Mail distribution method and device and electronic equipment

Abstract

The embodiment of the invention provides a mail distribution method and device and electronic equipment, and relates to the technical field of electronic information. The method comprises the steps of obtaining feature information of a target customer group; the feature information at least comprises content preference features and receiving environment features of the target customer group; the receiving environment feature is used for indicating a time environment and a server environment where the target customer group receives the mail; based on the content preference features, determining a mail oriented to the target customer group; based on the receiving environment characteristics, determining a sending rule for sending a mail to the target customer group; and sending the mail to the target customer group according to the sending rule. The embodiment of the invention can be used for a system or a platform which needs to execute batch distribution of mails, and can effectively improve the click rate of the mails by combining content preference matching and receiving environment self-adaption.

Description

Technical Field

[0001] This application relates to the field of electronic information technology, and in particular to a method, apparatus and electronic device for email distribution. Background Technology

[0002] With the rapid development of internet technology, email has become a widely used and efficient method of promotion in corporate marketing activities. While various email marketing solutions exist on the market, many problems still arise in practical applications.

[0003] Current email distribution systems primarily focus on automated email sending, aiming to help businesses send emails in bulk to reach a certain user base. These systems, with their relatively simple operation and low initial investment costs, have become common tools for SMEs to conduct online promotion. However, with increasing market competition and evolving user needs, the shortcomings of traditional systems in terms of accuracy and personalization are becoming increasingly apparent, making it difficult to meet businesses' pursuit of high-quality promotional results. Summary of the Invention

[0004] The purpose of this application is to provide an email distribution method, apparatus, and electronic device that aims to effectively improve the click-through rate of emails by combining content preference matching and adaptive receiving environment.

[0005] In a first aspect, this application provides an email distribution method, the method comprising: obtaining characteristic information of a target customer group; the characteristic information including at least the content preference characteristics and receiving environment characteristics of the target customer group; the receiving environment characteristics being used to indicate the time environment and server environment in which the target customer group receives the email; determining emails for the target customer group based on the content preference characteristics; determining sending rules for sending emails to the target customer group based on the receiving environment characteristics; and sending emails to the target customer group according to the sending rules.

[0006] The email distribution method provided in this application offers objective data support for the email distribution decision-making process by acquiring the content preference characteristics and receiving environment characteristics of the target customer group. Furthermore, determining email content based on content preference characteristics directly ensures that the email subject and body information are relevant to the recipient's individual interests, thereby increasing the email's attractiveness and click-through rate. Simultaneously, determining sending rules based on receiving environment characteristics allows email delivery to accurately match the time period when the recipient is likely to view the email (e.g., avoiding off-peak hours or peak data flow) and avoid server congestion periods, thus increasing the chances of the email being received and presented to the user in a timely manner. Finally, executing the sending according to these sending rules ensures that highly relevant email content is delivered at the time when it is most likely to be noticed and processed. The synergistic effect of these two methods effectively overcomes the two key obstacles leading to low click-through rates: irrelevance of content and inappropriate delivery timing, thereby improving the overall click-through rate of emails.

[0007] In conjunction with the first aspect mentioned above, in one possible implementation, determining emails for a target customer group based on content preference characteristics includes: determining an email content template based on content preference characteristics; and for each target customer in the target customer group, populating the email content template based on the target customer's individual data to determine emails for that target customer.

[0008] In conjunction with the first aspect mentioned above, in one possible implementation, the email content template can be determined in the following way: multiple versions of the email content template are determined for the same email subject; the first user set is divided into multiple test groups, and emails generated based on different versions of the email content template are sent to users in different test groups; the first user set is a set of customers related to the email subject; the target email content template for the email subject is determined based on the feedback behavior of users in each test group on the email.

[0009] In conjunction with the first aspect mentioned above, in one possible implementation, the sending rules for sending emails to the target customer group are determined based on the characteristics of the receiving environment. This includes: obtaining the time zone distribution and active time period distribution of the target customer group based on the time environment information in the receiving environment characteristics to determine the email sending time window; obtaining the historical bounce rate and historical sending success rate corresponding to the target email domain based on the server environment information in the receiving environment characteristics to determine the sending frequency limit; and generating sending rules based on the sending time window and the sending frequency limit.

[0010] In conjunction with the first aspect mentioned above, in one possible implementation, obtaining the characteristic information of the target customer group includes: obtaining the historical email interaction records of the target customer group to extract the content preference characteristics of the target customer group; and obtaining the historical email receiving logs of the target customer group to extract the receiving environment characteristics of the target customer group.

[0011] In conjunction with the first aspect mentioned above, in one possible implementation, before obtaining the characteristic information of the target customer group, the method further includes: obtaining a customer data set; the customer data set contains attribute information of multiple customers; based on a preset email subject, the customers in the customer data set are classified according to the attribute information to form multiple candidate customer groups.

[0012] In conjunction with the first aspect mentioned above, in one possible implementation, after sending emails to the target customer group according to the sending rules, the method further includes: generating adjustment suggestions for adjusting email distribution elements based on the target users' feedback behavior data on the emails.

[0013] In conjunction with the first aspect mentioned above, in one possible implementation, the feedback behavior data includes at least email open behavior data, link click behavior data, and customer intent data. Based on user feedback behavior data regarding emails, adjustment suggestions for adjusting email distribution elements are generated, including: generating a first adjustment suggestion based on email open behavior data by determining the email open rate; the first adjustment suggestion is used to indicate adjustments to the title of the email content template; generating a second adjustment suggestion based on link click behavior data by determining the link click rate in the email; the second adjustment suggestion is used to indicate adjustments to link settings or content in the email content template; and generating a third adjustment suggestion based on customer intent data by determining the content preferences of the target customers; the third adjustment suggestion is used to indicate adjustments to the characteristic information of the target customers.

[0014] Secondly, this application provides an email distribution device, comprising: a customer management module, an email generation module, a task planning module, and an email sending module. The customer management module is used to acquire characteristic information of a target customer group; the characteristic information includes at least the target customer group's content preference characteristics and receiving environment characteristics; the receiving environment characteristics indicate the time and server environment in which the target customer group receives the email; the email generation module is used to determine emails for the target customer group based on the content preference characteristics; the task planning module is used to determine the sending rules for sending emails to the target customer group based on the receiving environment characteristics; and the email sending module is used to send emails to the target customer group according to the sending rules.

[0015] In conjunction with the second aspect above, in one possible implementation, the customer management module is specifically used to: obtain historical email interaction records of the target customer group to extract the content preference characteristics of the target customer group; and obtain historical email receiving logs of the target customer group to extract the receiving environment characteristics of the target customer group.

[0016] In conjunction with the second aspect mentioned above, in one possible implementation, the customer management module can also be used to: acquire a customer data set; the customer data set contains attribute information of multiple customers; based on a preset email subject, classify the customers in the customer data set according to the attribute information to form multiple candidate customer groups.

[0017] In conjunction with the second aspect above, in one possible implementation, the email generation module is specifically used to: determine the email content template based on content preference features; and for each target customer in the target customer group, populate the email content template based on the target customer's individual data to determine the email for the target customer.

[0018] In conjunction with the second aspect above, in one possible implementation, the email generation module can also be used to: determine multiple versions of email content templates for the same email subject; divide the first user set into multiple test groups and send emails generated based on different versions of email content templates to users in different test groups; the first user set is a set of customers related to the email subject; and determine the target email content template for the email subject based on the feedback behavior of users in each test group on the email.

[0019] In conjunction with the second aspect above, in one possible implementation, the task planning module is specifically used to: obtain the time zone distribution and active time period distribution of the target customer group based on the time environment information in the receiving environment characteristics, so as to determine the email sending time window; obtain the historical bounce rate and historical sending success rate corresponding to the target email domain name based on the server environment information in the receiving environment characteristics, so as to determine the sending frequency limit; and generate sending rules based on the sending time window and the sending frequency limit.

[0020] In conjunction with the second aspect above, in one possible implementation, the apparatus further includes a suggestion generation module for generating adjustment suggestions for adjusting email distribution elements based on target user feedback behavior data.

[0021] In conjunction with the second aspect mentioned above, in one possible implementation, the feedback behavior data includes at least email open behavior data, link click behavior data, and customer intent data. The suggestion generation module is specifically used to: generate a first adjustment suggestion based on the email open behavior data by determining the email open rate; the first adjustment suggestion is used to indicate adjustments to the email content template title; generate a second adjustment suggestion based on the link click behavior data by determining the link click rate in the email; the second adjustment suggestion is used to indicate adjustments to the link settings or content in the email content template; and generate a third adjustment suggestion based on the customer intent data by determining the target customer's content preferences; the third adjustment suggestion is used to indicate adjustments to the target customer's characteristic information.

[0022] Thirdly, this application provides an electronic device comprising: a processor and a memory; the memory storing processor-executable instructions; when the processor is configured to execute the instructions, the electronic device implements the method of the first aspect described above.

[0023] Fourthly, this application provides a computer-readable storage medium comprising: computer software instructions; which, when executed in an electronic device, cause the electronic device to implement the method described in the first aspect.

[0024] Fifthly, this application provides a computer program product that, when run on a computer, causes the computer to perform the steps of the relevant method described in the first aspect above, so as to implement the method of the first aspect above.

[0025] The beneficial effects of the second to fifth aspects mentioned above can be referred to the corresponding description of the first aspect, and will not be repeated here. Attached Figure Description

[0026] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 A flowchart illustrating an email distribution method provided in an embodiment of this application; Figure 2 A flowchart illustrating a method for generating an email to be sent, provided in an embodiment of this application; Figure 3 A flowchart illustrating a method for adjusting email distribution elements provided in an embodiment of this application; Figure 4 This is a schematic diagram of the composition of an email distribution device provided in an embodiment of this application; Figure 5 A detailed schematic diagram of the composition of an email distribution device provided in this application embodiment; Figure 6 This is a schematic diagram of the structure of an email distribution device provided in an embodiment of this application. Detailed Implementation

[0028] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] It should be noted that in the embodiments of this application, the words "exemplarily" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplarily" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplarily" or "for example" is intended to present the relevant concepts in a specific manner.

[0030] In the embodiments of this application, the terms "first," "second," "third," "fourth," "fifth," and "sixth" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," "third," "fourth," "fifth," and "sixth" may explicitly or implicitly include one or more of that feature.

[0031] In embodiments of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0032] "A and / or B" includes the following three combinations: A only, B only, and a combination of A and B.

[0033] As described in the background section, existing mainstream email distribution systems mainly consist of a basic data management module, an email template module, a sending management module, and a statistics module. The basic data management module primarily stores basic user information, such as email address, name, and registration time. The data source is mostly registered user information collected by the enterprise itself, resulting in a relatively singular data dimension. The email template module provides a fixed library of email templates, allowing enterprises to select templates and manually fill in the content. Template types mainly include common types such as promotional and notification emails. The sending management module supports sending emails at preset times or instantly, and allows setting simple sending quantity limits and frequency controls. The statistics module can statistically analyze basic data such as email sending volume, open count, and click count, and generate simple reports.

[0034] However, such email distribution systems often suffer from problems such as low email open and conversion rates, severe template homogenization, unreasonable email sending strategies, limited means of evaluating email effectiveness, and limited data analysis capabilities.

[0035] Based on this, this application provides an email distribution method that provides objective data support for the email distribution decision-making process by acquiring the content preference characteristics and receiving environment characteristics of the target customer group. Furthermore, determining email content based on content preference characteristics directly ensures that the email subject and body information are relevant to the recipient's individual interests, thereby increasing the email's attractiveness and click-through rate. Simultaneously, determining sending rules based on receiving environment characteristics allows email delivery to accurately match the time period when the recipient is likely to view the email (e.g., avoiding off-peak hours or peak data flow) and avoid server congestion periods, thus increasing the chances of the email being received and presented to the user in a timely manner. Finally, sending is executed according to these sending rules, ensuring that highly relevant email content is delivered at the time when it is most likely to be noticed and processed. The synergistic effect of these two methods effectively overcomes the two key obstacles leading to low click-through rates: irrelevance of content and inappropriate delivery timing, thereby improving the overall click-through rate of the email.

[0036] The email distribution method provided in this application can be widely applied to various email systems that need to deliver and reach information to specific user groups. This application does not impose any restrictions on the specific nature of the email content, the distribution scale, or the target user group.

[0037] For example, the embodiments of this application can be applied to scenarios involving large-scale system notifications or knowledge pushes within an enterprise. Faced with the need to send differentiated policy documents, training materials, or business announcements to employees in different departments and positions, the method provided in this application can generate or match corresponding email content based on employees' content preferences (such as job position and historical reading records), and intelligently plan the sending time in conjunction with the receiving environment (such as the recipient's working hours in their time zone and the load cycle of the enterprise email server), thereby improving the read rate and execution efficiency of notifications.

[0038] For example, the embodiments of this application can be applied to user dynamics and information push scenarios of large online service platforms (such as e-commerce and content platforms). Faced with a massive number of users and diverse personal interests, the method provided by this application can accurately generate personalized recommendation emails based on users' real-time content preference characteristics (such as browsing and collection behavior), and select the optimal sending time and routing strategy according to the characteristics of the receiving environment (such as users' active time periods and the real-time load of the platform server), thereby effectively improving the click-through rate of users opening emails and accessing the platform, and enhancing user stickiness.

[0039] For example, the embodiments of this application can be applied to course update and learning reminder scenarios on online education or knowledge payment platforms. Faced with the need to send chapter updates, review materials, or live broadcast reminders based on students' learning progress and course preferences, the method provided in this application can customize email content based on students' content preference characteristics (such as purchased courses and learning records) and consider the characteristics of the receiving environment (such as students' usual study times and the platform's notification system's concurrent processing capabilities) to formulate sending rules, ensuring that reminder information is delivered at the time when students are most likely to view it, thereby improving student participation and course completion rates.

[0040] The following describes in detail an email distribution method provided by the embodiments of this application, with reference to specific examples and accompanying drawings.

[0041] The email distribution method provided in this application embodiment can be implemented by an email distribution system deployed on a server cluster. The system adopts a modular architecture design, which can be deployed as an independent platform in an enterprise private cloud or public cloud environment, or it can be seamlessly integrated with an existing customer data platform through standardized interfaces, and has full-process capabilities for customer feature processing, intelligent email generation, and multi-channel sending management.

[0042] Figure 1 This is a flowchart illustrating an email distribution method provided in an embodiment of this application. For example, as shown... Figure 1 As shown, it includes the following: S101. Obtain characteristic information of the target customer group.

[0043] The feature information includes at least the content preference characteristics and receiving environment characteristics of the target customer group. The receiving environment characteristics are used to indicate the time and server environment in which the target customer group receives the email.

[0044] In this embodiment, the target customer group refers to a specific set of customers with common attributes or needs targeted by the email promotion campaign; it is the core object of email sending. Content preference characteristics refer to relevant information reflecting the target customer group's preferences regarding email content type, presentation format, and core information; these are key bases for matching personalized email content. Receiving environment characteristics refer to information related to the external environment in which the target customer group receives the email; their core function is to provide a reference for formulating email sending rules. The time environment is an important component of the receiving environment characteristics, specifically referring to the time-related conditions under which the target customer group receives the email. The server environment also belongs to the receiving environment characteristics, specifically referring to the server-related attributes upon which the target customer group relies to receive the email.

[0045] In some embodiments, content preference features may include: the types of email subjects clicked by customers in the past, the frequency of response to different content types such as promotions, notifications, and event invitations, keywords related to products or services of interest mentioned in past feedback, the degree of attention paid to specific modules in emails such as promotional information, industry news, and feature introductions, and the content characteristics of emails that were not opened or quickly deleted in the past.

[0046] In some embodiments, the receiving environment characteristics may include: time environment and server environment. The time environment includes the customer's time zone, active periods during which historical emails were received and interacted with (opened, clicked) (e.g., weekday evenings, weekend afternoons), and periods when emails are avoided (e.g., early morning, peak work hours). The server environment includes the domain type corresponding to the customer's email address (e.g., corporate email domain, personal email domain), the historical response speed of the email receiving server corresponding to that domain, the bounce rate of historical emails sent to that domain, the success rate of delivery, and the probability of being marked as spam.

[0047] In some embodiments, the target customer group may include a set of customers with common attributes filtered based on a preset email subject. Examples include frequent business travelers in the financial industry related to air transport, individual customers with a history of responding to airfare promotions, and past ticket buyers currently in a product recall phase.

[0048] In some embodiments, the email distribution system can access a pre-set customer database and obtain characteristic information of the target customer group based on the feature tags already labeled in the database. The customer database stores standardized feature tags corresponding to the historical interaction data of all customers. Tags corresponding to content preference characteristics include "preferring promotional content," "following industry news," "responding to event invitations," and "rejecting hard-sell advertisements." Tags corresponding to receiving environment characteristics include "time zone - UTC+8," "active hours - 19:00-21:00," "email domain - corporate email," "low historical bounce rate," and "fast server response." The system extracts and integrates the content preference characteristics and receiving environment characteristics of the target customer group by filtering the customer data corresponding to these tags.

[0049] In some embodiments, the email distribution system may further: first, acquire a customer dataset containing attribute information for multiple customers. This attribute information covers basic customer information (name, email address, industry, region, age, etc.), historical email interaction information (received email records, open / click records, feedback content, unsubscribe records, etc.), and consumption-related information (past purchase records, consumption amounts, consumption preferences, etc.). Then, the customers in the dataset are categorized based on this attribute information. For example, they may be divided according to industry, region, consumption preferences, historical response behavior, lifecycle stage, etc., forming multiple candidate customer groups with distinct characteristics. Finally, based on the current email subject (such as off-season ticket promotions for airlines, new route opening announcements, etc.), at least one group with the highest match to the email subject is selected as the target customer group, and the content preference characteristics and receiving environment characteristics of this target customer group are further obtained.

[0050] In one possible implementation, the email distribution system can first initiate a customer data retrieval service, connecting to a customer database via a pre-defined database interface. Users can input filtering criteria corresponding to their current needs (such as "customers related to the air transport industry" or "email opening records in the last 6 months"), and extract raw customer data that meets these criteria from the database. The extracted raw customer data is then subjected to structured analysis. Keywords are extracted from historical email clicks and replies and mapped to content preference tags. Information such as timezone, active interaction time, email domain, and bounce records are extracted from email receiving logs and converted into receiving environment tags. Finally, all tags are deduplicated and merged to form a set of characteristic information for the target customer group, which can be quickly retrieved in subsequent steps.

[0051] As can be seen from step S101, by accurately obtaining the content preference characteristics and receiving environment characteristics of the target customer group, accurate and comprehensive data support can be provided for subsequent steps, effectively avoiding problems such as email content being out of touch with customer needs and unreasonable sending timing due to missing or ambiguous feature information, and reducing the amount of invalid emails sent.

[0052] S102. Based on content preference characteristics, determine emails targeting the target customer group.

[0053] In this embodiment of the application, emails targeting a specific customer group refer to emails that are customized and generated based on the content preference characteristics of the target customer group, which can meet the needs and preferences of the group and stimulate the willingness to interact. They are the core carrier connecting the email distribution system and the target customer.

[0054] In some embodiments, emails targeting a specific customer group may include: a subject title that matches the content preferences of that group, body content that fits industry attributes or needs scenarios, images or videos related to the core information, a dedicated link to a product details page or event registration page, a personalized salutation (such as the customer's name or company abbreviation), customized benefits information (such as discount coupons that match consumer preferences or introductions to service packages that meet needs), and a signature and contact information that conform to email standards.

[0055] In some embodiments, the email distribution system can access a built-in multi-industry template library, filter suitable basic templates based on tags corresponding to the content preference characteristics of the target customer group (such as "prefers promotional content" or "interested in new route information"), extract individual attribute information (such as name, historical consumption records, and affiliated company) of each customer in the group from the customer database, obtain personalized content to fill the template, and then integrate them to form an email for the target customer group.

[0056] In some embodiments, the email distribution system can also: perform format compatibility checks on the initially determined emails to ensure that the emails can be displayed correctly in different email clients (such as webmail and mobile email apps), and that images and links are not invalid or misaligned. Simultaneously, the email distribution system can perform sensitive word detection on the email content, removing expressions that violate promotional guidelines or may offend customers, ensuring the compliance and appropriateness of the email content.

[0057] In one possible implementation, the specific implementation of step S102 is described below. Figure 2 The corresponding implementation examples are described in detail here.

[0058] As can be seen from step S102, determining emails targeting specific customer groups based on content preference characteristics enables email content to accurately match customers' interests and needs, avoiding the problems of homogenization and irrelevant content in traditional promotional emails, effectively enhancing the attractiveness of emails to customers, and increasing the probability of customers opening emails and clicking links.

[0059] S103. Based on the characteristics of the receiving environment, determine the sending rules for sending emails to the target customer group.

[0060] In this embodiment, the sending rules refer to the specific specifications and operating guidelines that the email distribution system must follow when delivering emails to the target customer group. Its core function is to adapt to the receiving environment characteristics of the target customer group, ensure the effective delivery and reasonable reach of emails, and avoid affecting the promotion effect due to improper sending methods.

[0061] In some embodiments, the sending rules may include: a sending time window determined by the time environment, such as a specific sending time period set according to the customer's time zone to avoid rest periods or match historically active periods (e.g., 19:00-21:00 for customers in the East 8 zone, and 9:00-11:00 for customers in the West 5 zone); a sending frequency limit determined by the server environment, such as setting a higher sending frequency (no more than 30 emails per minute) for email domains with low historical bounce rates, and setting a lower sending frequency (no more than 5 emails per minute) for niche domains or domains with high historical bounce rates; and server configuration parameters, such as the Simple Mail Transfer Protocol (SMTP) server port settings adapted to the target email domain, and the selection of the sending link.

[0062] In some embodiments, the email distribution system can access receiving environment feature tags stored in the customer database to extract time-related data such as the time zone distribution and historical active time period records of the target customer group. Simultaneously, it can retrieve historical email sending logs to obtain server-related data such as historical bounce rate, sending success rate, and server response speed corresponding to the target email domain, and then integrate this data to determine sending rules.

[0063] In some embodiments, the email distribution system can also: dynamically fine-tune the determined sending time window based on real-time online status data of the target customer group to ensure that customers can receive emails more easily and promptly. It can also pre-validate the established sending rules, simulating email sending processes under different server environments and time conditions to detect any conflicts (such as logical contradictions between the sending time window and frequency limits) or unreasonable settings (such as insufficient sending efficiency due to excessively low frequency limits). Simultaneously, a dynamic rule adjustment trigger mechanism is established. When abnormal situations such as a sudden increase in the bounce rate of the target email domain or a significant slowdown in server response speed are detected, the rule adjustment process is automatically initiated to avoid email sending being hindered due to environmental changes.

[0064] In one possible implementation, the email distribution system can first retrieve time zone distribution data and historical active period data of the target customer group from the customer database. Using statistical analysis tools, it can calculate the high-frequency active periods for customers in each time zone and integrate this data to form a unified sending time window. Next, it extracts historical bounce rate and sending success rate data for the target email domain from historical sending logs. Combined with a preset security threshold (e.g., a bounce rate security threshold of 10%), it sets differentiated sending frequency limits for different domains. Subsequently, it prioritizes target customers based on metrics such as the number of historical interactions and the time of the most recent interaction, generating a sending queue priority ranking table. Finally, it integrates and encapsulates the sending time window, sending frequency limits, queue priority ranking rules, and bounce handling strategies to form a complete sending rule, which is stored in a rule configuration database for use during email sending.

[0065] As can be seen from step S103, determining email sending rules based on the characteristics of the receiving environment allows the email sending process to adapt to the time habits and server environment of the target customer group, avoiding sending emails during periods of customer inactivity, peak server load, or high bounce risk, effectively improving the delivery rate and customer viewing probability of emails, reducing problems such as server blacklisting and customer resentment caused by improper sending timing or unreasonable frequency, and ensuring the effective reach of promotional emails.

[0066] S104. Send emails to the target customer group according to the sending rules.

[0067] In some embodiments, the email distribution system can obtain all the key information required to send emails to the target customer group by accessing the rule configuration database, customer database, and email content cache. Specifically, it retrieves the complete set of sending rules (including sending time windows, sending frequency limits, queue priority sorting, and bounce message handling strategies) from the rule configuration database, extracts core delivery information such as valid email addresses and customer identifiers of the target customer group from the customer database, and obtains the complete content of the promotional email (including personalized titles, body text, images, and unique links) adapted to customer content preferences from the email content cache.

[0068] In some embodiments, the email distribution system can also: collect sending status data in real time during email sending, including the status of a single email such as pending, sending, delivered, bounced, or marked as spam, and record detailed information corresponding to each status. Simultaneously, for temporary failures occurring during sending (such as network interruption or server response timeout), a breakpoint resume mechanism is activated, automatically resuming the sending task from the breakpoint after the failure is resolved, thus avoiding missed or duplicate emails due to failures.

[0069] In one possible implementation, the email distribution system can first synchronously load the sending rules from the rule configuration database, the target customer email list from the customer database, and the promotional email content from the email content cache via a preset database access interface. Then, it verifies the sending time window in the sending rules. If the preset sending time period has not yet been reached, the sending task is stored in a queue for execution, and polled at fixed time intervals until the sending conditions are met. If the current time is within a suitable sending period, sending quotas are allocated to different email domains based on sending frequency limits. Target customers are batched according to queue priority sorting rules, and a multi-threaded technology is used to allocate an independent sending channel for each batch. Each channel is only responsible for delivering emails in its corresponding batch, avoiding interference between emails from different batches. During the sending process, a self-built email transmission channel is integrated to receive response information from the recipient's server in real time. Successfully sent emails record the delivery time and associate it with the customer identifier, storing it in the sending log database. For bounced emails, the bounce reason is extracted (e.g., email address does not exist, server is temporarily unavailable), and a bounce handling rule is used to determine whether to initiate a retry. For bounced emails with no retry value, relevant information is synchronized to the bounce database until all emails to all target customers are delivered.

[0070] As can be seen from step S104, sending emails to the target customer group according to the sending rules can strictly follow the sending constraints adapted to the customer's receiving environment, effectively reducing the risk of emails being rejected, blacklisted, or marked as spam, and improving the email delivery rate.

[0071] In this embodiment of the application, in step S102 above, the email distribution system can generate personalized emails to be sent based on content preference features and detailed customer data. For example, as shown... Figure 2 As shown, the above step S102 can be specifically implemented as S201-S202: S201. Determine the email content template based on content preference characteristics.

[0072] In this embodiment, the email content template refers to a pre-defined, structured basic email style. It includes fixed modules such as title, body, image placeholders, and link placeholders, and can be adapted and adjusted according to different email themes and customer content preferences. It serves as the basic carrier for quickly generating personalized marketing emails.

[0073] In some embodiments, email content templates may include: templates categorized by industry scenario, such as airline ticket promotion templates, financial product recommendation templates, real estate project promotion templates, and corporate event invitation templates; and templates categorized by marketing objective, such as new customer welcome templates, existing customer recall templates, and holiday discount templates. Each template has a pre-defined core content framework adapted to the scenario. For example, promotional templates include placeholders for discount details, event time, and participation methods, while notification templates include placeholders for instructions, operation guidelines, and contact information.

[0074] In some embodiments, the email distribution system can call a built-in multi-industry basic template library, which stores various template resources indexed by customer attributes. The system can then match the template resource with the highest degree of fit from the template library by combining the customer attributes indicated by the characteristic information of the target customer group (such as industry, consumption preferences, and marketing theme adaptation requirements).

[0075] In some embodiments, the email distribution system may also: perform format compatibility preprocessing on the matched email content templates to ensure that the templates display correctly on different email clients (web, mobile, and desktop clients) without image or link misalignment. Simultaneously, it performs sensitive word detection and compliance verification on the fixed content within the templates to avoid illegal expressions or content that does not conform to industry standards, thus ensuring the compliance of promotional email delivery.

[0076] S202. For each target customer in the target customer group, populate the email content template based on the individual data of the target customer to determine the emails to be sent to the target customer.

[0077] In this embodiment of the application, individual data refers to specific information unique to a single customer within the target customer group that can be used to personalize emails.

[0078] In some embodiments, the email distribution system can extract individual data (such as name, exclusive benefits eligibility, and customized information related to historical interaction records) of each customer in the target customer group from the customer database, thereby obtaining templates and data support for generating personalized marketing emails.

[0079] In some embodiments, the email distribution system may further: determine multiple versions of email content templates for the same email distribution topic, with differences in title style, body layout, image selection, and presentation of key information. A set of users related to the email topic is selected and randomly divided into multiple test groups, each corresponding to a different version of the email content template. Marketing emails generated based on the corresponding template are sent to users in different test groups. After the test period ends, feedback data on user behavior in each test group regarding the marketing emails (such as open rate, click-through rate, conversion rate, etc.) is collected. By comparing and analyzing the feedback effects of each template version, the best-performing template is selected as the target email content template for the email topic, and used for generating subsequent marketing emails for the target customer group of that email topic.

[0080] In one possible implementation, steps S201-S202 are implemented as follows: The email distribution system first parses the characteristic information of the target customer group, extracting the core customer attributes, such as "civil aviation industry + frequent travel + discount sensitivity". Based on this attribute combination, keyword matching and relevance scoring are performed in a multi-industry basic template library, and the "limited-time discount on civil aviation tickets" template with the highest score is selected as the email content template. Subsequently, for each target customer in the target customer group, individual data of that customer is extracted from the customer database, including name, frequently used travel routes, exclusive discount levels corresponding to historical booking records, and reserved contact phone numbers. This individual data is automatically filled into the corresponding reserved positions in the email content template, such as embedding the customer's name in the title, filling the discount information area with exclusive discount codes and discount information adapted to their travel routes, and linking their historical contact channels in the consultation guidance area, ultimately generating a personalized marketing email for each target customer.

[0081] As can be seen from steps S201-S202, determining the email content template based on customer attributes and filling it with individual data ensures that the basic framework of the email is highly compatible with the core needs of the customer group. At the same time, personalized information filling avoids the problem of template homogenization, allowing each customer to receive an email that fits their own situation, increasing the email's attractiveness and attention to customers, reducing the interference of irrelevant content to customers, laying the foundation for improving email open rate, click-through rate and conversion efficiency in the future, and also reducing the time and manpower costs of manually designing personalized emails.

[0082] In this embodiment, after sending emails to the target customer group, the email distribution system can also generate adjustment suggestions for adjusting email distribution elements based on the target users' feedback behavior data. This feedback behavior data includes at least email open behavior data, link click behavior data, and customer intent data. For example, such as... Figure 3 As shown, steps S301-S303 are included: S301. Based on email open behavior data, generate the first adjustment suggestion by determining the email open rate.

[0083] The first adjustment suggestion is used to indicate how to adjust the title of the email content template.

[0084] In this embodiment, email open behavior data refers to raw data related to email opening, collected by the email distribution system through technical means, such as whether each customer in the target customer group opened the promotional email, the time of opening the email, and the number of times the email was opened. Open rate refers to the proportion of customers who opened the email out of the total number of customers who successfully received the email in a single promotional email delivery task; it is a core indicator for measuring the attractiveness of the email title to customers. The first adjustment suggestion refers to specific guidance generated by the email distribution system based on the open rate analysis results, used to optimize the email content template title to improve subsequent open rates.

[0085] In some embodiments, email opening behavior data may include: the customer identifier corresponding to each promotional email, the email opening status (opened / not opened), the first opening time, the cumulative number of openings, the duration of each opening, the device type and email client type used when opening the email, etc.

[0086] In some embodiments, the first adjustment suggestion may include: adjusting the style of the email subject (e.g., changing from a straightforward style to a suspenseful style or highlighting benefits), optimizing the keyword layout of the email subject (e.g., adding keywords related to customer-focused offers, industry-related information, and service types), adjusting the number of words and sentence structure of the subject (e.g., simplifying redundant expressions and using short sentences to enhance readability), and optimizing the visual presentation settings of the subject (e.g., bolding the subject font and adjusting the subject color scheme to improve visual attention).

[0087] In some embodiments, the email distribution system can trigger data collection through pixel tags embedded in the email body. When a customer opens a promotional email, the pixel tag will send a trigger signal to the system. At the same time, combined with link redirection technology to assist in verification, the collected customer identification, opening time, device type and other data are synchronized to the behavior data database in real time, and then all email opening behavior data corresponding to the target promotion task are extracted from the database.

[0088] In some embodiments, the email distribution system may also: compare the calculated open rate with the average open rate of similar promotional emails in the same industry to analyze the industry competitiveness of the current email title's attractiveness. Simultaneously, it may segment the open rate data by customer group attributes (such as industry, region, and age) to identify specific customer segments with low open rates due to insufficient title relevance, providing supplementary evidence for generating more targeted primary adjustment suggestions related to title adjustments.

[0089] In one possible implementation, the email distribution system can first access a behavioral data database through a pre-defined data interface, filter out all email open behavior data corresponding to the target promotion task, extract the number of successfully delivered emails and the number of opened emails, and calculate the open rate of the promotion task according to the formula "open rate = number of opened emails / number of successfully delivered emails × 100%". Then, it retrieves the open rate threshold stored in the system (this threshold is set based on historical promotion data and industry benchmarks). If the calculated open rate is higher than or equal to the threshold, a first adjustment suggestion is generated: "Maintain the current core style of the email title, and you can choose to fine-tune the keyword expression to enhance attractiveness". If the open rate is lower than the threshold, it further analyzes the content preference characteristics of the customer group corresponding to the unopened emails, combines the title characteristics of historical high-open-rate emails, and generates specific and actionable first adjustment suggestions such as "optimize the presentation of benefits in the email title, supplement industry keywords that customers care about, and simplify redundant expressions in the title", and stores these suggestions in the adjustment suggestion database.

[0090] As shown in step S301, determining the open rate and generating the first adjustment suggestion based on email open behavior data can directly focus on the attractiveness of the email title to customers. This data-driven approach accurately identifies shortcomings in the title, avoiding the waste of promotional resources caused by blindly adjusting the title. Simultaneously, the generated specific adjustment suggestions can directly guide the optimization of the email content template's title, helping to improve the open rate of subsequent promotional emails, making promotional content more accessible to customers, and providing strong support for improving subsequent customer interaction and promotional effectiveness.

[0091] S302. Based on link click behavior data, generate a second adjustment suggestion by determining the click-through rate of links in the email.

[0092] The second adjustment suggestion is used to indicate adjustments to the link settings or content in the email content template.

[0093] In this embodiment, link click behavior data refers to the raw data collected by the email distribution system regarding links embedded in promotional emails by customers within the target customer group. This data is a crucial indicator reflecting the link's appeal to customers and the email content's conversion potential. Click-through rate (CTR) refers to the proportion of customers who click on links within an email out of the total number of customers who open the email during a single promotional email campaign. It is a core indicator for measuring the rationality of link settings and the effectiveness of content guidance. The second adjustment suggestion refers to specific guidance generated by the email distribution system based on CTR analysis results, used to optimize link settings or associated content in email content templates to improve subsequent link click conversion rates.

[0094] In some embodiments, link click behavior data may include: the customer identifier corresponding to each link click, the unique ID of the link, the click time, the number of clicks, the device type and browser type used when clicking the link, whether the link redirection was successful, the dwell time after the redirection, and whether the core operation of the page pointed to by the link (such as viewing details, submitting information, etc.) was completed.

[0095] In some embodiments, the second adjustment suggestion may include: adjusting the position of the link in the email content (e.g., moving it from the end of the body to near the core benefit at the beginning, or adding a link entry below the key information module), optimizing the display style of the link (e.g., changing text links to button links, or adjusting the link color and font to highlight it), modifying the name and description of the link (e.g., changing "Click to learn more" to "Claim exclusive offers" or "View new route details" to be more guiding), replacing the content page that the link points to (e.g., changing the general introduction page to a personalized details page that matches customer preferences), increasing or decreasing the number of links in the email (e.g., deleting redundant and invalid links, or adding related links to high-attention modules), etc.

[0096] In some embodiments, the email distribution system can acquire link click behavior data through link redirection technology, embedding a unique redirect address in the links of promotional emails. When a customer clicks the link, the system first captures data such as customer identifier, click time, and device information, then redirects the customer to the target page. Simultaneously, the captured raw data is synchronized to the behavior data database in real time, and subsequently, all link click behavior data corresponding to the target promotion task are filtered and extracted from this database.

[0097] In some embodiments, the email distribution system can also: split click-through rate (CTR) data by the ID of different links within the email, identify inefficient links with extremely low CTRs and efficient links with high CTRs, and analyze the differences between the two types of links in terms of settings, descriptions, and placement. Simultaneously, it compares the current link CTR with the CTR of historical promotional emails for the same customer group and the average CTR of similar promotional emails in the same industry to further clarify the advantages and disadvantages of the current link settings and associated content.

[0098] In one possible implementation, the email distribution system can first access the link click behavior data and email open behavior data corresponding to the target promotion task in the behavioral data database through a data access interface. It can then extract the total number of customers who opened emails and the total number of customers who clicked any link, and calculate the overall link click-through rate (CTR) for the promotion task using the formula: "CTR = (Total number of customers who clicked links / Total number of customers who opened emails) × 100%". Subsequently, it retrieves the system's preset CTR benchmark threshold (this threshold is set based on historical promotion performance data and industry standards). If the calculated CTR is higher than or equal to the benchmark threshold, a second adjustment suggestion is generated: "Maintain the settings and content of core links, and optionally fine-tune the display style of some links to improve visual attention." If the click-through rate is lower than the baseline threshold, further analysis is conducted on the characteristics of inefficient links (such as remote locations, vague descriptions, and links that do not meet expectations). Based on the successful experience of high-click-through-rate links, specific adjustment suggestions are generated, such as "changing the text links in the middle of the body to red button-style links, changing 'Click to view' to 'Book a flight and get a 200 yuan discount,' and deleting the generic links at the bottom that have no clicks." These suggestions are stored in the adjustment suggestion database for use when optimizing email content templates in the future.

[0099] As shown in step S302, determining the click-through rate based on link click behavior data and generating a second adjustment suggestion can accurately pinpoint the problems in link settings and content guidance within the email content template, avoiding the waste of promotional resources caused by blind adjustments. The generated specific adjustment suggestions can directly guide link-related optimization operations, effectively improving the click-through rate of links in subsequent promotional emails, promoting the conversion of customers from "opening emails" to "deep interaction," further realizing the value of promotional content, and providing strong support for improving the overall promotional effect.

[0100] S303. Based on customer intention data, generate a third adjustment suggestion by determining the content preference tendencies of the target customers.

[0101] The third adjustment recommendation is used to indicate adjustments to the characteristic information of the target customers.

[0102] In this embodiment, customer intent data refers to various data collected by the email distribution system that reflects the target customer's interest, needs, and attitudes towards promotional email content, related products, or services. It is the core basis for uncovering the customer's true preferences. Content preference refers to the target customer's explicit preferences or potential needs regarding the type of promotional content, core information, and presentation format, derived from the analysis of customer intent data. Third adjustment suggestions refer to specific guidance generated by the email distribution system based on the content preference analysis results, used to correct, supplement, or update the target customer characteristic information to improve the accuracy of subsequent promotions.

[0103] In some embodiments, customer intent data may include: the body of the customer's reply to the promotional email and extracted keywords (such as "inquire about new routes", "promotion period", "not needed at the moment"), the customer's interaction focus on different content modules in the email (such as clicking a certain service introduction link multiple times, quickly skipping a certain type of information), the reason for unsubscribing when unsubscribing from the email (such as "content is irrelevant", "too frequent", "no need"), and requests or suggestions related to the promotional content provided through online consultation channels.

[0104] In some embodiments, the third adjustment suggestion may include: adding segmentation preference tags for target customers (such as "interested in international route discounts" or "demand for business class services"), updating the weights of existing feature information (such as adjusting the weight of "preference for promotional content" from medium to high), correcting inaccurate feature classifications (such as correcting "preference for personal travel" to "preference for business travel"), and supplementing feature information corresponding to potential customer needs (such as supplementing the feature "interested in travel-related services" based on "inquiry about baggage allowance" feedback).

[0105] In some embodiments, the email distribution system can acquire customer intent data through multiple channels. For example, it can extract the text content of customer replies to emails and records of unsubscribe reason selections from a feedback data database. It can retrieve customer interaction data on different content modules within emails from a behavioral data database. Simultaneously, it can integrate customer request records synchronized from an online consultation system to a related database, and use data fusion technology to aggregate these into a complete set of customer intent data.

[0106] In some embodiments, the email distribution system may also: verify the credibility of the collected customer intent data, remove obviously invalid and interfering data (such as meaningless garbled replies, unsubscription records generated by accidental operations), and ensure the authenticity of the data. Simultaneously, it may compare customer intent data with historical intent data according to customer identifiers, analyze the dynamic trends of customer preferences, and provide support for generating more timely third-party adjustment suggestions.

[0107] In one possible implementation, the email distribution system can first access relevant data from feedback data databases, behavioral data databases, and relational databases via data interfaces, summarizing them into a set of target customer intent data. Then, natural language processing algorithms are used to extract keywords and perform semantic analysis on the text-based intent data. Combined with the interaction trajectory characteristics of the behavioral intent data, a customer intent scoring model is constructed to quantify customer interest in different types of promotional content. Based on the scoring results, the target customer's content preference tendencies are determined (e.g., "High interest: International Business Class Discounts," "Medium interest: Airport VIP Services," "Low interest: Travel Packages"). The current content preference tendencies are then compared with the customer's existing feature information, identifying missing, biased, and updateable items, generating specific third-party adjustment suggestions such as "adding an 'International Business Class Preference' tag to the customer, updating the 'Travel Package Preference' feature to 'Low Interest,' and supplementing the 'Focus on Airport VIP Services' feature." Finally, these suggestions are synchronized to the customer database, triggering the target customer feature information update process to ensure that the feature information is consistent with the customer's true preferences.

[0108] As shown in step S303, determining content preferences based on customer intent data and generating third adjustment suggestions ensures that the target customer's characteristic information continuously aligns with their actual needs and dynamic changes, correcting potential deviations or lags in the initial characteristic information. With optimized characteristic information, subsequent promotional email content customization and targeted delivery become more precise, effectively reducing the waste of promotional resources caused by inaccurate characteristic information, increasing customer acceptance and willingness to interact with promotional content, and providing reliable data support for continuous optimization of subsequent promotional effects.

[0109] In summary, in this embodiment, the email distribution system can also integrate various data from behavioral data databases, feedback data databases, customer databases, and adjustment suggestion databases to build a visual data presentation function. Through various chart formats such as line charts, bar charts, funnel charts, and pie charts, it can intuitively display the trends and detailed distribution of core metrics for promotional emails. This visualization function supports data breakdown by time dimension (day / week / month / quarter), customer group dimension (industry, region, feature tags), and promotion task dimension (different email content templates, sending rules), clearly presenting changes in key metrics such as open rate, click-through rate, content preference distribution, and the effectiveness of feature information adjustments. It also provides a visual display of single-customer behavior trajectories, connecting data such as email opening time, link click order, and intention feedback content in a timeline format, facilitating users to trace the entire interaction process of a single customer. It also supports exporting visual reports to PDF or Excel format to meet the needs of data archiving and secondary analysis. Furthermore, the visualization interface simultaneously marks the optimization direction of the corresponding indicators for the adjustment suggestions (such as "open rate increased by 15% after adjusting email title" and "precision reach rate improved by 20% after updating customer feature tags"), allowing users to intuitively perceive the actual value of the adjustment suggestions and helping to quickly formulate subsequent email distribution strategies.

[0110] The email distribution method provided in this application provides objective data support for the email distribution decision-making process by acquiring the content preference characteristics and receiving environment characteristics of the target customer group. Furthermore, determining email content based on content preference characteristics directly ensures that the email subject and body information are relevant to the recipient's individual interests, thereby increasing the email's attractiveness and click-through rate. Simultaneously, determining sending rules based on receiving environment characteristics allows email delivery to accurately match the time period when the recipient is likely to view the email (e.g., avoiding off-peak hours or peak data flow) and avoid server congestion periods, thus increasing the chances of the email being received and presented to the user in a timely manner. Finally, sending is executed according to these sending rules, ensuring that highly relevant email content is delivered at the time when it is most likely to be noticed and processed. The synergistic effect of these two methods effectively overcomes the two key obstacles leading to low click-through rates: irrelevance of content and inappropriate delivery timing, thereby improving the overall click-through rate of the email.

[0111] In an exemplary embodiment, Figure 4 This is a schematic diagram illustrating the composition of an email distribution device provided in an embodiment of this application. Figure 4As shown, the email distribution device includes: a customer management module 401, an email generation module 402, a task planning module 403, and an email sending module 404. The customer management module 401 is used to acquire characteristic information of the target customer group. This characteristic information includes at least the target customer group's content preference characteristics and receiving environment characteristics. The receiving environment characteristics indicate the time and server environment in which the target customer group receives the email. The email generation module 402 is used to determine emails for the target customer group based on the content preference characteristics. The task planning module 403 is used to determine the sending rules for sending emails to the target customer group based on the receiving environment characteristics. The email sending module 404 is used to send emails to the target customer group according to the sending rules.

[0112] In this embodiment, the customer management module 401 is specifically used to: obtain historical email interaction records of the target customer group to extract content preference features of the target customer group; and obtain historical email receiving logs of the target customer group to extract receiving environment features of the target customer group.

[0113] In this embodiment, the customer management module 401 can also be used to: acquire a customer data set. The customer data set contains attribute information for multiple customers. Based on a preset email subject, the customers in the customer data set are categorized according to the attribute information to form multiple candidate customer groups.

[0114] In this embodiment, the email generation module 402 is specifically used to: determine an email content template based on content preference features. For each target customer in the target customer group, the email content template is populated based on the target customer's individual data to determine the email addressed to the target customer.

[0115] In this embodiment, the email generation module 402 can also be used to: determine multiple versions of email content templates for the same email subject; divide the first user set into multiple test groups and send emails generated based on different versions of email content templates to users in different test groups; wherein, the first user set is a set of customers related to the email subject; and determine the target email content template for the email subject based on the feedback behavior of users in each test group.

[0116] In this embodiment, the task planning module 403 is specifically used to: obtain the time zone distribution and active time period distribution of the target customer group based on the time environment information in the receiving environment characteristics, so as to determine the email sending time window; obtain the historical bounce rate and historical sending success rate corresponding to the target email domain name based on the server environment information in the receiving environment characteristics, so as to determine the sending frequency limit; and generate sending rules based on the sending time window and the sending frequency limit.

[0117] In this embodiment of the application, the apparatus further includes a suggestion generation module, which is used to generate adjustment suggestions for adjusting email distribution elements based on the target user's feedback behavior data on the email.

[0118] In this embodiment, the feedback behavior data includes at least email open behavior data, link click behavior data, and customer intent data. The suggestion generation module is specifically used to: generate a first adjustment suggestion based on the email open behavior data by determining the email open rate. The first adjustment suggestion indicates how to adjust the title of the email content template. Generate a second adjustment suggestion based on the link click behavior data by determining the link click rate in the email. The second adjustment suggestion indicates how to adjust the link settings or content in the email content template. Generate a third adjustment suggestion based on the customer intent data by determining the content preferences of the target customer. The third adjustment suggestion indicates how to adjust the characteristic information of the target customer.

[0119] In summary, in some embodiments, the customer management module 401 can be specifically a customer data management module, the email generation module 402 can be specifically an email generation and management module, the task planning module 403 can be specifically a task management module, the email sending module 404 can be specifically an email sending and receiving module, and the suggestion generation module can be further refined into a user behavior tracking module, a data analysis module, and a strategy optimization suggestion module. For example, Figure 5As shown, the modular architecture of the email distribution device can be further refined into a customer data management module, an email generation and management module, a task management module, an email sending and receiving module, a user behavior tracking module, a data analysis module, and a strategy optimization suggestion module. Among these, the customer data management module, as the core data source module, is responsible for integrating customer attribute information from multiple channels, classifying customers, and filtering target customer groups with matching topics. Simultaneously, it synchronizes the characteristic information of the target customer groups with individual data to the email generation and management module. The email generation and management module uses email content templates matched to customer attributes and combines them with individual data to complete personalized content filling, generating exclusive emails for each target customer. The generated emails are then delivered to the task management module. The task management module determines the sending time zone based on the target customer's geographic attributes, plans the email sending time sequence based on active periods, and sets the sending rate according to the frequency limit rules of the email service domain. These sending rules, along with the emails to be sent, are then passed to the email sending and receiving module for outbound operations. After the email sending and receiving module completes email delivery, the user behavior tracking module captures remote resource requests through embedded pixel tags in the email to obtain open behavior data, obtains click behavior data through link redirection mechanisms, and simultaneously receives customer reply emails and extracts intent data. All feedback behavior data is then uniformly transmitted to the data analysis module. The data analysis module quantifies the feedback behavior, generating multiple metrics representing effectiveness, such as email open rate and click-through rate. These metrics are compared with preset reference thresholds to identify target metrics, and the analysis results are then output to the strategy optimization suggestion module. Based on the type of target metric, the strategy optimization suggestion module adjusts the email content generation strategy or email sending rules accordingly, thereby continuously improving the accuracy and effectiveness of email distribution.

[0120] In an exemplary embodiment, this application also provides an electronic device, which may be the email distribution device in the above method embodiments. Figure 6 This is a schematic diagram of the structure of an email distribution device provided in an embodiment of this application. Figure 6 As shown, the email distribution device may include a processor 601 and a memory 602. The memory 602 stores instructions executable by the processor 601. When the processor 601 is configured to execute the instructions, it causes an electronic device, network device, or manager to perform the system functions described in the foregoing method embodiments.

[0121] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0122] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0123] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0124] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0125] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0126] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for distributing emails, characterized in that, The method includes: Obtain characteristic information of the target customer group; the characteristic information includes at least the content preference characteristics and receiving environment characteristics of the target customer group; the receiving environment characteristics are used to indicate the time environment and server environment in which the target customer group receives emails. Based on the content preference characteristics, determine emails targeting the target customer group; Based on the characteristics of the receiving environment, the sending rules for sending emails to the target customer group are determined; The email is sent to the target customer group according to the sending rules.

2. The method according to claim 1, characterized in that, The step of determining emails targeting the target customer group based on the content preference characteristics includes: Based on the aforementioned content preference characteristics, determine the email content template; For each target customer in the target customer group, the email content template is populated based on the individual data of the target customer to determine the emails to be sent to the target customer.

3. The method according to claim 2, characterized in that, The email content template can be determined in the following ways: Create multiple versions of email content templates for the same email subject; The first user set is divided into multiple test groups, and emails generated based on different versions of email content templates are sent to users in different test groups; the first user set is a set of customers related to the email subject. Based on the feedback behavior of users in each test group regarding the email, the target email content template for the email subject is determined.

4. The method according to claim 1, characterized in that, The step of determining the sending rules for emails to the target customer group based on the characteristics of the receiving environment includes: Based on the time environment information in the receiving environment characteristics, the time zone distribution and active time period distribution of the target customer group are obtained to determine the email sending time window; Based on the server environment information in the received environment characteristics, the historical bounce rate and historical sending success rate of the target email domain are obtained to determine the sending frequency limit; Based on the aforementioned sending time window and sending frequency limit, sending rules are generated.

5. The method according to claim 1, characterized in that, The acquisition of characteristic information of the target customer group includes: Obtain historical email interaction records of the target customer group to extract content preference characteristics of the target customer group; Obtain historical email receiving logs of the target customer group to extract the receiving environment characteristics of the target customer group.

6. The method according to claim 1, characterized in that, Before obtaining the characteristic information of the target customer group, the method further includes: Obtain a customer data set; the customer data set contains attribute information for multiple customers; Based on the preset email subject, customers in the customer data set are categorized according to the attribute information to form multiple candidate customer groups.

7. The method according to any one of claims 1-6, characterized in that, After sending the email to the target customer group according to the sending rules, the method further includes: Based on the target users' feedback behavior data on the emails, adjustment suggestions are generated for adjusting email distribution elements.

8. The method according to claim 7, characterized in that, The feedback behavior data includes at least email open behavior data, link click behavior data, and customer intent data; the step of generating adjustment suggestions for adjusting email distribution elements based on user feedback behavior data includes: Based on the email open behavior data, a first adjustment suggestion is generated by determining the open rate of the email; the first adjustment suggestion is used to indicate the adjustment of the title of the email content template. Based on the link click behavior data, a second adjustment suggestion is generated by determining the link click rate in the email; the second adjustment suggestion is used to indicate the adjustment of link settings or content in the email content template. Based on the customer intention data, a third adjustment suggestion is generated by determining the target customer's content preference tendencies; the third adjustment suggestion is used to indicate the adjustment of the target customer's characteristic information.

9. An email distribution device, characterized in that, The device includes: The customer management module is used to obtain characteristic information of the target customer group; the characteristic information includes at least the content preference characteristics and receiving environment characteristics of the target customer group; the receiving environment characteristics are used to indicate the time environment and server environment in which the target customer group receives emails. The email generation module is used to determine emails targeting the target customer group based on the content preference features. The task planning module is used to determine the sending rules for sending emails to the target customer group based on the characteristics of the receiving environment. The email sending module is used to send the email to the target customer group according to the sending rules.

10. An electronic device, characterized in that, The electronic device includes: a processor and a memory; The memory stores instructions that the processor can execute; When the processor is configured to execute the instructions, the electronic device performs the method as described in any one of claims 1-8.

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