Unit promotion management system and method based on visual model

Abstract

The invention relates to the technical field of promotion management, and particularly discloses a unit promotion management system and method based on a visual model, and the method comprises the following steps: 1, carrying out the comparative analysis of an existing unit of a target customer and a product unit, and obtaining a customer promotion coefficient of the target customer; 2, judging whether the target customer is a promotion object or not based on the customer promotion coefficient, and generating a customer judgment signal; 3, analyzing a matched unit in the existing units of the target customer based on the promotion signal, and obtaining a unit promotion coefficient of the matched unit; step 4, based on the unit promotion coefficients of the plurality of target clients, respectively distributing promotion personnel to the target clients; according to the embodiment of the invention, through refined customer analysis, scientific promotion potential evaluation and efficient resource allocation, precision and high efficiency of product promotion are realized, and powerful market competitive advantages are provided for enterprises.

Description

Technical Field

[0001] The present invention relates to the technical field of promotion management, and in particular to a unit promotion management system and method based on a visualization model. Background Art

[0002] A unit refers to a group of machines, such as a compressor unit, a refrigeration unit, a generator unit, etc. It is generally a system consisting of a prime mover driving the compressor and auxiliary machines.

[0003] However, in the promotion management of the unit, the existing technology does not have a multi-dimensional assessment of the promotion potential of target customers, which makes it impossible to accurately identify target customers with potential demand for the product, resulting in waste of resources and ineffective promotion. It also cannot make scientific and reasonable allocation of promotion personnel based on the unit promotion coefficient and the ability assessment results of the promotion personnel, resulting in irrational use of resources. Summary of the invention

[0004] The purpose of the present invention is to provide a unit promotion management system and method based on a visualization model to solve the technical problems in the above background.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] In a first aspect, the present invention provides a unit promotion management method based on a visualization model, comprising the following steps:

[0007] Step 1: Compare and analyze the target customer's existing units with the product units to obtain the target customer's customer promotion coefficient;

[0008] Step 2: Based on the customer promotion coefficient, determine whether the target customer is a promotion object, and generate a customer determination signal;

[0009] Customer judgment signals include: promotion signals and non-promotion signals;

[0010] Step 3: Based on the promotion signal, analyze the matching units among the target customer's existing units to obtain the unit promotion coefficient of the matching units;

[0011] Step 4: Based on the unit promotion coefficients of multiple target customers, assign promotion personnel to each target customer.

[0012] As a further solution of the present invention: the process of obtaining the customer promotion coefficient is:

[0013] Based on the product unit, extract all product features in the product unit;

[0014] At the same time, the existing features of each existing unit group of the target customer are extracted respectively, and based on the existing features of the existing unit group and the product features of the product unit, the matching coefficient of each existing unit group is calculated;

[0015] Based on the matching coefficient of each existing unit, determine whether the existing unit is a matching unit;

[0016] The matching coefficients of all matching units of the target customer are summed up to obtain the customer promotion coefficient of the target customer.

[0017] As a further solution of the present invention: the process of obtaining the matching coefficient is:

[0018] Based on the existing features of any existing unit, the existing features of the existing unit are compared and analyzed with the product features of the product unit, and the overlapping feature information is extracted and marked as overlapping features;

[0019] Based on all the overlapping features, all the overlapping features are scored one by one, and the scores of all the overlapping features are summed up to obtain the matching coefficient.

[0020] As a further solution of the present invention: As a further solution of the present invention: The process of determining whether the existing unit is a matching unit is as follows:

[0021] Preset a matching coefficient threshold, and compare and analyze the matching coefficient with the matching coefficient threshold;

[0022] If the matching coefficient is less than or equal to the matching coefficient threshold, a unit mismatch signal is generated, and the existing unit is marked as a mismatched unit;

[0023] If the matching coefficient is greater than the matching coefficient threshold, a unit matching signal is generated, and the existing unit is marked as a matching unit.

[0024] As a further solution of the present invention: the process of generating the client decision signal is:

[0025] Preset a customer promotion coefficient threshold, and compare and analyze the customer promotion coefficient with the customer promotion coefficient threshold;

[0026] If the customer promotion coefficient is greater than the customer promotion coefficient threshold, a promotion signal is generated;

[0027] If the customer promotion coefficient is ≤ the customer promotion coefficient threshold, a no promotion signal is generated.

[0028] As a further solution of the present invention: the process of obtaining the unit promotion coefficient is:

[0029] Based on the promotion signal, extract all matching groups of target customers and calculate the optimization coefficient of each matching group respectively;

[0030] The optimization coefficients of all matching units are summed up to obtain the unit promotion coefficient.

[0031] As a further solution of the present invention: the process of obtaining the optimization coefficient is:

[0032] Based on any matching unit, obtain the service life and maintenance times of the matching unit;

[0033] At the same time, based on the coincidence characteristics of the matching unit and the coincidence characteristics of other matching units, the potential coefficient of the matching unit is calculated;

[0034] By formula: The optimization coefficient YH is calculated, where a1, a2, and a3 are preset proportional factors and are all greater than 0, SY is the service life, WX is the number of maintenance times, and XL is the potential coefficient.

[0035] As a further solution of the present invention: the process of obtaining the potential coefficient is:

[0036] Extract the coincident features of the matching unit and mark them as target features;

[0037] At the same time, the overlapping features of other matching units are extracted and marked as other features;

[0038] Based on any target feature, extract the number of times other features of the target feature appear in different other matching units to obtain the feature importance coefficient;

[0039] Calculate the ratio of the characteristic importance coefficient to the total number of matching units to obtain the characteristic importance coefficient ratio;

[0040] Then, the characteristic importance coefficient ratio is multiplied by the matching coefficient of the matching unit to obtain the potential coefficient.

[0041] As a further solution of the present invention: the process of allocating promotion personnel to target customers is:

[0042] Based on the unit promotion coefficients of multiple target customers, the unit promotion coefficients of all target customers are sorted from high to low according to the values;

[0043] At the same time, the idle promotion personnel are sorted in descending order according to the length of time they have been in the job, and the idle promotion personnel are matched one by one with the unit promotion coefficients of the target customers, so as to complete the promotion personnel allocation task.

[0044] In a second aspect, the present invention provides a unit promotion management system based on a visualization model, the system is used to execute the method described in any one of claims 1 to 9, and the system comprises:

[0045] Customer data collection module: compare and analyze the target customer's existing units with the product units to obtain the target customer's customer promotion coefficient;

[0046] Customer determination module: based on the customer promotion coefficient, determines whether the target customer is a promotion object and generates a customer determination signal;

[0047] Customer judgment signals include: promotion signals and non-promotion signals;

[0048] Unit data collection module: Based on the promotion signal, analyze the matching units among the target customers' existing units to obtain the unit promotion coefficients of the matching units;

[0049] Personnel allocation module: Based on the unit promotion coefficients of multiple target customers, promotion personnel are allocated to each target customer.

[0050] Beneficial effects of the present invention:

[0051] (1) The present invention first calculates the matching coefficient of each existing unit and the product unit by comparing the technical specifications, unique functions and other features of the existing units of the target customer and the product unit in detail. In this step, the overlapping features of the two are extracted and compared, and based on the importance of these features and the needs of the target customer, they are scored one by one, and finally summed up to obtain the matching coefficient. Subsequently, according to a preset matching coefficient threshold, we mark each existing unit as matched or unmatched, and further sum the matching coefficients of all matching units to obtain the customer promotion coefficient of the target customer. The customer promotion coefficient reflects the overall matching degree and promotion potential between the target customer and the product unit to be promoted. In order to formulate an accurate promotion strategy, a customer promotion coefficient threshold is preset and compared with the customer promotion coefficient. If the customer promotion coefficient is higher than the threshold, a promotion signal will be generated, indicating that the target customer is an ideal promotion target; conversely, if it is lower than or equal to the threshold, a non-promotion signal will be generated, indicating that the target customer and the product unit have a low matching degree and are not suitable for immediate promotion. The present invention helps enterprises achieve optimal allocation of market resources and improve the pertinence and effectiveness of promotion activities through precise data analysis and scientific decision-making processes.

[0052] (2) The present invention first compares the target customer's existing units with the product units, calculates the matching coefficient based on technical specifications, unique functions and other features, and generates a promotion signal; then, for target customers with promotion potential, further analyzes the optimization potential of the matching units, considers the service life, number of maintenance times and the importance of the features in the group, and obtains the unit promotion coefficient by comprehensively calculating the optimization coefficient; this coefficient comprehensively reflects the target customer's acceptance of the new product unit and its promotion potential; in the promotion execution stage, the strategy sorts the idle promotion personnel according to experience or ability, and matches them with the target customer's unit promotion coefficient, ensuring that high-potential target customers receive better promotion services; the promotion personnel allocation method based on data analysis not only improves the utilization efficiency of promotion resources, but also enhances the pertinence and effectiveness of promotion activities; the present invention achieves precision and efficiency in product promotion through refined customer analysis, scientific promotion potential assessment and efficient resource allocation, providing enterprises with a strong market competitive advantage. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The present invention will be further described below in conjunction with the accompanying drawings.

[0054] Figure 1 It is a flowchart of the present invention;

[0055] Figure 2 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] Embodiment 1:

[0058] See also Figure 1 As shown, a unit promotion management method based on a visualization model according to an embodiment of the present invention comprises the following steps:

[0059] Step 1: Compare and analyze the target customer's existing units with the product units to obtain the target customer's customer promotion coefficient;

[0060] It should be explained that the existing units are the types of units that the target customers already have; the product units are the types of units to be promoted;

[0061] In some embodiments, based on the product group, all product features in the product group are extracted;

[0062] It should be explained that product features include but are not limited to: technical specifications of the product unit (such as power, efficiency, noise level, etc.), unique functions (such as intelligent control, remote monitoring, etc.);

[0063] At the same time, the existing features of each existing unit of the target customer are extracted respectively, and based on the existing features of the existing unit and the product features of the product unit, the matching coefficient of each existing unit is calculated;

[0064] It should be explained that existing features include but are not limited to: technical specifications of existing units (such as power, efficiency, noise level, etc.), unique functions (such as intelligent control, remote monitoring, etc.);

[0065] Based on the matching coefficient of any existing organic group, a matching coefficient threshold is preset, and the matching coefficient is compared and analyzed with the matching coefficient threshold;

[0066] If the matching coefficient is less than or equal to the matching coefficient threshold, it means that the similarity or compatibility between the existing unit and the product unit is low, that is, a unit mismatch signal is generated, and the existing unit is marked as a mismatch unit;

[0067] If the matching coefficient is greater than the matching coefficient threshold, it means that the similarity or compatibility between the existing unit and the product unit is high, that is, a unit matching signal is generated, and the existing unit is marked as a matching unit;

[0068] The matching coefficients of all matching units of the target customer are summed up to obtain the customer promotion coefficient of the target customer;

[0069] Specifically, the process of obtaining the matching coefficient is:

[0070] Based on the existing features of any existing unit, the existing features of the existing unit are compared and analyzed with the product features of the product unit, and the overlapping feature information is extracted and marked as overlapping features;

[0071] Based on all the overlapping features, all the overlapping features are scored one by one, and the scores of all the overlapping features are summed up to obtain the matching coefficient;

[0072] It should be explained that the purpose of scoring all overlapping features one by one and summing up the scores of all overlapping features is to show the importance of different features in the overall matching evaluation, wherein the weighting is based on the importance of each feature according to the product characteristics and the needs of the target customers; for example, if the product unit performs well in a certain feature (such as high energy efficiency and low noise), it will be given a higher score; conversely, if the performance is poor, it will be given a lower score;

[0073] It should be further explained that the scoring can be carried out using a five-point scale, a ten-point scale or any other appropriate scale; for example, in a five-point scale, 5 points represent "excellent", 4 points represent "good", 3 points represent "average", 2 points represent "poor", and 1 point represents "very poor";

[0074] Step 2: Based on the customer promotion coefficient, determine whether the target customer is a promotion object, and generate a customer determination signal;

[0075] Customer judgment signals include: promotion signals and non-promotion signals;

[0076] In some implementation schemes, a customer promotion coefficient threshold is preset, and the customer promotion coefficient is compared and analyzed with the customer promotion coefficient threshold;

[0077] If the customer promotion coefficient is greater than the customer promotion coefficient threshold, it means that the target customer and the product unit to be promoted have a high degree of match, and a promotion signal is generated;

[0078] If the customer promotion coefficient is ≤ the customer promotion coefficient threshold, it means that the matching degree between the target customer and the product unit to be promoted is low, and a non-promotion signal is generated;

[0079] The technical solution of the embodiment of the present invention is mainly as follows: first, by comparing the technical specifications, unique functions and other features of the existing units of the target customer and the product units in detail, the matching coefficient of each existing unit and the product unit is calculated; in this step, the overlapping features of the two are extracted and compared, and based on the importance of these features and the needs of the target customer, they are scored one by one, and finally the matching coefficient is summed up; then, according to the preset matching coefficient threshold, we mark each existing unit as matched or unmatched, and further sum the matching coefficients of all matching units to obtain the customer promotion coefficient of the target customer; the customer promotion coefficient reflects the overall matching degree and promotion potential between the target customer and the product unit to be promoted; in order to formulate an accurate promotion strategy, a customer promotion coefficient threshold is preset and compared with the customer promotion coefficient; if the customer promotion coefficient is higher than the threshold, a promotion signal will be generated, indicating that the target customer is an ideal promotion object; on the contrary, if it is lower than or equal to the threshold, a non-promotion signal will be generated, indicating that the target customer and the product unit have a low matching degree and are not suitable for immediate promotion; the embodiment of the present invention helps enterprises achieve optimal allocation of market resources and improve the pertinence and effectiveness of promotion activities through precise data analysis and scientific decision-making processes.

[0080] Embodiment 2:

[0081] Based on Example 1, please refer to Figure 1 As shown, the unit promotion management method based on the visualization model described in the embodiment of the present invention further includes the following steps:

[0082] Step 3: Based on the promotion signal, analyze the matching units among the target customer's existing units to obtain the unit promotion coefficient of the matching units;

[0083] In some implementation schemes, based on the promotion signal, all matching groups of target customers are extracted, and the optimization coefficient of each matching group is calculated respectively;

[0084] The optimization coefficients of all matching units are summed up to obtain the unit promotion coefficient;

[0085] Specifically, the process of obtaining the optimization coefficient is:

[0086] Based on any matching unit, obtain the service life and maintenance times of the matching unit;

[0087] It should be explained that the larger the values ​​of service life and maintenance times are, the greater the probability that the matching unit needs to be optimized, that is, it is easier to promote;

[0088] At the same time, based on the overlapping characteristics of the matching group and the overlapping characteristics of other matching groups (other matching groups refer to other matching groups of the target user after removing the matching group), the potential coefficient of the matching group is calculated;

[0089] By formula: The optimization coefficient YH is calculated, where a1, a2, and a3 are preset proportional factors, all greater than 0, SY is the service life, WX is the number of maintenance times, and XL is the potential coefficient;

[0090] Furthermore, the process of obtaining the potential coefficient is:

[0091] Extract the coincident features of the matching unit and mark them as target features;

[0092] At the same time, the overlapping features of other matching units are extracted and marked as other features;

[0093] Based on any target feature, extract the number of times other features of the target feature appear in different other matching units to obtain the feature importance coefficient;

[0094] Calculate the ratio of the feature importance coefficient to the total number of matching units (the total number of matching units is the total number of all matching units of the target customers) to obtain the feature importance coefficient ratio;

[0095] It should be explained that the feature importance coefficient ratio indicates the prevalence and importance of the feature in the matching unit group; the larger the value of the feature importance coefficient ratio, the higher the importance of the feature, that is, it is easier to promote the matching unit;

[0096] Then, the characteristic importance coefficient ratio is multiplied by the matching coefficient of the matching unit to obtain the potential coefficient;

[0097] Step 4: Based on the unit promotion coefficients of multiple target customers, assign promotion personnel to each target customer;

[0098] In some implementation schemes, based on the unit promotion coefficients of multiple target customers, the unit promotion coefficients of all target customers are sorted from high to low according to the numerical values;

[0099] At the same time, the idle promotion personnel are sorted in descending order according to the length of employment, and the idle promotion personnel are matched with the unit promotion coefficients of the target customers one by one, so as to complete the promotion personnel allocation task;

[0100] It should be explained that the length of time a promoter has been employed indicates their experience or ability. The longer the length of time they have been employed, the higher their experience or ability.

[0101] The technical solution of the embodiment of the present invention is mainly as follows: first, the existing units of the target customers are compared with the product units, and the matching coefficient is calculated based on the characteristics such as technical specifications and unique functions to generate a promotion signal; then, for the target customers with promotion potential, the optimization potential of the matching units is further analyzed, and the importance of the service life, maintenance times and characteristics in the group is considered, and the unit promotion coefficient is obtained by comprehensively calculating the optimization coefficient; this coefficient comprehensively reflects the target customers' acceptance and promotion potential of the new product units; in the promotion execution stage, the strategy sorts the idle promotion personnel according to experience or ability, and matches them with the unit promotion coefficients of the target customers to ensure that high-potential target customers receive better promotion services; the promotion personnel allocation method based on data analysis not only improves the utilization efficiency of promotion resources, but also enhances the pertinence and effect of promotion activities; the embodiment of the present invention realizes the precision and efficiency of product promotion through refined customer analysis, scientific promotion potential assessment and efficient resource allocation, and provides enterprises with a strong market competitive advantage.

[0102] Embodiment 3:

[0103] Based on Example 1 and Example 2, please refer to Figure 2 As shown, a unit promotion management system based on a visualization model according to an embodiment of the present invention includes:

[0104] Customer data collection module: compare and analyze the target customer's existing units with the product units to obtain the target customer's customer promotion coefficient;

[0105] Customer determination module: based on the customer promotion coefficient, determines whether the target customer is a promotion object and generates a customer determination signal;

[0106] Customer judgment signals include: promotion signals and non-promotion signals;

[0107] Unit data collection module: Based on the promotion signal, analyze the matching units among the target customers' existing units to obtain the unit promotion coefficients of the matching units;

[0108] Personnel allocation module: Based on the unit promotion coefficients of multiple target customers, promotion personnel are allocated to each target customer.

[0109] The above thresholds are set for the convenience of comparison. The thresholds depend on the amount of sample data and the number of bases set by technicians in this field for each set of sample data.

[0110] The above formulas are obtained by collecting a large amount of data and performing software simulation to select a formula close to the actual value. The factors in the formula are set by technicians in this field according to actual conditions; for example: Formula A technician in this field collects multiple groups of service life, maintenance times and potential coefficients and sets corresponding optimization coefficients for each group of service life, maintenance times and potential coefficients; substitutes the set optimization coefficients and the collected service life, maintenance times and potential coefficients into the formula, any three formulas constitute a three-variable linear equation system, and the calculated factors are screened and averaged to obtain the values ​​of a1, a2 and a3, which are 2.27, 2.65 and 2.43 respectively;

[0111] The size of the factor is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the factor depends on the service life, number of repairs and potential coefficient, as well as the initial setting of the corresponding optimization coefficient for each group of service life, number of repairs and potential coefficient by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantified value, such as the optimization coefficient is proportional to the value of the service life.

[0112] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A unit promotion management method based on a visualization model, characterized in that: The following steps are involved: Step 1: Compare and analyze the target customer's existing units with the product units to obtain the target customer's customer promotion coefficient; Step 2: Based on the customer promotion coefficient, determine whether the target customer is a promotion object, and generate a customer determination signal; Customer judgment signals include: promotion signals and non-promotion signals; Step 3: Based on the promotion signal, analyze the matching units among the target customer's existing units to obtain the unit promotion coefficient of the matching units; Step 4: Based on the unit promotion coefficients of multiple target customers, assign promotion personnel to each target customer.

2. The unit promotion management method based on a visualization model according to claim 1 is characterized in that: The process of obtaining the customer promotion coefficient is as follows: Based on the product unit, extract all product features in the product unit; At the same time, the existing features of each existing unit group of the target customer are extracted respectively, and based on the existing features of the existing unit group and the product features of the product unit, the matching coefficient of each existing unit group is calculated; Based on the matching coefficient of each existing unit, determine whether the existing unit is a matching unit; The matching coefficients of all matching units of the target customer are summed up to obtain the customer promotion coefficient of the target customer.

3. The unit promotion management method based on a visualization model according to claim 2 is characterized in that: The process of obtaining the matching coefficient is: Based on the existing features of any existing unit, the existing features of the existing unit are compared and analyzed with the product features of the product unit, and the overlapping feature information is extracted and marked as overlapping features; Based on all the overlapping features, all the overlapping features are scored one by one, and the scores of all the overlapping features are summed up to obtain the matching coefficient.

4. The unit promotion management method based on a visualization model according to claim 2 is characterized in that: The process of determining whether the existing unit is a matching unit is as follows: Preset a matching coefficient threshold, and compare and analyze the matching coefficient with the matching coefficient threshold; If the matching coefficient is less than or equal to the matching coefficient threshold, a unit mismatch signal is generated, and the existing unit is marked as a mismatched unit; If the matching coefficient is greater than the matching coefficient threshold, a unit matching signal is generated, and the existing unit is marked as a matching unit.

5. The unit promotion management method based on a visualization model according to claim 1 is characterized in that: The process of generating the customer decision signal is: Preset a customer promotion coefficient threshold, and compare and analyze the customer promotion coefficient with the customer promotion coefficient threshold; If the customer promotion coefficient is greater than the customer promotion coefficient threshold, a promotion signal is generated; If the customer promotion coefficient is ≤ the customer promotion coefficient threshold, a no promotion signal is generated.

6. The unit promotion management method based on a visualization model according to claim 1 is characterized in that: The process of obtaining the unit promotion coefficient is as follows: Based on the promotion signal, extract all matching groups of target customers and calculate the optimization coefficient of each matching group respectively; The optimization coefficients of all matching units are summed up to obtain the unit promotion coefficient.

7. The unit promotion management method based on a visualization model according to claim 6 is characterized in that: The process of obtaining the optimization coefficient is: Based on any matching unit, obtain the service life and maintenance times of the matching unit; At the same time, based on the coincidence characteristics of the matching unit and the coincidence characteristics of other matching units, the potential coefficient of the matching unit is calculated; By formula: The optimization coefficient YH is calculated, where a1, a2, and a3 are preset proportional factors and are all greater than 0, SY is the service life, WX is the number of maintenance times, and XL is the potential coefficient.

8. The unit promotion management method based on a visualization model according to claim 7 is characterized in that: The process of obtaining the potential coefficient is: Extract the coincident features of the matching unit and mark them as target features; At the same time, the overlapping features of other matching units are extracted and marked as other features; Based on any target feature, extract the number of times other features of the target feature appear in different other matching units to obtain the feature importance coefficient; Calculate the ratio of the characteristic importance coefficient to the total number of matching units to obtain the characteristic importance coefficient ratio; Then the characteristic importance coefficient ratio is multiplied by the matching coefficient of the matching unit to obtain the potential coefficient.

9. The unit promotion management method based on a visualization model according to claim 1, characterized in that: The process of assigning promoters to target customers is as follows: Based on the unit promotion coefficients of multiple target customers, the unit promotion coefficients of all target customers are sorted from high to low according to the values; At the same time, the idle promotion personnel are sorted in descending order according to the length of time they have been in the job, and the idle promotion personnel are matched one by one with the unit promotion coefficients of the target customers, so as to complete the promotion personnel allocation task.

10. A unit promotion management system based on a visualization model, characterized in that: The system is used to execute the method described in any one of claims 1 to 9, and the system comprises: Customer data collection module: compare and analyze the target customer's existing units with the product units to obtain the target customer's customer promotion coefficient; Customer determination module: based on the customer promotion coefficient, determines whether the target customer is a promotion object and generates a customer determination signal; Customer judgment signals include: promotion signals and non-promotion signals; Unit data collection module: Based on the promotion signal, analyze the matching units among the target customers' existing units to obtain the unit promotion coefficients of the matching units; Personnel allocation module: Based on the unit promotion coefficients of multiple target customers, promotion personnel are allocated to each target customer.

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