And perform data cleaning and data format standardization, data cleaning includes removing duplicate records, correcting errors, processing missing values, etc. Standardized data formats can ensure that data from different data sources can be analyzed consistently. Next, cluster analysis and other techniques are applied to group customers according to purchase behavior, product preferences, customer feedback, etc. to obtain multiple customer groups. And use statistical analysis or machine learning algorithms to perform feature analysis on each segmented group to identify the behavior patterns (such as purchase frequency, average consumption amount) and preferences (such as product type, service requirements) of each customer group. Then, according to the results of the identification, assign corresponding labels to each customer group, such as "high-value customers", "price-sensitive", "loyal customers", etc. Create customer labels that are easy to understand and operate to support subsequent customer management and communication. Then, based on the labels of each customer group, extract relevant data and features, and build a detailed portrait for each customer group, including lifestyle, purchase motivation, media usage habits, etc. Then, based on the user portrait, draw a customer journey map to determine the key stages of the customer journey, such as awareness, consideration, purchase, use and recommendation. Identify key touchpoints at each stage, such as advertising, recommendations, product experience, after-sales service, etc. Finally, determine the communication goals and content for each journey stage based on the customer journey map and user portraits. And design personalized message templates and marketing campaigns to ensure the relevance and attractiveness of the information.

[0075] Beneficial effects of the above technical solution: The maintenance method of this application helps to better understand and serve customers, improve customer satisfaction and business results. Through customer segmentation and portrait creation, it is possible to have a deeper understanding of customer behavior patterns and preferences, so as to better meet customer needs and expectations. Based on the refined user portraits and journey maps, personalized message push plans can be formulated to improve the relevance and attractiveness of communication, thereby improving customer engagement and satisfaction.

[0076] Based on Example 1, drawing a customer journey map also includes a customer consumption recommendation mechanism;

[0077] A multi-level cell memory array pre-configured at a target location; wherein,

[0078] Each storage cell of the multi-level cell memory array corresponds to a target location, and each storage cell stores a plurality of different product information;

[0079] The target location is the current customer's scan location;

[0080] According to the multi-level cell memory array, a point-to-point matching mechanism based on user profile is set to determine the target product set;

[0081] According to the target product information set, product recommendation planning is carried out to generate a visual interface for customer consumption recommendation;

[0082] In the above scheme, firstly, the information of different recommended products is stored through a multi-level cell memory array, including the product location, product advantages, suitable groups, etc. of the recommended products; because the multi-level cell memory array can store different data through multiple bits, the information of the products can be reflected in different states, so as to perform matching calculations through the user portrait and each storage unit, that is, the user portrait is matched with different types of product information in each storage unit, and the storage unit with a relatively high matching value is determined, that is, the target product suitable for the customer is determined, so that it can be recommended to the customer;

[0083] Then, after determining the target product, by planning the recommendation priority between different target products, we can ensure that customers can quickly find each recommended product in their current area, thereby generating recommendation information to guide customers to find recommended products, thereby improving customer retention. At the same time, it allows fixed customers to regularly maintain customer information when purchasing products, and based on the recommendation information, allow customers to find target products more quickly.

[0084] In the above product recommendation process, because the customer consumption recommendation mechanism has been generated, when recommending different products, it is necessary to constantly receive product update information and change information of the target products that customers want. Therefore, an automatic recommendation product planning network should be set up;

[0085] In actual implementation, first, through the customer consumption recommendation mechanism, set the initial recommended product, and set the initial recommended product as the recommendation origin;

[0086] The origin of recommendation is the real-time demand products of customers. All demand products belong to the customer's current scanning channel, can be arranged, and have product inventory;

[0087] According to the recommendation origin, set the preference recommendation model and troubleshooting recommendation model; among them,

[0088] The preference recommendation model is constructed by combining the customer's real-time demand parameters for different product elements with the content recommendation algorithm, and is used to find specific demand products and similar alternative products;

[0089] The troubleshooting recommendation model is based on real-time updated customer demand products, existing unfavorable factors and rule-based obstacle avoidance algorithms. It is used to determine whether the demand product can be purchased at the customer's current location, the purchase probability, and the purchase probability and availability of similar alternative products.

[0090] Determine the recommended values ​​of target products and alternative products based on the preference recommendation model and troubleshooting recommendation model;

[0091] According to the recommended values, a recommendation interface for the target product and alternative products is generated, and the real-time locations of the target product and alternative products are marked on the recommendation interface.

[0092] In the above scheme, by setting the recommendation origin, the product that the customer needs can be determined, and then the tea product is used as the recommendation origin to determine the product preference of the customer's product. Through the product preference, the probability of the target product and the alternative product being available for purchase is determined. Thus, the user is recommended to purchase the product that the customer needs and the recommended alternative product. In this way, not only the customer information is maintained regularly, but also the customer's needs and preferences are maintained at all times, so as to recommend the most suitable products that the customer can buy.

[0093] Based on Example 1, the step of segmenting the pre-processed customer data and identifying the behavioral characteristics and preferences of each customer group includes:

[0094] Select key features from pre-processed customer data; and perform exploratory analysis on customer data to identify basic features and distribution of customer data;

[0095] Based on key features and exploratory analysis results, multiple customers are divided into groups with similar behaviors and preferences using clustering algorithms;

[0096] For each cluster group, analyze its behavior patterns and preferences on key features.

[0097] Working principle of the above technical solution: see Figure 2, in order to identify the behavioral characteristics and preferences of each customer group, we must first determine which features are most important for distinguishing different customer groups. Specifically, we select candidate features related to customer behavior and preferences from the preprocessed customer data, such as purchase history, browsing behavior, course ratings, learning progress, etc. Then, we perform correlation analysis between the candidate features and the target variables of customer segmentation (such as purchase behavior, course preferences) to identify the most influential features. Finally, we use machine learning models (such as random forests) to evaluate feature importance and select the features that contribute most to customer segmentation. Next, we explore the distribution, trends, and potential patterns of the data through data visualization and statistical methods. Specifically, we use charts (such as bar charts, box plots, and scatter plots) to show the distribution of features and the differences between customer groups. We apply statistical tests (such as ANOVA and chi-square tests) to verify whether the differences in features between different customer groups are significant. We identify and process outliers in the data to avoid their influence on cluster analysis. Then, based on the selected features, we divide customers into groups with similar behaviors and preferences. Specific process: Select a suitable clustering algorithm (such as K-means, hierarchical clustering) according to the characteristics of the data. Use methods such as silhouette coefficient analysis to determine the optimal number of clusters. Finally, define specific behavioral characteristics and preferences for each clustered customer group. Specific process: Analyze the average, distribution, and pattern of each group on key characteristics. Based on the characteristic description, summarize the behavioral patterns of each group, such as "high-value buyers", "regular learners", etc. Identify each group's course preferences, price sensitivity, learning time preferences, etc.

[0098] Beneficial effects of the above technical solution: This application can more accurately segment customers and identify the behavioral characteristics and preferences of each customer group. This will help provide more personalized and effective services and marketing strategies for different customer groups, thereby improving customer satisfaction and sales conversion rate.

[0099] Based on Example 1, determine the elements that need to be included in the customer group portrait;

[0100] Obtain relevant data based on the determined portrait elements;

[0101] Create a preliminary draft portrait for each customer group based on relevant data;

[0102] Add a personalized description for each portrait and create one or more typical cases for each portrait to obtain a user portrait.

[0103] Working principle of the above technical solution: see Figure 3First, determine the elements that need to be included in the user portrait, such as basic information (age, gender, occupation, etc.), consumption habits, lifestyle, values, purchasing motivations, media usage habits, etc. Then, based on the determined portrait elements, collect relevant detailed data. This may include historical sales data, customer survey results, social media behavior analysis, customer service records, etc. Then, based on the collected data, create a preliminary portrait draft for each customer group. This includes assigning specific labels and attributes to each group, such as "price-sensitive housewives", "newcomers in the workplace who pursue quality", etc. Secondly, add personalized descriptions to each portrait to make it vivid and contextual. For example, add a description to the portrait of "newcomers in the workplace who pursue quality": "They are between 22 and 28 years old, have just entered the workplace, pursue fashion and quality, like to learn about the latest trends through social media, and are interested in brand stories and the meaning behind products." Finally, create one or more typical cases or stories for each portrait. These cases should contain specific background information, behavior patterns, and demand scenarios to help team members better understand and empathize with customers. For example: "Li Ming, 25 years old, is an urban white-collar worker. He often works overtime and likes to participate in outdoor sports on weekends. He has high requirements for outdoor sports equipment."

[0104] Beneficial effects of the above technical solution: This application creates more detailed and personalized user portraits through each customer group, which will provide a solid foundation for formulating effective customer relationship management and marketing strategies.

[0105] Based on Example 1, the steps of drawing a customer journey map based on user portraits and identifying key touch points from awareness to purchase specifically include:

[0106] Integrate user portrait information;

[0107] Based on user personas, identify the key stages of the customer journey;

[0108] Based on user profiles and customer behavior data, identify the touchpoints that customers may encounter at each stage of the journey;

[0109] Map the touchpoints on a journey map and describe the customer’s experience and feelings at each touchpoint;

[0110] Create a visual journey map based on the stages and touchpoints of the journey.

[0111] Here’s how the above technical solution works: First, connect the information in the user profile with actual customer interaction data and purchase history to better understand customer behavior. Then, based on the purchase motivations and behavior patterns in the user profile, identify the main stages of the journey, such as awareness, consideration, purchase, use, and recommendation. And associate the specific behaviors and preferences of each customer group with the stages of the journey to reveal the characteristics and needs of different groups at each stage. Second, based on the user profile and behavior data, identify the touchpoints that customers may encounter at each stage of the journey, such as advertising, social media, customer service, etc. And map these touchpoints on the journey map and describe the customer’s experience and feelings at each touchpoint. Finally, use a graphic design tool or specialized journey mapping software to design the various stages and touchpoints of the journey into a chart. You can add text descriptions to the map to explain the customer’s behavior, feelings, and needs at each touchpoint. You can also use visual elements such as color, icons, and pictures to enhance the readability and appeal of the map.

[0112] Beneficial effects of the above technical solution: This application can truly reflect the customer experience during the purchase process and provide guidance for optimizing key touch points. This will help improve customer satisfaction and conversion rate.

[0113] On the basis of Example 1, according to the customer journey map and user portrait, the steps of determining the message push plan for each customer group specifically include:

[0114] Analyze the customer journey map to determine when customers are most likely to accept information successfully;

[0115] Determine the timing of message push based on user portraits, journey maps, and the time when customers are most likely to accept information;

[0116] Develop personalized message content;

[0117] Generate and execute message push plans based on the timing, channels and frequency of message push;

[0118] According to the monitoring and feedback results, the effectiveness of message push is analyzed. When the effectiveness is lower than the preset threshold, the message push plan is adjusted.

[0119] The working principle of the above technical solution: This application first analyzes the customer journey map to determine the time when the customer is most likely to accept the information, such as providing comparative information in the consideration stage and providing preferential information in the purchase stage. Then, based on the user portrait and journey map, the most appropriate message push time is selected to improve the relevance and opening rate of the message. Next, the personalized message content is formulated. When formulating the personalized message content, the key elements in the user portrait can be analyzed, such as the customer's interests, purchase motivations, lifestyles and values. Review the customer journey map to understand the needs and expectations of customers at different stages. Determine the theme of the message content based on the customer's needs and journey stage. For example, in the awareness stage, educational content can be provided. You can also choose the appropriate communication tone and style based on the customer's preferences and brand image. Or add the customer's name or other personalized information to the message. Then, a message push plan is generated and executed based on the message push timing, channel and frequency. The message push channel can be determined based on the customer portrait and journey map, such as email, social media, SMS, etc. The frequency of message push should not be too frequent to avoid excessively disturbing customers while ensuring the effective communication of information. When arranging the specific time for message push, you should consider the customer's active time and the urgency of the message. Finally, based on the monitoring and feedback results, analyze the effectiveness of message push and adjust the strategy to improve the effectiveness of future push.

[0120] Beneficial effects of the above technical solution: This application ensures that the message push solution is closely connected with the customer journey and profile, improves the relevance and personalization of messages, and thus improves customer engagement and satisfaction. This will help improve customer loyalty and promote sales conversion.

[0121] On the basis of embodiment 1, after the step of determining the message push scheme for each customer group, the method further comprises the steps of: allowing the user to perform customer monetization and group monetization operations on potential customers;

[0122] Customer monetization operations include:

[0123] Create a push notification plan for users;

[0124] Receive basic information filled in by users and create rules based on the basic information;

[0125] When receiving a customer's request to add a new customer, add a corresponding tag to the current customer based on the channel code and set rules;

[0126] Determine current customer tags and needs, and generate personal SOPs based on customer tags and needs;

[0127] When the time to add a potential customer exceeds the preset threshold, the user is reminded to send a push message to the customer.

[0128] The working principle and beneficial effects of the above technical solution: In order to improve customer operation efficiency, while determining the message push plan for each customer group, users are allowed to monetize individual customers or groups. Single customer monetization includes SOP functions such as timed reminders, adding new friends, group activities, birthday reminders, etc., which can automate customer follow-up and significantly improve customer operation efficiency. Group monetization includes support for various excellent speech types such as pictures and adaptations, and one-click sending of established speech files to avoid wasting time and effectively improve customer conversion.

[0129] The steps of customer monetization operation include: first create a message push plan for the user. The user can fill in the basic information in the created user message push plan. The basic information includes the rule name, which is convenient for recording the records of multiple SOP lists set. Customer service member settings can set exclusive customer service personnel, or automatically serve by a professional through certain keywords; set the push cycle content, such as birthday cards, and set unique words to get closer to customers. According to the above information, you can create rules. Creating a message push plan can remind employees to push messages to customers multiple times after a period of time after the customer adds employees. In this way, the purpose of cultivating new customers, maintaining old customers, and activating dormant customers is achieved. Generally used for: new customer cultivation, customer return visits, and activation of dormant customers. When receiving a customer's request to add a customer, the current customer is automatically labeled according to the channel code and set rules for subsequent personalized operations. Automatic labeling can improve conversion rate and reduce costs; improve user experience and increase brand reputation. When labeling customers, they can be labeled according to the preset automatic labeling rules. Labeling methods include keyword labeling, customer group entry behavior labeling, and time period labeling. When you choose to tag with keywords, you need to create a rule name, bind the employees who can make this rule effective, add keywords, you can add multiple keywords, and set the frequency of keyword appearance. When you choose to tag customers' group joining behavior, you need to create a rule name, bind the group, and select tags, you can select multiple tags. When you choose to tag by time period, you need to create a rule name, bind the employees who can make this rule effective, and you can set to add keywords in the dimensions of day / week / month. Next, determine the current customer tags and needs, and generate personal SOPs based on customer tags and needs. Regular SOPs, maintain potential students, communicate efficiently, and improve conversion rates. When the time to add potential customers exceeds the preset threshold, remind users to push messages to customers.

[0130] The functions of group monetization include standardizing the group message push process to ensure the quality of operation; operating multiple communities to avoid wrong or missed messages; simplifying repetitive preliminary operation work to save time. Group monetization operations include: creating a group sop: setting the rule name, adding rule content, setting the content to be sent, including the form of the content, text / picture, and the time of sending, etc.

[0131] Embodiment 2 provides a customer information regular maintenance system, the system includes an integration module, an identification module, a user portrait creation module, a journey map drawing module and a push plan determination module, wherein:

[0132] The integration module is used to integrate customer data from multiple channels and pre-process the data;

[0133] The identification module is used to segment the pre-processed customer data and identify the behavioral characteristics and preferences of each customer group;

[0134] The user portrait creation module is used to label each customer group according to the identification results; and to create a user portrait of the current customer group according to the customer label;

[0135] The journey mapping module is used to draw a customer journey map based on the user portrait and identify key touch points from awareness to purchase;

[0136] The push scheme determination module is used to determine the message push scheme for each customer group based on the customer journey map and user portrait.

[0137] Working principle of the above technical solution: This application includes an integration module, an identification module, a user portrait creation module, a journey map drawing module and a push solution determination module. The integration module is responsible for integrating customer data from multiple channels, including but not limited to sales records, customer service logs, social media interactions, website access data, etc. And perform data cleaning and data format standardization, data cleaning includes removing duplicate records, correcting errors, processing missing values, etc. Standardized data formats can ensure that data from different data sources can be analyzed consistently. The identification module is responsible for applying cluster analysis and other technologies to group customers according to purchasing behavior, product preferences, customer feedback, etc. to obtain multiple customer groups. And use statistical analysis or machine learning algorithms to perform feature analysis on each segmented group to identify the behavior patterns (such as purchase frequency, average consumption amount) and preferences (such as product type, service requirements) of each customer group. The user portrait creation module is responsible for assigning corresponding labels to each customer group based on the identification results, such as "high-value customers", "price-sensitive", "loyal customers", etc. Create customer labels that are easy to understand and operate to support subsequent customer management and communication. And according to the label of each customer group, relevant data and features are extracted to build a detailed portrait for each customer group, including lifestyle, purchase motivation, media usage habits, etc. The journey mapping module is used to draw a customer journey map based on the user portrait and determine the key stages of the customer journey, such as awareness, consideration, purchase, use and recommendation. Identify the key touch points in each stage, such as advertising, recommendation, product experience, after-sales service, etc. The push plan determination module is responsible for determining the communication goals and content of each journey stage based on the customer journey map and user portrait. And design personalized message templates and marketing activities to ensure the relevance and attractiveness of the information.

[0138] Beneficial effects of the above technical solution: The maintenance method of this application helps to better understand and serve customers, improve customer satisfaction and business results. Through customer segmentation and portrait creation, it is possible to have a deeper understanding of customer behavior patterns and preferences, so as to better meet customer needs and expectations. Based on the refined user portraits and journey maps, personalized message push plans can be formulated to improve the relevance and attractiveness of communication, thereby improving customer engagement and satisfaction.

[0139] Based on Example 2, the identification module includes a feature extraction submodule, an analysis submodule, a group division submodule and a behavior pattern and preference determination submodule; wherein:

[0140] The feature extraction submodule is used to select key features from the pre-processed customer data;

[0141] The analysis submodule is used to perform exploratory analysis on customer data to identify basic characteristics and distribution of customer data;

[0142] The group segmentation submodule is used to segment multiple customers into groups with similar behaviors and preferences based on key features and exploratory analysis results and using a clustering algorithm;

[0143] The behavior pattern and preference determination submodule is used to analyze the behavior pattern and preference of each cluster group on key features.

[0144] Working principle of the above technical solution: The recognition module of the present application includes a feature extraction submodule, an analysis submodule, a group segmentation submodule and a behavior pattern and preference determination submodule. The feature extraction submodule is responsible for selecting candidate features related to customer behavior and preferences from the preprocessed customer data, such as purchase history, browsing behavior, course ratings, learning progress, etc. Then, the candidate features are correlated with the target variables of customer segmentation (such as purchase behavior, course preferences) to identify the most influential features. Finally, a machine learning model (such as random forest) is used to evaluate the importance of features and select the features that contribute most to customer segmentation. The analysis submodule is responsible for exploring the distribution, trends and potential patterns of the data. The group segmentation submodule is responsible for dividing customers into groups with similar behaviors and preferences based on the selected features. The behavior pattern and preference determination submodule is responsible for summarizing the behavior patterns and preferences of each group based on the feature description.

[0145] Beneficial effects of the above technical solution: This application can more accurately segment customers and identify the behavioral characteristics and preferences of each customer group. This will help provide more personalized and effective services and marketing strategies for different customer groups, thereby improving customer satisfaction and sales conversion rate.

[0146] Based on Example 2, the user portrait creation module includes an element determination submodule, a data acquisition submodule, a draft creation submodule and a case creation submodule; wherein,

[0147] The element determination submodule is used to determine the elements that need to be included in the customer group portrait;

[0148] The data acquisition submodule is used to acquire relevant data according to the determined portrait elements;

[0149] The draft creation submodule is used to create a preliminary draft portrait for each customer group based on relevant data;

[0150] The case creation submodule is used to add a personalized description to each portrait, and to create one or more typical cases for each portrait to obtain a user portrait.

[0151] The working principle of the above technical solution: The user portrait creation module of this application includes an element determination submodule, a data acquisition submodule, a draft creation submodule and a case creation submodule. The element determination submodule is used to determine the elements that need to be included in the user portrait, such as basic information (age, gender, occupation, etc.), consumption habits, lifestyle, values, purchasing motivations, media usage habits, etc. The data acquisition submodule is responsible for collecting relevant detailed data based on the determined portrait elements. The draft creation submodule is responsible for creating a preliminary portrait draft for each customer group based on the collected data. This includes assigning specific labels and attributes to each group. The case creation submodule is responsible for creating one or more typical cases or stories for each portrait. These cases should contain specific background information, behavior patterns, and demand scenarios.

[0152] Beneficial effects of the above technical solution: This application creates more detailed and personalized user portraits through each customer group, which will provide a solid foundation for formulating effective customer relationship management and marketing strategies.

[0153] Based on Example 2, the journey map drawing module includes a stage determination submodule, a contact point identification submodule, a description submodule and a map creation submodule; wherein:

[0154] The stage determination submodule is used to integrate user profile information; based on the user profile, determine the key stages of the customer journey;

[0155] The contact point identification submodule is used to identify the contact points that customers may encounter at each journey stage based on user portraits and customer behavior data;

[0156] The description submodule is used to map the contact points on the journey map and describe the customer's experience and feelings at each contact point;

[0157] The map creation submodule is used to create a visual journey map according to various stages and contact points of the journey.

[0158] The working principle of the above technical solution: The journey mapping module includes a stage determination submodule, a touchpoint identification submodule, a description submodule and a map creation submodule. The stage determination submodule is used to connect the information in the user portrait with the actual customer interaction data and purchase history to better understand customer behavior. Then, based on the purchase motivations and behavior patterns in the user portrait, the main stages of the journey, such as awareness, consideration, purchase, use and recommendation, are determined. The specific behaviors and preferences of each customer group are associated with the various stages of the journey to reveal the characteristics and needs of different groups at each stage. The touchpoint identification submodule is used to identify the touchpoints that customers may encounter at each stage of the journey, such as advertising, social media, customer service, etc., based on the user portrait and behavior data. The description submodule is responsible for mapping these touchpoints on the journey map and describing the customer's experience and feelings at each touchpoint. The map creation submodule is responsible for designing the various stages and touchpoints of the journey into a chart using a graphic design tool or a dedicated journey mapping software. Text descriptions can be added to the map to explain the customer's behavior, feelings and needs at each touchpoint. Visual elements such as colors, icons and pictures can also be used to enhance the readability and attractiveness of the map.

[0159] Beneficial effects of the above technical solution: This application can truly reflect the customer experience during the purchase process and provide guidance for optimizing key touch points. This will help improve customer satisfaction and conversion rate.

[0160] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for periodically maintaining customer information, characterized in that: The following steps are involved: Integrate and pre-process customer data from multiple channels; Perform customer segmentation on the pre-processed customer data and identify the behavioral characteristics and preferences of each customer group; Label each customer group based on the identification results; Create user profiles for current customer groups based on customer tags; Based on user portraits, draw a customer journey map and identify the touchpoints from awareness to purchase, including: integrating user portrait information; determining the key stages of the customer journey based on user portraits; identifying the touchpoints that customers may encounter at each key stage based on user portraits and customer behavior data; mapping the touchpoints on the journey map and describing the customer's experience and feelings at each touchpoint; creating a visual journey map based on the key stages and touchpoints of the journey; Determine the message push plan for each customer group based on the customer journey map and user portrait, including: Analyze the customer journey map to determine the time when customers have the highest success rate in accepting information; Determine the message push timing based on user portrait, journey map and the time when customers have the highest success rate in accepting information; Develop personalized message content; Generate and execute the message push plan based on the message push timing, channel and frequency; Analyze the effectiveness of message push based on monitoring and feedback results, and adjust the message push plan when the effectiveness is lower than the preset threshold; After the step of determining the message push plan for each customer group, it also includes: allowing the user to perform customer realization and group realization operations on potential customers; the customer realization operation specifically includes: creating a message push plan for the user; receiving the basic information filled in by the user, and creating rules based on the basic information; when receiving a customer's add request, marking the current customer with a corresponding label according to the channel code and the set rules; determining the current customer's label and needs, and generating a personal sop according to the customer's label and needs; when the time to add a potential customer exceeds the preset threshold, reminding the user to push a message to the customer; Among them, drawing a customer journey map also includes a customer consumption recommendation mechanism; pre-configuring a multi-level unit memory array of a target location; setting a point-to-point matching mechanism based on the user portrait according to the multi-level unit memory array to determine the target product set; based on the target product information set, making a product recommendation plan and generating a customer consumption recommendation visualization interface; wherein each storage unit of the multi-level unit memory array corresponds to a target location, and each storage unit stores multiple different product information; the target location is the location where the current customer scans the code.

2. A method for periodically maintaining customer information as claimed in claim 1, characterized in that: The steps of segmenting the pre-processed customer data and identifying the behavioral characteristics and preferences of each customer group include: Select key features from pre-processed customer data; and perform exploratory analysis on customer data to identify basic features and distribution of customer data; Based on key features and exploratory analysis results, multiple customers are divided into groups with similar behaviors and preferences using clustering algorithms; For each cluster group, analyze its behavior patterns and preferences on key features.

3. A method for periodically maintaining customer information as claimed in claim 2, characterized in that: The step of creating a user profile of the current customer group based on the customer tags specifically includes: Determine the elements that need to be included in the customer profile; Obtain relevant data based on the determined portrait elements; Create a preliminary draft portrait for each customer group based on relevant data; Add a personalized description for each portrait and create one or more typical cases for each portrait to obtain a user portrait.

4. A customer information regular maintenance system, characterized in that: The system includes an integration module, an identification module, a user portrait creation module, a journey map drawing module and a push plan determination module, among which: The integration module is used to integrate customer data from multiple channels and pre-process the data; The identification module is used to segment the pre-processed customer data and identify the behavioral characteristics and preferences of each customer group; The user portrait creation module is used to label each customer group according to the identification results; and to create a user portrait of the current customer group according to the customer label; The journey map drawing module is used to draw a customer journey map based on user portraits and identify the contact points from awareness to purchase, including: a stage determination submodule, a contact point identification submodule, a description submodule and a map creation submodule; the stage determination submodule is used to integrate user portrait information; based on the user portrait, determine the key stages of the customer journey; the contact point identification submodule is used to identify the contact points that the customer may encounter at each key stage according to the user portrait and customer behavior data; the description submodule is used to map the contact points on the journey map and describe the customer's experience and feelings at each contact point; the map creation submodule is used to create a visual journey map according to the key stages and contact points of the journey; the journey map drawing module also includes a customer consumption recommendation mechanism when drawing the customer journey map; a multi-level unit memory array of a target location is pre-configured; according to the multi-level unit memory array, a point-to-point matching mechanism based on the user portrait is set to determine the target product set; according to the target product information set, product recommendation planning is performed to generate a customer consumption recommendation visualization interface; wherein each storage unit of the multi-level unit memory array corresponds to a target location, and each storage unit stores a plurality of different product information; the target location is the current customer's code scanning location; The push scheme determination module is used to determine the message push scheme for each customer group based on the customer journey map and user portrait, including: analyzing the customer journey map to determine the time when the customer has the highest success rate in accepting information; determining the message push timing based on the user portrait, journey map and the time when the customer has the highest success rate in accepting information; formulating personalized message content; generating and executing a message push scheme based on the message push timing, channel and frequency; analyzing the effectiveness of message push based on monitoring and feedback results, and adjusting the message push scheme when the effectiveness is lower than a preset threshold; and after the step of determining the message push scheme for each customer group, it also includes: allowing users to perform customer realization and group realization operations on potential customers; customer realization operations specifically include: creating a message push plan for users; receiving basic information filled in by users, and creating rules based on the basic information; when receiving a customer's add request, labeling the current customer accordingly according to the channel code and set rules; determining the current customer's tags and needs, and generating a personal sop based on the customer's tags and needs; and when the time to add a potential customer exceeds the preset threshold, reminding the user to push a message to the customer.

5. A customer information regular maintenance system as claimed in claim 4, characterized in that: The identification module includes a feature extraction submodule, an analysis submodule, a group division submodule and a behavior pattern and preference determination submodule; wherein: The feature extraction submodule is used to select key features from the pre-processed customer data; The analysis submodule is used to perform exploratory analysis on customer data to identify basic characteristics and distribution of customer data; The group segmentation submodule is used to segment multiple customers into groups with similar behaviors and preferences based on key features and exploratory analysis results and using a clustering algorithm; The behavior pattern and preference determination submodule is used to analyze the behavior pattern and preference of each cluster group on key features.

6. A customer information regular maintenance system as claimed in claim 5, characterized in that: The user portrait creation module includes an element determination submodule, a data acquisition submodule, a draft creation submodule and a case creation submodule; wherein, The element determination submodule is used to determine the elements that need to be included in the customer group portrait; The data acquisition submodule is used to acquire relevant data according to the determined portrait elements; The draft creation submodule is used to create a preliminary draft portrait for each customer group based on relevant data; The case creation submodule is used to add a personalized description to each portrait, and to create one or more typical cases for each portrait to obtain a user portrait.

Images