Customer classification management method and device, electronic equipment and storage medium

Abstract

The invention provides a customer classification management method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining customer contact data of target customers in different databases, and carrying out the data preprocessing of the customer contact data, and obtaining target customer contact data; performing data analysis on the target customer contact data, judging a target transaction stage of the target customer, and determining a customer classification corresponding to the target transaction stage; and based on the target customer contact data, generating display information of the target customer under the customer classification, and sending the display information to the target terminal for display. Compared with the prior art, the embodiment of the invention has the advantages that the customer contact data of the target customer is acquired from the plurality of databases, and data analysis is performed on the target customer contact data, so that the transaction stage of the target customer can be accurately judged; according to the analysis result of the target customer contact data, the customers can be automatically classified and managed, so that an enterprise can formulate personalized marketing strategies for different types of customers, and the marketing efficiency and the conversion rate are improved.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a method and device for customer classification management, an electronic device, and a storage medium. Background Art

[0002] In the insurance marketing field, insurance marketers are often faced with the challenge of managing a large number of customers with scattered information. These customers are at different stages of transaction, from initial contact, interest cultivation, decision-making considerations to final transaction, and each stage contains different follow-up needs and marketing strategies. However, the traditional method of dividing customer transaction stages has many limitations.

[0003] Traditionally, the customer's transaction stage is often manually divided by marketers after the customer has made a purchase or clearly expressed the purchase motivation. This lag makes it difficult for marketers to capture the customer's potential needs and intention changes in a timely manner, thus missing the best follow-up opportunity. In addition, the traditional division stage is usually rough, lacks sufficient detail and depth, and cannot fully reflect the customer's complex behavior and mentality changes during the transaction process. More importantly, traditional methods lack the ability to predict customer intentions through multi-dimensional data collection. In the modern marketing environment, customers' purchase decisions are often affected by many factors, including but not limited to browsing behavior, consultation records, interaction frequency, social media activities, etc. These factors together constitute a complex map of customer intentions, but traditional methods have failed to make full use of this valuable information to guide sales strategies. Therefore, how to automatically and accurately classify and manage customers has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] The present disclosure provides a method and device for customer classification management, electronic equipment and storage medium, which is mainly intended to solve the problem of being unable to classify and manage customers during insurance marketing.

[0005] According to a first aspect of the present disclosure, a method for customer classification management is provided, comprising:

[0006] Acquire the customer contact data of target customers from different databases, and perform data preprocessing on the customer contact data to obtain target customer contact data;

[0007] Performing data analysis on the target customer contact data to determine the target transaction stage of the target customer, and determining the customer classification corresponding to the target transaction stage;

[0008] Based on the target customer contact data, display information of the target customer under the customer category is generated, and the display information is sent to the target terminal for display.

[0009] In some embodiments, the step of obtaining the target customer contact data from different databases and performing data preprocessing on the target customer contact data to obtain the target customer contact data includes:

[0010] Acquire the customer contact data of the target customer recorded in different contact systems;

[0011] Aggregating the customer data in databases of different touch surface systems according to the customer identifier in the customer data;

[0012] The aggregated customer contact data is cleaned to obtain the target customer contact data.

[0013] In some embodiments, the step of analyzing the target customer contact data, determining the target transaction stage of the target customer, and determining the customer classification corresponding to the target transaction stage includes:

[0014] Performing data analysis on the target customer contact data to determine the customer contact data category to which each customer contact data in the target customer contact data belongs, wherein the customer contact data category includes: potential customer category, interactive customer category, intended customer category, closed customer category, and intended repurchase customer category;

[0015] Based on the customer contact data classification, the target transaction stage of the target customer is determined, and the customer classification corresponding to the target transaction stage is determined.

[0016] In some embodiments, the step of determining the target transaction stage of the target customer based on the customer contact data classification, and determining the customer classification corresponding to the target transaction stage, includes:

[0017] Based on the correspondence between customer contact data classification and transaction stage, identify and determine the customer contact data classification in the later stage of transaction as the target customer contact data classification;

[0018] The transaction stage corresponding to the target customer contact data classification is used as the target transaction stage, and the customer classification corresponding to the target transaction stage is determined.

[0019] In some embodiments, before obtaining the customer contact data of target customers from different databases and performing data preprocessing on the customer contact data to obtain the target customer contact data, the method further includes:

[0020] The customer contact data of different customers are collected, and the customer contact data are classified to obtain the customer contact data classification, and a corresponding relationship between the customer contact data classification and the transaction stage is established.

[0021] In some embodiments, the method further comprises:

[0022] In response to a customer classification instruction, the transaction stage of the target customer in the customer classification instruction is obtained, and the customer type of the target customer is adjusted to the target customer classification.

[0023] According to a second aspect of the present disclosure, there is provided a device for customer classification management, comprising:

[0024] A preprocessing unit, used to obtain the customer contact data of target customers from different databases, and perform data preprocessing on the customer contact data to obtain target customer contact data;

[0025] A judgment unit, configured to analyze the target customer contact data, judge the target transaction stage of the target customer, and determine the customer classification corresponding to the target transaction stage;

[0026] A generating unit is used to generate display information of the target customer under the customer category based on the target customer contact data, and send the display information to a target terminal for display.

[0027] In some embodiments, the pre-processing unit comprises:

[0028] An acquisition module, used for acquiring the customer contact data of the target customer recorded in different touch surface systems;

[0029] A summarizing module, used to summarize the customer data in the databases of different touch surface systems according to the customer identifier in the customer data;

[0030] The cleaning module is used to clean the aggregated customer contact data to obtain the target customer contact data.

[0031] In some embodiments, the determining unit includes:

[0032] An analysis module is used to perform data analysis on the target customer contact data to determine the customer contact data category to which each customer contact data in the target customer contact data belongs, wherein the customer contact data category includes: potential customer category, interactive customer category, intended customer category, closed customer category, and intended repurchase customer category;

[0033] A determination module is used to determine the target transaction stage of the target customer based on the customer contact data classification, and determine the customer classification corresponding to the target transaction stage.

[0034] In some embodiments, the determining module is further configured to:

[0035] Based on the correspondence between customer contact data classification and transaction stage, identify and determine the customer contact data classification in the later stage of transaction as the target customer contact data classification;

[0036] The transaction stage corresponding to the target customer contact data classification is used as the target transaction stage, and the customer classification corresponding to the target transaction stage is determined.

[0037] In some embodiments, the apparatus further comprises:

[0038] A unit is established to obtain the customer contact data of target customers in different databases in the preprocessing unit, and perform data preprocessing on the customer contact data. Before obtaining the target customer contact data, the customer contact data of different customers are collected, and the customer contact data are classified to obtain the customer contact data classification, and a corresponding relationship between the customer contact data classification and the transaction stage is established.

[0039] In some embodiments, the apparatus further comprises:

[0040] The adjustment unit is used to respond to the customer classification instruction, obtain the transaction stage of the target customer in the customer classification instruction, and adjust the customer type of the target customer to the target customer classification.

[0041] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0042] at least one processor; and

[0043] a memory communicatively connected to the at least one processor; wherein,

[0044] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect.

[0045] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the method described in the first aspect.

[0046] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method as described in the first aspect above.

[0047] The present disclosure provides a method and device, electronic device and storage medium for customer classification management, which obtains the customer contact data of target customers from different databases, and performs data preprocessing on the customer contact data to obtain target customer contact data; performs data analysis on the target customer contact data to determine the target transaction stage of the target customer, and determines the customer classification corresponding to the target transaction stage; based on the target customer contact data, generates display information of the target customer under the customer classification, and sends the display information to the target terminal for display. Compared with the related art, the embodiment of the present disclosure can integrate scattered information resources and form a comprehensive understanding of customers by obtaining the customer contact data of target customers from multiple databases; by performing data analysis on the target customer contact data, it can accurately determine the transaction stage of the target customer, which helps enterprises seize sales opportunities; according to the analysis results of the target customer contact data, customers can be automatically classified and managed, which helps enterprises formulate personalized marketing strategies for different categories of customers and improve marketing efficiency and conversion rate.

[0048] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0050] Figure 1 A flowchart of a customer classification management method provided by an embodiment of the present disclosure;

[0051] Figure 2 A flowchart of another method for customer classification management provided by an embodiment of the present disclosure;

[0052] Figure 3 A schematic diagram of the structure of a device for customer classification management provided by an embodiment of the present disclosure;

[0053] Figure 4 A schematic diagram of the structure of another device for customer classification management provided by an embodiment of the present disclosure;

[0054] Figure 5 A schematic block diagram of an exemplary electronic device provided for an embodiment of the present disclosure. DETAILED DESCRIPTION

[0055] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0056] The following describes the customer classification management method and device, electronic device and storage medium according to the embodiments of the present disclosure with reference to the accompanying drawings.

[0057] Figure 1 A flowchart of a customer classification management method provided in an embodiment of the present disclosure.

[0058] like Figure 1 As shown, the method comprises the following steps:

[0059] Step 101, obtaining the customer contact data of target customers from different databases, and performing data preprocessing on the customer contact data to obtain target customer contact data.

[0060] In the embodiments of the present disclosure, in order to conduct marketing and customer management more effectively, enterprises need to obtain customer contact data about target customers from different databases. This step usually involves the integration of multiple data sources, including customer relationship management systems (CRM), social media platforms, email marketing tools, and enterprise resource planning (ERP) systems. After collecting these extensive customer contact data, the next step is to preprocess the data. Data preprocessing is a complex process, which includes but is not limited to data cleaning, deduplication, format unification, missing value processing, and outlier detection, aiming to improve the quality and accuracy of data. Through data cleaning, duplicate information and irrelevant noise can be removed to ensure that each record is unique and valuable; format unification can make data from different sources comparable and easy to analyze; missing value processing is to fill in the blanks in the data through interpolation, deletion, or estimation to reduce information loss; and outlier detection helps to identify and process extreme values ​​that may be caused by input errors or special events to avoid them from misleading data analysis. After this series of carefully designed preprocessing steps, the original and complex customer contact data is refined into more accurate and usable target customer contact data, laying a solid foundation for subsequent customer portrait construction, marketing strategy formulation and personalized promotion.

[0061] Step 102, performing data analysis on the target customer contact data, determining the target transaction stage of the target customer, and determining the customer classification corresponding to the target transaction stage.

[0062] In the embodiments of the present disclosure, in order to gain an in-depth understanding of the target customer's purchasing intention and current market position, it is necessary to conduct in-depth data analysis on the pre-processed target customer contact data. First, determine the target transaction stage that the target customer is in. This can be an evaluation of multiple key indicators such as customer behavior patterns, purchase history, interaction frequency, and response time. For example, by monitoring the customer's browsing trajectory on the website, the dwell time on the product page, the number of items in the shopping cart, and past purchase records, it can be roughly inferred whether the customer is in the initial understanding stage, the purchase consideration stage, the order-about-to-be stage, or has become a loyal repeat customer.

[0063] After accurately determining the target transaction stage, the next step is to determine the customer classification corresponding to this stage. Customer classification is based on multiple dimensions such as customer characteristics, purchasing behavior, and value contribution. Different transaction stages often correspond to different types of customers. For example, customers in the initial understanding stage may belong to the "potential customer" category. They are actively looking for solutions, but have not yet formed a clear understanding of the company's products or services; while customers who are about to place an order may belong to the "potential customer" category. They have already developed a strong interest in the product or service and are considering making a purchase decision.

[0064] Through this series of detailed data analysis steps, we can not only more accurately grasp the current status of target customers, but also provide strong data support for the subsequent marketing strategy formulation. This will help companies allocate marketing resources more efficiently, improve customer conversion rates, and ultimately achieve sustained growth in sales performance.

[0065] Step 103: Based on the target customer contact data, generate display information of the target customer under the customer category, and send the display information to the target terminal for display.

[0066] In an embodiment of the present disclosure, based on the collected and accurately analyzed target customer contact data, the system can intelligently generate personalized display information for specific target customers under the preset customer classification dimension. This display information not only contains key information such as the target customers' basic attributes, interest preferences, and historical interaction records, but also undergoes in-depth mining and customized design through data processing algorithms to ensure the accuracy and attractiveness of the information. Subsequently, through an efficient data transmission mechanism, these carefully prepared display information are quickly and securely sent to the target terminal used by the target customer, whether it is a smartphone, tablet computer or personal computer, to achieve seamless docking and accurate display. On the target terminal, the display information is presented in an intuitive, vivid and easy-to-understand form, such as dynamic charts, customized recommendation lists or interactive interfaces, aiming to maximize the attention of target customers and promote subsequent interactions and conversions.

[0067] The present disclosure provides a method for customer classification management, which obtains the customer contact data of target customers from different databases, and performs data preprocessing on the customer contact data to obtain target customer contact data; performs data analysis on the target customer contact data to determine the target transaction stage of the target customer, and determines the customer classification corresponding to the target transaction stage; based on the target customer contact data, generates display information of the target customer under the customer classification, and sends the display information to the target terminal for display. Compared with the related art, the embodiment of the present disclosure can integrate scattered information resources and form a comprehensive understanding of customers by obtaining the customer contact data of target customers from multiple databases; by performing data analysis on the target customer contact data, it can accurately determine the transaction stage of the target customer, which helps enterprises seize sales opportunities; according to the analysis results of the target customer contact data, customers can be automatically classified and managed, which helps enterprises formulate personalized marketing strategies for different categories of customers and improve marketing efficiency and conversion rate.

[0068] In order to clearly illustrate the embodiment of the present disclosure, this embodiment provides a flowchart of another method for customer classification management.

[0069] like Figure 2 As shown, the method comprises the following steps:

[0070] Step 201, collect customer contact data of different customers, classify the customer contact data, obtain customer contact data classification, and establish a corresponding relationship between the customer contact data classification and the transaction stage.

[0071] Specifically, in step 201, in order to establish the correspondence between the customer contact data classification and the transaction stage, it is necessary to obtain the customer contact data of different customers for data analysis to determine the correspondence between different customer contact data and the transaction stage. In short, customer contact data refers to various information records of the contact between customers and enterprises during the sales process, including but not limited to customers' browsing behavior, consultation records, interactive feedback, purchase history, participation in activities, social media interactions, etc.

[0072] The collection of customer data is a basic step. This requires companies to establish a comprehensive data capture mechanism and use technical tools such as CRM systems, website analysis tools, social media monitoring software, etc. to automatically or manually capture every interaction data between customers and the company. At the same time, the accuracy and completeness of the data must be ensured to avoid analysis biases caused by data omissions or errors. Data classification of customer data can be based on multiple dimensions, such as data sources (online or offline), contact types (active visits, marketing email clicks, telephone consultations, etc.), customer behavior stages (cognition, interest, decision-making, purchase, loyalty, etc.) and customer attributes (age, gender, region, occupation, etc.). Through these classifications, the data can be organized to facilitate subsequent analysis and insights. To establish a correspondence between the classification of customer data and the transaction stage, it is necessary to deeply explore the meaning behind these data.

[0073] In this embodiment, customers corresponding to different transaction stages are divided into: potential customers, interactive customers, intended customers, transaction customers, and intended repurchase customers. Different transaction stages correspond to different types of customer contact data. For example, transaction customers in the transaction stage will generate data such as order contracts, and other types of data, such as interactive data, will also be generated during the transaction process. Therefore, data analysis and classification of these data are required to accurately classify and manage customers.

[0074] According to the different transaction stages of customers and the analysis results of customer contact data of different customers, in this embodiment, customer contact data is divided into the following categories: potential customer classification, interactive customer classification, intended customer classification, transaction customer classification, and intended repurchase customer classification. These data classifications correspond to their respective transaction stages. For example, the transaction stage corresponding to the potential customer classification is potential customer.

[0075] Step 202, obtaining the target customer's touch data recorded in different touch systems.

[0076] Specifically, in step 202, in order to fully and deeply understand the needs, preferences and behavior patterns of target customers, the enterprise needs to obtain and integrate customer contact data about the target customers from multiple touch surface systems. These touch surface systems are widely distributed in various links of customer-enterprise interaction, including but not limited to online platforms (such as official websites, mobile applications, social media channels), offline stores, customer service hotlines, email marketing systems, SMS push platforms, and partner networks. As the main battlefield for modern enterprises to interact with customers, online platforms contain rich customer contact data. By deploying advanced data collection tools and technologies, such as website analysis tools, user behavior tracking scripts, social media monitoring software, etc., it is possible to capture the target customers' browsing history, click behavior, search keywords, stay time, sharing and comments on official websites, mobile applications and social media in real time. These data not only reflect the customer's interests and demand trends, but also reveal their online behavior patterns, providing strong support for subsequent personalized recommendations and marketing strategies.

[0077] Secondly, as an important part of physical retail and services, offline channels also record a large amount of valuable customer contact data. Through POS systems, membership management systems, customer feedback collection forms and other means, companies can obtain information such as target customers' shopping history, consumption preferences, membership levels, and after-sales service needs in stores. These data help companies understand customers' offline consumption habits, optimize product layout and service processes, and improve customer satisfaction and loyalty. In addition, customer service hotlines and email marketing systems are also key channels for obtaining target customers' customer contact data. Customer service hotlines record voice and text interaction information such as customers' consultation content, complaints and suggestions, and problem-solving processes, reflecting customers' direct feedback and expectations on products and services. The email marketing system helps companies evaluate the effectiveness of marketing activities and further optimize email content and sending timing by sending personalized emails and tracking email open rates, click-through rates, and conversion rates.

[0078] As another communication method to directly reach customers, SMS push platforms can also collect data on target customers' responses to SMS marketing, such as reading rate, reply rate, participation in activities, etc. These data are crucial for evaluating the effectiveness of SMS marketing and adjusting strategies. Finally, partner networks, such as e-commerce platforms and third-party service providers, are also important sources for obtaining target customer contact data. By cooperating with these platforms, companies can obtain target customers' behavioral data in a wider range of scenarios, such as browsing footprints, purchase records, payment preferences, etc., so as to more comprehensively portray customer portraits and achieve cross-channel data integration and analysis.

[0079] Step 203: According to the customer identifier in the customer touch data, the customer touch data in the databases of different touch surface systems are aggregated.

[0080] Step 204, clean the aggregated customer contact data to obtain the target customer contact data.

[0081] Specifically, in steps 203 to 204, in order to build a complete and accurate customer portrait, the company needs to effectively aggregate the customer data scattered in different touch surface systems (such as online e-commerce platforms, offline store systems, customer service systems, social media platforms, etc.) based on the customer identifiers contained in the customer data, such as customer ID, mobile phone number, email address and other unique identification information.

[0082] In the process of data aggregation, it is necessary to ensure the accuracy and consistency of the data to avoid analytical deviations caused by data differences between different systems. This usually requires enterprises to unify and standardize the data formats, encoding rules, timestamps, etc. of each interface system before data integration to ensure seamless connection and accurate matching of data when it flows across systems.

[0083] After the data is aggregated, the next step is to clean the huge amount of customer data. Data cleaning is a complex and meticulous process that aims to remove duplicate data, correct erroneous data, fill in missing data, and handle abnormal data to ensure the accuracy and completeness of the data. This step usually includes the following key links:

[0084] Deduplication: Identify and delete duplicate records to ensure that each customer has only one unique record in the database, avoiding duplicate calculations during analysis.

[0085] Error correction: Check and correct errors in the data, such as spelling errors, format errors, logical errors, etc., to ensure the accuracy and consistency of the data.

[0086] Missing value processing: For missing data items, fill-in strategies are adopted according to the specific situation, such as using statistical methods such as mean, median, mode, etc. for estimation, or filling in according to business rules to reduce information loss.

[0087] Outlier detection and processing: Identify and process outliers in the data. These outliers may be caused by entry errors, data corruption or special events. They need to be corrected or eliminated based on the actual situation to avoid misleading the analysis results.

[0088] Data standardization: Standardize data from different sources and in different formats, such as unifying date formats and standardizing numerical data, to facilitate subsequent data analysis and mining.

[0089] After the above data cleaning steps, the original customer contact data is refined into more accurate and reliable target customer contact data. These data not only provide a solid foundation for building customer portraits, but also provide strong data support for subsequent marketing strategy formulation, customer segmentation, personalized recommendations, etc. Enterprises can conduct more in-depth data analysis and mining based on these target customer contact data to gain insight into customer needs, optimize product design, and improve service experience, ultimately achieving sustainable business growth and innovation.

[0090] Step 205, performing data analysis on the target customer contact data to determine the customer contact data category to which each customer contact data in the target customer contact data belongs, wherein the customer contact data category includes: potential customer category, interactive customer category, intended customer category, closed customer category, and intended repurchase customer category.

[0091] Specifically in step 205, the customer data classification system is carefully divided into several key categories so that we can better understand and use this data. First, the data of potential customer classification is identified. This part of the data represents the group that has initially shown interest in our products or services, but has not yet interacted or expressed further. They may have first come into contact with our brand through marketing activities, advertising or social media, but have not yet entered a deeper contact stage.

[0092] Next, we identified the data of interactive customers. These customers have had some form of direct interaction with our brand or product, such as browsing the company's website, participating in a survey, or communicating with the company on social media. These interactive behaviors show that they have a certain curiosity or interest in the product or service, which is an important step for potential customers to convert to a higher level. For example, they have made an appointment for a product or service, browsed information, and communicated online.

[0093] Furthermore, we also determined the data of potential customers. These customers have shown clear purchase intention or demand based on the interaction. They may have consulted our service details, submitted a purchase intention form, or had in-depth communication with our sales representatives. These customers are the group closest to the transaction and need more attention and follow-up from us. For example, they have made product reservations, product browsing, and plan browsing for products or services.

[0094] Then, we screened out the data of the transaction customers. These customers have completed the purchase and become our formal customers. Their transaction records are not only a direct reflection of our performance, but also an important basis for subsequent service maintenance and word-of-mouth communication.

[0095] Finally, we also paid special attention to the data of customers with the intention to repurchase. After the previous purchase experience, these customers showed the intention to buy again. They may be satisfied with our products or services and plan to make repeat purchases. They are an important part of our loyal customer base and the key driving force for our continued growth in future performance.

[0096] By conducting such detailed classification and analysis of target customer data, we can more accurately grasp the different stages and needs of customers, thereby formulating more effective marketing strategies and service plans to enhance customer experience and satisfaction.

[0097] Step 206, based on the customer contact data classification, determine the target transaction stage of the target customer and determine the customer classification corresponding to the target transaction stage.

[0098] Step 2061, based on the correspondence between the customer contact data classification and the transaction stage, identify and determine the customer contact data classification in the later stage of the transaction as the target customer contact data classification.

[0099] Step 2062, taking the transaction stage corresponding to the target customer contact data classification as the target transaction stage, and determining the customer classification corresponding to the target transaction stage.

[0100] Specifically, in step 206, based on an in-depth analysis of the close correspondence between the customer contact data classification and the transaction stage, it is necessary to accurately identify and determine those customer contact data classifications that clearly point to the later stages of the transaction as target customer contact data classifications. This requires the ability to accurately capture the transaction intention strength and maturity information implicit in the data. For example, if the customer contact data contains a large number of records about detailed product consultation, price negotiation, and purchase intention confirmation, these are usually regarded as signals of the later stages of the transaction and should be classified as target customer contact data.

[0101] The transaction stage corresponding to these target customer contact data classifications is directly determined as the target transaction stage. This identification is based on the previous in-depth analysis and precise matching of the relationship between customer contact data and transaction stages. Subsequently, based on the company's internal customer classification system or the industry's common customer classification standards, the customer classification corresponding to the target transaction stage can be further determined. Customer classification may be based on a variety of factors, such as purchase potential, loyalty, demand urgency, etc. Each classification is designed to help companies better understand customer needs and optimize sales strategies. For example, if the target transaction stage shows that the customer is close to the final purchase decision, then they may be classified as "high-intent potential customers" or "customers about to close a deal." Such classification helps the sales team concentrate resources and adopt more targeted sales strategies to improve transaction rates and customer satisfaction.

[0102] Step 207, in response to the customer classification instruction, obtaining the transaction stage of the target customer in the customer classification instruction, and adjusting the customer type of the target customer to the target customer classification.

[0103] Specifically, in step 207, in response to the customer classification instruction issued by the terminal marketing personnel, the system will first accurately capture the specific transaction stage information of the target customer currently specified in the instruction. This step is the basis for ensuring the accuracy of subsequent customer classification adjustments, which requires the system to be able to quickly parse the instruction content and extract key target customers and their transaction stage data from it.

[0104] Subsequently, the system will automatically perform a series of logical judgment and classification rule matching operations based on the target customer's transaction stage information. These rules are carefully designed based on the company's internal customer management strategy, sales process and industry best practices to ensure that each customer can be accurately classified into the customer type that best suits their current status.

[0105] In this process, it is particularly important that the system will manually adjust the customer type of the target customer based on the customer classification instructions issued by the marketer. The design of this manual adjustment mechanism fully takes into account the need for marketers to flexibly adjust customer classification according to actual conditions when facing complex and changing market environments and customer needs. Through this mechanism, marketers can make necessary corrections and optimizations to the results of the system's automatic classification based on their own professional judgment and experience to ensure the accuracy and effectiveness of customer classification.

[0106] Specifically, when marketers find that the results of the system's automatic classification deviate from the actual situation, they can use customer classification instructions to clearly instruct the system to adjust the customer type of the target customer to a more accurate target customer classification. This adjustment process not only reflects the marketers' requirements for refined and personalized customer management, but also further enhances the flexibility and adaptability of the system in practical applications.

[0107] It should be noted that the embodiments of the present disclosure may include multiple steps. For the convenience of description, these steps are numbered, but these numbers do not limit the execution time slots or execution order between the steps; these steps can be implemented in any order, and the embodiments of the present disclosure do not limit this.

[0108] Corresponding to the above customer classification management method, the present invention also provides a customer classification management device. Since the device embodiment of the present invention corresponds to the above method embodiment, the details not disclosed in the device embodiment can be referred to the above method embodiment, and will not be repeated in the present invention.

[0109] Figure 3 A schematic diagram of a structure of a device for customer classification management provided by an embodiment of the present disclosure, such as Figure 3 As shown, including:

[0110] A preprocessing unit 31 is used to obtain the customer contact data of target customers from different databases, and perform data preprocessing on the customer contact data to obtain target customer contact data;

[0111] The judgment unit 32 is used to analyze the target customer contact data, judge the target transaction stage of the target customer, and determine the customer classification corresponding to the target transaction stage;

[0112] The generating unit 33 is used to generate the display information of the target customer under the customer category based on the target customer contact data, and send the display information to the target terminal for display.

[0113] The present disclosure provides a device for customer classification management, which obtains the customer contact data of target customers from different databases, and performs data preprocessing on the customer contact data to obtain target customer contact data; performs data analysis on the target customer contact data to determine the target transaction stage of the target customer, and determines the customer classification corresponding to the target transaction stage; based on the target customer contact data, generates display information of the target customer under the customer classification, and sends the display information to the target terminal for display. Compared with the related art, the embodiment of the present disclosure can integrate scattered information resources and form a comprehensive understanding of customers by obtaining the customer contact data of target customers from multiple databases; by performing data analysis on the target customer contact data, it can accurately determine the transaction stage of the target customer, which helps enterprises seize sales opportunities; according to the analysis results of the target customer contact data, customers can be automatically classified and managed, which helps enterprises formulate personalized marketing strategies for different categories of customers and improve marketing efficiency and conversion rate.

[0114] Furthermore, in a possible implementation of this embodiment, as Figure 4 As shown, the pre-processing unit 31 includes:

[0115] An acquisition module 311 is used to acquire the customer contact data of the target customer recorded in different touch surface systems;

[0116] A summarizing module 312, for summarizing the customer contact data in the databases of different touch surface systems according to the customer identifier in the customer contact data;

[0117] The cleaning module 313 is used to clean the aggregated customer contact data to obtain the target customer contact data.

[0118] Furthermore, in a possible implementation of this embodiment, as Figure 4 As shown, the judging unit 32 includes:

[0119] The analysis module 321 is used to perform data analysis on the target customer contact data to determine the customer contact data category to which each customer contact data in the target customer contact data belongs, wherein the customer contact data category includes: potential customer category, interactive customer category, intended customer category, closed customer category, and intended repurchase customer category;

[0120] The determination module 322 is used to determine the target transaction stage of the target customer based on the customer contact data classification, and determine the customer classification corresponding to the target transaction stage.

[0121] Furthermore, in a possible implementation of this embodiment, the determining module 322 is further configured to:

[0122] Based on the correspondence between customer contact data classification and transaction stage, identify and determine the customer contact data classification in the later stage of transaction as the target customer contact data classification;

[0123] The transaction stage corresponding to the target customer contact data classification is used as the target transaction stage, and the customer classification corresponding to the target transaction stage is determined.

[0124] Furthermore, in a possible implementation of this embodiment, as Figure 4 As shown, the device also includes:

[0125] Establishing unit 34 is used to obtain the customer contact data of target customers in different databases in the preprocessing unit, and perform data preprocessing on the customer contact data. Before obtaining the target customer contact data, the customer contact data of different customers are collected, and the customer contact data are classified to obtain the customer contact data classification, and the corresponding relationship between the customer contact data classification and the transaction stage is established.

[0126] Furthermore, in a possible implementation of this embodiment, as Figure 4 As shown, the device also includes:

[0127] The adjustment unit 35 is used to respond to the customer classification instruction, obtain the transaction stage of the target customer in the customer classification instruction, and adjust the customer type of the target customer to the target customer classification.

[0128] It should be noted that the above explanation of the method embodiment is also applicable to the device of this embodiment, and the principle is the same, which is not limited in this embodiment.

[0129] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0130] Figure 5 A schematic block diagram of an example electronic device 300 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0131] like Figure 5 As shown, the device 300 includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in a ROM (Read-Only Memory) 302 or a computer program loaded from a storage unit 308 to a RAM (Random Access Memory) 303. In the RAM 303, various programs and data required for the operation of the device 300 can also be stored. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An I / O (Input / Output) interface 305 is also connected to the bus 304.

[0132] A number of components in the device 300 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the device 300 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0133] The computing unit 301 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a CPU (Central Processing Unit), a GPU (Graphic Processing Units), various dedicated AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, a DSP (Digital Signal Processor), and any appropriate processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as a method for customer classification management. For example, in some embodiments, the method for customer classification management may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the computing unit 301, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to execute the aforementioned customer classification management method in any other appropriate manner (for example, by means of firmware).

[0134] Various embodiments of the systems and techniques described above herein may be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application Specific Standard Products), SOCs (System On Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that may be executed and / or interpreted on a programmable system including at least one programmable processor that may be a special purpose or general purpose programmable processor that may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0135] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0136] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a RAM, a ROM, an EPROM (Electrically Programmable Read-Only-Memory) or a flash memory, an optical fiber, a CD-ROM (Compact Dis sc Read-Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0137] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball), through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0138] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: LAN (Local Area Network), WAN (Wide Area Network), the Internet, and blockchain networks.

[0139] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0140] It should be noted that artificial intelligence is a discipline that studies how computers can simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), and includes both hardware-level and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing; artificial intelligence software technologies mainly include computer vision technology, speech recognition technology, natural language processing technology, as well as machine learning / deep learning, big data processing technology, knowledge graph technology, and other major directions.

[0141] The various numerical numbers such as first and second involved in the present disclosure are only for the convenience of description and are not used to limit the scope of the embodiments of the present disclosure, but also indicate the order of precedence.

[0142] At least one in the present disclosure may also be described as one or more, and a plurality may be two, three, four or more, which is not limited in the present disclosure. In the embodiments of the present disclosure, for a technical feature, the technical features in the technical feature are distinguished by "first", "second", "third", "A", "B", "C" and "D", etc., and there is no order of precedence or size between the technical features described by the "first", "second", "third", "A", "B", "C" and "D".

[0143] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0144] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for customer classification management, characterized in that: include: Acquire the customer contact data of target customers from different databases, and perform data preprocessing on the customer contact data to obtain target customer contact data; Performing data analysis on the target customer contact data to determine the target transaction stage of the target customer, and determining the customer classification corresponding to the target transaction stage; Based on the target customer contact data, display information of the target customer under the customer category is generated, and the display information is sent to the target terminal for display.

2. The method according to claim 1, characterized in that The step of obtaining the target customer contact data from different databases and performing data preprocessing on the target customer contact data to obtain the target customer contact data includes: Acquire the customer contact data of the target customer recorded in different contact systems; Aggregating the customer data in databases of different touch surface systems according to the customer identifier in the customer data; The aggregated customer contact data is cleaned to obtain the target customer contact data.

3. The method according to claim 1, characterized in that The step of analyzing the target customer contact data, determining the target transaction stage of the target customer, and determining the customer classification corresponding to the target transaction stage includes: Performing data analysis on the target customer contact data to determine the customer contact data category to which each customer contact data in the target customer contact data belongs, wherein the customer contact data category includes: potential customer category, interactive customer category, intended customer category, closed customer category, and intended repurchase customer category; Based on the customer contact data classification, the target transaction stage of the target customer is determined, and the customer classification corresponding to the target transaction stage is determined.

4. The method according to claim 3, characterized in that The step of determining the target transaction stage of the target customer based on the customer contact data classification, and determining the customer classification corresponding to the target transaction stage, includes: Based on the correspondence between customer contact data classification and transaction stage, identify and determine the customer contact data classification in the later stage of transaction as the target customer contact data classification; The transaction stage corresponding to the target customer contact data classification is used as the target transaction stage, and the customer classification corresponding to the target transaction stage is determined.

5. The method according to claim 1, characterized in that: Before obtaining the target customer contact data from different databases and performing data preprocessing on the target customer contact data to obtain the target customer contact data, the method further includes: The customer contact data of different customers are collected, and the customer contact data are classified to obtain the customer contact data classification, and a corresponding relationship between the customer contact data classification and the transaction stage is established.

6. The method according to claim 1, characterized in that The method further comprises: In response to a customer classification instruction, the transaction stage of the target customer in the customer classification instruction is obtained, and the customer type of the target customer is adjusted to the target customer classification.

7. A device for customer classification management, characterized in that: include: A preprocessing unit, used to obtain the customer contact data of target customers from different databases, and perform data preprocessing on the customer contact data to obtain target customer contact data; A judgment unit, configured to analyze the target customer contact data, judge the target transaction stage of the target customer, and determine the customer classification corresponding to the target transaction stage; A generating unit is used to generate display information of the target customer under the customer category based on the target customer contact data, and send the display information to a target terminal for display.

8. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-6.

10. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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