Variable observation value scoring method for multiple-pass index model

Abstract

The invention discloses a variable observation value scoring method for a doublet index model, which relates to the technical field of index models, and comprises the following steps: collecting and sorting customer data, industry data and sales terminal data, and defining a variable index system; respectively carrying out Min-Max standardization processing on the customer data index, the industry data index and the sales terminal data index; respectively determining weights of the customer data index, the industry data index and the sales terminal data index; and calculating a point-of-sale potential score based on a point-of-sale potential correlation variable scoring algorithm. According to the invention, a comprehensive perspective can be provided to evaluate the potential of the point-of-sale terminal, and the comprehensiveness and accuracy of the evaluation result are ensured. Through standardization and weighting of variable indexes, interference of subjective judgment is reduced through a model scoring method, and objectivity and credibility of an evaluation result are improved. The step of weight distribution allows the importance of each index to be adjusted according to market changes and business strategies, so that the model can adapt to different market environments and business requirements.

Description

Technical Field

[0001] The present invention relates to the technical field of exponential models, and particularly to a method for scoring variable observation values for the Beitong exponential model. Background Art

[0002] The Beitong exponential model is a type of exponential model that can integrate customer data and industry data to form a comprehensive medical and health sales terminal potential evaluation system, covering the potential of medical and health sales terminals both inside and outside the customer's existing channels. At the same time, it tracks and analyzes market dynamics, enabling customers to promptly understand the latest market trends. By covering different levels of medical and health sales terminals, it can help customers discover growth opportunities, optimize market coverage strategies, and improve overall sales efficiency.

[0003] The current problem with the Beitong exponential model is how to objectively and accurately score each variable therein, that is, the potential correlation parameters of medical and health sales terminals, so as to provide a parameter basis for the more objective and accurate output of the terminal potential evaluation result by the Beitong exponential model. Summary of the Invention

[0004] To solve the technical problem of objectively and accurately scoring the correlation parameters of the above exponential model, the present invention provides a method for scoring variable observation values for the Beitong exponential model. The following technical solutions are adopted:

[0005] A method for scoring variable observation values for the Beitong exponential model includes the following steps:

[0006] Step 1, collect and sort out customer data, industry data, and sales terminal data, and define a variable index system, which includes customer data indexes, industry data indexes, and sales terminal data indexes;

[0007] Step 2, perform Min - Max normalization processing on the customer data indexes, industry data indexes, and sales terminal data indexes respectively;

[0008] Step 3, determine the weights of the customer data indexes, industry data indexes, and sales terminal data indexes respectively;

[0009] Step 4, calculate the sales terminal potential score based on the sales terminal potential correlation variable scoring algorithm.

[0010] By adopting the above technical solution, by integrating customer data, industry data, and sales terminal data, the model can provide a comprehensive perspective to evaluate the potential of sales terminals, ensuring the comprehensiveness and accuracy of the evaluation results. Min-Max normalization ensures the comparability between different metrics, eliminates the influence of dimensional and scale differences, and enables comprehensive analysis of each metric on the same scale. By standardizing and weighting variable metrics, the scoring method of the model reduces the interference of subjective judgment and improves the objectivity and credibility of the evaluation results. The step of weight allocation allows for adjusting the importance of each metric according to market changes and business strategies, enabling the model to adapt to different market environments and business requirements. This technical solution is easy to operate and implement in actual business, contributing to improving work efficiency.

[0011] By regularly updating data and adjusting weights, the model can timely reflect market dynamics and help customers capture market trends and changes.

[0012] The potential scores of sales terminals can reveal the growth potential of different sales terminals and help customers identify and prioritize the most promising markets.

[0013] Based on the potential scores, customers can optimize their market coverage strategies, reasonably allocate resources, and improve market penetration and sales efficiency.

[0014] Through scientific data analysis and market evaluation, the model helps reduce the risk of market decisions and avoid wasting resources on sales terminals with no potential.

[0015] The feedback mechanism of the model enables customers to continuously adjust and optimize their sales strategies based on the evaluation results, achieving continuous business improvement and innovation.

[0016] Optionally, in step 1, the customer data metrics include customer scale metrics, customer purchasing power metrics, customer loyalty metrics, and customer growth rate metrics;

[0017] The industry data metrics include industry market size metrics, industry growth rate metrics, industry competition degree metrics, and industry policy support degree metrics;

[0018] The sales terminal data metrics include terminal coverage metrics, terminal sales ability metrics, terminal satisfaction metrics, and terminal growth rate metrics.

[0019] Optionally, the calculation formula for the customer scale metric C1 is:

[0020] C1 = w1·C 1s + w2·C 2s + w3·C 3s ;

[0021] where w1 is the weight of turnover, C1s is the standardized turnover volume, w2 is the weight of the company's floor area, C 2s is the standardized company floor area, w3 is the weight of the number of employees, C 3s is the standardized number of employees;

[0022] The calculation formulas for each standardized quantity are as follows:

[0023] where C s is the standardized value of the customer scale indicator, C d is the original volume of the customer scale indicator, minC d is the minimum volume of the customer scale indicator among all customers, maxC d ) is the maximum volume of the customer scale indicator among all customers;

[0024] For example, there are the following data:

[0025] The original turnover of customer A is 1 million.

[0026] The minimum turnover among all customers is 0.1 million.

[0027] The maximum turnover among all customers is 5 million.

[0028] The adjustment coefficient of the industry where customer A is located is 1.2 (because the average turnover of this industry is higher than the overall market average).

[0029] According to the above formula, calculate the customer scale indicator (C1) of customer A:

[0030] Standardization process:

[0031] Industry adjustment: C1 = 0.1837×1.2 ≈ 0.2204

[0032] The calculation formula for the customer purchasing power indicator C2 is: where Pur is the total purchase amount of the customer, and nt is the number of purchase times of the customer;

[0033] The calculation formula for the customer loyalty indicator C3 is: where rc is the number of repeat customers within the set time, and nc is the total number of customers;

[0034] The calculation formula for the customer growth rate indicator C4 is: where Cc is the number of customers in the current period, and Lc is the number of customers in the previous period.

[0035] Optionally, the industry data indicator L1 is the total sales of the industry;

[0036] The calculation formula for the industry growth rate indicator L2 is: where Cl is the industry sales amount in the current period, and Ll is the industry sales amount in the previous period;

[0037] The calculation formula for the industry competition degree indicator L3 is: COMr is the number of competitors in the market, and COMn is the total number of market participants;

[0038] The industry policy support degree indicator L4 is the industry policy score, and the industry policy score is obtained by experts' scoring according to the policy strength and influence scope.

[0039] Optionally, the calculation formula for the terminal coverage rate indicator S1 is: where Str is the number of covered sales terminals, and STn is the total number of sales terminals in the target market;

[0040] The terminal sales ability indicator S2 is the average sales amount of the sales terminal;

[0041] The calculation formula for the terminal satisfaction degree indicator S3 is: where Satr is the number of satisfied customers, and Satn is the total number of surveyed customers;

[0042] The calculation formula for the terminal growth rate indicator S4 is: where CS is the industry sales amount of the terminal in the current period, and LS is the industry sales amount of the terminal in the previous period.

[0043] By adopting the above technical solutions, by evaluating the customer scale, the enterprise can more accurately identify and position the target market, and formulate differentiated strategies for customers of different scales.

[0044] It helps the enterprise to reasonably allocate marketing and sales resources according to the customer scale, and improve the resource utilization efficiency.

[0045] Understanding the customer purchasing power helps to formulate price strategies and promotional activities that are more in line with the customer payment ability.

[0046] Identifying customers with high purchasing power, and specifically improving the service or product to increase sales opportunities.

[0047] For the customer loyalty indicator, by monitoring the loyalty, the enterprise can take measures to improve customer satisfaction and loyalty, thereby increasing the customer retention rate. It helps to predict the future purchase behavior of customers, and provides a basis for market promotion and customer relationship management.

[0048] For the customer growth rate indicator, it identifies customer groups with rapid growth, provides new growth points and market expansion directions for the enterprise. Adjust the market development strategy according to the customer growth rate to ensure the continuous growth of the enterprise.

[0049] Industry market size indicators help enterprises evaluate the overall capacity of the industry market, formulate reasonable market entry and expansion strategies, provide data support for enterprises' investment decisions, and reduce investment risks.

[0050] Industry growth rate indicators predict the market development trend through the industry growth rate, help enterprises seize market opportunities, provide a basis for enterprises' medium- and long-term strategic planning, and ensure that the enterprise development direction is consistent with the market trend.

[0051] Understanding the degree of industry competition helps enterprises formulate effective competition strategies, enhance market competitiveness, identify highly competitive market environments, and help enterprises avoid unnecessary competition risks.

[0052] Industry policy support indicators help enterprises make full use of industry policies, obtain policy dividends, ensure that enterprise operations comply with policy requirements, and reduce the risks brought by policy changes.

[0053] Through the coverage rate indicator, enterprises can optimize the layout of sales terminals, improve market coverage efficiency, help enterprises evaluate and manage sales channels, and ensure the healthy operation of channels.

[0054] Identifying terminals with strong sales capabilities can improve the overall sales efficiency. Adjusting sales strategies according to the sales capabilities of terminals can improve sales performance.

[0055] By improving terminal satisfaction, enhancing the customer experience, and strengthening the brand image. Improving terminal services based on satisfaction feedback can enhance customer loyalty.

[0056] Identifying terminals with rapid growth as a breakthrough for business growth. Allocating resources to terminals with great growth potential can accelerate business development.

[0057] By comprehensively considering indicators from multiple dimensions, it can provide enterprises with a comprehensive and dynamic market evaluation tool, help enterprises better understand the market environment, formulate effective market strategies, thereby enhancing competitiveness and achieving sustainable development.

[0058] Optionally, the formula for Min-Max normalization in step 2 is:

[0059]

[0060] Where X is the original data of the indicator, X min and X max are the minimum and maximum values of the indicator respectively, and X norm is the Min-Max standard value of the indicator.

[0061] By adopting the above technical solutions, in order to eliminate the influence of dimensions between different indicators, it is necessary to standardize the data.

[0062] Optionally, in step 3, according to the importance of each indicator, weights are assigned to each indicator, and the weight assignment adopts the expert scoring method or the analytic hierarchy process (AHP).

[0063] By adopting the above technical solutions, according to the importance of each indicator, weights are assigned to each indicator. The weight assignment can adopt methods such as the expert scoring method and the analytic hierarchy process (AHP).

[0064] Optionally, in step 4, the formula for calculating the potential score of the sales terminal is:

[0065]

[0066] where wc, wI, and wS are the weights of the customer scale indicator, industry data indicator, and terminal coverage rate indicator respectively, and wc i , wI i and wS i are the weights of the sub-indicators of the customer scale indicator, the sub-indicators of the industry data indicator, and the sub-indicators of the terminal coverage rate indicator respectively.

[0067] By adopting the above technical solutions, through the above algorithms and formulas, the potential of the medical and health sales terminal can be evaluated more objectively and accurately, helping customers improve the overall sales efficiency. It should be noted that in actual applications, the indicator system and weight assignment can be adjusted according to the actual situation to adapt to market changes.

[0068] The scoring method reduces the interference of subjective judgment and improves the objectivity and credibility of the evaluation results. The steps of weight assignment allow the importance of each indicator to be adjusted according to market changes and business strategies, enabling the model to adapt to different market environments and business requirements. This technical solution is easy to operate and implement in actual business and helps improve work efficiency.

[0069] By regularly updating the data and adjusting the weights, the model can timely reflect market dynamics and help customers capture market trends and changes.

[0070] In summary, the present invention includes at least one of the following beneficial technical effects:

[0071] The present invention can provide a method for scoring variable observation values for the Beitong index model. By integrating customer data, industry data, and sales terminal data, this model can provide a comprehensive perspective to evaluate the potential of sales terminals, ensuring the comprehensiveness and accuracy of the evaluation results. Min-Max normalization processing ensures the comparability between different indicators, eliminates the influence of dimension and scale differences, and enables comprehensive analysis of each indicator on the same scale. By standardizing and weighting variable indicators, the scoring method of the model reduces the interference of subjective judgment and improves the objectivity and credibility of the evaluation results. The step of weight allocation allows for adjusting the importance of each indicator according to market changes and business strategies, enabling the model to adapt to different market environments and business needs. This technical solution is easy to operate and implement in actual business, helping to improve work efficiency.

[0072] By regularly updating data and adjusting weights, the model can promptly reflect market dynamics, assisting customers in capturing market trends and changes.

[0073] The potential scores of sales terminals can reveal the growth potential of different sales terminals, helping customers identify and prioritize the most promising markets.

[0074] Based on the potential scores, customers can optimize their market coverage strategies, reasonably allocate resources, and improve market penetration and sales efficiency.

[0075] Through scientific data analysis and market evaluation, this model helps reduce the risk of market decisions and avoid wasting resources on sales terminals with no potential.

[0076] The feedback mechanism of the model enables customers to continuously adjust and optimize their sales strategies based on the evaluation results, achieving continuous business improvement and innovation. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 is a schematic flowchart of the method for scoring variable observation values for the Beitong index model of the present invention; DETAILED DESCRIPTION OF THE EMBODIMENTS

[0078] The present invention will be further described in detail below with reference to the accompanying drawings.

[0079] An embodiment of the present invention discloses a method for scoring variable observation values for the Beitong index model.

[0080] Refer to Figure 1 , Embodiment 1, a method for scoring variable observation values for the Beitong index model, includes the following steps:

[0081] Step 1, collect and organize customer data, industry data, and sales terminal data, and define a variable indicator system, which includes customer data indicators, industry data indicators, and sales terminal data indicators;

[0082] Step 2, perform Min-Max standardization processing on customer data indicators, industry data indicators, and sales terminal data indicators respectively;

[0083] Step 3, determine the weights of customer data indicators, industry data indicators, and sales terminal data indicators respectively;

[0084] Step 4, calculate the sales terminal potential score based on the sales terminal potential correlation variable scoring algorithm.

[0085] By integrating customer data, industry data, and sales terminal data, this model can provide a comprehensive perspective to evaluate the potential of sales terminals, ensuring the comprehensiveness and accuracy of the evaluation results. Min-Max standardization processing ensures the comparability between different indicators, eliminates the influence of dimension and scale differences, enabling comprehensive analysis of each indicator on the same scale. By standardizing and weighting variable indicators, the scoring method of the model reduces the interference of subjective judgment and improves the objectivity and credibility of the evaluation results. The step of weight allocation allows adjustment of the importance of each indicator according to market changes and business strategies, enabling the model to adapt to different market environments and business requirements. This technical solution is easy to operate and implement in actual business, helping to improve work efficiency.

[0086] By regularly updating data and adjusting weights, the model can timely reflect market dynamics, helping customers capture market trends and changes.

[0087] The sales terminal potential score can reveal the growth potential of different sales terminals, helping customers identify and prioritize the most promising markets.

[0088] Based on the potential score, customers can optimize the market coverage strategy, reasonably allocate resources, and improve market penetration and sales efficiency.

[0089] Through scientific data analysis and market evaluation, this model helps to reduce the risk of market decisions and avoid wasting resources on sales terminals with no potential.

[0090] The feedback mechanism of the model enables customers to continuously adjust and optimize the sales strategy according to the evaluation results, achieving continuous business improvement and innovation.

[0091] In Example 2, in Step 1, customer data indicators include customer scale indicator, customer purchasing power indicator, customer loyalty indicator, and customer growth rate indicator;

[0092] Industry data indicators include industry market scale indicator, industry growth rate indicator, industry competition degree indicator, and industry policy support degree indicator;

[0093] The sales terminal data metrics include terminal coverage metrics, terminal sales ability metrics, terminal satisfaction metrics, and terminal growth rate metrics.

[0094] Example 3, the calculation formula for the customer scale metric C1 is:

[0095] C1 = w1·C 1s + w2·C 2s + w3·C 3s ;

[0096] where w1 is the weight of the turnover, C 1s is the standardized turnover, w2 is the weight of the company's floor area, C 2s is the standardized company's floor area, w3 is the weight of the number of employees, C 3s is the standardized number of employees;

[0097] The calculation formulas for each standardized quantity are:

[0098] where C s is the standardized quantity value of the customer scale metric, C d is the original quantity of the customer scale metric, minC d is the minimum quantity of the customer scale metric among all customers, max(C d ) is the maximum quantity of the customer scale metric among all customers;

[0099] For example, there are the following data:

[0100] The original turnover of customer A is 1 million.

[0101] The minimum turnover among all customers is 0.1 million.

[0102] The maximum turnover among all customers is 5 million.

[0103] The adjustment coefficient of the industry where customer A is located is 1.2 (because the average turnover of this industry is higher than the overall market average level).

[0104] According to the above formula, calculate the customer scale metric (C1) of customer A:

[0105] Standardization process:

[0106] Industry adjustment: C1 = 0.1837×1.2 ≈ 0.2204

[0107] The calculation formula for the customer purchasing power metric C2 is: where Pur is the total purchase amount of the customer, and nt is the number of purchase times of the customer;

[0108] The calculation formula for the customer loyalty indicator C3 is: where rc is the number of repeat customers within a set time period, and nc is the total number of customers;

[0109] The calculation formula for the customer growth rate indicator C4 is: where Cc is the number of customers in the current period, and Lc is the number of customers in the previous period.

[0110] In Example 4, the industry data indicator L1 is the total sales of the industry;

[0111] The calculation formula for the industry growth rate indicator L2 is: where Cl is the industry sales in the current period, and Ll is the industry sales in the previous period;

[0112] The calculation formula for the industry competition degree indicator L3 is: COMr is the number of competitors in the market, and COMn is the total number of market participants;

[0113] The industry policy support degree indicator L4 is the industry policy score, which is obtained by experts scoring according to the policy strength and influence scope.

[0114] In Example 5, the calculation formula for the terminal coverage rate indicator S1 is: where Str is the number of covered sales terminals, and STn is the total number of sales terminals in the target market;

[0115] The terminal sales ability indicator S2 is the average sales amount of the sales terminal;

[0116] The calculation formula for the terminal satisfaction indicator S3 is: where Satr is the number of satisfied customers, and Satn is the total number of surveyed customers;

[0117] The calculation formula for the terminal growth rate indicator S4 is: where CS is the industry sales of the terminal in the current period, and LS is the industry sales of the terminal in the previous period.

[0118] By evaluating the customer scale, the enterprise can more accurately identify and position the target market, and formulate differentiated strategies for customers of different scales.

[0119] It helps the enterprise to reasonably allocate marketing and sales resources according to the customer scale and improve the resource utilization efficiency.

[0120] Understanding the customer purchasing power helps to formulate price strategies and promotional activities that are more in line with the customer's payment ability.

[0121] Identify customers with high purchasing power, specifically improve the service or product, and increase sales opportunities.

[0122] Customer loyalty metrics. By monitoring loyalty, enterprises can take measures to improve customer satisfaction and loyalty, thereby enhancing customer retention rates. It helps predict customers' future purchasing behaviors and provides a basis for marketing promotion and customer relationship management.

[0123] Customer growth rate metrics identify customer groups with rapid growth, providing new growth points and market expansion directions for enterprises. Adjust market development strategies according to the customer growth rate to ensure the continuous growth of enterprises.

[0124] Industry market size metrics help enterprises evaluate the overall capacity of the industry market, formulate reasonable market entry and expansion strategies. Provide data support for enterprises' investment decisions and reduce investment risks.

[0125] Industry growth rate metrics predict market development trends through the industry growth rate, helping enterprises seize market opportunities. Provide a basis for enterprises' medium- and long-term strategic planning to ensure that the enterprise development direction is consistent with market trends.

[0126] Industry competition degree metrics. Understanding the competition degree helps enterprises formulate effective competition strategies and enhance market competitiveness. Identify highly competitive market environments and help enterprises avoid unnecessary competition risks.

[0127] Industry policy support degree metrics help enterprises make full use of industry policies and obtain policy dividends. Ensure that enterprise operations comply with policy requirements and reduce risks brought by policy changes.

[0128] Terminal coverage rate metrics. Through the coverage rate metrics, enterprises can optimize the layout of sales terminals and improve market coverage efficiency. It helps enterprises evaluate and manage sales channels to ensure the healthy operation of channels.

[0129] Terminal sales ability metrics identify terminals with strong sales ability and improve overall sales efficiency. Adjust sales strategies according to the terminal sales ability to improve sales performance.

[0130] Terminal satisfaction metrics. By improving terminal satisfaction, enhance the customer experience and strengthen the brand image. Improve terminal services according to satisfaction feedback to enhance customer loyalty.

[0131] Terminal growth rate metrics identify terminals with rapid growth as a breakthrough for business growth. Allocate resources to terminals with great growth potential to accelerate business development.

[0132] By comprehensively considering metrics from multiple dimensions, it can provide enterprises with a comprehensive and dynamic market evaluation tool, helping enterprises better understand the market environment, formulate effective market strategies, thereby enhancing competitiveness and achieving sustainable development.

[0133] In Example 6, the formula for Min-Max normalization in step 2 is:

[0134]

[0135] Where X is the original data of the index, X min and X max are the minimum and maximum values of the index respectively, and X norm is the Min - Max standard value of the index.

[0136] In order to eliminate the influence of dimensions between different indices, it is necessary to standardize the data.

[0137] In Example 7, in Step 3, according to the importance of each index, weights are assigned to each index, and the weight assignment adopts the expert scoring method or the analytic hierarchy process (AHP).

[0138] According to the importance of each index, weights are assigned to each index. The weight assignment can adopt methods such as the expert scoring method, the analytic hierarchy process (AHP), etc.

[0139] In Example 8, in Step 4, the formula for calculating the potential score of the sales terminal is:

[0140]

[0141] Where wc, wI, and wS are the weights of the customer scale index, industry data index, and terminal coverage rate index respectively, and wc i , wI i and wS i are the weights of the sub - indices of the customer scale index, sub - indices of the industry data index, and sub - indices of the terminal coverage rate index respectively.

[0142] Through the above algorithms and formulas, the potential of the medical and health sales terminal can be evaluated more objectively and accurately, helping customers improve the overall sales efficiency. It should be noted that in actual applications, the index system and weight assignment can be adjusted according to the actual situation to adapt to market changes.

[0143] The scoring method reduces the interference of subjective judgment and improves the objectivity and credibility of the evaluation results. The steps of weight assignment allow the importance of each index to be adjusted according to market changes and business strategies, enabling the model to adapt to different market environments and business needs. This technical solution is easy to operate and implement in actual business and helps to improve work efficiency.

[0144] By regularly updating data and adjusting weights, the model can timely reflect market dynamics and help customers capture market trends and changes.

[0145] The following is a specific implementation case to illustrate how to use the Beiton Index Model to evaluate the potential of the medical and health sales terminal:

[0146] Implementation Case: Evaluating the Potential of a Medical and Health Product Sales Terminal

[0147] Step 1: Define the Variable Index System and Collect Data

[0148] The following index system is defined:

[0149] Customer Data Index (C): Customer Scale Index (C1), Customer Purchasing Power Index (C2), Customer Loyalty Index (C3), Customer Growth Rate Index (C4)

[0150] Industry Data Index (I): Industry Market Scale Index (I1), Industry Growth Rate Index (I2), Industry Competition Degree Index (I3), Industry Policy Support Degree Index (I4)

[0151] Sales Terminal Data Index (S): Terminal Coverage Index (S1), Terminal Sales Ability Index (S2), Terminal Satisfaction Index (S3), Terminal Growth Rate Index (S4)

[0152] It is assumed that the following data has been collected:

[0153] The original turnover of Customer A is 1 million, the smallest turnover among all customers is 0.1 million, and the largest turnover is 5 million.

[0154] The adjustment coefficient of the industry where Customer A is located is 1.2.

[0155] The total purchase amount of Customer A is 1.5 million, and the number of purchase times is 300.

[0156] The number of repeat customers within the set time is 250, and the total number of customers is 300.

[0157] The number of customers in the current period is 350, and the number of customers in the previous period is 300.

[0158] The total sales of the industry is 1 billion, the industry sales in the current period is 1.1 billion, and the industry sales in the previous period is 1 billion.

[0159] The number of competitors in the market is 20, and the total number of market participants is 50.

[0160] The industry policy score is 80 (out of 100).

[0161] The number of covered sales terminals is 30, and the total number of sales terminals in the target market is 100.

[0162] The average sales of the sales terminal is 2 million.

[0163] The number of satisfied customers is 200, and the total number of surveyed customers is 250.

[0164] The industry sales of the terminal in the current period is 2.2 million, and the industry sales of the terminal in the previous period is 2 million.

[0165] Step 2: Min-Max normalization processing;

[0166] Normalize each indicator using the formula:

[0167] The normalized value of the customer scale indicator (C1): [Normalized value of C1: $\frac{1000000 - 100000}{5000000 - 100000} \times \frac{900000}{4900000} \approx 0.1837$];

[0168] The normalized value of the industry market scale indicator (I1): [Normalized value of I1: $\frac{110000000 - 100000000}{120000000 - 100000000} \times \frac{10000000}{20000000} \approx 0.5$];

[0169] The normalized value of the terminal coverage rate indicator (S1): [Normalized value of S1: $\frac{30 - 0}{50 - 0} \times \frac{30}{0} \approx 0.6$]

[0170] Step 3: Determine the weights;

[0171] Assume that through the expert scoring method or the analytic hierarchy process, we have determined the following weights:

[0172] The weight of the customer data indicator (C) is 0.4

[0173] The weight of the industry data indicator (I) is 0.3

[0174] The weight of the sales terminal data indicator (S) is 0.3

[0175] The sub-indicator weight distribution is as follows:

[0176] The sub-indicator weights of the customer scale indicator (C1): turnover 0.5, area 0.3, number of employees 0.2

[0177] The sub-indicator weights of the industry market scale indicator (I1): total sales 0.4, growth rate 0.3, degree of competition 0.2, policy support 0.1;

[0178] The sub-indicator weights of the terminal coverage rate indicator (S1): coverage rate 0.4, sales ability 0.3, satisfaction 0.2, growth rate 0.1;

[0179] Step 4: Calculate the sales terminal potential score;

[0180] Calculate the sales terminal potential score (TPS) using the formula:

[0181] [TPS0.4 times 0.5 times 0.1837 0.3 times 0.5 0.3 times 0.6 approximately 0.09185 0.15 0.18 approximately 0.42585]

[0182] Therefore, the sales terminal potential score of Customer A is 0.42585.

[0183] According to the calculation results, the sales terminal potential score of Customer A is 0.42585, indicating that this sales terminal has certain growth potential. Enterprises can decide whether to invest resources in this sales terminal and how to optimize the market coverage strategy based on this score. By regularly updating data and adjusting weights, enterprises can continuously monitor market dynamics and optimize sales strategies.

[0184] The above are all preferred embodiments of the present invention, and the protection scope of the present invention is not limited thereby. Therefore, all equivalent changes made according to the structure, shape, and principle of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for scoring variable observations of the Bitong index model, characterized in that It includes the following steps: Step 1: Collect and sort out customer data, industry data, and sales terminal data, and define a variable index system, which includes customer data indexes, industry data indexes, and sales terminal data indexes; Step 2: Perform Min-Max standardization processing on customer data indexes, industry data indexes, and sales terminal data indexes respectively; Step 3: Determine the weights of customer data indexes, industry data indexes, and sales terminal data indexes respectively; Step 4: Calculate the sales terminal potential score based on the sales terminal potential correlation variable scoring algorithm.

2. The method for scoring variable observation values for the Bitong index model according to claim 1, wherein: In Step 1, the customer data indexes include customer scale index, customer purchasing power index, customer loyalty index, and customer growth rate index; The industry data indexes include industry market scale index, industry growth rate index, industry competition degree index, and industry policy support degree index; The sales terminal data indexes include terminal coverage rate index, terminal sales ability index, terminal satisfaction index, and terminal growth rate index.

3. The variable observation value scoring method for the Bitong index model according to claim 2, characterized in that: The calculation formula of the customer scale index C1 is: C1 = w1·C 1s + w2·C 2s + w3·C 3s ; where w1 is the weight of turnover, C 1s is the standardized turnover, w2 is the weight of the company's floor area, C 2s is the standardized company's floor area, w3 is the weight of the number of employees, C 3s is the standardized number of employees; The calculation formulas for each standardized quantity are as follows: Among them, C s is the standardized value of the customer scale indicator, and C d is the original value of the customer scale indicator. min(C d ) is the minimum value of the customer scale indicator among all customers, and max(C d ) is the maximum value of the customer scale indicator among all customers; The calculation formula for the customer purchasing power indicator C2 is: where Pur is the total purchase amount of the customer, and nt is the number of purchase times of the customer; The calculation formula for the customer loyalty indicator C3 is: where rc is the number of repeat customers within a set time period, and nc is the total number of customers; The calculation formula for the customer growth rate indicator C4 is: where Cc is the number of customers in the current period and Lc is the number of customers in the previous period.

4. The method for scoring variable observations for the Bitong index model according to claim 3, characterized in that: The industry data index L1 is the total sales volume of the industry; The calculation formula for the industry growth rate indicator L2 is: where Cl is the industry sales volume in the current period, and Ll is the industry sales volume in the previous period; The calculation formula for the industry competition degree indicator L3 is: COMr is the number of competitors in the market, and COMn is the total number of market participants; The industry policy support degree index L4 is the industry policy score, and the industry policy score is obtained by experts' scoring according to the policy strength and influence scope.

5. The method for scoring variable observation values for the Betong index model according to claim 4, wherein: The calculation formula for the terminal coverage rate indicator S1 is: where STr is the number of covered sales terminals, and STn is the total number of sales terminals in the target market; The terminal sales ability index S2 is the average sales volume of the sales terminal; The calculation formula for the terminal satisfaction index S3 is: where Satr is the number of satisfied customers and Satn is the total number of surveyed customers; The calculation formula for the terminal growth rate indicator S4 is: where CS is the industry sales volume of the terminal in the current period, and LS is the industry sales volume of the terminal in the previous period.

6. The method for scoring variable observations for the Betong index model according to claim 5, characterized in that: The formula for Min-Max standardization in Step 2 is: where X is the original data of the index, X min and X max are the minimum and maximum values of the index respectively, and X norm is the Min-Max standard value of the index.

7. The method for scoring variable observation values for the Bitong index model according to claim 6, wherein: In Step 3, according to the importance of each index, weights are assigned to each index, and the weight assignment adopts the expert scoring method or the analytic hierarchy process.

8. The method for scoring variable observation values for the Bitong index model according to claim 7, characterized in that: In Step 4, the formula for calculating the sales terminal potential score is: Among them, wc, wI, and wS are the weights of the customer scale index, industry data index, and terminal coverage rate index respectively, and wc i , wI i and wS i are the weights of the sub-indices of the customer scale index, the sub-indices of the industry data index, and the sub-indices of the terminal coverage rate index respectively.

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