Express industry multi-source data fusion latent guest truth rate evaluation and business opportunity mining method

Abstract

The invention discloses a potential customer truth rate evaluation and business opportunity mining method based on multi-source data fusion in the express industry, and relates to the technical field of logistics information, and the method comprises the following steps: S1, obtaining potential customer original data from a plurality of independent channels through a data collection module, the channels comprise an e-commerce platform non-cooperative customer data source, a centralized signing / returning address data source, an employee recommendation customer clue data source and a third-party API data source; and S2, performing structured processing on the original data in the data processing module. According to the latent customer truth rate evaluation and business opportunity mining method provided by the invention, multi-source data such as uncooperative customers, centralized signing / returning addresses, employee recommendation clues, third-party APIs and the like of an e-commerce platform are integrated, so that comprehensive acquisition and accurate analysis of latent customer information are realized, the problems of data dispersion and high integration difficulty in a traditional method are solved, and the real rate of latent customers is evaluated. Repeated and invalid information is effectively removed, and quick identification and efficient distribution of business opportunities are realized.

Description

Technical Field

[0001] This invention relates to the field of logistics information technology, specifically to a method for assessing the authenticity rate of potential customers and mining business opportunities through multi-source data fusion in the express delivery industry. Background Technology

[0002] With the booming development of e-commerce, the express delivery industry has generated massive amounts of data. This data covers various aspects, including customer information, order details, and transportation routes. By integrating and analyzing this multi-source data, it is possible to gain a deeper understanding of customer needs, optimize operational processes, and improve service quality. In terms of potential customer authenticity assessment and business opportunity mining, multi-source data fusion technology can integrate data from different channels to accurately determine the authenticity and value of potential customers, helping companies seize business opportunities and improve market competitiveness, which is of great significance to the development of the express delivery industry.

[0003] However, the express delivery industry currently faces several shortcomings in assessing the authenticity of potential customers and identifying business opportunities. On one hand, data sources are scattered and difficult to integrate, making it challenging to comprehensively obtain potential customer information and affecting the accuracy of assessments. On the other hand, existing analytical methods often rely on only a single or a few data sources, failing to fully explore potential correlations between data and making it difficult to uncover hidden business opportunities. Furthermore, existing technologies lack flexibility and adaptability when dealing with dynamically changing market environments and customer needs, hindering timely adjustments to assessment and opportunity mining strategies. To address these issues, we propose a multi-source data fusion method for assessing the authenticity of potential customers and identifying business opportunities in the express delivery industry. Summary of the Invention

[0004] To address the aforementioned technical issues, this solution provides a method for assessing the authenticity rate of potential customers and uncovering business opportunities through multi-source data fusion in the express delivery industry. This technical solution resolves the problems in potential customer discovery in the express delivery industry, such as data fragmentation and difficulty in integration, reliance on a single data source, difficulty in discovering hidden business opportunities through analytical methods, and insufficient flexibility in responding to market changes, making it difficult to accurately assess the authenticity rate of potential customers and uncover business opportunities.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for assessing the authenticity rate of potential customers and mining business opportunities through multi-source data fusion in the express delivery industry includes the following steps: S1. Obtain raw data of potential customers from multiple independent channels through the data acquisition module. The channels include data sources of customers who have not cooperated with e-commerce platforms, centralized data sources of delivery / return addresses, data sources of customer leads recommended by employees, and third-party API data sources. S2. In the data processing module, perform structured processing on the raw data, specifically including: semantic parsing of non-standard addresses to extract the province, city, district, street, and building fields and generate standardized addresses; verifying the status of empty and virtual numbers in contact information; performing deduplication based on the address-contact person-phone triplet; adding customer tags according to industry classification and shipping frequency; and constructing a behavioral profile that includes historical shipment volume, return frequency, and order placement behavior. S3. In the potential customer assessment module, the authenticity score is calculated using a scoring model. The input parameters include address reliability score, contact information validity score, industry matching score, historical behavior score, and channel trust level. The score is calculated using a weighted formula. S4. In the opportunity output module, classify the opportunities according to the accuracy rate score range: ≥0.85 is AAA grade, 0.70-0.84 is AA grade, 0.50-0.69 is A grade, and <0.50 is B / C grade. Then push the classification results to the sales task allocation system or customer relationship management system.

[0006] Preferably, the specific implementation method of multi-source data acquisition in step S1 is as follows: The e-commerce platform obtains information on active merchants on the platform who have not signed contracts with the courier company through API interfaces. This information includes store name, business category, monthly order volume, and shipping address. The centralized delivery address data is extracted from the last-mile delivery station management system for addresses whose daily delivery volume exceeds a preset threshold, and the positive delivery records of that address are linked to determine whether a cooperative relationship already exists. Return address data is obtained by scanning the return package label, identifying sender information, and aggregating shipments to the same address; Employees submit customer names, contact information, and business scale descriptions through the internal system for recommended leads. Third-party API data access to enterprise credit reporting platforms or industry databases supplements the validity verification results of enterprise registration information, operating status, and contact information.

[0007] Preferably, the structuring process in step S2 further includes: Address standardization uses natural language processing technology to parse the original address text, identify and separate administrative region levels from detailed geographic identifiers, and generate structured fields containing province, city, district / county, street / township, road name, house number and building number; Contact verification checks whether a mobile number is a reachable physical number by calling the telecom operator's interface or a third-party number verification service, and marks virtual operator number segments and inactive numbers. The deduplication operation generates a unique identifier for the address-contact-phone triplet based on a hash algorithm, and eliminates duplicate records by comparing them with the database index. Customer tags are attached based on a preset industry classification library to match the customer's business type, and are also divided into high-frequency / medium-frequency / low-frequency tags according to the shipping frequency in the past 30 days. Behavioral profiling is built by aggregating six months of historical express delivery records to quantify and calculate monthly average shipment volume, return rate, and regularity indicators of order placement time.

[0008] Preferably, the deduplication operation includes rules for handling abnormal data: When the same customer provides conflicting information through different channels, the contact information provided by employee referrals should be prioritized; for records with the same address but different contact persons, the company's business registration information should be used to verify whether they are related companies. For phone number change records, if the old number expires within 30 days, the new number will be retained and the historical behavior data will be merged. All deduplication operations are recorded in the audit log, including the data source, processing time, and conflict resolution rules.

[0009] Preferably, the behavioral profile construction specifically includes: Historical shipment volume index is calculated based on the average monthly express delivery order volume over the past 180 days. The order volume range is defined as low frequency (0, 50), medium frequency (51, 200), and high frequency (201, ∞). The return frequency metric is the percentage of returned packages out of the total number of packages shipped. When the percentage exceeds 15%, a risk flag is triggered. Order behavior analysis uses time series models to detect the regularity of shipping time. Customers who ship at fixed times on weekdays are labeled "stable" and customers whose order volume increases by more than 200% during promotional seasons are labeled "volatile". A multi-dimensional customer profile matrix is ​​generated by combining industry classification tags and behavioral tags.

[0010] Preferably, the calculation of the accuracy score by the scoring model in step S3 is specifically as follows:

[0011] In the formula, Score the accuracy rate. To score the address reliability, Rate the validity of the contact information. Industry matching score Rate historical behavior Based on channel trust level, to Preset weighting coefficients; The configuration rules for the weighting coefficients are as follows: Weight to The default values ​​are 0.25, 0.20, 0.15, 0.25, and 0.15, respectively. The weight values ​​can be dynamically adjusted through the management interface. The adjustment range is limited to a single weight not exceeding 0.40 and the sum always being 1.0. The weight adjustment is based on the feedback mechanism of historical transaction data. When the correlation between a specific parameter score and the final transaction rate exceeds a preset threshold, the weight coefficient of that parameter is automatically increased. The weight of address reliability score has been increased to 0.30 for cross-border e-commerce clients, and the weight of contact information validity score has been increased to 0.25 for individual merchant clients.

[0012] Preferably, the logic for generating the address reliability score is as follows: The address was verified to exist and belong to a commercial building through a geographic information system. The basic score for a commercial address was 0.80, and the basic score for a residential address was 0.50. Address integrity coefficient: Add 0.10 when building number is included, subtract 0.15 when street information is missing; Based on historical data verification: if the address has a successful delivery record in the express delivery database, add 0.05; if it is marked as a fake address, the score is 0. The final score is normalized to the [0,1] interval.

[0013] Preferably, the method for dynamically assigning the channel trust level is as follows: The initial level of employee referral leads is 0.90, the initial level of data from non-cooperative customers on e-commerce platforms is 0.75, the initial level of data from third-party APIs is 0.70, and the initial level of data from centralized delivery addresses is 0.80. When customer information provided by a certain channel is verified as false by sales, the trust level of that channel will be lowered by 0.05 for that month. If the conversion rate of business opportunities provided by a channel exceeds 10% for three consecutive months, the trust level will be permanently increased by 0.10; the channel level will be recalibrated every quarter.

[0014] Preferably, the scoring model in step S3 includes a dynamic optimization mechanism: Each month, the transaction results data of allocated business opportunities for the past 30 days are extracted. Logistic regression algorithm is used to analyze the correlation coefficient between each scoring parameter and the transaction rate. When the correlation coefficient of a certain parameter is lower than 0.3 for two consecutive months, its weight coefficient is reduced by 0.05. When a newly accessed data source causes the variance of the industry matching score to increase by more than 15%, the recalibration process of the weight coefficient w3 is triggered. Model version changes must be verified through a test set to ensure that the new model's AUC value is not lower than 0.75 before it can be deployed online.

[0015] Preferably, in step S4, the hierarchical rules are expanded as follows: AAA-level business opportunities must simultaneously meet the following requirements: authenticity rate ≥ 0.85, address reliability score ≥ 0.85, and contact information validity score ≥ 0.80. The system will automatically assign the opportunity to the sales team and initiate the first contact within 24 hours. AA-level business opportunities trigger the standard allocation process and require follow-up within 48 hours; When allocating Class A business opportunities, detailed data on historical behavior scores (H) are attached for sales staff to manually review and follow up. B / C level business opportunities are transferred to the pending review pool and will only be reactivated for evaluation if the address subsequently shows new positive shipping records or the channel trust level improves to 0.85 or above.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention proposes a method for assessing the authenticity of potential customers and mining business opportunities. By integrating multi-source data, including data from non-cooperative customers on e-commerce platforms, centralized delivery / return addresses, employee referral leads, and third-party APIs, it achieves comprehensive acquisition and accurate analysis of potential customer information. This solves the problems of data dispersion and integration difficulties in traditional methods. The method utilizes structured processing technology to standardize and verify non-standard addresses and contact information, effectively removing duplicate and invalid information and improving data quality. A scoring model calculates the potential customer authenticity score, and business opportunities are pushed to the sales system according to tiered rules, enabling rapid identification and efficient allocation of opportunities. The scoring parameter weights are adjusted based on historical transaction data feedback to ensure the accuracy and adaptability of the assessment model, improving the precision of potential customer authenticity assessment and the efficiency of business opportunity mining. This provides strong support for express delivery companies to seize market opportunities and enhance their competitiveness. Attached Figure Description

[0017] Figure 1 This is a mind map of the method of the present invention; Figure 2 This is a diagram illustrating the hierarchical classification rules of the present invention. Detailed Implementation

[0018] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0019] Reference Figure 1 As shown, in the express delivery industry, in order to accurately assess the authenticity of potential customers and effectively explore business opportunities, an assessment and mining method based on multi-source data fusion has been developed. The core process of this method first involves extensively acquiring raw potential customer data from multiple independent but complementary channels.

[0020] These channels specifically include: systematically acquiring information on active merchants on e-commerce platforms who have not yet established a cooperative relationship with the courier company through their open API interfaces, covering their store names, main product categories, average monthly order volume, and commonly used shipping addresses; extracting address information from the management systems of last-mile delivery stations or distribution outlets that significantly exceed preset thresholds for the number of packages signed for in a single day, and further analyzing whether the address has a history of being a sender to determine whether a potential or actual business relationship already exists; accurately identifying sender information by automatically scanning electronic waybills on a large number of returned packages, and aggregating and statistically analyzing the total number of packages sent from the same address; establishing internal system channels that allow company employees to proactively submit potential customer leads they are aware of, including customer names, valid contact information, and an estimated business scale description; in addition, accessing third-party enterprise credit reporting platforms or industry database APIs to supplement the acquisition of target companies' business registration information, current operating status (such as existence, cancellation, etc.), and the validity results of contact information verified by professional services.

[0021] After acquiring the raw data, the crucial data processing stage begins. The primary task in this stage is to perform deep structuring processing on the raw data. For non-standardized address text, advanced natural language processing technology is used for intelligent parsing, accurately identifying and separating administrative region levels (such as province, city, district / county, street / township) and detailed geographic identifiers (such as road name, house number, building number), ultimately generating highly structured standard address fields.

[0022] For the contact information provided by the customer, the system will call the official interface provided by the telecom operator or the professional third-party number verification service in real time to strictly check the current status of the mobile phone number (whether it is a physical number that can be reached), and clearly mark the identified virtual operator number segments and numbers that have been suspended or are invalid.

[0023] To eliminate data redundancy, a unique identifier is generated using a highly efficient hash algorithm based on a triplet consisting of address, contact person, and phone number. This identifier is then quickly compared with existing indexes in the database to intelligently identify, merge, or delete duplicate records. Simultaneously, based on a pre-built industry classification knowledge base, each customer is matched with a corresponding business type tag (e.g., apparel, electronics, fresh produce), and categorized into high-frequency (e.g., high daily shipment volume), medium-frequency, or low-frequency customer tags according to their shipping activity over the past 30 days.

[0024] More importantly, by aggregating the customer's historical express delivery records over the past 6 months, a detailed behavioral profile is constructed. This includes quantitatively calculating the average monthly shipment volume, the proportion of returned packages to the total shipment volume (return rate), and using time series analysis models to deeply analyze the temporal regularity of the customer's ordering behavior (e.g., whether shipments are made at fixed times on weekdays, or whether there is an explosive increase in order volume during promotional seasons).

[0025] In the deduplication process, specific rules for handling abnormal data were designed: when the same customer provides contradictory information from different sources (such as different phone numbers), the system will prioritize the contact information provided in employee referral leads; for records with identical addresses but different contact persons, the system will query an authoritative corporate registration information database to verify whether these contacts belong to the same company (such as employees from different departments); for detected phone number change records, if the old number expires within 30 days, the new number will be retained and the customer's historical behavior data will be automatically merged into the new record; all deduplication processes will be recorded in detail in the audit log, including data source, processing timestamp, and the conflict resolution rules finally applied.

[0026] The behavioral profile construction further refines the indicators: the historical shipment volume indicator is divided into low-frequency (0-50 orders), medium-frequency (51-200 orders), and high-frequency (201 orders or more) ranges based on the average monthly order volume over the past 180 days; the return frequency indicator calculates the proportion of returned packages to the total shipment volume, and when this proportion exceeds 15%, the system will automatically trigger a risk label; the order behavior analysis detects the time pattern of customer shipments through a time series model, and adds a "stable" label to customers who send shipments steadily during fixed time periods on weekdays, while adding a "fluctuating" label to customers whose order volume increases by more than 200% during promotional seasons; finally, the industry classification labels are combined with the above behavioral labels to form a multi-dimensional customer profile matrix.

[0027] In the potential customer assessment module, the core task is to calculate the authenticity score for each potential customer using a comprehensive scoring model. This model employs a weighted summation formula:

[0028] In the formula, Score the accuracy rate. To score the address reliability, Rate the validity of the contact information. Industry matching score Rate historical behavior Based on channel trust level, to The preset weighting coefficients are configured as follows: 0.25, 0.20, 0.15, 0.25, and 0.15.

[0029] These weighting coefficients can be dynamically adjusted through the management interface, but are subject to strict constraints: the adjustment limit for a single weighting coefficient cannot exceed 0.40, and the sum of all weights must be constant at 1.0. Weighting adjustments are not arbitrary, but rather based on a feedback mechanism of historical business opportunity transaction data: when the system analysis finds that the statistical correlation between the score of a certain rating parameter (such as address reliability A) and the final actual transaction rate continuously exceeds a preset threshold, the weighting coefficient of that parameter will be automatically increased. Furthermore, the model also has intelligent scenario adaptation capabilities. For example, when processing cross-border e-commerce customers, the weight of address reliability score A will be automatically increased to 0.30; while when processing individual merchant customers, the weight of contact information validity score C will be increased to 0.25.

[0030] The calculation logic for each scoring parameter is also quite sophisticated. The generation of the address reliability score A involves: first, verifying the authenticity and attributes (commercial or residential) of the address through a geographic information system. Commercial addresses receive a base score of 0.80, while residential addresses receive 0.50. Then, an address completeness coefficient is added; for example, if the address text explicitly contains a building number, 0.10 points are added, and if street information is missing, 0.15 points are subtracted. Finally, verification is performed using the courier company's own historical operational data. If the address has a successful delivery record in the database, an additional 0.05 points are added; if it has been explicitly marked as a fake address, the score is directly 0. The final score is normalized to a range of 0 to 1.

[0031] The channel trust level S is not fixed but dynamically assigned: employee referral leads start with the highest level, set at 0.90; data from non-partner customers on e-commerce platforms starts at 0.75; third-party API data starts at 0.70; and data from centralized delivery addresses starts at 0.80. The dynamic adjustment rule is as follows: when customer information provided by a channel is confirmed as false by the sales team on-site, the channel's trust level for that month will be immediately lowered by 0.05; conversely, if a channel's conversion rate for leads provided for three consecutive months exceeds 10%, its trust level will be permanently increased by 0.10; the trust levels of all channels will be systematically recalibrated quarterly.

[0032] The scoring model itself also incorporates a dynamic optimization mechanism: each month, the system automatically extracts the final transaction results data of allocated business opportunities from the past 30 days and uses a logistic regression algorithm to deeply analyze the statistical correlation coefficients between the scores of each scoring parameter (A, C, B, H, S) and the final transaction rate. If the correlation coefficient of a certain parameter is found to be below 0.3 for two consecutive months, indicating a decrease in its contribution to predicted transactions, the system will automatically reduce its corresponding weight coefficient by 0.05. In addition, when the introduction of a new data source causes the variance of the industry matching score B to suddenly increase by more than 15%, a recalibration process for the weight coefficient w3 will be immediately triggered.

[0033] Any modification to the weights or structure of the scoring model (i.e., model version change) must first undergo rigorous validation on an independent test set to ensure that the new model's AUC value (a metric for measuring the model's discriminative ability) in predicting transactions is not lower than 0.75 before it can be deployed in the production environment.

[0034] In the business opportunity output module, potential customers are classified and managed in a refined manner based on the calculated authenticity rate score.

[0035] refer to Figure 2 As shown, the grading rules clearly define: potential customers with an authenticity score ≥ 0.85, and simultaneously meeting the requirements of an address reliability score ≥ 0.85 and a contact information validity score ≥ 0.80, are rated as AAA-level business opportunities. These highest priority business opportunities will be automatically assigned to the sales team by the system, and an initial contact attempt is required within 24 hours.

[0036] Potential customers with an authenticity rate score between 0.70 and 0.84 are rated as AA-level opportunities, triggering the standard sales allocation process, requiring sales personnel to follow up within 48 hours. Potential customers with an authenticity rate score between 0.50 and 0.69 are rated as A-level opportunities. When assigning them to sales personnel, the system will specifically attach detailed historical behavioral score H data for that potential customer (such as specific shipment volume, return rate, regularity tags, etc.) for sales personnel to manually review and evaluate before deciding whether and how to follow up.

[0037] Potential customers with an authenticity score below 0.50 are categorized as B / C level opportunities and will not be directly assigned to a service. Instead, they will be placed in a waiting pool for observation. The system will continuously monitor changes in the relevant information of these low-scoring potential customers. Only when the customer's address subsequently shows new positive shipping records in the courier system (indicating that they have started actively shipping), or when the trust level (S) of their data source channel increases to 0.85 or higher, will the evaluation process for that potential customer be reactivated, giving them the opportunity to be upgraded to a higher-level opportunity.

[0038] The grading results will be pushed to the company's sales task allocation system or customer relationship management system in real time to drive sales actions.

[0039] The following is a specific implementation example: A courier company hopes to use this system to identify potential customers and optimize its opportunity allocation process. Data collection: Obtain data from non-partner merchants on the Taobao platform; obtain centralized delivery addresses from last-mile delivery stations; extract sender information from returned packages; and have internal employees submit recommended customer information.

[0040] Data cleaning and structuring: Standardize the address "Province X City X District X Road X Number" and extract structured fields; verify the validity of the mobile phone number "138XXXX1111" by calling a third-party interface; determine whether the customer already exists in the system database; and tag the customer with "e-commerce customer", "high-frequency delivery", and "popular business district".

[0041] Authenticity score calculation: Address reliability score A=0.9; Contact information validity score C=0.8; Industry matching score B=0.7; Historical behavior score H=0.85; Channel trust level S=0.8; calculate:

[0042] The calculation result is 0.8275, which is classified as AA level and can be distributed to sales outlets for follow-up.

[0043] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for assessing the authenticity rate of potential customers and mining business opportunities through multi-source data fusion in the express delivery industry, characterized by: Includes the following steps: S1. Obtain raw data of potential customers from multiple independent channels through the data acquisition module. The channels include data sources of customers who have not cooperated with e-commerce platforms, centralized data sources of delivery / return addresses, data sources of customer leads recommended by employees, and third-party API data sources. S2. In the data processing module, perform structured processing on the raw data, specifically including: semantic parsing of non-standard addresses to extract the province, city, district, street, and building fields and generate standardized addresses; verifying the status of empty and virtual numbers in contact information; performing deduplication based on the address-contact person-phone triplet; adding customer tags according to industry classification and shipping frequency; and constructing a behavioral profile that includes historical shipment volume, return frequency, and order placement behavior. S3. In the potential customer assessment module, the authenticity score is calculated using a scoring model. The input parameters include address reliability score, contact information validity score, industry matching score, historical behavior score, and channel trust level. The score is calculated using a weighted formula. S4. In the opportunity output module, classify the opportunities according to the accuracy rate score range: ≥0.85 is AAA grade, 0.70-0.84 is AA grade, 0.50-0.69 is A grade, and <0.50 is B / C grade. Then push the classification results to the sales task allocation system or customer relationship management system.

2. The method for assessing the authenticity rate of potential customers and mining business opportunities through multi-source data fusion in the express delivery industry according to claim 1, characterized in that, The specific implementation method for multi-source data acquisition in step S1 is as follows: The e-commerce platform obtains information on active merchants on the platform who have not signed contracts with the courier company through API interfaces. This information includes store name, business category, monthly order volume, and shipping address. The centralized delivery address data is extracted from the last-mile delivery station management system for addresses whose daily delivery volume exceeds a preset threshold, and the positive delivery records of that address are linked to determine whether a cooperative relationship already exists. Return address data is obtained by scanning the return package label, identifying sender information, and aggregating shipments to the same address; Employees submit customer names, contact information, and business scale descriptions through the internal system for recommended leads. Third-party API data access to enterprise credit reporting platforms or industry databases supplements the validity verification results of enterprise registration information, operating status, and contact information.

3. The method for assessing the authenticity rate of potential customers and mining business opportunities through multi-source data fusion in the express delivery industry according to claim 1, characterized in that, The structuring process in step S2 further includes: Address standardization uses natural language processing technology to parse the original address text, identify and separate administrative region levels from detailed geographic identifiers, and generate structured fields containing province, city, district / county, street / township, road name, house number and building number; Contact verification checks whether a mobile number is a reachable physical number by calling the telecom operator's interface or a third-party number verification service, and marks virtual operator number segments and inactive numbers. The deduplication operation generates a unique identifier for the address-contact-phone triplet based on a hash algorithm, and eliminates duplicate records by comparing them with the database index. Customer tags are attached based on a preset industry classification library to match the customer's business type, and are also divided into high-frequency / medium-frequency / low-frequency tags according to the shipping frequency in the past 30 days. Behavioral profiling is built by aggregating six months of historical express delivery records to quantify and calculate monthly average shipment volume, return rate, and regularity indicators of order placement time.

4. The method for assessing the authenticity rate of potential customers and mining business opportunities through multi-source data fusion in the express delivery industry according to claim 3, characterized in that, The deduplication operation includes rules for handling abnormal data: When the same customer provides conflicting information through different channels, the contact information provided by employee referrals should be prioritized; for records with the same address but different contact persons, the company's business registration information should be used to verify whether they are related companies. For phone number change records, if the old number expires within 30 days, the new number will be retained and the historical behavior data will be merged. All deduplication operations are recorded in the audit log, including the data source, processing time, and conflict resolution rules.

5. The method for assessing the authenticity rate of potential customers and mining business opportunities through multi-source data fusion in the express delivery industry according to claim 3, characterized in that, The construction of the behavioral profile specifically includes: Historical shipment volume index is calculated based on the average monthly express delivery order volume over the past 180 days. The order volume range is defined as low frequency (0, 50), medium frequency (51, 200), and high frequency (201, ∞). The return frequency metric is the percentage of returned packages out of the total number of packages shipped. When the percentage exceeds 15%, a risk flag is triggered. Order behavior analysis uses time series models to detect the regularity of shipping time. Customers who ship at fixed times on weekdays are labeled "stable" and customers whose order volume increases by more than 200% during promotional seasons are labeled "volatile". A multi-dimensional customer profile matrix is ​​generated by combining industry classification tags and behavioral tags.

6. The method for assessing the authenticity rate of potential customers and mining business opportunities through multi-source data fusion in the express delivery industry according to claim 1, characterized in that, The calculation of the accuracy score by the scoring model in step S3 is specifically as follows: ； In the formula, Score the accuracy rate. To score the address reliability, Rate the validity of the contact information. Industry matching score Rate historical behavior Based on channel trust level, to Preset weighting coefficients; The configuration rules for the weighting coefficients are as follows: Weight to The default values ​​are 0.25, 0.20, 0.15, 0.25, and 0.15, respectively. The weight values ​​can be dynamically adjusted through the management interface. The adjustment range is limited to a single weight not exceeding 0.40 and the sum always being 1.

0. The weight adjustment is based on the feedback mechanism of historical transaction data. When the correlation between a specific parameter score and the final transaction rate exceeds a preset threshold, the weight coefficient of that parameter is automatically increased. The weight of address reliability score has been increased to 0.30 for cross-border e-commerce clients, and the weight of contact information validity score has been increased to 0.25 for individual merchant clients.

7. The method for assessing the authenticity rate of potential customers and mining business opportunities through multi-source data fusion in the express delivery industry according to claim 6, characterized in that, The logic for generating the address reliability score is as follows: The address was verified to exist and belong to a commercial building through a geographic information system. The basic score for a commercial address was 0.80, and the basic score for a residential address was 0.

50. Address integrity coefficient: Add 0.10 when building number is included, subtract 0.15 when street information is missing; Based on historical data verification: if the address has a successful delivery record in the express delivery database, add 0.05; if it is marked as a fake address, the score is 0. The final score is normalized to the [0,1] interval.

8. The method for assessing the authenticity rate of potential customers and mining business opportunities through multi-source data fusion in the express delivery industry according to claim 6, characterized in that, The method for dynamically assigning the channel trust level is as follows: The initial level of employee referral leads is 0.90, the initial level of data from non-cooperative customers on e-commerce platforms is 0.75, the initial level of data from third-party APIs is 0.70, and the initial level of data from centralized delivery addresses is 0.

80. When customer information provided by a certain channel is verified as false by sales, the trust level of that channel will be lowered by 0.05 for that month. If the conversion rate of business opportunities provided by a channel exceeds 10% for three consecutive months, the trust level will be permanently increased by 0.10; the channel level will be recalibrated every quarter.

9. The method for assessing the authenticity rate of potential customers and mining business opportunities through multi-source data fusion in the express delivery industry according to claim 1, characterized in that, The scoring model in step S3 includes a dynamic optimization mechanism: Each month, the transaction results data of allocated business opportunities for the past 30 days are extracted. Logistic regression algorithm is used to analyze the correlation coefficient between each scoring parameter and the transaction rate. When the correlation coefficient of a certain parameter is lower than 0.3 for two consecutive months, its weight coefficient is reduced by 0.

05. When a newly accessed data source causes the variance of the industry matching score to increase by more than 15%, the recalibration process of the weight coefficient w3 is triggered. Model version changes must be verified through a test set to ensure that the new model's AUC value is not lower than 0.75 before it can be deployed online.

10. The method for assessing the authenticity rate of potential customers and mining business opportunities through multi-source data fusion in the express delivery industry according to claim 1, characterized in that, The hierarchical rules for step S4 are expanded as follows: AAA-level business opportunities must simultaneously meet the following requirements: authenticity rate ≥ 0.85, address reliability score ≥ 0.85, and contact information validity score ≥ 0.

80. The system will automatically assign the opportunity to the sales team and initiate the first contact within 24 hours. AA-level business opportunities trigger the standard allocation process and require follow-up within 48 hours; When allocating Class A business opportunities, detailed data on historical behavior scores (H) are attached for sales staff to manually review and follow up. B / C level business opportunities are transferred to the pending review pool and will only be reactivated for evaluation if the address subsequently shows new positive shipping records or the channel trust level improves to 0.85 or above.

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