A unit promotion management system and method based on visualization model

Abstract

The present invention relates to the technical field of promotion management, and specifically discloses a unit promotion management system and method based on a visualization model, comprising the following steps: Step 1: Comparing and analyzing 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, determining whether the target customer is a promotion object, and generating a customer determination signal; Step 3: Based on the promotion signal, analyzing the matching units in 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, respectively assigning promotion personnel to the target customers; The embodiment of the present invention achieves precision and efficiency in product promotion through refined customer analysis, scientific promotion potential assessment, and efficient resource allocation, providing a strong market competitive advantage for the enterprise.

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, refrigeration unit, generator unit, etc., which is generally a system consisting of a prime mover driving a compressor and auxiliary machines.

[0003] However, in the existing technology, there is no multi-dimensional evaluation of the promotion potential of target customers in the promotion management of the unit, which makes it impossible to accurately identify target customers with potential demand for the product, resulting in waste of resources and ineffective promotion. It is also impossible to scientifically and rationally allocate promotion personnel based on the unit promotion coefficient and the ability evaluation results of the promoters, 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 target and generate a customer determination signal;

[0009] Customer decision 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 characteristics of each existing unit group of the target customer are extracted respectively, and the matching coefficient of each existing unit group is calculated based on the existing characteristics of the existing unit group and the product characteristics of the product unit;

[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 as follows:

[0025] Preset the 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 less than or equal to 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 the target feature appears in other features of different matching groups 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 as follows:

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

[0043] At the same time, the idle promotion personnel are sorted in descending order according to the length of time they have been employed, and the idle promotion personnel are matched one-to-one with the unit promotion coefficients of the target customers, thereby completing 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 being used to execute the method according to any one of claims 1 to 9 above, the system comprising:

[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, it determines whether the target customer is a promotion target and generates a customer determination signal;

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

[0048] Unit data acquisition module: Based on the promotion signal, analyze the matching units among the target customer's existing units and obtain the unit promotion coefficient of the matching units;

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

[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 target customer's existing units with 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 the sum is used to obtain the matching coefficient; then, according to the preset matching coefficient threshold, each existing unit is marked as matched or unmatched, and the matching coefficients of all matched units are further summed 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; 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 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 with reference to 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 clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making any creative efforts shall fall within the scope of protection of the present invention.

[0057] Example 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 includes 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 existing units are the types of units that target customers already own; 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 characteristics of each existing unit group of the target customer are extracted respectively, and based on the existing characteristics of the existing unit group and the product characteristics of the product unit, the matching coefficient of each existing unit group 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 indicates that the similarity or compatibility between the existing unit and the product unit is low, and a unit mismatch signal is generated, and the existing unit is marked as a mismatched unit;

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

[0068] Sum up the matching coefficients of all matching units of the target customer 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 the scores of all overlapping features is to show the importance of different features in the overall matching evaluation. The weighting is based on the importance of each feature determined 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 rating 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 target and generate a customer determination signal;

[0075] Customer decision 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 matching, and a promotion signal is generated;

[0078] If the customer promotion coefficient is less than or equal to 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 features, and other characteristics of the target customer's existing units with 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 each feature is scored based on its importance and the needs of the target customer. The matching coefficient is finally summed to obtain the matching coefficient. Subsequently, based on a preset matching coefficient threshold, each existing unit is marked as matching or non-matching. The matching coefficients of all matching units are further summed 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. To formulate a precise promotion strategy, a customer promotion coefficient threshold is preset and compared with the customer promotion coefficient. If the customer promotion coefficient is above the threshold, a promotion signal is generated, indicating that the target customer is an ideal promotion target. Conversely, if it is below or equal to the threshold, a non-promotion signal is generated, indicating that the target customer has a low matching degree with the product unit and is not suitable for immediate promotion. Through precise data analysis and a scientific decision-making process, the embodiment of the present invention helps enterprises achieve optimal allocation of market resources and improve the relevance and effectiveness of promotion activities.

[0080] Example 2:

[0081] Based on Example 1, please refer to Figure 1 As shown, the unit promotion management method based on the visualization model according to 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] Sum up the optimization coefficients of all matching units 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 excluding the matching group), the potential coefficient of the matching group is calculated;

[0089] By formula: Calculate and obtain the optimization coefficient YH, 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;

[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 the target feature appears in other features of different matching groups to obtain the feature importance coefficient;

[0094] Calculate the ratio of the characteristic 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 characteristic 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 more important the feature is, which means 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 embodiments, 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 their values;

[0099] At the same time, the idle promotion personnel are sorted in descending order according to their employment time, and the idle promotion personnel are matched one-to-one with the unit promotion coefficients of the target customers, thereby completing 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 employment, 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 customer are compared with the product units, and the matching coefficient is calculated based on characteristics such as technical specifications and unique functions to generate a promotion signal; then, for target customers with promotion potential, the optimization potential of the matching units is further analyzed, and the service life, number of maintenance times and the importance of the characteristics in the group are taken into consideration. The unit promotion coefficient is obtained by comprehensively calculating the optimization coefficient; this coefficient comprehensively reflects the target customer's acceptance of the new product unit and the 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 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 effectiveness of promotion activities; the embodiment of the present invention achieves precise and efficient product promotion through refined customer analysis, scientific promotion potential assessment and efficient resource allocation, providing enterprises with a strong market competitive advantage.

[0102] Example 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, it determines whether the target customer is a promotion target and generates a customer determination signal;

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

[0107] Unit data acquisition module: Based on the promotion signal, analyze the matching units among the target customer's existing units and obtain the unit promotion coefficient of the matching units;

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

[0109] The above thresholds are set to facilitate comparison. The thresholds depend on the amount of sample data and the number of bases set by those skilled in the art 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 those skilled in the art according to the actual situation; for example: A technician in this field collected multiple sets of service life, number of repairs, and potential coefficients and set corresponding optimization coefficients for each set of service life, number of repairs, and potential coefficients; substituted the set optimization coefficients and the collected service life, number of repairs, and potential coefficients into a formula, and any three formulas formed a three-variable linear equation system. The calculated factors were screened and averaged, and the values of a1, a2, and a3 were obtained as 2.27, 2.65, and 2.43, respectively;

[0111] The size of the factor is to quantify each parameter to obtain a specific value, which is convenient 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 set 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. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope 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; 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 characteristics of each existing unit group of the target customer are extracted respectively, and the matching coefficient of each existing unit group is calculated based on the existing characteristics of the existing unit group and the product characteristics of the product unit; Based on the matching coefficient of each existing unit, determine whether the existing unit is a matching unit; Sum up the matching coefficients of all matching units of the target customer to obtain the customer promotion coefficient of the target customer; 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; Step 2: Based on the customer promotion coefficient, determine whether the target customer is a promotion target and generate a customer determination signal; Customer decision 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; 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; Sum up the optimization coefficients of all matching units to obtain the unit promotion coefficient; 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: , calculate and obtain the optimization coefficient YH, 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; 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 the target feature appears in other features of different matching groups 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.

2. The unit promotion management method based on a visualization model according to claim 1 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.

3. 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 the 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 less than or equal to the customer promotion coefficient threshold, a no-promotion signal is generated.

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

5. A unit promotion management system based on a visualization model, characterized in that: The system is used to perform the method according to any one of claims 1 to 4 above, 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, it determines whether the target customer is a promotion target and generates a customer determination signal; Customer decision signals include: promotion signals and non-promotion signals; Unit data acquisition module: Based on the promotion signal, analyze the matching units among the target customer's existing units and obtain the unit promotion coefficient of the matching units; Personnel allocation module: Based on the unit promotion coefficients of multiple target customers, promotion personnel are assigned to target customers respectively.

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