Telephone marketing system-oriented real-time clue intelligent distribution method

Abstract

The invention discloses a telephone marketing system-oriented real-time clue intelligent distribution method, and relates to the cross technical field of computer software and communication technologies, and the method comprises the specific steps of multi-dimensional data access, comprehensive adaptation score calculation, accurate clue order distribution, elastic quota adjustment and whole-process monitoring and early warning. According to the method, multi-dimensional information such as salesman real-time load, historical transaction data, customer portrait and regional clue data and the like is obtained from a CRM system at regular time through a bidirectional API interface, then the obtained multi-dimensional data is comprehensively processed and analyzed, and the salesman comprehensive adaptation score is obtained based on calculation. According to the technical scheme, accurate matching of the clues and the salesmen can be achieved, the current working states of the salesmen are considered, the historical performance and customer characteristics of the salesmen are further deeply analyzed, and therefore optimization of resource allocation is ensured, the efficiency and accuracy of telemarketing are improved through the technical innovation point, and higher market response speed and transaction rate are brought to enterprises.

Description

Technical Field

[0001] This invention relates to the field of computer software and communication technology, specifically to a real-time intelligent lead allocation method for telephone marketing systems. Background Technology

[0002] With increasingly fierce market competition, telemarketing has become an important means for enterprises to expand their markets and improve sales performance. Its efficiency and accuracy have become the focus of enterprises' attention. In order to achieve more efficient telemarketing, enterprises generally use CRM (Customer Relationship Management) systems to manage customer information, track sales opportunities, and evaluate salesperson performance. However, when faced with a massive number of customer leads and diverse market demands, how to accurately allocate these leads to the most suitable salesperson has become a key issue in improving the efficiency of telemarketing.

[0003] However, in traditional telemarketing models, lead allocation mechanisms primarily rely on manual experience or automated allocation based on simple rules. These methods suffer from inefficiency and uneven resource distribution when dealing with complex and ever-changing market environments and customer needs. Specifically, manual judgment heavily depends on the allocator's subjective experience and intuition, making it difficult to comprehensively and objectively assess a salesperson's current performance, such as the number of leads pending and historical daily average processing volume. Furthermore, it fails to accurately measure a salesperson's past performance, including the number of leads closed and industry-specific expertise, and struggles to deeply analyze customer characteristics, such as industry distribution and regional preferences. The traditional allocation method is not only inefficient and prone to uneven resource distribution due to human factors, but it may also miss the best marketing opportunities, affecting the overall marketing effect. While automated allocation based on simple rules improves allocation efficiency to some extent, the rules are often too simplistic to flexibly respond to rapid changes in the market environment and customer needs. For example, allocation may be based solely on the salesperson's availability or the customer's geographic information, ignoring important factors such as the industry matching degree between the salesperson and the lead, and historical transaction records. This results in unsatisfactory allocation results, failing to meet the needs of enterprises for efficient and precise marketing, and thus has significant limitations. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a real-time intelligent lead allocation method for telephone marketing systems. This invention periodically obtains multi-dimensional information from the CRM system, including real-time workload of salespersons, historical transaction data, customer profiles, and regional lead data, through a two-way API interface. This multi-dimensional data is then comprehensively processed and analyzed, and based on the calculated comprehensive suitability score of the salesperson, precise matching of leads and salespersons can be achieved. This not only considers the current working status of the salesperson but also deeply analyzes their historical performance and customer characteristics, thereby ensuring optimal resource allocation. This technological innovation improves the efficiency and accuracy of telephone marketing, bringing enterprises higher market response speed and conversion rates.

[0005] To solve the above-mentioned technical problems, this invention provides the following technical solution: a real-time intelligent lead allocation method for telephone marketing systems, the specific steps of which are as follows: Multi-dimensional data access: The extraction layer of the bidirectional API adopts the HTTPS protocol, calls the CRM system interface at regular intervals to complete identity verification, and synchronously obtains three types of multi-dimensional data. All data is encapsulated in JSON format and stored in the data buffer after being not empty and formatted. Comprehensive Adaptability Score Calculation: The calculation process is started periodically by the allocation engine. Based on the multi-dimensional data in the data buffer, the comprehensive adaptation score of the salesperson is calculated and written to the Redis cache through atomic operations. The cache key value is set according to the business line identifier + salesperson ID. Precise lead assignment: After new customer leads enter the system and are marked as pending assignment, the assignment unit filters the salespersons based on their comprehensive suitability scores in the Redis cache, and records the assignment information after determining the matching salesperson. Then, the assignment results are written back to the CRM system by the push layer of the two-way API using the HTTPS protocol. Flexible quota adjustment: Quota monitoring is initiated during lead assignment to calculate regional saturation and industry matching degree between salesperson and lead. When the calculation result deviates from the average benchmark value of the past week by more than a threshold, the quota adjustment mechanism is triggered. Full-process monitoring and early warning: The entire process data is obtained through the visual monitoring unit, and the load balancing degree of salespersons and lead conversion rate are calculated respectively. When the calculation result deviates from the benchmark value of the same period in the past week by more than the threshold, the alarm level is divided according to the deviation magnitude and the alarm information is output to locate the root cause of the problem.

[0006] Furthermore, in the multi-dimensional data access, the three types of multi-dimensional data include: the first type is salesperson real-time load-related data, including the number of leads currently pending for each salesperson. and the historical average number of clues processed per day The first category includes a unique identifier for the salesperson; the second category contains the salesperson's historical transaction data, including the number of leads. Total number of follow-up clues and the number of transaction leads by industry The third category is customer profiles and regional lead data, including information on the customer's industry and region, and summing the number of unassigned leads and newly added unassigned leads in the region to form the total number of potential customers in the region. And the number of clues from various industries among all types of clues. Total number of clues of all types .

[0007] Furthermore, in the calculation of the comprehensive suitability score, based on the multi-dimensional data in the data buffer, the comprehensive suitability score of the salesperson is calculated using the salesperson comprehensive suitability score formula, which is: ,in, For the first The overall suitability score of each salesperson These are weighting coefficients, obtained from historical data. The load adjustment factor is calculated using the following formula: ; The historical transaction rate is calculated using a formula: ; The profile matching score is calculated using a formula, which is: ,in, For the first The industry overlap rate between a salesperson's past closed clients and current leads. For the first The overlap rate between the regions of a salesperson's historical closed deals and current leads.

[0008] Furthermore, in the precise lead assignment process, when selecting suitable salespersons, when multiple salespersons... If the difference is less than 5%, a random allocation mechanism is used for balanced allocation. After the matching salesperson is determined, the dispatch unit records the matching salesperson ID, dispatch time, and dispatch reason code. A timeout retry mechanism is set during the write-back process: when writing the dispatch result back to the CRM system, the timeout time for a single request is 0.5-1 seconds. If no acceptance confirmation is received from the CRM system within 0.5-1 seconds, a timeout retry is triggered, with a total of 3-4 retries, each retry interval being 100-200 milliseconds. If the write-back is still unsuccessful after 3-4 retries, the dispatch result is temporarily stored in the local backup database, and an exception log is generated to indicate the handling.

[0009] Furthermore, in the aforementioned flexible quota adjustment, quota monitoring is initiated during the lead dispatching process, and the saturation of each region is calculated using the regional saturation calculation formula. Then, the industry matching degree between the salesperson and the lead is calculated using the industry matching degree calculation formula. ,when or When the deviation from the average benchmark value of the past week exceeds 15%, the quota adjustment mechanism is triggered, the current order dispatch is suspended and the salesperson's comprehensive suitability score is recalculated. And based on the new Update the order dispatch strategy.

[0010] Furthermore, in the aforementioned flexible quota adjustment, the formula for calculating regional saturation is: ,in, For the first Regional saturation of each region For the first The number of online sales representatives in each region For the first Total number of potential customers in each region.

[0011] Furthermore, in the aforementioned flexible quota adjustment, the industry matching degree is calculated using the following formula: ,in, For the first The salesperson and the first Industry matching degree of similar clues This represents the total number of industries covered by the business. For the first The number of historical sales leads for a salesperson in the t-th industry. For the first Total number of sales leads for each salesperson in history For the first The number of clues belonging to the t-th industry among the clues of category t. For the first The total number of clues of each type.

[0012] Furthermore, in the aforementioned end-to-end monitoring and early warning system, the end-to-end data includes: multi-dimensional data, comprehensive adaptation score, order dispatch information, regional saturation, and industry matching degree; and the load balancing degree of all online salespersons is calculated using the salesperson load balancing degree calculation formula. Then, calculate the lead conversion rate using the lead conversion rate calculation formula. ;when or When the deviation from the baseline value for the same period in the past week exceeds 20%, alarm levels are categorized according to the deviation magnitude: 20%-30% is a general alarm, 30%-50% is a critical alarm, and over 50% is an emergency alarm. Alarm information including salesperson load balancing, lead conversion rate, deviation ratio, and preliminary optimization suggestions is output. Based on the alarm information, the root cause of the problem is located, and the weighting coefficients are optimized. Or the average baseline value over the past week, thereby completing closed-loop management.

[0013] Furthermore, in the aforementioned full-process monitoring and early warning system, the formula for calculating the salesperson load balancing is: ,in, For load balancing of all online salespersons, The total number of online salespersons For the first Number of leads currently pending for each salesperson This represents the average number of leads currently pending for all online sales representatives.

[0014] Furthermore, in the aforementioned full-process monitoring and early warning system, the formula for calculating the lead conversion rate is: ,in, This represents the lead conversion rate over the past T days, where T is 7. This represents the number of leads that were ultimately closed out among those that had been assigned orders within the past T days. This represents the total number of dispatched leads within the last T days.

[0015] Compared with existing technologies, this real-time intelligent lead allocation method for telephone marketing systems has the following advantages: I. This invention periodically retrieves multi-dimensional information from the CRM system via a two-way API interface, including real-time salesperson workload, historical transaction data, customer profiles, and regional lead data. This data is then comprehensively processed and analyzed. Based on the calculated salesperson comprehensive suitability score, precise matching of leads and salespersons is achieved. This approach not only considers the salesperson's current work status but also deeply analyzes their historical performance and customer characteristics, ensuring optimal resource allocation. This technological innovation improves the efficiency and accuracy of telephone marketing, bringing enterprises higher market response speed and conversion rates.

[0016] Second, by introducing a flexible quota adjustment mechanism, this invention can adjust the order dispatch strategy in real time based on regional saturation and the industry matching degree between salespersons and leads, ensuring the rational allocation of resources in different market environments. At the same time, the full-process monitoring and early warning function displays the system's operating status in real time through a visual interface, including core indicators such as salesperson load balancing and lead conversion rate, and issues timely warnings when anomalies occur, helping managers to quickly locate problems and take corresponding measures. This combination of dynamic adjustment and full-process monitoring enhances the system's adaptability and stability, providing a strong guarantee for the successful implementation of enterprise telemarketing activities.

[0017] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0019] Figure 1 A flowchart of a real-time intelligent lead allocation method for telephone marketing systems; Figure 2 This is a framework diagram of a real-time intelligent lead allocation method for telephone marketing systems. Detailed Implementation

[0020] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0021] Example 1 Multi-dimensional data access: In telemarketing scenarios involving financial products such as credit card applications and wealth management fund recommendations, the extraction layer uses HTTPS protocol to periodically call the financial industry CRM system interface and complete identity verification through a two-way API. Simultaneously, it obtains three types of multi-dimensional data. For example, the synchronized financial product leads cover types such as credit card applications and wealth management fund recommendations, and the salesperson's unique identifier is associated with their bank branch or financial business team. The first type is salesperson's real-time load-related data, including the number of credit card, wealth management fund, and other financial product leads that each salesperson is currently waiting to process and the historical daily average number of such financial product leads processed, accompanied by the salesperson's unique identifier. The second category is salesperson's historical transaction data, including the number of transaction leads for financial products such as credit card applications and wealth management fund recommendations, the total number of financial leads followed up, and the number of transaction leads in specific financial sectors, such as retail finance and wealth management. The third category is customer profile and regional lead data, including the customer's industry, such as corporate employees or freelancers, and their regional information. The number of financial leads to be assigned in the region and the number of newly added but unassigned financial leads are aggregated to form the total number of potential financial customers in the region. At the same time, the number of leads in each sub-sector of each type of financial lead and the total number of financial leads in each type are counted. All data is encapsulated in JSON format and stored in the data buffer after being not empty and formatted.

[0022] Comprehensive Fit Score Calculation: The calculation process is initiated periodically by the allocation engine. Based on multi-dimensional financial data in the data buffer, the comprehensive fit score of each salesperson is calculated using the salesperson's comprehensive fit score formula. The salesperson's comprehensive fit score formula is as follows: ,in, For the first The overall suitability score of each salesperson These are weighting coefficients, obtained from historical data. The load adjustment factor is calculated using the following formula: ; The historical transaction rate is calculated using a formula: ; The profile matching score is calculated using a formula, which is: ,in, For the first The industry overlap rate between a salesperson's past closed clients and current leads. For the first The overlap rate between a salesperson's historical closed deals and current leads in terms of region; for example, for leads related to wealth management funds, the historical conversion rate will focus on the salesperson's past sales data for recommended fund products. In the profile matching, the industry overlap rate will consider whether the customer has previous investment experience, and the region overlap rate will correspond to the cities or business districts frequently served by the salesperson. The calculation results are written to the Redis cache via atomic operations. The cache key-value pair is set using the financial business line identifier + salesperson ID, such as... Figure 1 As shown.

[0023] Precise lead assignment: After new financial customer leads enter the system and are marked as pending assignment, the assignment unit filters and matches salespersons based on their comprehensive suitability scores stored in the Redis cache. New leads may be large-scale financial needs proactively inquired by customers. When assigning an order, if the difference in comprehensive suitability scores between two salespersons is less than 5%, a random assignment mechanism is used for balanced allocation. After determining the matching salesperson, the matching salesperson ID, assignment time, and assignment reason code are recorded. The assignment reason code clearly indicates the basis for allocation, such as allocation through a random assignment mechanism. Then, the bidirectional API push layer uses the HTTPS protocol to write the assignment result back to the financial industry CRM system, ensuring that bank branches or financial business teams can view the assignment progress in real time. The timeout for a single request during the write-back process is 0.7 seconds. If no confirmation of receipt is received from the CRM system within 0.7 seconds, a timeout retry is triggered, with a total of 3 retries. Each retrieval interval is 150 milliseconds. If the write-back is still unsuccessful after 3 retries, the assignment result is temporarily stored in the local backup database, and an exception log is generated to prompt maintenance personnel for handling.

[0024] Flexible quota adjustment: Quota monitoring is activated during lead dispatching, and the saturation of each region is calculated using the regional saturation calculation formula. The regional saturation calculation formula is as follows: ,in, For the first Regional saturation of each region For the first The number of online sales representatives in each region For the first The total number of potential customers in each region; then, the industry matching degree between the salesperson and the financial leads is calculated using the industry matching degree calculation formula, which is: ,in, For the first The salesperson and the first Industry matching degree of similar clues This represents the total number of industries covered by the business. For the first The number of historical sales leads for a salesperson in the t-th industry. For the first Total number of sales leads for each salesperson in history For the first The number of clues belonging to the t-th industry among the clues of category t. For the first The total number of leads; when the deviation between the regional saturation or industry matching degree and the average benchmark value of the past week exceeds 15%, the quota adjustment mechanism is triggered, the current order assignment is suspended and the salesperson's comprehensive suitability score is recalculated, and orders are assigned to the region with priority given to salespeople who are good at credit card business, and the order assignment strategy is updated based on the new comprehensive suitability score.

[0025] Full-process monitoring and early warning: The system acquires multi-dimensional data, comprehensive adaptation scores, order dispatch information, regional saturation, and industry matching data through a visual monitoring unit. It then calculates the load balancing of all online sales personnel using a specific formula: ,in, For load balancing of all online salespersons, The total number of online salespersons For the first Number of leads currently pending for each salesperson This is the average number of leads currently pending from all online sales representatives; then, the lead conversion rate is calculated using the lead conversion rate calculation formula: ,in, This represents the lead conversion rate over the past T days, where T is 7. This represents the number of leads that were ultimately closed out among those that had been assigned orders within the past T days. This represents the total number of leads dispatched in the past T days. When the load balancing or lead conversion rate deviates from the baseline value for the same period in the past week by more than 20%, alarm levels are divided according to the deviation: 20%-30% is a general alarm, 30%-50% is a critical alarm, and more than 50% is an emergency alarm. Alarm information including the salesperson's load balancing, lead conversion rate, deviation ratio, and preliminary optimization suggestions is output. The alarm information suggests adjusting the lead allocation weight for the team, optimizing the baseline value for the past week to balance the load, and then locating the root cause of the problem based on the alarm information, optimizing the weight coefficient or the average baseline value for the past week, thereby completing closed-loop management.

[0026] In summary, for telemarketing scenarios involving financial products, including credit card applications and investment fund recommendations, this system utilizes multi-dimensional data access to obtain financial-related salesperson workload, historical transactions, customer profiles, and regional lead data. After verification, this data is stored in a buffer. Salesperson comprehensive suitability scores are calculated based on historical financial industry data and cached. New financial leads are filtered by salesperson based on their scores; leads exceeding thresholds are randomly assigned, and the data is written back to the CRM system to handle anomalies. Regional saturation and industry matching are monitored, and quotas are adjusted when deviation thresholds are exceeded. The entire process is monitored to calculate load balancing and lead conversion rates, with alarm levels categorized based on deviations and parameters optimized. This effectively adapts to the needs of financial businesses, ensuring accurate lead allocation and process stability.

[0027] Example 2 Multi-dimensional data access: In educational course telemarketing scenarios covering academic tutoring and adult vocational training courses, a two-way API extraction layer uses HTTPS protocol to periodically call the education industry CRM system interface and complete identity verification, simultaneously acquiring three types of multi-dimensional data, such as... Figure 2 As shown, the synchronized educational course leads include types such as subject tutoring and adult vocational training courses. Each salesperson's unique identifier is associated with the course system or teaching area they are responsible for. The first category is salesperson's real-time workload-related data, including the number of educational leads such as subject tutoring and adult vocational training courses that each salesperson is currently processing and the historical daily average number of such educational leads processed, accompanied by the salesperson's unique identifier. The second category is salesperson's historical transaction data, including the number of transaction leads for subject tutoring and adult vocational training courses, the total number of educational leads followed up, and the number of transaction leads by subject. The third category is customer profile and regional lead data, including the customer's industry, such as students' parents or office workers, and their regional information. The number of educational leads to be assigned in the region and the number of newly added unassigned educational leads are summarized into the total number of potential educational customers in the region. At the same time, the number of leads for each subject in each type of educational lead and the total number of all types of educational leads are counted. All data is encapsulated in JSON format and stored in the data buffer after being not empty and formatted.

[0028] Comprehensive Fit Score Calculation: The calculation process is initiated periodically by the allocation engine. Based on multi-dimensional education-related data in the data buffer, the comprehensive fit score of each salesperson is calculated using the salesperson's comprehensive fit score formula. The salesperson's comprehensive fit score formula is as follows: For example, for math tutoring leads, the historical conversion rate is based on the salesperson's past math tutoring course signing data. In the profile matching degree, the industry overlap rate focuses on the grade level of the customer's children, and the regional overlap rate corresponds to the school district or community that the salesperson often serves. The calculation results are written to the Redis cache through atomic operations, and the cache key value is set according to the education business line identifier + salesperson ID.

[0029] Precise lead assignment: After new education customer leads enter the system and are marked as pending assignment, the assignment unit filters and matches salespersons based on their comprehensive suitability scores stored in the Redis cache. For example, a new lead might be a parent's inquiry about middle school physics tutoring. If the difference in comprehensive suitability scores among multiple salespersons is less than 5%, a random assignment mechanism is used for balanced allocation. After determining the matching salesperson, the matching salesperson ID, assignment time, and assignment reason code are recorded. Then, the bidirectional API push layer uses HTTPS protocol to write the assignment result back to the education industry CRM system, ensuring that training schools can promptly arrange salespersons to follow up on leads. The timeout for each request during the write-back process is 0.8 seconds. If no confirmation is received from the CRM system within 0.8 seconds, a timeout retry is triggered, up to 4 retries with a 100-millisecond interval between each retry. If the write-back still fails after 4 retries, the assignment result is temporarily stored in the local backup database, and an exception log is generated to prompt maintenance personnel for handling.

[0030] Flexible quota adjustment: Quota monitoring is activated during lead dispatching, and the saturation of each region is calculated using the regional saturation calculation formula. The regional saturation calculation formula is as follows: Next, the industry matching degree between the salesperson and the education lead is calculated using the industry matching degree calculation formula. The industry matching degree calculation formula is as follows: When the deviation between the regional saturation or industry matching degree and the average benchmark value of the past week exceeds 15%, the quota adjustment mechanism is triggered, the current order dispatch is suspended and the comprehensive matching score of the salesperson is recalculated, and the salesperson who is good at adult vocational training is given priority in order dispatch to the region. The order dispatch strategy is updated based on the new comprehensive matching score.

[0031] Full-process monitoring and early warning: The visual monitoring unit acquires multi-dimensional data, comprehensive adaptation scores, order dispatch information, regional saturation, and industry matching degree, etc., and calculates the load balancing of all online salespersons using the salesperson load balancing calculation formula. The salesperson load balancing calculation formula is as follows: Then, the lead conversion rate is calculated using the lead conversion rate calculation formula, which is: When the load balancing or lead conversion rate deviates from the baseline value for the same period in the past week by more than 20%, alarm levels are divided according to the deviation range: 20%-30% is a general alarm, 30%-50% is an important alarm, and more than 50% is an emergency alarm. The alarm information includes the salesperson's load balancing, lead conversion rate, deviation ratio, and preliminary optimization suggestions. Based on the alarm, the root cause of the problem is located, and the weighting coefficient or the average baseline value of the past week is optimized to complete closed-loop management.

[0032] In summary, for telemarketing scenarios in education courses, including academic tutoring and adult vocational training courses, this approach integrates multi-dimensional data access with synchronized educational leads and relevant data from sales representatives and customers, including subject classification information. It calculates the comprehensive suitability score for sales representatives based on education industry data, linking subject and regional factors. New educational leads are assigned orders according to their scores, matching sales representatives to the needs of parents or working professionals, and the data is updated back to the education CRM system. It monitors regional saturation and industry matching, adjusting quotas to address changes in regional course demand. It monitors load balancing and lead conversion rates, analyzes conversion issues, optimizes parameters, adapts to the characteristics of the education business, balances sales representative workload, and improves lead conversion effectiveness.

[0033] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A real-time intelligent lead allocation method for telephone marketing systems, characterized in that, The specific steps of this method are as follows: Multi-dimensional data access: The extraction layer of the bidirectional API adopts the HTTPS protocol, calls the CRM system interface at regular intervals to complete identity verification, and synchronously obtains three types of multi-dimensional data. All data is encapsulated in JSON format and stored in the data buffer after being not empty and formatted. Comprehensive Adaptability Score Calculation: The calculation process is started periodically by the allocation engine. Based on the multi-dimensional data in the data buffer, the comprehensive adaptation score of the salesperson is calculated and written to the Redis cache through atomic operations. The cache key value is set according to the business line identifier + salesperson ID. Precise lead assignment: After new customer leads enter the system and are marked as pending assignment, the assignment unit filters the salespersons based on their comprehensive suitability scores in the Redis cache, and records the assignment information after determining the matching salesperson. Then, the assignment results are written back to the CRM system by the push layer of the two-way API using the HTTPS protocol. Flexible quota adjustment: Quota monitoring is initiated during lead assignment to calculate regional saturation and industry matching degree between salesperson and lead. When the calculation result deviates from the average benchmark value of the past week by more than a threshold, the quota adjustment mechanism is triggered. Full-process monitoring and early warning: The entire process data is obtained through the visual monitoring unit, and the load balancing degree of salespersons and lead conversion rate are calculated respectively. When the calculation result deviates from the benchmark value of the same period in the past week by more than the threshold, the alarm level is divided according to the deviation magnitude and the alarm information is output to locate the root cause of the problem.

2. The real-time intelligent lead allocation method for a telephone marketing system according to claim 1, characterized in that, In the multi-dimensional data access, the three types of multi-dimensional data include: the first type is salesperson real-time load-related data, including the number of leads currently pending for each salesperson. and the historical average number of clues processed per day The first category includes a unique identifier for the salesperson; the second category contains the salesperson's historical transaction data, including the number of leads. Total number of follow-up clues and the number of transaction leads by industry The third category is customer profiles and regional lead data, including information on the customer's industry and region, and summing the number of unassigned leads and newly added unassigned leads in the region to form the total number of potential customers in the region. And the number of clues from various industries among all types of clues. Total number of clues of all types .

3. The real-time intelligent lead allocation method for a telephone marketing system according to claim 1, characterized in that, In the calculation of the comprehensive suitability score, based on the multi-dimensional data in the data buffer, the comprehensive suitability score of the salesperson is calculated using the comprehensive suitability score formula, which is as follows: ,in, For the first The overall suitability score of each salesperson These are weighting coefficients, obtained from historical data. The load adjustment factor is calculated using the following formula: ; The historical transaction rate is calculated using a formula: ; The profile matching score is calculated using a formula, which is: ,in, For the first The industry overlap rate between a salesperson's past closed clients and current leads. For the first The overlap rate between the regions of a salesperson's historical closed deals and current leads.

4. The real-time intelligent lead allocation method for a telephone marketing system according to claim 1, characterized in that, In the precise lead assignment process, when selecting suitable salespersons, if multiple salespersons... If the difference is less than 5%, a random allocation mechanism is used for balanced allocation. After the matching salesperson is determined, the dispatch unit records the matching salesperson ID, dispatch time, and dispatch reason code. A timeout retry mechanism is set during the write-back process: when writing the dispatch result back to the CRM system, the timeout time for a single request is 0.5-1 seconds. If no acceptance confirmation is received from the CRM system within 0.5-1 seconds, a timeout retry is triggered, with a total of 3-4 retries and an interval of 100-200 milliseconds between each retry. If the write-back is still unsuccessful after 3-4 retries, the dispatch result is temporarily stored in the local backup database, and an exception log is generated to indicate the handling.

5. The real-time intelligent lead allocation method for a telephone marketing system according to claim 1, characterized in that, In the aforementioned flexible quota adjustment, quota monitoring is initiated during the lead dispatching process, and the saturation of each region is calculated using the regional saturation calculation formula. Then, the industry matching degree between the salesperson and the lead is calculated using the industry matching degree calculation formula. ,when or When the deviation from the average benchmark value of the past week exceeds 15%, the quota adjustment mechanism is triggered, the current order dispatch is suspended and the salesperson's comprehensive suitability score is recalculated. And based on the new Update the order dispatch strategy.

6. The real-time intelligent lead allocation method for a telephone marketing system according to claim 5, characterized in that, In the aforementioned flexible quota adjustment, the formula for calculating regional saturation is: ,in, For the first Regional saturation of each region For the first The number of online sales representatives in each region For the first Total number of potential customers in each region.

7. A real-time intelligent lead allocation method for a telephone marketing system according to claim 5, characterized in that, In the aforementioned flexible quota adjustment, the industry matching degree calculation formula is as follows: ,in, For the first The salesperson and the first Industry matching degree of similar clues This represents the total number of industries covered by the business. For the first The number of historical sales leads for a salesperson in the t-th industry. For the first Total number of sales leads for each salesperson in history For the first The number of clues belonging to the t-th industry among the clues of category t. For the first The total number of clues of each type.

8. The real-time intelligent lead allocation method for a telephone marketing system according to claim 1, characterized in that, In the full-process monitoring and early warning system, the full-process data includes: multi-dimensional data, comprehensive adaptation score, order dispatch information, regional saturation, and industry matching degree; the load balancing degree of all online salespersons is calculated using the salesperson load balancing degree calculation formula. Then, calculate the lead conversion rate using the lead conversion rate calculation formula. ;when or When the deviation from the baseline value for the same period in the past week exceeds 20%, alarm levels are categorized according to the deviation magnitude: 20%-30% is a general alarm, 30%-50% is a critical alarm, and over 50% is an emergency alarm. Alarm information including salesperson load balancing, lead conversion rate, deviation ratio, and preliminary optimization suggestions is output. Based on the alarm information, the root cause of the problem is located, and the weighting coefficients are optimized. Or the average baseline value over the past week, thereby completing closed-loop management.

9. A real-time intelligent lead allocation method for a telephone marketing system according to claim 8, characterized in that, In the full-process monitoring and early warning system, the formula for calculating the salesperson load balancing is as follows: ,in, For load balancing of all online salespersons, The total number of online salespersons For the first Number of leads currently pending for each salesperson This represents the average number of leads currently pending for all online sales representatives.

10. A real-time intelligent lead allocation method for a telephone marketing system according to claim 8, characterized in that, In the aforementioned full-process monitoring and early warning system, the formula for calculating the lead conversion rate is as follows: ,in, This represents the lead conversion rate over the past T days, where T is 7. This represents the number of leads that were ultimately closed out among those that had been assigned orders within the past T days. This represents the total number of dispatched leads within the last T days.

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