Automatic marketing method and system

Abstract

The invention discloses an automatic marketing method and system, and the method comprises the steps: S1, screening a target enterprise, and calculating the basic weight of the target enterprise and the weight of a target contact person; extracting an activeness parameter of the target enterprise from the historical reach record, and generating a reach frequency weight of the target enterprise; s2, calculating the comprehensive weight of the target enterprise based on the basic weight of the target enterprise, the weight of the target contact person and the reaching frequency weight; s3, based on the pseudo-random number sequence, generating a plurality of touch resource nicks for the touch task, and sorting the touch resource nicks according to time; s4, screening a target contact person based on the push channel; through a bipartite graph maximum matching method, matching a plurality of arriving resource niches which are ranked in the first place with a plurality of target contacts under the corresponding pushing channels, calculating matching weights between the arriving resource niches and the target contacts, and obtaining an optimal matching scheme when the sum of the matching weights is maximum; and S5, based on the optimal matching scheme, executing a touch task to the corresponding target contact person through the corresponding push channel.

Description

Technical Field

[0001] This invention relates to the field of automated marketing technology, and in particular to an automated marketing method and system. Background Technology

[0002] In today's rapidly developing digital marketing landscape, companies are adopting various marketing ranking and push solutions to improve marketing efficiency, accurately reach target customers, and reduce the waste of marketing resources. By prioritizing customers, allocating resource slots, and matching channels, they can achieve reasonable scheduling and efficient utilization of marketing resources, thereby improving customer conversion efficiency and cooperation conversion rate. This has become one of the core components of a company's digital marketing system.

[0003] Currently, mainstream marketing ranking and push solutions in the industry all have obvious technical limitations in practical applications, making it difficult to meet the refined and efficient marketing needs of enterprises. The main shortcomings are as follows: customer priority is ranked based on only a few static indicators, resulting in a single evaluation metric; customer contact contact time slots are fixed according to priority, lacking a dynamic adjustment mechanism; real-time contact priority calculation and contact task allocation rely on daily contact data, resulting in high dependence on system computing power; all push channels adopt a uniform resource allocation strategy, failing to achieve channel differentiation; and invalid contact information is not dynamically identified and filtered, leading to low contact success rate and wasted communication resources and push costs.

[0004] The statements herein provide only background information in relation to this invention and do not necessarily constitute prior art. Summary of the Invention

[0005] The purpose of this invention is to provide an automated marketing method and system that improves the accuracy of identifying high-value enterprises and provides a fair allocation mechanism for reach resources to high-value and high-potential enterprises. This invention also filters suitable target contacts based on the characteristics of each push channel and matches reach resources with target contacts based on the time sequence of reach resources and the comprehensive weight of the enterprise to which the target contact belongs, effectively improving reach success rate and ensuring the rationality and accuracy of reach priority allocation. This invention can automatically filter out lost contact and enterprises with no cooperation intention, reducing ineffective reach consumption, and has a forward-looking time dimension, effectively avoiding the risk of insufficient system computing power when deploying reach tasks in real time.

[0006] To achieve the above objectives, the present invention provides an automated marketing method, comprising the following steps: S1. Filter target companies based on historical contact records; calculate the basic weight of the target companies based on their value parameters. Define the contact person of the target company as the target contact person, and calculate the target contact person weight based on the decision-making authority level of the target contact person; The activity parameters of the target enterprise are extracted from the historical outreach records, and an outreach frequency weight is generated for the corresponding target enterprise based on the activity parameters. S2. Calculate the comprehensive weight of the target company based on its basic weight, target contact weight, and contact frequency weight. S3. Based on a pseudo-random number sequence, generate multiple reach resource bits for the reach task and sort them in chronological order; different reach resource bits correspond to different times of the reach task execution day; S4. Filter the corresponding target contacts based on the push channel; use the bipartite graph maximum matching method to match the multiple first-ranked reach resource positions with the multiple target contacts under the corresponding push channel; Based on the time corresponding to the reach resource position and the comprehensive weight of the target enterprise to which the target contact belongs, the matching weight between the reach resource position and the matched target contact is calculated under the corresponding push channel. The optimal matching solution is obtained when the sum of the matching weights between the first-ranked reach resource positions and the multiple target contacts under the corresponding push channel is maximized. S5. Based on the optimal matching scheme, perform the outreach task to the corresponding target contact through the corresponding push channel.

[0007] Optionally, step S1 includes: S11. Filter target companies based on historical contact records; S12. The value parameters of the target enterprise include the target enterprise's qualification weight a and cooperation potential weight b; Let r1 represent the basic weight of the target company, and r1 = h1 + h2 × a + h3 × b; Among them, a and b are assigned values ​​manually based on market research results and are updated regularly; h1 represents the base weight, h2 represents the qualification weight coefficient, and h3 represents the cooperation potential weight coefficient. S13. Let r2 represent the target contact weight; r2 = (c + h4) / h5; Where c represents the decision-making authority level of the target contact person, and c is a natural number. The higher the decision-making authority level of the target contact person, the larger the value of c; h4 is the decision-making authority level correction parameter, and h5 is the normalization parameter. S14. The activity parameters include: the number of times the target contact was successfully reached f in the last few days, the total number of times the target company was successfully reached F in the last few days, the duration of cooperation with the target company g, and the number of days since the last successful reach to the target contact s. Let r3 represent the reach frequency weight of the target enterprise, r3 = h6×g - h7×f - h8×F + s; Among them, h6, h7, and h8 are the weighting coefficients for successful contact with the target contact person, successful contact with the target company, and cooperation duration, respectively.

[0008] Optionally, in step S11, if none of the following conditions are found in the enterprise's historical outreach records, the enterprise will be designated as the target enterprise: Scenario 1: The company contact person's mobile phone number and email address are both unreachable; Scenario 2: The number of consecutive failures to reach the task exceeds the preset threshold; Scenario 3: The company contact person refuses to receive calls.

[0009] Optionally, let R represent the overall weight of the target firm, R = r1 × r2 × r3 / (r1 × r2 + r1 × r3 + r2 × r3).

[0010] Optionally, the push channel includes an instant push channel, and step S4 includes: S41. Add all target contacts to the first set; determine whether the target contact simultaneously meets the following conditions: the target contact's mobile phone number is reachable, the time difference between the target contact's location and the area where the task is executed is less than or equal to p hours, and the target company's industry matches the business area of ​​the task; if so, add the target contact to the second set and delete it from the first set. S42. Using the bipartite graph maximum matching method, match the sorted first to Nth reach resource positions with the target contacts in the second set; In the context of instant push notifications, when the i-th target contact in the second set is matched with the k-th reach resource, the matching weight between them is denoted as... , ; Among them, R i This represents the overall weight of the i-th target contact in the second set. M represents the total number of target contacts in the second set; h9 represents the basic weight constant of the instant push channel; t_k represents the time interval between the time corresponding to the kth reach resource position and the preset start time of the reach task execution day. S43. Calculate the sum of matching weights for each feasible matching scheme obtained; the feasible matching scheme with the largest sum of matching weights is taken as the best matching scheme under the instant push channel.

[0011] Optionally, the push channel includes a non-real-time push channel, and step S4 further includes: S44. Determine whether the target contact in the first set simultaneously meets the following conditions: the target contact's email address is reachable, and the target company's industry matches the business area of ​​the outreach task; if so, add the target contact to the third set. S45. Using the bipartite graph maximum matching method, move the (N+1)th reachable resource bit to the (N+2)th reachable resource bit. Each contact resource is matched with the target contact in the third set; In non-real-time push channels, when the third set of... The target contact person and the first When matching two access resource positions, the matching weight between them is denoted as: , ; in, Represents the third set of... The overall weight of each target contact person. , The total number of target contacts in the third set; This represents the basic weight constant for non-real-time push channels. ; Indicates the first The time interval between the time corresponding to each reachable resource and the preset start time of the reachable task execution day; S46. Calculate the sum of matching weights for each feasible matching scheme obtained; the feasible matching scheme with the largest sum of matching weights is taken as the best matching scheme under the non-instant push channel.

[0012] Optionally, the pseudo-random number sequence is defined as y_1, y_2, ..., y_m; m represents the length of the pseudo-random number sequence, k <m; ; The unit is seconds, and q is the optimization coefficient.

[0013] Optionally, the execution time of steps S1 to S4 is earlier than the time corresponding to the earliest access resource among the multiple access resource positions, and the time interval between the two is greater than the preset duration.

[0014] The present invention also provides an automated marketing system for implementing the automated marketing method as described in the present invention, comprising: The target company filtering module is configured to filter target companies based on historical contact records; the contacts of the target companies are referred to as target contacts. The calculation module is configured to calculate the basic weight of the target enterprise based on the value parameters of the target enterprise, calculate the weight of the target contact person based on the decision-making authority level of the target contact person, extract the activity parameters of the target enterprise based on the historical contact records, and generate a contact frequency weight for the target enterprise based on the activity parameters; the calculation module is also configured to calculate the comprehensive weight of the target enterprise based on the basic weight, the target contact person weight, and the contact frequency weight. The reach resource bit generation module is configured to generate multiple reach resource bits for the reach task based on a pseudo-random number sequence; different reach resource bits correspond to different times. The reach resource allocation module is configured to filter the corresponding target contacts based on the push channel, and match multiple reach resource positions sorted by time with multiple target contacts under the corresponding push channel based on the time corresponding to the reach resource position and the comprehensive weight of the target contact, using the bipartite graph maximum matching algorithm. The outreach task execution module is configured to perform outreach tasks to the corresponding target contacts through the corresponding push channels based on the best matching scheme.

[0015] Optionally, the automated marketing system further includes a storage module for storing historical outreach records, the enterprise's value parameters, the contact person's decision-making authority level, and contact information; the historical outreach records include: the target of the outreach task, the execution result, and the execution time.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention can automatically filter out companies that are out of contact or have no intention to cooperate, retaining only the target companies as the objects to be reached, thereby reducing the waste of ineffective outreach and improving overall outreach efficiency and execution efficiency.

[0017] This invention integrates core factors such as the target company's value parameters, activity parameters, and the decision-making authority level of the target contact person to calculate the target company's comprehensive weight, thereby assessing the target company's priority. This achieves comprehensive quantification of the target company's static attributes (company qualifications, contact person's job level) and dynamic behaviors (including the frequency of successful outreach). Not only does it improve the accuracy of identifying high-value companies by over 30%, ensuring that high-value companies have priority access to premium resource slots, but it also significantly improves the accuracy of identifying companies with high cooperation potential. This breaks the rigid pattern of outreach resource allocation, avoids the long-term monopoly of premium outreach time slots, guarantees fair outreach opportunities for low-value companies with conversion potential, and ensures that outreach resources are tilted towards companies with genuine conversion potential. Through this invention, the overall outreach response rate is increased by over 25%, and marketing accuracy is significantly improved.

[0018] This invention generates multiple reach resource positions through pseudo-random sequences, so that each reach resource position is randomly distributed on the time axis, avoiding concentrated congestion during the execution time of reach tasks, reducing time period conflicts, and improving the stability of reach task execution.

[0019] The present invention also selects suitable target contacts based on the characteristics of each push channel to avoid undue disturbance across time zones, and ensures that the industry described by the target contact matches the business area being reached, thus ensuring the relevance of the business being reached.

[0020] This invention matches reachable resource slots with target contacts based on the timing of the reach and the comprehensive weight of the target contact's company. Randomness is introduced during the matching weight calculation to avoid repeatedly outputting the same fixed optimal matching scheme, and to prevent high-quality resource slots from being concentrated in a few specific companies for extended periods. This ensures the rationality and accuracy of reach priority allocation, improving the overall reach success rate.

[0021] This invention is forward-looking in terms of time, enabling pre-planning of the allocation of reach resources and prioritizing computationally intensive processing. It effectively avoids the risk of insufficient system computing power when issuing reach tasks in real time, preventing deployment delays and missed customer activity periods due to excessive system load, thus ensuring reach response rates. Attached Figure Description

[0022] Figure 1 This is a flowchart of an automated marketing method in an embodiment of the present invention.

[0023] Figure 2 This is a schematic diagram of the optimal matching scheme obtained by the bipartite graph maximum matching method in one embodiment of the present invention.

[0024] Figure 3 This is a schematic diagram of another matching scheme in one embodiment of the present invention.

[0025] Figure 4 This is a schematic diagram of an automated marketing system in an embodiment of the present invention. Detailed Implementation

[0026] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The advantages and features of the present invention will become clearer from the following description. It should be noted that the drawings are in a very simplified form and use non-precise proportions, used only to facilitate clear explanation of the embodiments of the present invention. Please refer to the drawings to make the objective features and advantages of the present invention more apparent and understandable. It should be understood that the structural proportions and sizes shown in the accompanying drawings are only for illustrative purposes and to aid those skilled in the art, and are not intended to limit the implementation conditions of the present invention. Therefore, they have no substantial technical significance. Any changes to the proportions or adjustments to the size of the structure, without affecting the effects and objectives achieved by the present invention, should still fall within the scope of the technical content disclosed in the present invention.

[0027] Currently, the mainstream marketing ranking and push solutions in the industry mainly suffer from the following problems: 1) Limited Prioritization Dimensions for Enterprises. Currently, only one or two static indicators, such as historical cooperation amount and contact person's position, are used as the criteria for prioritizing enterprises, without considering dynamic factors such as cooperation potential, historical contact frequency, cooperation cycle, and recent business needs. Since enterprises' willingness to cooperate is constantly changing, relying solely on static indicators for prioritization can easily lead to the underestimation of high-potential customers and the overemphasis on low-value customers, resulting in a misallocation of marketing resources and an inability to achieve accurate prioritization and efficient outreach, which contradicts the current trend of data-driven, refined marketing.

[0028] 2) The resource allocation scheme is fixed, and fixed contact time slots are assigned to enterprises only based on their priority ranking. For example, contacts of high-priority enterprises are contacted at 9 am, while low-value but potential customers are continuously ignored, resulting in insufficient fairness in resource allocation and untapped market conversion potential.

[0029] 3) Lack of differentiated push solutions for multiple push channels. A uniform set of rules is applied to both instant push channels (SMS, WeChat) and non-instant push channels (such as email), failing to select suitable enterprise contacts based on the characteristics of each push channel. This results in issues such as inappropriate disruptions across time zones and mismatches between push channels and contact needs.

[0030] 4) On the day of outreach, enterprise priorities are typically calculated based on real-time data, and outreach tasks are assigned accordingly. This places high demands on the system's computing power. During peak marketing periods and periods of surging data volume, insufficient computing power can easily occur, leading to slow calculations, delayed task allocation, and consequently, untimely outreach, missed outreach, or incorrect outreach. This makes it difficult to guarantee the stability and efficiency of marketing outreach, increasing the uncertainty of enterprise marketing execution.

[0031] 5) Invalid data severely interferes with outreach deployment. During marketing push execution, invalid contact methods (such as invalid numbers, invalid email addresses, customer rejections, and disconnected numbers) are not dynamically identified and updated, and outreach resources continue to be allocated to these invalid contact methods. This not only seriously wastes marketing costs but also makes it difficult to optimize and adjust subsequent marketing ranking and push strategies, failing to form a virtuous data closed loop of "push-feedback-optimization," which is inconsistent with the end-to-end requirements of intelligent marketing.

[0032] This invention provides an automated marketing method and system that improves the accuracy of identifying high-value enterprises and provides a fair allocation mechanism for reach resources to high-value and high-potential enterprises. The invention also filters suitable target contacts based on the characteristics of each push channel and matches reach resources with target contacts based on the time sequence of reach resources and the comprehensive weight of the enterprise to which the target contact belongs, effectively improving reach success rate and ensuring the rationality and accuracy of reach priority allocation. This invention can automatically filter out lost contact and enterprises with no cooperation intention, reducing ineffective reach consumption. This invention is forward-looking in the time dimension, effectively avoiding the risk of insufficient system computing power when deploying reach tasks in real time.

[0033] This invention is applicable to marketing scenarios that require large-scale customer outreach, such as B2B / B2C e-commerce, enterprise services, education, and finance. It can be directly integrated into enterprise customer relationship management (CRM) systems, marketing automation platforms (MAP), or customer outreach tools (this is only an example and is not intended to limit the invention).

[0034] To achieve the above objectives, such as Figure 1 As shown, the present invention provides an automated marketing method, including the following steps: S1. Filter target companies based on historical contact records; calculate the basic weight of target companies based on their value parameters; calculate the weight of target contacts based on their decision-making authority level; extract the activity parameters of target companies from historical contact records, and generate contact frequency weights for the corresponding target companies based on these activity parameters.

[0035] In this embodiment, step S1 includes: S11. Filter target companies based on historical outreach records.

[0036] If no of the following conditions are found in the company's historical outreach records, the company will be considered a target company: Scenario 1: The company contact person's mobile phone number and email address are both unreachable.

[0037] Scenario 2: The number of consecutive failed outreach tasks exceeds a preset threshold. In this embodiment, failed outreach tasks include: push messages not being delivered (such as being unsubscribed from emails, having one's phone number or WeChat account blocked by a contact), or messages being delivered but not replied to and without business feedback. This threshold can be adjusted according to actual needs, for example, 2 times.

[0038] Scenario 3: The company contact person refuses to be contacted. For example, proactively informing them via email, SMS, or telephone.

[0039] This invention can automatically filter out companies that are out of contact or have no intention to cooperate, retaining only the target companies as the objects to be reached, thereby reducing the waste of ineffective outreach and improving overall outreach efficiency and execution efficiency.

[0040] S12. The value parameters of the target company include the target company's qualification weight a and cooperation potential weight b; Let r1 represent the basic weight of the target company, and r1 = h1 + h2 × a + h3 × b; Where 'a' represents a quantitative value indicating core qualifications such as company size and industry position. 'b' represents a quantitative score indicating the matching degree and cooperation intention between the target company and the current company; in this embodiment, the value of 'b' ranges from [0, 10]. 'a' and 'b' are assigned manually based on market research results and updated periodically (e.g., every 3 months). 'h1' represents the base weight, 'h2' represents the qualification weight coefficient, and 'h3' represents the cooperation potential weight coefficient. In this embodiment, 'r1' = 1.5 + 3 × a + ×b.

[0041] S13. Let r2 represent the target contact weight; r2 = (c + h4) / h5; Where 'c' represents the decision-making authority level of the target contact person, and 'c' is a natural number. The higher the decision-making authority level of the target contact person, the larger the value of 'c', ensuring that contacts with high decision-making authority are reached first. In this embodiment, the value range of 'c' is [1, 6]. A value of 1 corresponds to a junior employee, a value of 2 corresponds to a department specialist, a value of 3 corresponds to a department supervisor, a value of 4 corresponds to a department manager, a value of 5 corresponds to a senior executive, and a value of 6 corresponds to the company's decision-making level.

[0042] h4 is the decision authority level correction parameter, and h5 is the normalization parameter.

[0043] In this embodiment, r2 = (c + 2) / 8.

[0044] S14. The target company's activity parameters include: The number of successful contacts f in the past few days, the total number of successful contacts F in the past few days, the duration of cooperation with the target company g (i.e., the cumulative number of days from the first business contact to the present, g≥0), and the number of days s since the most recent successful contact with the target contact (s≥0, s=0 indicates that there is already a valid contact record on that day).

[0045] Successful contact refers to receiving a non-negative cooperation intention from the contact person, including but not limited to inquiries about quotations, requests for product details, and general polite replies. In some embodiments, a target company has multiple target contacts; the total number of successful contacts for the target company in the most recent few days is the sum of the number of successful contacts for each of the multiple target contacts.

[0046] In this embodiment, let r3 represent the reach frequency weight of the target enterprise, r3=h6×g-h7×f-h8×F+s.

[0047] h6, h7, and h8 are the weighting coefficients for successful contact with the target contact person, successful contact with the target company, and cooperation duration, respectively. In this embodiment, r3 = 0.9g - 0.6f - 0.8F + s.

[0048] In this embodiment, f represents the number of times the target contact has been successfully reached in the last 30 days, with a value range of [0, 20]. The upper limit for the number of reach is set to 20 to avoid excessive disturbance. F represents the total number of times the target enterprise has been reached in the last 30 days, with a value range of [0, 50]. The upper limit for the number of reach is set to 50 to avoid excessive disturbance.

[0049] The expression for the contact frequency weight r3 shows that the more times a target contact has been contacted in the past, the lower the r3, to avoid excessive disturbance. The longer the cooperation period with the target company, the higher the r3, to prioritize existing customers. The shorter the interval between the most recent successful contact, the higher the r3, to maintain recent relationships and comprehensively balance contact frequency with the target company's cooperation intentions.

[0050] S2. Calculate the overall weight of the target company based on its basic weight, reach frequency weight, and target contact weight.

[0051] The overall weight of the target enterprise reflects its ranking priority when allocating resource slots. In this embodiment, let R represent the overall weight of the target enterprise, R = r1 × r2 × r3 / (r1 × r2 + r1 × r3 + r2 × r3).

[0052] This invention integrates core factors such as the target company's value parameters, activity parameters, and the decision-making authority level of the target contact person to calculate the target company's comprehensive weight and assess its priority. It achieves comprehensive quantification of the target company's static attributes (company qualifications, contact person's job level) and dynamic behaviors (including the frequency of successful outreach). Not only does it improve the accuracy of identifying high-value companies by over 30%, ensuring that high-value companies have priority access to premium resource slots, but it also significantly improves the accuracy of identifying companies with high cooperation potential. This breaks the rigid pattern of outreach resource allocation, prevents the long-term monopoly of premium outreach time slots, guarantees fair outreach opportunities for low-value companies with conversion potential, and ensures that outreach resources are tilted towards companies with genuine conversion potential.

[0053] S3. Based on a pseudo-random number sequence, generate multiple reach resource bits for the reach task and sort them in chronological order; different reach resource bits correspond to different times on the reach task execution day.

[0054] The pseudo-random number sequence is defined as y_1, y_2, ..., y_m; m represents the length of the pseudo-random number sequence. In this embodiment, m = 1200, generating a total of 1200 independent pseudo-random numbers, y_j ∈ [1.00, 5.00] (j = 1, 2, ..., 1200), corresponding to the 1200 reach resource bits for the reach task execution date. In one embodiment, the day after the current date is taken as the reach task execution date. In another embodiment, the current date is taken as day 1, and the execution date of the reach task is day D after the current date, where D is a positive integer greater than or equal to 3.

[0055] make Indicates the sorted order of the first... The time interval between the time corresponding to each reachable resource and the preset start time (e.g., 00:00:00) of the reachable task execution day. The unit is seconds.

[0056] .

[0057] 86400 represents the total number of seconds per day, and q is an optimization coefficient to avoid uneven distribution of reachable resource positions due to an excessively small total numerator. In this embodiment, the value of q is 2.

[0058] In this invention, each target contact is allocated only one contact resource slot per day, balancing the priority resources for high-priority customers with fair outreach opportunities for high-potential customers. It should be noted that the pseudo-random number range and the number of contact resource slots can be adjusted as needed to accommodate different scales of marketing outreach requirements. For example, a pseudo-random number range of [1.50, 2.50] and 800 contact resource slots per day.

[0059] This invention generates multiple reach resource positions through pseudo-random sequences, so that each reach resource position is randomly distributed on the time axis, avoiding concentrated congestion during the execution time of reach tasks, reducing time period conflicts, and improving the stability of reach task execution.

[0060] S4. Filter the corresponding target contacts based on the push channel; use the bipartite graph maximum matching method to match the multiple first-ranked reach resource positions with the multiple target contacts under the corresponding push channel.

[0061] Based on the time corresponding to the reach resource position and the comprehensive weight of the target enterprise to which the target contact belongs, the matching weight between the reach resource position and the matched target contact is calculated under the corresponding push channel.

[0062] The optimal matching scheme is obtained when the sum of the matching weights between the first-ranked reach resource positions and the multiple target contacts under the corresponding push channel is maximized.

[0063] This invention selects suitable target contacts based on the characteristics of each push channel, avoids undue disturbance across time zones, and ensures that the industry mentioned by the target contact matches the business area being reached, thus ensuring the relevance of the business being reached.

[0064] In this embodiment, the push channel includes instant push channels (such as telephone, WeChat, and SMS push), and step S4 includes: S41. Add all target contacts to the first set; determine whether the target contact simultaneously meets the following conditions: the target contact's mobile phone number is reachable, the time difference between the target contact's location and the area where the task is executed is less than or equal to p hours (e.g., 4 hours), and the target company's industry matches the business area of ​​the task. If so, add the target contact to the second set and delete it from the first set.

[0065] S42. Using the bipartite graph maximum matching method, match the sorted first to Nth reach resource positions with the target contacts in the second set.

[0066] In the context of instant push notifications, when the i-th target contact in the second set is matched with the k-th reach resource, the matching weight between them is denoted as... ,

[0067] .

[0068] Among them, R i This represents the overall weight of the i-th target contact in the second set. M represents the total number of target contacts in the second set; h9 represents the basic weight constant of the instant push channel; and t_k represents the time interval between the time corresponding to the kth reach resource position and the preset start time of the reach task execution day.

[0069] S43. Calculate the sum of matching weights for each feasible matching scheme obtained; the feasible matching scheme with the largest sum of matching weights is taken as the best matching scheme under the instant push channel.

[0070] In one embodiment, four reach resource bits (the first to the fourth reach resource bits) are matched with five target contacts (the first to the fifth target contacts) in a second set. The four reach resource bits form one vertex set, and the five target contacts form another vertex set. Edges represent the matching weights between reach resource bits and their corresponding target contacts. Figure 2 , Figure 3 Two matching schemes are shown respectively, in which Figure 2 The middle option is the best match. Figure 2 The sum of matching weights in + + Greater than Figure 3 The sum of matching weights in + + .

[0071] In this embodiment, the push channel also includes a non-real-time push channel, and step S4 further includes: S44. Determine whether the target contact in the first set simultaneously meets the following conditions: the target contact's email address is reachable, and the target company's industry matches the business area of ​​the outreach task; if so, add the target contact to the third set.

[0072] S45. Using the bipartite graph maximum matching method, move the (N+1)th reachable resource bit to the (N+2)th reachable resource bit. Each reachable resource is matched with the target contact in the third set.

[0073] In non-real-time push channels, when the third set of... The target contact person and the first When matching two access resource positions, the matching weight between them is denoted as: ,

[0074] .

[0075] in, Represents the third set of _____. The overall weight of each target contact person. , The total number of target contacts in the third set; This represents the basic weight constant for non-real-time push channels. ; Indicates the first The time interval between the time corresponding to each reachable resource location and the preset start time of the reachable task execution day.

[0076] S46. Calculate the sum of matching weights for each feasible matching scheme obtained; the feasible matching scheme with the largest sum of matching weights is taken as the best matching scheme under the non-instant push channel.

[0077] In this embodiment, N=25. In other words, under the instant push channel, resource slots from the first to the 25th are matched and allocated. Under the non-instant push channel, resource slots from the 26th to the 185th are allocated.

[0078] This invention matches reachable resource slots with target contacts based on the timing of the reach and the comprehensive weight of the target contact's company. Randomness is introduced during the matching weight calculation to avoid repeatedly outputting the same fixed optimal matching solution, thus preventing high-quality resource slots from being concentrated in a few specific companies for extended periods. This ensures the rationality and accuracy of reach priority allocation, improving the overall reach success rate. In one embodiment, the failure rate of instant push channels is reduced by 40%, and the cost of invalid reach through non-instant push channels is reduced by 50%.

[0079] S5. Based on the best matching scheme, perform the outreach task to the corresponding target contact through the corresponding push channel.

[0080] In this invention, the execution time of steps S1 to S4 is earlier than the time corresponding to the earliest access resource bit among the multiple access resource bits, and the time interval between the two is greater than the preset duration.

[0081] This invention employs a cold calculation mechanism, advancing the calculation of the optimal matching scheme for outreach task resources to 20:00 the day before the outreach task execution date (this is merely an example and not a limitation of the invention). For instance, if an outreach task is planned for October 5th, target customers are screened and the optimal matching scheme is planned starting at 20:00 on October 4th. In one embodiment, the historical outreach records used to calculate the overall weight are: historical outreach records before 24:00 on October 3rd (excluding failed outreach records corresponding to invalid contact methods) + outreach data scheduled for October 4th and marked as "successfully sent". By default, all outreach scheduled for October 4th is successful; if subsequent failures are detected, the corresponding historical outreach records are corrected in the historical outreach database. In another embodiment, the overall weight can also be calculated based solely on historical outreach records before 24:00 on October 3rd.

[0082] In other embodiments, the cold computing start time can be set as needed, for example, to reach 18:00 the day before the task execution date, to adapt to the response time requirements of different services.

[0083] This invention demonstrates foresight in the time dimension, enabling pre-planning of resource allocation and prioritizing computationally intensive processing. It effectively mitigates the risk of insufficient system computing power when issuing outreach tasks in real-time, avoiding deployment delays and missed customer activity periods due to excessive system load, thus ensuring a high outreach response rate. In one embodiment, through a cold computation mechanism, the on-time execution rate of outreach tasks is increased to 99.8%, and execution efficiency is improved by 35%.

[0084] like Figure 4 As shown, the present invention also provides an automated marketing system 1 for implementing the automated marketing method described in the present invention, comprising: a storage module 11, a target enterprise screening module 12, a calculation module 13, a reach resource position generation module 14, a reach resource position allocation module 15, and a reach task execution module 16.

[0085] The storage module 11 is used to store historical contact records, enterprise value parameters, contact person's decision-making authority level, and contact information. The historical contact records include: the target of the contact task, the execution result, and the execution time.

[0086] The target enterprise screening module 12 communicates with the storage module 11 and is configured to screen target enterprises based on historical contact records.

[0087] The calculation module 13 is connected to the storage module 11 and is configured to calculate the basic weight of the target enterprise based on the value parameters of the target enterprise, calculate the weight of the target contact person based on the decision-making authority level of the target contact person, extract the activity parameters of the target enterprise based on historical contact records, and generate a contact frequency weight for the target enterprise based on the activity parameters. The calculation module 13 is also configured to calculate the comprehensive weight of the target enterprise based on the basic weight, contact frequency weight, and target contact person weight of the target enterprise.

[0088] The reach resource bit generation module 14 is configured to generate multiple reach resource bits for the reach task based on a pseudo-random number sequence, with different reach resource bits corresponding to different times.

[0089] The reach resource allocation module 15 is configured to filter the corresponding target contacts based on the push channel, and based on the time corresponding to the reach resource position and the comprehensive weight of the target enterprise to which the target contact belongs, match multiple reach resource positions sorted first by time with multiple target contacts under the corresponding push channel through the bipartite graph maximum matching algorithm.

[0090] The outreach task execution module 16 is configured to perform outreach tasks to the corresponding target contacts through the corresponding push channel based on the best matching scheme.

[0091] It should be noted that, in this document, 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 said element.

[0092] In the description of this invention, it should be understood that the terms "center," "height," "thickness," "upper," "lower," "vertical," "horizontal," "top," "bottom," "inner," "outer," "axial," "radial," and "circumferential," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0093] In the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0094] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0095] Although the present invention has been described in detail through the preferred embodiments above, it should be understood that the above description should not be considered as a limitation of the present invention. Various modifications and substitutions to the present invention will be apparent to those skilled in the art after reading the above description. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. An automated marketing method, characterized in that, Including the following steps: S1. Filter target companies based on historical contact records; calculate the basic weight of the target companies based on their value parameters. Define the contact person of the target company as the target contact person, and calculate the target contact person weight based on the decision-making authority level of the target contact person; The activity parameters of the target enterprise are extracted from the historical outreach records, and an outreach frequency weight is generated for the corresponding target enterprise based on the activity parameters. S2. Calculate the comprehensive weight of the target company based on its basic weight, target contact weight, and contact frequency weight. S3. Based on a pseudo-random number sequence, generate multiple reach resource bits for the reach task and sort them in chronological order; different reach resource bits correspond to different times of the reach task execution day; S4. Filter the corresponding target contacts based on the push channel; use the bipartite graph maximum matching method to match the multiple first-ranked reach resource positions with the multiple target contacts under the corresponding push channel; Based on the time corresponding to the reach resource position and the comprehensive weight of the target enterprise to which the target contact belongs, the matching weight between the reach resource position and the matched target contact is calculated under the corresponding push channel. The optimal matching solution is obtained when the sum of the matching weights between the first-ranked reach resource positions and the multiple target contacts under the corresponding push channel is maximized. S5. Based on the optimal matching scheme, perform the outreach task to the corresponding target contact through the corresponding push channel.

2. The automated marketing method as described in claim 1, characterized in that, Step S1 includes: S11. Filter target companies based on historical contact records; S12. The value parameters of the target enterprise include the target enterprise's qualification weight a and cooperation potential weight b; Let r1 represent the basic weight of the target company, and r1 = h1 + h2 × a + h3 × b; Among them, a and b are assigned values ​​manually based on market research results and are updated regularly; h1 represents the base weight, h2 represents the qualification weight coefficient, and h3 represents the cooperation potential weight coefficient. S13. Let r2 represent the target contact weight; r2 = (c + h4) / h5; Where c represents the decision-making authority level of the target contact person, and c is a natural number. The higher the decision-making authority level of the target contact person, the larger the value of c; h4 is the decision-making authority level correction parameter, and h5 is the normalization parameter. S14. The activity parameters include: the number of times the target contact was successfully reached f in the last few days, the total number of times the target company was successfully reached F in the last few days, the duration of cooperation with the target company g, and the number of days since the last successful reach to the target contact s. Let r3 represent the reach frequency weight of the target enterprise, r3 = h6×g - h7×f - h8×F + s; Among them, h6, h7, and h8 are the weighting coefficients for successful contact with the target contact person, successful contact with the target company, and cooperation duration, respectively.

3. The automated marketing method as described in claim 1, characterized in that, In step S11, if none of the following conditions are found in the enterprise's historical outreach records, the enterprise will be designated as the target enterprise: Scenario 1: The company contact person's mobile phone number and email address are both unreachable; Scenario 2: The number of consecutive failures to reach the task exceeds the preset threshold; Scenario 3: The company contact person refuses to receive calls.

4. The automated marketing method as described in claim 2, characterized in that, Let R represent the overall weight of the target firm, R = r1 × r2 × r3 / (r1 × r2 + r1 × r3 + r2 × r3).

5. The automated marketing method as described in claim 1, characterized in that, The push channel includes an instant push channel, and step S4 includes: S41. Add all target contacts to the first set; determine whether the target contact simultaneously meets the following conditions: the target contact's mobile phone number is reachable, the time difference between the target contact's location and the area where the task is executed is less than or equal to p hours, and the target company's industry matches the business area of ​​the task; if so, add the target contact to the second set and delete it from the first set. S42. Using the bipartite graph maximum matching method, match the sorted first to Nth reach resource positions with the target contacts in the second set; In the context of instant push notifications, when the i-th target contact in the second set is matched with the k-th reach resource, the matching weight between them is denoted as... , ； Among them, R i This represents the overall weight of the i-th target contact in the second set. M represents the total number of target contacts in the second set; h9 represents the basic weight constant of the instant push channel; t_k represents the time interval between the time corresponding to the kth reach resource position and the preset start time of the reach task execution day. S43. Calculate the sum of matching weights for each feasible matching scheme obtained; the feasible matching scheme with the largest sum of matching weights is taken as the best matching scheme under the instant push channel.

6. The automated marketing method as described in claim 5, characterized in that, The push channel includes non-real-time push channels, and step S4 further includes: S44. Determine whether the target contact in the first set simultaneously meets the following conditions: the target contact's email address is reachable, and the target company's industry matches the business area of ​​the outreach task; if so, add the target contact to the third set. S45. Using the bipartite graph maximum matching method, move the (N+1)th reachable resource bit to the (N+2)th reachable resource bit. Each contact resource is matched with the target contact in the third set; In non-real-time push channels, when the third set of... The target contact person and the first When matching two access resource positions, the matching weight between them is denoted as: , ； in, Represents the third set of... The overall weight of each target contact person. , The total number of target contacts in the third set; This represents the basic weight constant for non-real-time push channels. ; Indicates the first The time interval between the time corresponding to each reachable resource and the preset start time of the reachable task execution day; S46. Calculate the sum of matching weights for each feasible matching scheme obtained; the feasible matching scheme with the largest sum of matching weights is taken as the best matching scheme under the non-instant push channel.

7. The automated marketing method as described in claim 5, characterized in that, The pseudo-random number sequence is defined as y_1, y_2, ..., y_m; m represents the length of the pseudo-random number sequence, k <m; ； The unit is seconds, and q is the optimization coefficient.

8. The automated marketing method as described in claim 1, characterized in that, The execution time of steps S1 to S4 is earlier than the time corresponding to the earliest access resource among the multiple access resource positions, and the time interval between the two is greater than the preset duration.

9. An automated marketing system for implementing the automated marketing method as described in any one of claims 1 to 8, characterized in that, include: The target company filtering module is configured to filter target companies based on historical contact records; The contact person of the target company is referred to as the target contact person; The calculation module is configured to calculate the basic weight of the target enterprise based on the value parameters of the target enterprise, calculate the weight of the target contact person based on the decision-making authority level of the target contact person, extract the activity parameters of the target enterprise based on the historical contact records, and generate a contact frequency weight for the target enterprise based on the activity parameters. The calculation module is also configured to calculate the comprehensive weight of the target enterprise based on the basic weight, the target contact weight, and the reach frequency weight; The reach resource bit generation module is configured to generate multiple reach resource bits for the reach task based on a pseudo-random number sequence; different reach resource bits correspond to different times. The reach resource allocation module is configured to filter the corresponding target contacts based on the push channel, and match multiple reach resource positions sorted by time with multiple target contacts under the corresponding push channel based on the time corresponding to the reach resource position and the comprehensive weight of the target contact, using the bipartite graph maximum matching algorithm. The outreach task execution module is configured to perform outreach tasks to the corresponding target contacts through the corresponding push channels based on the best matching scheme.

10. The automated marketing system as described in claim 9, characterized in that, It also includes a storage module for storing historical contact records, the enterprise's value parameters, and the decision-making authority level and contact information of the contact person; The historical outreach records include: the target of the outreach task, the execution result, and the execution time.

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