Medical sales support information processing device and method

Abstract

[Problem] There is a need to overcome the social issue of "lives that could be saved are not being saved" by achieving efficient marketing activities by pharmaceutical and other companies and by precisely reducing waste in sales promotional activities, thus making it possible to lower marketing costs and optimize pharmaceutical costs. [Solution] Provided are a medical sales support information processing device, program, and method, said device including: an acquisition unit that acquires data containing attributes about individual physicians and outcomes of sales promotional activities; a storage unit storing a program and the data; a calculation unit that, in order to reflect differences in prescribing activities or the diversity of activities by multiple physicians in a facility, calculates a sales proportion score by calculating a score in which a correlation coefficient for each of only sales promotional activities for which the p-value is below a prescribed threshold and at a significant level in relation to sales for each facility, a relative impact, and a weight on activity level for each physician are multiplied together, constructs a multivariate logistic regression model, fixes an explanatory variable related to the attributes of physicians therein, assumes that at least one of an explanatory variable related to sales promotional activities or explanatory variable related to a sales representative is an adjustable variable of the model, calculates an optimal combination of adjustable variables necessary to achieve a preset target prescription probability, and calculates, for each physician, a minimum level of sales promotional activity or a target level of the explanatory variable related to a sales representative necessary to achieve the target prescription probability; and an output unit.

Description

Medical sales support information processing device and method

[0001] The present invention relates to a medical sales support information processing device and method.

[0002] The soaring prices of pharmaceuticals have become a serious social problem, especially in developed countries, resulting in lives that could be saved going unspent. One factor behind this is the current situation in which pharmaceutical companies are investing more money in marketing and sales promotion activities than in research and development (R&D).

[0003] As a result, in the United States, marketing costs have driven up drug prices, making it difficult to receive the latest treatments, and this has become a social problem.In Japan, where the reimbursement prices for pharmaceuticals are set by the government, the Japanese market, with its low reimbursement prices, has lost its appeal, and the country is now in a situation where 143 pharmaceutical products have been approved in Europe and the United States but have not yet been approved in Japan, and 86 of these products have not yet begun development as of March 2023.

[0004] On the other hand, some marketing expenditures do not reach the appropriate targets, resulting in waste. One of the major causes of this waste is the failure to determine the optimal sales targets for a product from the user's perspective. In order to reduce the waste caused by such inefficient marketing and sales promotion activities and curb rising drug prices, it is effective to improve the accuracy of marketing activities and optimize resource allocation.

[0005] In the past, pharmaceutical and medical device marketing activities were primarily based on qualitative perspectives, relying on experience and intuition. However, the external environment is changing rapidly, and decision-making that relies on experience and intuition is highly uncertain. Therefore, in recent years, with the aim of improving the efficiency of sales activities, Customer Relationship Management systems (CRM), which apply information technology to sales and marketing activities, have been proposed and are being used in some areas.

[0006] JP 2022-042617 A

[0007] However, there is a problem with the low adoption rate of sales support systems that have actually been implemented. It has even been reported that up to 70% of those who are implemented end up not continuing. This is partly due to the weak theoretical models used in conventional sales support systems, particularly because they were not based on theoretical models based on statistical processing that clearly targeted individual doctors, who are the decision makers on pharmaceutical adoption. At the same time, the strong sales management aspect has also had an impact. Another factor cited is that they do not achieve results that exceed expectations.

[0008] Unlike conventional CRM, which supports goal achievement by efficiently managing sales activities, this invention is a system based on a new concept that visualizes the impact of sales and marketing activities on sales and achieves sales targets in the most efficient way.

[0009] This invention is an improvement over Japanese Patent No. 7636838. In other words, rather than allocating sales to each physician based on the single activity with the highest correlation, the invention provides a more accurate and efficient sales support technology based on a theoretical model based on statistical processing that makes full use of logit functions, including the latest logistic regression analysis, by eliminating statistically insignificant activities among multiple sales promotion activities targeted at multiple physicians within a facility, while weighting significant sales promotion activities according to calculated scores, etc., and clearly targeting individual physicians who make decisions about the adoption of pharmaceuticals, etc. The invention has multiple functions to achieve this objective.

[0010] According to the present invention, an acquisition unit acquires data including the attributes of each doctor, the number of sales promotion activities including face-to-face visits to the doctor, web interviews, telephone interviews, seminars, hands-on sessions, sending materials, email distribution, web seminars, or email newsletters, the history of email opening, web lecture viewing, login to a doctor portal site, playback of web detailing for medical professionals, or browsing of case introduction content, schedule, results of the sales promotion activities, or the attributes or evaluations of sales representatives of the doctor; a storage unit that stores a program and the data; and a program that calls up the program and the data, a calculation unit that calculates a correlation coefficient between the sales of each facility and only the sales promotion activities for which the realized value p of the probability when a null hypothesis of a hypothesis test is established in relation to at least the sales is less than a predetermined threshold and is judged to be at a significant level, normalizes the correlation coefficient to calculate a relative influence, calculates a score by multiplying each correlation coefficient for each sales promotion activity by a weight of the activity amount for each of the doctors, and calculates a sales apportionment value of the amount of sales of the facility based on the score; constructs a multivariate logistic regression model using multiple explanatory variables included in the data using a statistical method including penalized logistic regression or logistic regression analysis, fixes the explanatory variable related to the attributes of the doctor, and assumes that at least one of the explanatory variable related to the sales promotion activity or the explanatory variable related to the sales representative is an adjustable variable, calculates an optimal combination of the adjustable variables necessary to achieve a predetermined target prescription probability, and calculates, for each of the doctors, the minimum amount of sales promotion activity or the target level of the explanatory variable related to the sales representative necessary to achieve the target prescription probability; and an output unit that outputs the calculation result.

[0011] This invention helps pharmaceutical companies improve the efficiency and accuracy of their sales, marketing, and sales promotion activities, thereby reducing waste, reducing marketing costs, and optimizing the costs of pharmaceuticals, etc. This can contribute to improving the social issue of "lives that could be saved but are not."

[0012] [Fig. 1] A conceptual diagram showing the configuration of a medical sales support information processing device 1 when a server 10, customer information terminals, etc. are connected via a network N. [Fig. 2] A block diagram showing the functional configuration of the server 10 that enables the medical sales support information processing device 1 to function. [Fig. 3] A flowchart showing the operation of discarding data with a correlation coefficient less than a threshold and calculating sales apportionment values, etc. [Fig. 4] A diagram showing the data structure of a doctor table 1012 for initial value input. [Fig. 5] A diagram showing the data structure of a product table 1013 for initial value input. [Fig. 6] A diagram showing the data structure of an ROI analysis input table 1014 for initial value input. [Fig. 7] A diagram showing the data structure of an activity simulation table 1015. [Fig. 8] A diagram showing the data structure of an ROI simulation table 1016. [Fig. 9] A flowchart showing the operation of activity simulation. [Fig. 10] A flowchart showing the operation in the case of selecting doctors with high similarity. [Fig. 11] A flowchart showing the operation of ROI simulation. [Fig. 12] An example of an Excel A FORM for uploading initial values ​​for activity simulation. FIG. 13 is a block diagram showing the basic hardware configuration of a general computer 90 that constitutes an information processing apparatus.

[0013] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In all drawings illustrating the embodiments, common components are designated by the same reference numerals, and repeated explanations will be omitted. Note that the following embodiments do not unduly limit the content of the present invention described in the claims. Furthermore, not all components shown in the embodiments are necessarily essential components of the present invention. Furthermore, each drawing is a schematic diagram and is not necessarily a precise illustration.

[0014] <Configuration of the Medical Sales Support Information Processing Device> The following description will be made with reference to the drawings related to the information processing device. As shown in FIG. 1 , the medical sales support information processing device 1 of the present invention is composed of a connected server 10 and a management information processing terminal 15, and can also be connected to optional information processing terminals 20 and 30, such as customer information processing terminals. Some or all of these may be connected via a network N. While the network N is disclosed assuming the Internet or a mobile phone carrier line, it is not limited thereto and may be, for example, a dedicated line. Similarly, the server 10 is disclosed as being connected to the management information processing terminal 15 via the network N, but some or all of the information may be stored within the management information processing terminal 15.

[0015] <Configuration of Information Processing Apparatus> FIG. 2 is a block diagram showing the functional configuration of the server 10. As shown in FIG.

[0016] Each information processing device is configured by a computer equipped with a processing unit for control and calculation and a storage device. The basic hardware configuration of the computer and the basic functional configuration of the computer realized by the hardware configuration will be described later. For each of the server 10, the management information processing terminal 15, and the optional information processing terminals 20 and 30, etc., descriptions that overlap with the basic hardware configuration and basic functional configuration of the computer will be omitted.

[0017] <Configuration of Server 10> The server 10 is an information processing device that acquires, stores, and, if necessary, calculates and provides information related to the medical sales support information processing device 1. The server 10 includes a storage unit 101 and a control unit 104.

[0018] <Configuration of Storage Unit 101 of Server 10 > The storage unit 101 of the server 10 includes an application program 1011 , a doctor table 1012 , a product table 1013 , an ROI table 1014 , an activity simulation table 1015 , and an ROI simulation table 1016 .

[0019] <Configuration of Control Unit 104 of Server 10 > The control unit 104 of the server 10 includes an acquisition unit 1041 , a calculation unit 1042 , and an output unit 1043 .

[0020] <Functions of medical sales support information processing device>

[0021] <Optimal Sales Action Calculation Process> First, the present invention predicts and identifies doctors who are likely to use or prescribe a target drug, etc., and evaluates that probability in order to improve the accuracy of targeting sales targets. Therefore, similar target customers who are predicted to have a high probability of prescribing a specific drug, etc., can be identified and matched, enabling effective resource allocation. In other words, it is possible to identify doctors who are predicted to have a desired range of usage or prescription probability, such as a high probability, and present that probability.

[0022] Then, in a previous step, the attributes of doctors and sales representatives, the sales promotion activities they carried out, their results, and sales are correlated, and those with low correlations (p-values ​​of, for example, 0.05 or more) are first discarded, and then sales are allocated proportionally and distributed to each doctor at the facility using weights calculated using the r-value, which is the correlation coefficient.By adding this process, the accuracy of the prescription probability improvement activity model, which calculates prescription probability and targets by treating each subsequent sales promotion activity as an independent variable, is improved.

[0023] <Outline of the Optimized Sales Action Calculation Process> The optimized sales action calculation process is a technology that utilizes statistical methods, particularly logistic regression analysis, to quantify the impact and results of sales activities for each physician and derive the amount of activity required to maximize sales effectiveness. In addition, by providing sales support technology that conforms to a theoretical model based on statistical processing that makes full use of the latest logistic regression analysis and logit functions, etc., and that clearly targets individual physicians who decide on the adoption of pharmaceuticals, etc., while discarding sales promotion activities aimed at multiple physicians within a facility that are not significant, it is possible to maximize sales effectiveness with even greater precision.

[0024] In order to be able to discard insignificant sales promotion activities for multiple doctors within a facility, the inventor of the present invention first calculates the correlation coefficient between each facility's sales and the sales promotion activities for each doctor, normalizes it to determine the relative influence, and calculates a score by multiplying the product-moment correlation coefficient for each sales promotion activity by the weight of the activity amount for each doctor, as well as a sales allocation value of the facility's sales amount based on the score. Logistic regression analysis is then performed using this score or sales allocation value. Logistic regression analysis includes penalized logistic regression.

[0025] The inventors of the present invention discovered that multiple regression logistic regression analysis can be applied to calculate optimal sales actions. Specifically, by calculating the maximum probability when the regression coefficients related to physician attributes are fixed as a general rule and the remaining regression coefficients related to sales activities are freely set, it is possible to derive the maximum usage or prescription probability for each physician and calculate the minimum amount of sales activity required to maximize the usage or prescription probability for each sales activity. The fixed regression coefficients related to physician attributes represent the attributes of each physician, while the freely set regression coefficients related to sales activities represent the details of the sales activity. Therefore, the present invention provides an efficient device and method for calculating optimal sales actions by utilizing a logistics function (probability transformation) and its inverse function, the logit function (linear combination). Specifically, an optimal sales activity model is constructed using the following procedure.

[0026] 1. Data collection - Collect activity history data for each doctor Examples of data include, but are not limited to: - Face-to-face visits (F2F) - Telephone interviews - Email distribution and opening - Sending of materials (pamphlets, etc.) - Viewing history of online lectures - Login history to the doctor portal site - Playback history of online detailing for medical professionals - Number of views of case introduction content - Remote interviews (online interviews) - Collect attribute information of doctors (e.g., specialty, working facility, prescription tendency, etc.) - Collect attribute information of sales representatives (for doctors) (e.g., age, years of experience, gender, evaluation, etc.) - Predict or collect results of sales activities (e.g., drug adoption and prescription status) 2. Analysis using statistical methods (1) Using the collected data, aggregate each activity by facility and calculate the correlation coefficient r with sales. ｊ and calculate the p-value. (2) Extract only activities with p-values ​​below a predetermined threshold (e.g., 0.05). To evaluate the relationship between activities and sales, use the product-moment correlation coefficient (r) of Pearson et al. Definition of r (general formula) That is, Then, by combining x with the activity amount and y with the significance determination using the p-value, highly reliable contribution evaluation becomes possible. (Application to the present invention)

[0027]

[0028] (3) Sum the correlation coefficients of the extracted activities and calculate the relative weight of each individual activity (normalize the correlation coefficient to calculate the relative influence). For each activity j ∈ J, find the correlation coefficient r_j and calculate the weight w_j.

[0029] (4) A score is calculated by multiplying the amount of activity for each doctor by a weight, and facility sales are allocated based on the score ratio within the facility. A. Score calculation

[0030] A. Pro rata allocation of facility sales Pro rata allocation of sales for each doctor is calculated as facility sales S ｆ Calculated based on

[0031] (5) Using the apportioned amount or score, a logit function is applied to calculate the probability of use or prescription for each doctor 3. Building a sales optimization model (a model for each doctor with a desired range of use or adoption probability) - For doctors with a low probability of use or prescription (for example, less than 50%), it is possible to quantitatively calculate how much to increase which sales activities to improve sales - For doctors with a certain probability of use or prescription (for example, 50% or more), it is also possible to generate proposals to reduce unnecessary sales activities 4. Visualization and output of results - The analysis results are output to a file and made available in a form that can be viewed by sales marketing personnel - Optimization strategies for each sales activity are presented as a report, and a feasible action plan is presented Details of the process are described below.

[0032] <Cosine Similarity Analysis> Regarding the overview of the cosine similarity analysis of the present invention, the present invention particularly relates to a system and method for quantifying the similarity between medical professionals based on the attribute data of doctors or medical professionals (age, specialty, certification, working style, etc.) and outputting it in visual or data form.

[0033] This system converts attribute data into a numerical format, then standardizes continuous variables and calculates cosine similarity on a scale of 0 to 100. This allows the similarity between each healthcare professional to be presented in a format that is intuitively interpretable by the user. In other words, this system has the following steps: 1. Obtain healthcare professional attribute data in Excel or a similar format. 2. Perform data preprocessing to convert the attribute data into a numerical format and standardize continuous variables. 3. Calculate cosine similarity and generate a similarity score between each healthcare professional on a scale of 0 to 100. 4. Output the calculation results as an Excel file or database and provide them in a format that can be used for analysis purposes.

[0034] In more detail, the following is performed: A. Data input and preprocessing - Attribute data for each healthcare professional is received as input. - Attribute data includes age, qualifications, specialty, number of activities, etc. - Continuous variables are standardized and scale differences are adjusted. B. Similarity calculation - Cosine similarity is calculated based on the quantified attribute data. - The cosine similarity calculation result is converted to a 0-100 scale and provided as a score that can be intuitively interpreted. C. Results output - The calculation results are saved in an Excel file or database. - Similarity scores between each healthcare professional are visualized and can be used to optimize educational programs and propose team compositions. This invention enables advanced analysis based on the characteristics of healthcare professionals, promoting more efficient resource management and the discovery of new interactions. Furthermore, 1. Attribute-based similarities between healthcare professionals can be efficiently analyzed. 2. Similarity scores can be intuitively interpreted, making it possible to utilize high levels of agreement between healthcare professionals with scores of 70% or more, for example. 3. 3. Based on the analysis results, it is possible to optimize educational programs and resource allocation. 4. It is possible to process large amounts of data in a short time and provide highly accurate results.

[0035] <ROI Analysis> ROI stands for "Return on Investment" and means "return on investment." ROI analysis is an analysis of return on investment, i.e., the ratio of the resulting effect, i.e., sales, to the funds invested in sales support. According to the present invention, ROI is an index that shows the degree of increase in sales as a result of an event (campaign) invested in before and after the investment. According to the present invention, ROI is analyzed for each sales promotion activity, enabling optimal budget allocation. Conventionally, when multiple events (campaigns) are implemented over a certain period (e.g., one year), it has been difficult to clearly determine the extent to which each event contributed to sales, even if a sales effect was observed. Therefore, when multiple events are used in combination, even if an effective measure is used, it is impossible to quantitatively evaluate the cost-effectiveness of inefficient measures, and final decision-making has had to rely on experience and intuition.

[0036] <ROI Analysis Details> In the present invention, the ROI for each campaign can be calculated, which makes it easy to determine whether it is desirable to continue each campaign or not, thereby contributing to the efficiency of sales.

[0037] In the past, it was difficult to perform ROI analysis, which quantitatively evaluates the extent to which each campaign contributed to sales before and after a certain period, especially when multiple campaigns were run simultaneously within that period, and there was a lack of methods for doing so.

[0038] The inventors of the present invention have discovered a regression analysis model that fits to ROI analysis, which quantitatively evaluates the contribution of each campaign to sales when multiple campaigns are run in the same fiscal year, and have invented a technology that uses the regression coefficients calculated by the regression analysis model to calculate the impact of each campaign on sales increase.In other words, the present invention provides an efficient method and system for performing regression analysis on sales increase data and campaign data, quantitatively evaluating the impact of each campaign, and calculating ROI.

[0039] Specifically, the present invention further calculates the ROI for each campaign based on sales data and campaign data in the following steps: 1. Obtaining the Increase in Sales: The increase in sales is calculated based on the annual sales performance for the target year and the sales performance for the previous year. If the difference in sales performance for individual physicians is significant, the growth rate (before and after the relevant period) may also be used. 2. Assessing Impact Using Regression Analysis: The inventors of the present invention found that for each client running a campaign, the relationship between the implementation (zero-one) of each campaign for each physician, the amount of investment in each campaign, and the impact on the increase in sales for each physician closely matches the following multiple regression equation (1). They also found that the regression coefficients multiplied by each investment in each term of this equation indicate the weight, or impact, of each investment.

[0040]

[0041] In other words, y in the above formula is the amount of increase in sales, m is the number of investments made, and x １ x ２... is the investment amount for each campaign, and the calculated regression coefficient represents the weight, or influence, of each investment.

[0042] The above can be expressed as follows:

[0043]

[0044] The above formula can be used to calculate the regression coefficients using the least squares method. The formula for calculating each regression coefficient (= the impact of each campaign) is as follows: The calculation method may be a general method in the least squares method that calculates the regression coefficient β as follows, which minimizes the loss function:

[0045] In this way, a combination of the regression coefficient vector β=(β0 β1 . . . βm) is calculated.

[0046] Based on the regression coefficients obtained by the above regression analysis, the relative influence of each campaign n is calculated using the following formula: m is the number of campaigns (a natural number), and n is any natural number between 1 and m.

[0047]

[0048] Calculating total ROI: Calculate the total ROI by dividing the total increase in sales by the total investment for all campaigns. 4. Calculating ROI for each campaign: Calculate the ROI for each campaign by multiplying the relative impact of each campaign by the total ROI. 5. Visualizing and exporting results: Visualize or export the analysis results to use in decision-making.

[0049] To achieve the above functions, in the present invention, initial values ​​are set in the medical sales support information processing device. These initial values ​​are stored in the storage unit 101 of the server 10 as a doctor table 1012, a product table 1013, and an ROI table 1014. The doctor table 1012, the product table 1013, and the ROI table 1014 will be described in detail below.

[0050] FIG. 4 shows the data structure of the doctor table 1012. The doctor table 1012 is a table that stores and manages affiliation and attribute information for doctors who have the authority or actual authority to decide whether to adopt or prescribe medicines or medical devices. The administrator of the medical sales support information processing device inputs this information about doctors into the doctor table, which stores the information in records in the doctor table 1012. This allows users of the medical sales support information processing device to use the services of the present invention. The doctor table 1012 is a table with a facility doctor code as a primary key and columns for doctor no., doctor name, facility no., facility name, attribute 1, attribute 2, attribute 3, attribute 4, attribute 5, attribute 6, attribute 7, attribute 8, and registration date and time D, which is the date and time when this information was input.

[0051] The facility physician code is a field that stores identification information (e.g., numbers and symbols) related to the sales activity target, assigned to each combination of a physician and the facility to which it belongs. The facility physician code is a field that has a unique value assigned to each combination of a physician and a facility. The physician facility code may be a combination of a physician number and a facility number. The physician name and facility name are the personal name of the physician or the name of the facility, such as a hospital, indicated by the physician number and facility number, respectively. The physician name and facility name may be entered as any character string, such as an abbreviated name or nickname. Attributes 1 to 8 are fields that store attributes that may be related to the sales activity of the sales activity target. Publicly available information, such as age, gender, affiliated academic societies, and affiliated research groups, is often registered, but information not publicly available, such as the physician's email newsletter subscriptions and other information that is of interest to the physician, may also be used. The information registered in the records of the doctor table 1012 can be changed by an administrator, or it may be changed by information entered by the user of the medical sales support information processing device at the time of use and recorded in the activity simulation table 1015, described below. All input and output information to the medical sales support information processing device, including initial values, is recorded in an input and output record table (not shown), allowing the administrator to trace the history.

[0052] FIG. 5 shows the data structure of the product table 1013. The product table 1013 stores and manages information such as regression coefficients calculated in advance as a result of a logistics regression analysis performed on pharmaceuticals and other products targeted for sales activities. The product table 1013 is also a management table within the server. Therefore, it has fields for product number and product name, allowing for the distinct management of information for multiple users. The product table 1013 has columns for product number, product name, regression coefficients β01, β02, β03, β04, β05, β06, β07, and β08 corresponding to attributes 1 to 8, regression coefficients β09, β10, β11, β12, β13, β14, β15, β16, β17, β18, β19, β20, and β21 corresponding to activities 1 to 13, and a registration date and time P for the input date and time.

[0053] The product number is identification information that identifies the medical product targeted by multiple users of the medical sales support information processing device. The product name is the name of the product represented by the product number. Logistic regression analysis is performed for each individual product number, and regression coefficients, etc. are determined in advance. The product number is an item for which a unique value is assigned for each individual product. Note that individual users do not need to provide the product number each time they use the present invention. The regression coefficients, etc. for each individual product number are determined by logistic regression analysis based on pre-collected information on the attributes of each doctor for each individual product number, the type and frequency of sales activities, and the probability of product use and prescription. In addition, the technology described in Japanese Patent No. 7418877, a factor analysis method invented and patented by the inventor of the present invention, can also be utilized. The regression coefficients, etc. input as initial values ​​are pre-processed, such as by filling in missing data and processing outliers, as in general logistic regression analysis. Furthermore, a sufficient amount of data should be secured, and sufficient consideration should be given to avoiding multicollinearity and overfitting. As a more advanced method of logistic regression analysis, analysis using penalized logistic regression or multiclass logistic regression may also be performed. The regression coefficients obtained in this way (regression coefficients) corresponding to physician attributes 1 to 8 are stored in columns β01 to β08, and those corresponding to sales activities 1 to 13 are stored in columns β09 to β21. The date and time when these values ​​were input into the medical sales support information processing device is stored in the registration date and time P column.

[0054] The outline of the factor analysis method (Japanese Patent No. 7418877 (JP7418877B) obtained by the inventor of the present invention as described above) is a sales support technology based on factor analysis of doctor data.

[0055] 6 is a diagram showing the data structure of the ROI table 1014. The ROI table 1014 is a table for inputting initial values ​​for conducting an ROI analysis for each campaign when a medical customer or the like conducts a sales campaign for a product such as a pharmaceutical product that is the target of sales activities into the medical sales support device. That is, the ROI table 1014 is composed of calculations such as whether or not each campaign was conducted for each doctor who is a customer of the customer or the like, the monthly sales for each doctor, the monthly sales for each doctor in the previous year for each doctor, and the amount of sales increase calculated from these. The ROI table 1014 has the following columns: Customer_ID, which represents each doctor; Campaign_1, 2, 3, etc., which represent whether or not campaigns 1 to 3 (the numbers are examples; the same applies below) were implemented for each customer doctor; the year and month, which stores each month's sales to each doctor in the implementation year; the year and month, which stores each month's sales in the previous year; the total annual sales calculated from these; Incremental sales increase; and Incremental sales increase rate (%).

[0056] Customer_ID is identification information that identifies each doctor who is a customer, such as a client, or the facility to which the doctor belongs. This field is set with a unique value for at least each doctor. Campaign_1, 2, 3, etc. store a zero or one to indicate whether or not each of Campaigns 1 to 3 was implemented for each Customer_ID, and the year / month column stores the actual sales figures for each Customer_ID for the year the campaign was implemented, as well as the actual sales figures for the previous year. The total sales for the year calculated from these are stored in the TOTAL column, the sales increase amount in the Incremental column, and the sales increase rate in the Incremental (%) column. These values ​​may be input by creating an Excel list and uploading them all at once to the medical sales support information processing device.

[0057] <Configuration of control unit 104 of server 10> The control unit 104 of the server 10 includes an acquisition unit 1041, a calculation unit 1042, and an output unit 1043. The control unit 104 executes an application program 1011 stored in the storage unit 101 to realize the functions of each functional unit.

[0058] The acquisition unit 1041 acquires information input in advance as initial values ​​by the administrator of the medical sales support information processing device, and stores the information in the doctor table 1012, product table 1013, and ROI table 1014 of the storage unit 101. This completes the initial registration, and the information stored in the doctor table 1012, product table 1013, or ROI table 1014 becomes available for use. The acquisition unit 1041 also acquires information input by the user of the medical sales support information processing device, and stores the information in the activity simulation table 1015 and ROI simulation table 1016 of the storage unit 101. This enables the calculation unit to perform calculations.

[0059] The calculation unit 1042 extracts information from the activity simulation table 1015 and the ROI simulation table 1016 acquired by the acquisition unit 1041 and stored in the memory unit 101, performs calculations using logit functions, etc., by the application program 1011, and stores the results in specified parts of the activity simulation table 1015 and the ROI simulation table 1016.

[0060] The output unit 1043 retrieves information from the activity simulation table 1015 and the ROI simulation table 1016, including the results calculated by the calculation unit 1042 and stored in the memory unit 101, and displays the information on a monitor display, prints it, or writes an electronic file in an appropriate format such as Excel for a user of the medical sales support information processing device. The output unit 1043 may also display information from the doctor table 1012, product table 1013, or ROI table 1014 required by the user on a monitor display, print it, or write an electronic file in an appropriate format such as Excel. The output unit 1043 may also display information from the doctor table 1012, product table 1013, and ROI table 1014 stored in the memory unit 101, which information has been input as initial values ​​in advance by an administrator of the medical sales support information processing device, on a monitor display, print it, or write an electronic file in an appropriate format such as Excel, and display information stored in the input / output history table on a monitor display, print it, or write an electronic file in an appropriate format such as Excel.

[0061] The medical sales support information processing device may include a communication unit that executes communication processing with the customer's information processing terminal 20, 30 or any other information processing terminal.

[0062] <Configuration of Information Processing Terminals 20 and 30> The customer's information processing terminals 20 and 30 may be any terminal that can transmit the necessary information to the user of the medical sales support information processing device, and may be a server, desktop PC, laptop PC, mobile terminal, dedicated terminal, etc. Furthermore, the customer's information processing terminals 20 and 30 may have a function to display the information received by the user of the medical sales support information processing device from the server 10, but are not limited to this.

[0063] <Configuration of Storage Unit 201 of Information Processing Terminal 20 / 30> The storage unit 201 (not shown) of the information processing terminal 20 / 30 includes an application program 2011 and a terminal table 2012 (not shown).

[0064] The application program 2011 may be pre-stored in the storage unit 201, or may be downloaded from a web server operated by a service provider via a communication IF. The application program 2011 includes an application such as a web browser application. The application program 2011 may include an interpreter-type programming language such as JavaScript (registered trademark) that is executed on a web browser application stored in the information processing terminal 20 / 30.

[0065] <Operation of the Medical Sales Support Information Processing Device> Each process of the medical sales support information processing device is described in detail below. In the present invention, in the preliminary stages of each process, the attributes of doctors and sales representatives, the sales promotion activities they implemented, and their results are correlated with sales. Low correlations (e.g., p-values ​​of 0.05 or greater) are first discarded. Then, the preliminary discard and sales allocation processes are performed, in which sales are allocated and distributed to each doctor at the facility based on weights calculated using the r-value, which is the correlation coefficient. Adding this process improves the accuracy of each subsequent process. Figure 3 is a flowchart illustrating the operation of the <Preliminary Discard and Sales Allocation> process. Figure 7 shows the activity simulation table 1015 used in the <Optimized Sales Action Calculation Process>. Related to this, Figure 9 is a flowchart illustrating the operation of the <Optimized Sales Action Calculation Process>. Figure 10 is a flowchart illustrating the operation of the <Cosine Similarity Analysis> process. Figure 8 shows the ROI simulation table 1016 used in the <ROI Analysis> process. Related to this, Figure 11 is a flowchart illustrating the operation of the <ROI Analysis> process.

[0066] <Tables for Pre-stage Rejection and Pro rata Distribution of Sales, etc.> No dedicated tables are required for pre-stage rejection and pro rata distribution of sales, etc.

[0067] <Details of previous stage rejection and sales allocation, etc.> Figure 3 is a flowchart showing the operation of the <previous stage rejection and sales allocation, etc.>. The following describes the details of the operation of <previous stage rejection and sales allocation, etc.>.

[0068] 3, data on sales promotion activities, attributes, etc. are acquired from each table. For example, the control unit 104 of the server 10 acquires information for the doctor table 1012 and the product table 1013 from the management information processing terminal 15.

[0069] In step S10, the control unit 104 of the server 10 also acquires sales data (for each facility).

[0070] In step S20, the calculation unit of the control unit 104 of the server 10 calculates the correlation coefficient between each sales promotion activity and sales from the data acquired in step S10, and calculates normalization and relative influence. This result may be recorded in the input / output history table.

[0071] In step S30, the control unit 104 of the server 10 calculates the p-value, which is the realized probability when a null hypothesis for hypothesis testing is established in relation to the sales of each facility. This result may be recorded in the input / output history table.

[0072] In step S40, the control unit 104 of the server 10 determines whether the p-value calculated as a result of step S30 is less than a predetermined threshold. For example, if the threshold is 0.05, it is determined whether the p-value is less than 0.05. Data whose p-value is determined to be less than the threshold is rejected in step S45. Data that has been determined to be rejected is marked as rejected in the table storing the data (the corresponding column in each table is not shown). This result may be recorded in the input / output history table.

[0073] In step S50, the control unit 104 of the server 10 calculates a score obtained by multiplying the correlation coefficient by a weight and a sales distribution value. The results may be recorded in the input / output history table.

[0074] In step S60, the control unit 104 of the server 10 stores these calculated values ​​in each table (the relevant columns in each table are not shown), and then performs each subsequent process using a predetermined statistical method. For example, the probability of use or prescription for each doctor is calculated. This process will be described in detail in the following sections.

[0075] <Activity Simulation Table 1015 for Optimized Sales Action Calculation Processing> Figure 7 is a diagram showing the data structure of the activity simulation table 1015 used in the <Optimized Sales Action Calculation Processing>. Note that Figure 7 is an example of a table for management purposes and is not, in principle, displayed to customers. The activity simulation table 1015 is a table into which the attributes of doctors who were actually approached by a user of the medical sales support information processing device for products such as pharmaceuticals that are the subject of sales activities and the details of the sales activities conducted toward those doctors are input for the <Optimized Sales Action Calculation Processing>, and the calculated <Optimized Sales Actions> are stored and managed. The activity simulation table 1015 is stored on a server. The activity simulation table 1015 has columns for product number, product name, facility doctor code, doctor no., doctor name, facility no., facility name, use or prescription probability "before" p0, x01, x02, x03, x04, x05, x06, x07, x08 as explanatory variables corresponding to attributes 1 to 8 to be multiplied by the regression coefficients obtained by logistic regression analysis, x09, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20, x21 as explanatory variables corresponding to activities 1 to 13, the registration date and time a0 of the above, use or prescription probability "after" p1 for storing the results after the <optimized sales action calculation process>, R1, R2, R3 for storing the names of recommended activities, and the registration date and time a1 for storing the results after the <optimized sales action calculation process>. Note that if there are not enough columns, additional columns may be added.

[0076] The product number is identification information that identifies the medical product that the user of the medical sales support information processing device intends to use. The product name is the name of the product represented by the product number. However, this is used only by the management information processing terminal and the server 10 and does not need to be known to the user. The <Optimized Sales Action Calculation Process> is performed for each individual product number. The product number is an item for which a unique value is set for each individual product, and corresponds to the product number in the product table 1013.

[0077] The "before" probability of use or prescription p0 is the probability that the target product will be used or prescribed by the doctor in question, calculated from x01, x02, x03, x04, x05, x06, x07, and x08 as explanatory variables corresponding to attributes 1 to 8, and x09, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20, and x21 as explanatory variables corresponding to activities 1 to 13, which are stored in advance in activity simulation table 1015, and the regression coefficients stored as initial values ​​in the row having the same product number in product table 1013. On the other hand, the "after" probability of use or prescription p1 is the target probability that the target product will be used or prescribed by the doctor in question after the "optimized sales action calculation process" is performed. Furthermore, R1, R2, and R3, which store the names of recommended activities, are names of activities that contribute greatly to achieving the use or prescription probability "after" p1, but more activity names may be recorded by adding a recording column, not limited to the three with the highest contributions. The registration date and time a1 stores the date and time when the results after the <optimized sales action calculation process> were stored.

[0078] <Details of the Optimized Sales Action Calculation Process> Fig. 9, which is related to Fig. 7, is a flowchart showing the operation of the <Optimized Sales Action Calculation Process>. Details of the <Optimized Sales Action Calculation Process> will be explained below.

[0079] In step S100 of Figure 9, the control unit 104 of the server 10 acquires information for the doctor table 1012 and the product table 1013 input from the management information processing terminal 15, and stores the information as initial values ​​in each column of the doctor table 1012 and the product table 1013.

[0080] In step S110, the control unit 104 of the server 10 acquires information for the activity simulation table 1015 that the customer has collected from any terminal, such as the customer's information processing terminal 20 or 30, and stores the information in each column for before the <optimized sales action calculation process> in the activity simulation table 1015. Specifically, from any terminal, the following are stored: the target product name, facility doctor code, facility name, doctor name, x01, x02, x03, x04, x05, x06, x07, and x08 as explanatory variables corresponding to attributes 1 to 8, x09, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20, and x21 as explanatory variables corresponding to activities 1 to 13, and the registration date and time a0 for the above.

[0081] In step S120, the control unit 104 of the server 10 compares the doctor identification information and the explanatory variables for the doctor's attributes stored in the activity simulation table 1015 (i.e., the facility doctor code, facility name, doctor name, and the explanatory variables x01, x02, x03, x04, x05, x06, x07, and x08 corresponding to attributes 1 to 8) with the corresponding records stored in the doctor table 1012 (step S130). If there are any discrepancies, the record in the doctor table is overwritten with the information stored in the activity simulation table 1015 (step S135). The process then returns to step S120 and executes step S120 again. If there are no discrepancies, the process proceeds to step S140 without any further action. Because all input / output information is recorded in an input / output history table (no diagram), even if it is later discovered that the overwritten information was incorrect, it is possible to determine what the original data was.

[0082] In step S140, the <optimized sales action calculation process> is performed. Prior to this, the control unit 104 of the server 10 first reads out the regression coefficients and the like stored in the product table 1013 and corresponding to the product numbers in the activity simulation table 1015, and calculates the regression coefficients x01, x02, x03, x04, x05, x06, x07, and x08 as explanatory variables corresponding to attributes 1 to 8 in the activity simulation table 1015, and x09, x10, x11, x12, x13, x14, x15, x16, and x17 as explanatory variables corresponding to activities 1 to 13. The same product numbers are multiplied by β01, β02, β03, β04, β05, β06, β07, β08, β09, β10, β11, β12, β13, β14, β15, β16, β17, β18, β19, β20, β21 stored in product table 1013, and the probability of use or prescription "before" p0 is calculated using a logit function, which is stored in the corresponding column of activity simulation table 1015. In this case, an example of the formula for calculating p0 using the logit function is as follows (where pi = p0), but the formula is not limited to this.

[0083]

[0084] Furthermore, in step S140, the combination of the usage or prescription probability "after" p1, which is the maximum probability that the target product will be used or prescribed to the target doctor after the <optimized sales action calculation process> is performed, and the x values ​​related to the activity to obtain it are calculated and recorded. That is, while x01, x02, x03, x04, x05, x06, x07, and x08, which are explanatory variables corresponding to attributes 1 to 8 identifying the doctor stored in the activity simulation table 1015, are left fixed, x09, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20, and x21, which are explanatory variables corresponding to activities 1 to 13, are changed, and the combination of x09 to x21 that maximizes the usage or prescription probability "after" p1 is calculated. This calculated value is stored in the input / output history table, and at the same time, in step S150, the name of the activity with the greatest contribution, the use or prescription probability "after" p1, and the calculation date and time thereof are stored in the columns of the use or prescription probability "after" p1, R1, R2, R3, and registration date and time a1 of the activity simulation table 1015.

[0085] When calculating the combination of x09 to x21 in which the use or prescription probability "after" p1 reaches the target value, p1 is calculated by the above formula (5). ｉ = p １ To calculate the combination of x09 to x21 that maximizes the use or prescription probability "after" p1, general methods for calculating optimal combinations using logistic functions or logit functions may be used in addition to the formula shown in this specification. The activity with the greatest impact is selected from the terms with β09 to β21 (terms related to activities) in the following formula.

[0086]

[0087] That is, for each term in the following formula, for example, three items with the largest calculated values ​​may be selected, and the names of the activities corresponding to these items may be stored as the top three activities with the highest contributions.

[0088]

[0089] Furthermore, the targets for this <optimized sales action calculation process> may be limited to only those doctors whose usage or prescription probability "before" p0 is low, by setting a threshold (for example, less than 50%).

[0090] In step S160, the control unit 104 of the server 10 may output, via the output unit, the use or prescription probability "after" p1, which is the probability that the target product will be used or prescribed to the doctor after the <optimized sales action calculation process> is performed, a combination of the value of x related to the activity to achieve that probability, and the name of the activity that contributes most. The output may be displayed on a monitor display, printed, or written to an electronic file.

[0091] Table for Cosine Similarity Analysis No dedicated table is required for the cosine similarity analysis.

[0092] <Details of Cosine Similarity Analysis> Figure 10 is a flowchart showing the operation of the <Cosine Similarity Analysis> calculation process. Details of the <Cosine Similarity Analysis> are explained below. The above <Optimized Sales Action Calculation Process> makes it possible to analyze the similarity for doctors who have a high probability of using or prescribing a drug. Therefore, it can be said that the <Cosine Similarity Analysis> after the <Optimized Sales Action Calculation Process> is performed produces a synergistic effect.

[0093] In step S200 of FIG. 10, the control unit 104 of the server 10 acquires information for the doctor table 1012 and the product table 1013 from the management information processing terminal 15 and stores the information in each column of the doctor table 1012 and the product table 1013 as initial values.

[0094] In step S210, the control unit 104 of the server 10 acquires, through the acquisition unit, the facility doctor code stored in the product table 1013 after performing the <Optimized Sales Action Calculation Process> or the facility doctor code specified by the customer from any terminal such as the customer's information processing terminal 20 / 30, as information representing the similar source.

[0095] In step S220, the calculation unit of the control unit 104 of the server 10 calculates the cosine similarity between the attribute information of the doctor associated with the facility doctor code acquired in step S210 and the attribute information of each doctor stored in the doctor table. The results of these similarities may be recorded in the input / output history table. Furthermore, in step S230, the control unit 104 of the server 10 may store the results in the doctor table 1012. A storage column may be added to the doctor table 1012.

[0096] In step S240, the control unit 104 of the server 10 selects a doctor with a high degree of similarity from the result of step S220. Note that the selection of a doctor with a high degree of similarity is just one example, and a desired range of similarity can also be specified for selection.

[0097] In step S240, the output unit 1043 of the control unit 104 of the server 10 outputs information about doctors who have the desired similarity. The output may be displayed on a monitor display, printed, or written to an electronic file.

[0098] Figure 8 shows the data structure of an ROI simulation table 1016 used for ROI analysis. The ROI simulation table 1016 stores and manages campaign activities conducted by customers for products such as pharmaceuticals targeted for sales activities, sales results for the target period for performance verification, sales results for the previous year, and ROI analysis results. The ROI simulation table 1016 has columns for customer no., customer name, investment amount for each campaign activity conducted for campaigns 1 to 5, total number of campaigns conducted, absolute sum of relative impacts for the conducted campaigns, calculated relative impacts for campaigns 1 to 5, monthly settlement 1 (Jan. to Dec., Total) which is sales results for the target period, monthly settlement 0 (Jan. to Dec., Total) which is sales results for the previous year, sales increase amount which is the increase in sales results, total ROI, ROI for each campaign for campaigns 1 to 5, and the registration date and time r in which these are stored. The ROI analysis according to the present invention evaluates the impact of the entire campaign by statistically aggregating and analyzing the individual customer data stored in the ROI simulation table 1016. Therefore, the "ROI per campaign" and "relative impact" listed in the table are not values ​​for each individual customer, but aggregated indicators based on the overall analysis.

[0099] A customer is a company that uses a medical sales support information processing device, such as a pharmaceutical company or a medical device manufacturer / distributor. The customer no. is an item in which a unique value corresponding to the customer name is set. A campaign is a special event held to promote sales to doctors, and although it incurs expenses, it is held in the hope of increasing sales. The amount invested in the campaign activities held is the actual amount invested by the customer in each campaign. Even if the same campaign is held multiple times in the same target year, each is considered to be a separate campaign event by assigning a number or other identifier to distinguish them. The number of times held is the total number of campaigns held by the customer in the year.

[0100] The regression coefficient β value corresponding to each campaign is calculated by the ROI analysis. The sum of the absolute values ​​of the regression coefficient β values ​​corresponding only to the implemented campaigns shown in the ROI simulation table 1016 is stored in the "Implemented β Value Absolute Value Sum" field. The relative influence is calculated by using the actual absolute value sum of the β values ​​as the denominator and the β value of each campaign as the numerator, and is stored.

[0101] The actual sales figures for each month of the target product from January to December of the target year are read from the ROI table 1014, which is the default table, and stored in Monthly Settlement 1 (January-December column), with the total amount stored in the Total 1 column. The actual sales figures for each month of the target product from January to December of the year prior to the target year are stored in Monthly Settlement 0 (January-December column), with the total amount stored in the Total 0 column. The amount obtained by subtracting the Total 0 column from the Total 1 column is then stored in the Sales Increase column. Note that this sales increase amount may be acquired by the acquisition unit based on the numerical value entered by the user at the time of use. As long as the corresponding months of the current and previous years are stored, it is acceptable for some months to have missing sales figures, and it is not necessary for all December figures to be filled in. In this case, numerical values ​​such as campaign investment amounts will also be calculated for the entered sales period.

[0102] The total ROI column stores the value obtained by dividing the increase in sales by the total amount invested in the campaigns. The ROI columns for each campaign, Campaigns 1 to 5, store the percentage obtained by dividing the funds invested in each campaign by the value obtained by multiplying the increase in sales by the relative impact. The registration date and time r stores the date and time when the ROI was calculated. Note that the number of campaigns is not limited to five, and columns for Campaigns 1 to 5, etc. may be added as needed.

[0103] Furthermore, if it is possible to calculate the total cost of regular sales activities that users normally conduct with doctors over the target period, this can be treated as the investment amount for one campaign. If this is possible, it will be possible to grasp the factors that affect the increase in sales more comprehensively and consider whether each other campaign is more or less efficient than regular sales activities.

[0104] <ROI Analysis Details> Fig. 11, which is related to Fig. 8, is a flowchart showing the operation of <ROI analysis>. Details of <ROI analysis> are explained below. In step S300 of Fig. 11, the control unit 104 of the server 10 acquires information for the ROI table 1014 from the management information processing terminal 15 and stores the information.

[0105] In steps S310 and S320, the control unit 104 of the server 10 acquires the information of the ROI table 1014, which is the initial value, and stores it in each column for the investment amount for each campaign activity carried out, the number of activities carried out, monthly settlement 0, and monthly settlement 1.

[0106] The number of campaigns implemented and the amount of investment in each campaign activity for Campaigns 1 to 5 are acquired from a specified terminal and stored. Note that this information may be acquired using general-purpose AI or prompts using Python (registered trademark), etc.

[0107] In step S330, the calculation unit 1042 of the control unit 104 of the server 10 applies each of the acquired values ​​to the aforementioned formula to calculate the regression coefficient for each campaign activity. The control unit 104 of the server 10 stores the sum of the absolute values ​​of these regression coefficients in the "total absolute β value of implementation" field. The calculation unit 1042 of the control unit 104 of the server 10 also stores the value obtained by dividing the regression coefficient β value corresponding to the campaign 1-5 fields in the ROI simulation table 1016, where a numerical value is stored in the "investment amount for each implemented campaign activity" field, by the value in the "total absolute β value of implementation" field, in the corresponding campaign field for each campaign 1-5 corresponding to the relative influence. The calculation unit 1042 of the control unit 104 of the server 10 also stores the value obtained by dividing the sales increase amount by the total investment amount for campaign activities in the "total ROI" field.

[0108] In step S340, the calculation unit of the control unit 104 of the server 10 stores the value obtained by multiplying the sales increase amount by the relative impact of each campaign, divided by the investment amount for each campaign, in the campaign columns for campaigns 1 to 5 of the ROI. That is, the calculation result of the following formula is stored.

[0109]

[0110] In step S350, the output unit of the control unit 104 of the server 10 outputs the ROI of each campaign. The output may be displayed on a monitor display, printed, or written to an electronic file.

[0111] 13 is a block diagram showing the basic hardware configuration of a typical computer 90. The computer 90 includes at least a processor 901, a main storage device 902, an auxiliary storage device 903, and a communication IF 991 (interface). These components are electrically connected to one another by a communication bus 921.

[0112] The processor 901 is hardware for executing an instruction set written in a program, and is composed of an arithmetic unit, a register, a peripheral circuit, and the like.

[0113] The main storage device 902 is used to temporarily store programs, data to be processed by the programs, etc. For example, it is a volatile memory such as a DRAM (Dynamic Random Access Memory).

[0114] The auxiliary storage device 903 is a storage device for saving data and programs, such as a flash memory, a hard disk drive (HDD), a magneto-optical disk, a CD-ROM, a DVD-ROM, or a semiconductor memory.

[0115] The communication IF 991 is an interface for inputting and outputting signals for communicating with other computers via a network using a wired or wireless communication standard. The network is composed of the Internet, a LAN, various mobile communication systems constructed using wireless base stations, etc. For example, the network includes 3G, 4G, and 5G mobile communication systems, LTE (Long Term Evolution), and wireless networks (e.g., Wi-Fi (registered trademark)) that can connect to the Internet via a predetermined access point. In the case of a wireless connection, communication protocols include, for example, Z-Wave (registered trademark), ZigBee (registered trademark), Bluetooth (registered trademark), etc. In the case of a wired connection, the network also includes a direct connection via a USB (Universal Serial Bus) cable or the like.

[0116] It is also possible to virtually realize the computer 90 by distributing all or part of each hardware configuration across multiple computers 90 and interconnecting them via a network. In this way, the concept of the computer 90 includes not only a computer 90 housed in a single housing but also a virtualized computer system.

[0117] <Basic Functional Configuration of Computer 90> A description will be given of the functional configuration of the computer realized by the basic hardware configuration (FIG. 13) of the computer 90. The computer includes at least the functional units of a control unit, a storage unit, and a communication unit.

[0118] The functional units of the computer 90 can also be realized by distributing all or part of the functional units among multiple computers 90 interconnected via a network. The computer 90 is a concept that includes not only a single computer 90 but also a virtualized computer system.

[0119] The control unit executes processing in accordance with the programs by the processor 901 reading various programs stored in the auxiliary storage device 903 and loading them into the main storage device 902. The control unit can realize the functions of functional units that perform various types of information processing depending on the type of program. This allows the computer to function as an information processing device that performs information processing.

[0120] The storage unit is realized by a main storage device 902 and an auxiliary storage device 903. The storage unit stores data, various programs, and various databases. The processor 901 can allocate a storage area corresponding to the storage unit in the main storage device 902 or the auxiliary storage device 903 in accordance with the programs. The control unit can cause the processor 901 to add, update, and delete data stored in the storage unit in accordance with the various programs.

[0121] A database refers to a relational database, which manages data sets called tables and masters in a tabular format structurally defined by rows and columns, by associating them with each other. In a database, a table is called a table or master, a column in a table is called a column, and a row in a table is called a record. In a relational database, relationships between tables and masters can be set and associated. Typically, each table and each master has a column set as a primary key to uniquely identify a record, but setting a primary key to a column is not required. The control unit can cause the processor 901 to add, delete, or update records in specific tables and masters stored in the storage unit according to various programs.

[0122] Each table, database, and master in the present invention may include any data structure in which information is structurally defined (such as a list, dictionary, associative array, or object). Data structures also include data that can be considered as a data structure by combining data with functions, classes, methods, etc. written in any programming language.

[0123] The communication unit is realized by the communication IF 991. The communication unit realizes the function of communicating with other computers 90 via a network. The communication unit can receive information transmitted from other computers 90 and input the information to the control unit. The control unit can cause the processor 901 to execute information processing on the received information in accordance with various programs. Furthermore, the communication unit can transmit information output from the control unit to other computers 90. Specific examples of implementation

[0124] As specific examples of the implementation, first, second and third embodiments will be described below.

[0125] <Embodiment 1> Company A is a pharmaceutical company that developed a new drug, Drug P, an anti-obesity drug, and launched it two years ago. Company A's MRs (Medical Representatives; same below) and wholesalers have conducted various sales activities by visiting doctors at various hospitals and other facilities, and as a result, have gained a 15% market share. Clinical prescription results for Drug P have been good, and Company A believes it can still increase sales. However, for some reason, some doctors are reluctant to prescribe Drug P, and the company believes that it would be a waste to allocate sales resources uniformly to such doctors. Therefore, the company decides to utilize the present invention.

[0126] In the medical sales support information processing device of the present invention, in step S100 of Figure 9, the control unit 104 of the server 10 obtains information for the doctor table 1012 and the product table 1013 from the management information processing terminal 15 and stores the information in each column of the doctor table 1012 and the product table 1013, with the initial values ​​already having been entered, as data on each hospital facility since the new drug P was launched, the attributes of the doctors affiliated with those facilities, the details of sales activities, and the results (usage or prescription).

[0127] Company A's MR will compile in an Excel A form as much information as they know about the attributes of each hospital facility and doctor they are going to approach about the new drug P, as well as the planned sales activities they are going to carry out.

[0128] In the medical sales support information processing device of the present invention, in step S110, the control unit 104 of the server 10 acquires the information necessary for the <optimized sales action calculation process> for the activity simulation table 1015 and stores it in each column of the activity simulation table 1015.

[0129] Specifically, the facility doctor code, facility name, doctor name, attributes (gender, age, affiliated academic society, etc.), planned activities 1 to 13 (future sales activities such as face-to-face interviews, online interviews, email awareness campaigns, and email newsletter distribution), product names, product numbers, other affiliation information that have been stored in advance on the server, and regression coefficients for logistic regression analysis that have been calculated in advance using another technology and appropriately preprocessed are obtained.

[0130] In step S120, the control unit 104 of the server 10 compares the doctor identification information and the explanatory variables for the doctor's attributes stored in the activity simulation table 1015 (i.e., the facility doctor code, facility name, doctor name, and the explanatory variables x01, x02, x03, x04, x05, x06, x07, and x08 corresponding to attributes 1 to 8) stored in the activity simulation table 1015 with the corresponding records stored in the doctor table 1012 (step S130). If there are any discrepancies, the record in the doctor table 1012 is overwritten with the information stored in the activity simulation table 1015 (step S135). Then, the process returns to step S120 and executes step S120 again. If there are no discrepancies, the process proceeds to step S140 without any further action. Because all input / output information is recorded in the input / output history table, even if it is later discovered that the overwritten information was incorrect, it is possible to determine what the original data was.

[0131] In this case, the facility's sales are allocated to the doctors. For example, if only three activities A, B, and C have a p-value of <0.05 among the various sales promotion activities mentioned above, there are only two doctors, and other conditions are as follows: the allocation of sales is calculated as follows:

[0132]

[0133] In step S140, the control unit 104 of the server 10 performs the <Optimization Sales Action Calculation Process>. Prior to this, the control unit 104 of the server 10 first reads out the regression coefficients and the like stored in the product table 1013 and corresponding to the product numbers in the activity simulation table 1015, and calculates the regression coefficients and the like as explanatory variables corresponding to attributes 1 to 8 in the activity simulation table 1015, such as x01, x02, x03, x04, x05, x06, x07, and x08, as well as the explanatory variables corresponding to activities 1 to 13, such as x09, x10, x11, x12, x13, x14, x15, x16, and x17. The same product numbers are multiplied by β01, β02, β03, β04, β05, β06, β07, β08, β09, β10, β11, β12, β13, β14, β15, β16, β17, β18, β19, β20, β21 stored in product table 1013, and the use or prescription probability "before" p0 is calculated using a logit function and stored in the corresponding column of activity simulation table 1015. The above registration date and time a0 is also stored.

[0134] Furthermore, in step S140, the system calculates and records the "after" usage or prescription probability p1, which is the target probability that the target product will be used or prescribed to the doctor after the <Optimized Sales Action Calculation Process> is performed, and the combination of x values ​​related to the activities to achieve that probability. That is, while x01, x02, x03, x04, x05, x06, x07, and x08, which are explanatory variables corresponding to attributes 1 to 8 identifying the doctor stored in the activity simulation table 1015, remain fixed, the values ​​of x09, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20, and x21, which are explanatory variables corresponding to activities 1 to 13, are opened, and the combination of x09 to x21 that maximizes the "after" usage or prescription probability p1 is calculated using the logit function described above, etc. This calculated value is stored in the input / output history table, and at the same time, in step S150, the names of three activities with the highest contribution, the use or prescription probability "after" p1, and the calculation date and time are stored in the columns of use or prescription probability "after" p1, R1, R2, R3, and registration date and time a1 of the activity simulation table 1015.

[0135] In step S160, the control unit 104 of the server 10 displays on the monitor display via the output unit 1043 the use or prescription probability “after” p1, which is the maximum probability that the target product will be used or prescribed by the doctor after the <optimized sales action calculation process> is performed, as well as the combination of x values ​​related to the activities required to achieve this probability and the names of the three activities with the greatest contribution.

[0136] The results showed that even if the currently planned activities of eight interview visits, 12 email awareness campaigns, and two mailings of information were carried out, the probability that new drug P would be adopted and prescribed by Doctor D, whom Company A's MR was trying to approach, would only be 44%. However, if 19 interview visits, 15 email awareness campaigns, or five mailings of information were carried out, the probability that new drug P would be adopted and prescribed by Doctor D would rise to 83%, and the above three activities would be the activities that would contribute greatly to this. On the other hand, for Doctor F, who the MR from Company A is also trying to approach, even if the currently planned activities of 11 home visits, 15 email awareness campaigns, and 7 mailings of materials are carried out, the probability that Doctor D will adopt and prescribe new drug P is only 21%. Even if the activities are maximized to 29 home visits, 29 email awareness campaigns, and 13 mailings of materials, which would achieve the highest predicted probability, the probability that Doctor D will adopt and prescribe new drug P is only increased to 26%.

[0137] Upon seeing this, the MR at Company A writes the details into an Excel file and decides to prioritize activities with Doctor D over Doctor F.

[0138] In addition, the MR manager of Company A wants to know whether each of the sales campaigns that have been implemented this fiscal year has been effective and how efficient each campaign's approach has been, so he decides to carry out the ROI analysis provided by the present invention.

[0139] Specifically, to conduct a campaign ROI analysis, the medical sales support information processing device of the present invention acquires information on sales results for the current and previous years, the total number of campaigns implemented, and the investment amount for each campaign, and after carrying out the procedure of the ROI analysis flowchart, it is possible to display the investment efficiency for each campaign. According to this, it is clear that out of the total ROI of 43.6%, the ROI of Campaign 3 was 217% efficient, but Campaign 1 was 38%, which was below the total ROI value, and Campaign 2 was only 23%, dragging down the overall ROI.

[0140] Therefore, the MR manager at Company A decides to focus on campaign 3 from now on, cancel campaign 2, and run campaign 1 for another year to see how the ROI changes.

[0141] At the very least, by canceling Campaign 2, which is inefficient for both the company and its customers, it appears possible to reduce investment costs.

[0142] <Embodiment 2> Company B is a pharmaceutical company that developed a new anti-allergy drug, AL, for the treatment of hay fever and launched it one and a half years ago. Due to the increasing number of patients and subtle changes in allergens year by year, new anti-allergy drugs for the treatment of hay fever are being launched by various companies almost every year. Sales of long-standing treatments are declining, making it important to switch to new drugs. Company B's medical representatives and wholesalers have not been fully engaged in sales activities, such as visiting doctors at hospitals and other facilities. However, a competitor, Company C, launched an anti-allergy drug with very similar efficacy and mechanism of action three years ago as an anti-allergy drug. This anti-allergy drug still has a low market share of less than 10%, and Company B would like to target the same market. While the medical sales support information processing device of the present invention has only a small amount of registered activity data for the company's new drug, AL, it has accumulated a considerable amount of data on anti-allergy drugs for the treatment of hay fever, including anti-allergy drugs. Therefore, the present invention will be utilized.

[0143] As described above, data on the attributes of each hospital facility and its affiliated physicians, as well as sales activities and results (usage or prescription) for anticoagulants and other products similar to the new drug AL since their launch, are registered in advance by the administrator of the medical sales support information processing device of the present invention. Each time this information becomes available in the past, in step S100 of the medical sales support information processing device of the present invention, the control unit 104 of the server 10 acquires information for the doctor table 1012 and the product table 1013 from the management information processing terminal 15 and stores it in each column of the doctor table 1012 and the product table 1013, providing sufficient storage information as initial values. Data on anticoagulants and other similar drugs similar to the new drug AL is then integrated in preprocessing and registered as initial values ​​with new similar product numbers.

[0144] Company B's MR will compile in an Excel A form as much information as they know about the attributes of each hospital facility and doctor that they are planning to approach regarding the new drug AL, as well as the planned sales activities that they are planning to carry out.

[0145] In the medical sales support information processing device of the present invention, in step S110, the control unit 104 of the server 10 acquires information for these activity simulation tables 1015 and stores it in each column required to perform the <optimized sales action calculation process> of the activity simulation table 1015.

[0146] Specifically, the facility doctor code, facility name, doctor name, attributes (gender, age, affiliated academic society, etc.), planned activities 1 to 13 (future sales activities such as face-to-face interviews, online interviews, email awareness campaigns, and email newsletter distribution), product names, product numbers, other affiliation information that have been stored in advance on the server, and regression coefficients for logistic regression analysis that have been calculated in advance using another technology and appropriately preprocessed are obtained.

[0147] In step S120, the control unit 104 of the server 10 compares the information identifying the doctor stored in the activity simulation table 1015 with explanatory variables regarding the doctor's attributes and performs any necessary updates based on the called application program 1011.

[0148] In step S140, the <Optimization Sales Action Calculation Process> is performed. Prior to this, the control unit 104 of the server 10 first reads the regression coefficients and the like corresponding to the aforementioned similar product numbers in the activity simulation table 1015 stored in the product table 1013, and multiplies the explanatory variables corresponding to attributes 1 to 8 in the activity simulation table 1015 and the explanatory variables corresponding to activities 1 to 13 by β01, β02, β03, β04, β05, β06, β07, β08, β09, β10, β11, β12, β13, β14, β15, β16, β17, β18, β19, β20, and β21 stored in the product table 1013, each for the same product number, and calculates the usage or prescription probability "before" p0 using a logit function in the same manner as in embodiment 1, and stores this in the corresponding column of the activity simulation table 1015. The above registration date and time a0 are also stored.

[0149] Furthermore, in step S140, the combination of the use or prescription probability "after" p1, which is the maximum probability that the target product will be used or prescribed to the doctor after the <optimized sales action calculation process> is performed, and the value of x related to the activity to achieve it is calculated and recorded.

[0150] In step S160, the control unit 104 of the server 10 displays on the monitor display via the output unit 1043 the use or prescription probability “after” p1, which is the target probability that the target product will be used or prescribed to the doctor after the <optimized sales action calculation process> is performed, as well as the combination of x values ​​related to the activities to achieve this and the names of the activities with the highest contribution.

[0151] According to the information, even if the currently planned activities of three online interviews, three email awareness campaigns, and two mailings of materials are carried out for Doctor G, who the MR from Company B is trying to approach, the probability that Doctor G will adopt and prescribe the new drug AL will only be 38%, but if 22 online interviews, 17 email awareness campaigns, or seven mailings of materials are carried out, the probability that Doctor G will adopt and prescribe the new drug AL will rise to 89%, and the above three activities will be activities that will greatly contribute to this. On the other hand, for Doctor H, who is also being approached by Company B's MR, even if the currently planned activities of 9 interview visits, 13 email awareness campaigns, and 9 mailings of materials are carried out, the probability that Doctor D will adopt and prescribe the new drug AL is only expected to be 29%, and even if the activities are maximized to 39 interview visits, 25 email awareness campaigns, or 9 mailings of materials, which would achieve the highest predicted probability, the probability that Doctor G will adopt and prescribe the new drug AL is only expected to increase to 31%.

[0152] Furthermore, in order to search for doctors who are likely to be able to take an efficient approach instead of Doctor H and who have high similarity in attributes with Doctor G, who will have a higher prescription probability based on the above results, the MR of Company B clicks a button on the screen for obtaining a similarity analysis list from the customer information processing terminal 30 of the medical sales support information processing device of the present invention. The medical sales support information processing device of the present invention obtains this information, executes the flowchart of <Cosine Similarity Analysis> (Figure 10), and then displays a list of doctors who are highly similar to Doctor G. Note that recommended activities and usage or prescription probability can also be calculated for these highly similar doctors, just as for Doctor G.

[0153] The MR from Company B will write the above information into an Excel file and immediately begin sales activities to Doctor G and several other doctors who are highly similar to Doctor G.

[0154] Third Embodiment Company T is a medical device manufacturer and distributor that developed and launched a new digital microscope five years ago, combining the functions of a compact camera with high image quality and 8K resolution with the functions of a microscope. The company sells the device to physicians and dentists (hereinafter collectively referred to as "physicians"). While sales were steady initially, sales performance over the past year has been disappointing. However, the launch of a new product is still some way off, and the company hopes to recover sales by narrowing down the target audience and enhancing sales activities. The medical sales support information processing device of the present invention has accumulated a fair amount of registered data on the activities and results of Company T's digital microscope, so the company will begin using the present invention.

[0155] As described above, the attributes of each hospital facility and its doctors, as well as sales activities and results (usage or prescription) of Company T's digital microscope since its launch, have been registered in advance by the administrator of the medical sales support information processing device of the present invention. In the medical sales support information processing device of the present invention, in step S100 of Fig. 9, whenever this information became available in the past, the control unit 104 of the server 10 acquires information for the doctor table 1012 and the product table 1013 from the management information processing terminal 15 and stores it in each column of the doctor table 1012 and the product table 1013.

[0156] Company T's MR will compile, in an Excel A form, as much as they know about the attributes of each hospital facility and doctor that they are going to approach about Company T's digital microscope, as well as the planned sales activities that they are going to carry out.

[0157] In the medical sales support information processing device of the present invention, in step S110, the control unit 104 of the server 10 acquires information for the activity simulation table 1015 uploaded by the sales representative of Company T from the management information processing terminal and the server 10, and stores the information in each column of the activity simulation table 1015 for carrying out the <optimized sales action calculation process>.

[0158] Specifically, the facility doctor code, facility name, doctor name, attributes (gender, age, affiliated academic society, etc.), planned activities 1 to 13 (future sales activities such as face-to-face interviews, online interviews, email awareness campaigns, and email newsletter distribution), product names, product numbers, other affiliation information that have been stored in advance on the server, and regression coefficients for logistic regression analysis that have been calculated in advance using another technology and appropriately preprocessed are obtained.

[0159] In step S120, the control unit 104 of the server 10 compares the information identifying the doctor stored in the activity simulation table 1015 with explanatory variables regarding the doctor's attributes and performs any necessary updates based on the called application program 1011.

[0160] In step S140, the <Optimization Sales Action Calculation Process> is performed. Prior to this, the control unit 104 of the server 10 first reads the regression coefficients and the like corresponding to the aforementioned similar product numbers in the activity simulation table 1015 stored in the product table 1013, and multiplies the explanatory variables corresponding to attributes 1 to 8 in the activity simulation table 1015 and the explanatory variables corresponding to activities 1 to 13 by β01, β02, β03, β04, β05, β06, β07, β08, β09, β10, β11, β12, β13, β14, β15, β16, β17, β18, β19, β20, and β21 stored in the product table 1013, each with the same product number, and calculates the usage or prescription probability "before" p0 using a logit function in the same way as in embodiment 1, and stores the calculated value in the corresponding column of the activity simulation table 1015. The above registration date and time a0 are also stored.

[0161] Furthermore, in step S140, the combination of the usage or prescription probability "after" p1, which is the target probability that the target product will be used by the doctor after the <optimized sales action calculation process> is performed, and the value of x related to the activity to achieve it is calculated and recorded.

[0162] In step S160, the control unit 104 of the server 10 displays on the monitor display via the output unit 1043 the use or prescription probability "after" p1, which is the maximum probability that the target product will be used by the doctor after the <optimized sales action calculation process> is performed, as well as the combination of x values ​​related to the activities required to achieve this and the names of the seven activities with the highest contribution (calculated in step 150).

[0163] According to the information, even if the currently planned activities, namely three face-to-face interviews, three email promotions, and two mailings of materials, are carried out for Doctor J, who the sales representative from Company T is trying to approach, the probability that Doctor J will adopt and prescribe Company T's digital microscope will only be 44%; however, if six online interviews, seven email promotions, or six mailings of materials are carried out, the probability that Doctor J will adopt and use Company T's digital microscope will rise to 66%, and the above three activities will be the activities that will contribute greatly to this.

[0164] On the other hand, for Dr. K, who the sales representative from Company T is also trying to approach, even if the currently planned activities of 11 online interviews, 15 email promotions, and 9 mailings of materials are carried out, the probability that Dr. K will adopt and prescribe Company T's digital microscope is only expected to be 23%. Even if the activities are further increased to 49 in-person interviews, 29 email promotions, and 19 mailings of materials, which would achieve the maximum predicted probability, the probability that Dr. K will adopt Company T's digital microscope is only expected to increase to 25%.

[0165] Company T's sales representatives and / or sales managers can write the above information into an Excel file and immediately put into practice focused sales activities aimed at the selected doctors and new sales strategies devised based on the improvement measures.

[0166] <Actions and Effects> The present invention provides the following actions and effects.

[0167] 1. Reduction of sales costs: Costs can be reduced by eliminating unnecessary sales activities and focusing on effective activities. 2. Optimization of sales resources: By creating optimal sales strategies for each physician, it is possible to efficiently allocate limited sales personnel. 3. Improvement of performance prediction: A data-based probabilistic approach improves the accuracy of sales performance predictions. 4. Strengthening relationships with physicians: It becomes possible to approach physicians in a way that meets their needs, building better relationships.

[0168] <Supplementary Note 1> An acquisition unit that acquires data including the attributes of each doctor, the number of sales promotion activities including face-to-face visits to doctors, web interviews, telephone interviews, holding seminars, conducting hands-on sessions, sending materials, email distribution, web seminars, or sending email newsletters, the history of email openings, web lecture viewings, logging in to the doctor portal site, playing back web detailing for medical professionals, or viewing case introduction content, schedules, the results of sales promotion activities, or the attributes or evaluations of sales representatives toward doctors; a storage unit that stores programs and data; and calling up the programs and data, and in order to reflect the differences in prescription activities or diversity of activities among multiple doctors within a facility, calculating correlation coefficients between the sales of each facility and only sales promotion activities for which the realized value p of the probability when a null hypothesis in a hypothesis test is established in relation to at least sales is judged to be at a significant level, being less than a predetermined threshold, normalizing the correlation coefficients to calculate relative influence, calculating a score by multiplying each correlation coefficient for each sales promotion activity by a weighting factor for the amount of activity for each doctor, and calculating a sales allocation value for the amount of facility sales based on the scores, a calculation unit that uses a statistical method including penalized logistic regression or logistic regression analysis to construct a multivariate logistic regression model using multiple explanatory variables included in data, fixes explanatory variables related to doctor attributes, and assumes that at least one of explanatory variables related to sales promotion activities or explanatory variables related to sales representatives is an adjustable variable, calculates an optimal combination of adjustable variables necessary to achieve a predetermined target prescription probability, and calculates, for each doctor, the minimum amount of sales promotion activities or the target level of explanatory variables related to sales representatives necessary to achieve the target prescription probability; and an output unit that outputs the calculation results.

[0169] <Supplementary Note 2> The medical sales support information processing device according to Supplementary Note 1, wherein the threshold value is 0.05.

[0170] <Supplementary Note 3> The medical sales support information processing device according to Supplementary Note 1 or 2, wherein the statistical method includes the use of a machine learning model.

[0171] <Supplementary Note 4> The medical sales support information processing device according to any one of Supplementary Notes 1 to 3, wherein the calculation of sales promotion activities is performed using a linear or non-linear prediction model.

[0172] <Supplementary Note 5> The medical sales support information processing device according to any one of Supplementary Notes 1 to 4, wherein the calculation unit further performs sales promotion activities for doctors and data-based sales promotion activities using ROI analysis, which quantifies the relationship between investment amount and results, applies either a statistical method, a machine learning model, or a regression analysis method, calculates a β value for an increase in sales for each sales promotion activity by regression analysis, sums up the individual β values ​​to determine the relative impact, and calculates the ROI by apportioning the increase.

[0173] <Supplementary Note 6> A medical sales support information processing device according to any one of Supplementary Notes 1 to 5, wherein the calculation unit further calculates similarities between doctors using cosine similarity analysis for doctors with a desired range of usage or prescription probability based on the doctor's attributes, classifies the doctors based on the calculated similarities, and uses the calculated similarities to list the names of doctors within the desired similarity range.

[0174] <Supplementary Note 7> The medical sales support information processing device according to any one of Supplementary Notes 1 to 6, wherein the sales promotion activity includes calculation of the type of sales activity and the amount of increase or decrease.

[0175] <Supplementary Note 8> The medical sales support information processing device according to any one of Supplementary Notes 1 to 7, wherein the output unit can write out an electronic file or display it on a screen.

[0176] <Supplementary Note 9> The medical sales support information processing device according to any one of Supplementary Notes 1 to 7, wherein the output unit is further capable of writing out or displaying on a screen an electronic file including recommended tactics or recommended actions for each sales activity.

[0177] <Supplementary Note 10> The medical sales support information processing device according to any one of Supplementary Notes 1 to 9, wherein the medical sales are sales of pharmaceuticals.

[0178] <Supplementary Note 11> The medical sales support information processing device according to any one of Supplementary Notes 1 to 9, wherein the medical sales are sales of medical equipment.

[0179] <Supplementary Note 12> A computer program for causing the acquisition unit, storage unit, calculation unit, or output unit according to any one of Supplementary Notes 1 to 11 to perform an operation.

[0180] <Supplementary Note 13> A step of acquiring data including the attributes of each doctor, the number of sales promotion activities including face-to-face visits to doctors, web interviews, telephone interviews, holding seminars, conducting hands-on sessions, sending materials, email distribution, web seminars, or sending email newsletters, the history of email opening, web lecture viewing, logging in to a doctor portal site, playing back web detailing for medical professionals, or viewing case introduction content, schedules, the results of sales promotion activities, or the attributes or evaluations of sales representatives toward doctors; a step of storing a program and data; and a step of calling up the program and data, calculating correlation coefficients between the sales of each facility and only sales promotion activities for which the realized value p of the probability when a null hypothesis of a hypothesis test is made in relation to at least the sales is judged to be at a significant level, being less than a predetermined threshold, in order to reflect the differences in prescription activities or diversity of activities among multiple doctors within the facility, normalizing the correlation coefficients to calculate relative influences, calculating a score by multiplying each correlation coefficient for each sales promotion activity by a weighting factor for the amount of activity for each doctor, and calculating a sales allocation value of the amount of facility sales based on the scores, A medical sales support method comprising the steps of: constructing a multivariate logistic regression model using a plurality of explanatory variables contained in data using a statistical method including penalized logistic regression or logistic regression analysis, fixing explanatory variables relating to doctor attributes among the models, and assuming that at least one of explanatory variables relating to sales promotion activities or explanatory variables relating to sales representatives is an adjustable variable, calculating an optimal combination of adjustable variables necessary to achieve a predetermined target prescription probability, and calculating, for each doctor, the minimum amount of sales promotion activities or the target level of explanatory variables relating to sales representatives necessary to achieve the target prescription probability; and outputting the calculation results.

[0181] <Supplementary Note 14> The medical sales support method according to Supplementary Note 13, wherein the threshold value is 0.05.

[0182] <Supplementary Note 15> The medical sales support method according to Supplementary Note 13 or 14, wherein the statistical method includes the use of a machine learning model.

[0183] <Supplementary Note 16> The medical sales support method according to any one of Supplementary Notes 13 to 15, wherein the calculation of the sales promotion activity is performed using a linear or non-linear prediction model.

[0184] <Supplementary Note 17> The medical sales support method according to any one of Supplementary Note 13 to 16, further comprising the step of performing sales promotion activities for doctors and data-based sales promotion activities using ROI analysis, which quantifies the relationship between investment amount and results, applies either a statistical method, a machine learning model, or a regression analysis method, calculates a β value for an increase in sales for each sales promotion activity by regression analysis, calculates a relative impact by summing the individual β values, and calculates an ROI by apportioning the increase.

[0185] <Appendix 18> The medical sales support method described in any of Appendices 13 to 17 further includes a step of calculating the similarity between doctors using cosine similarity analysis for doctors who have a usage or prescription probability in a desired range based on the doctor's attributes, classifying the doctors based on the calculated similarity, and utilizing it to list the names of doctors in the desired similarity range.

[0186] <Supplementary Note 19> The medical sales support method according to any one of Supplementary Notes 13 to 18, wherein the sales promotion activity includes calculating the type of sales activity and the amount of increase or decrease.

[0187] <Supplementary Note 20> The medical sales support method according to any one of Supplementary Notes 13 to 19, including a step of writing out or displaying an electronic file on a screen.

[0188] <Supplementary Note 21> The medical sales support method according to any one of Supplementary Notes 13 to 19, including a step of writing out or displaying on a screen an electronic file including recommended tactics or recommended actions for each sales activity.

[0189] <Supplementary Note 22> The medical sales support method according to any one of Supplementary Notes 13 to 21, wherein the medical sales are sales of pharmaceuticals.

[0190] <Supplementary Note 23> The medical sales support method according to any one of Supplementary Notes 13 to 21, wherein the medical sales are sales of medical equipment.

[0191] <Supplementary Note 24> A computer including a memory that is a non-transitory computer-readable medium that stores a computer program, and a processor that executes the computer program, wherein the processor stores in the memory data for medical sales support, including attributes of each doctor, the number of sales promotion activities including face-to-face visits to doctors, web interviews, telephone interviews, holding seminars, conducting hands-on sessions, sending materials, email distribution, web seminars, or sending email newsletters, the opening of emails, viewing of web lectures, logging into a doctor portal site, playing back web detailing for medical professionals, or browsing history of case introduction content, schedules, results of sales promotion activities, or attributes or evaluations of sales representatives toward doctors, the processor, after inputting the data, calls up the program and data, and calculates a correlation coefficient between the sales of each facility and only the sales promotion activities for which the realized p-value of the probability when a null hypothesis of a hypothesis test is made in relation to at least the sales is judged to be at a significant level below a predetermined threshold, in order to reflect the differences or diversity of activities among the prescription activities of multiple doctors within the facility, normalizes the correlation coefficient to calculate the relative influence, calculates a score by multiplying each correlation coefficient for each sales promotion activity by the weight of the activity amount for each doctor, and calculates the sales apportionment value of the amount of facility sales based on the score; the processor uses a statistical method including penalized logistic regression or logistic regression analysis to construct a multivariate logistic regression model using multiple explanatory variables contained in the data, fixes explanatory variables related to the doctor's attributes, and assumes that at least one of the explanatory variables related to the sales promotion activity or the explanatory variables related to the sales representative is an adjustable variable, calculates the optimal combination of adjustable variables necessary to achieve a predetermined target prescription probability, and calculates, for each doctor, the target level of the minimum amount of sales promotion activity or the explanatory variable related to the sales representative necessary to achieve the target prescription probability; A computer outputs the calculated recommended sales activities.

[0192] <Supplementary Note 25> The computer according to Supplementary Note 24, wherein the threshold is 0.05.

[0193] <Supplementary Note 26> The computer according to Supplementary Note 24 or 25, wherein the statistical method includes the use of a machine learning model.

[0194] <Supplementary Note 27> The computer according to any one of Supplementary Notes 24 to 26, wherein the calculation of the sales promotion activity is performed using a linear or non-linear prediction model.

[0195] <Supplementary Note 28> The computer according to any one of Supplementary Notes 24 to 27, wherein the calculation unit further performs sales promotion activities for doctors and data-based sales promotion activities using ROI analysis, which quantifies the relationship between investment amount and results, applies either a statistical method, a machine learning model, or a regression analysis method, calculates a β value for an increase in sales for each sales promotion activity by regression analysis, sums the individual β values ​​to determine a relative impact, and calculates an ROI by apportioning the increase.

[0196] <Supplementary Note 29> The computer according to any one of Supplementary Notes 24 to 28, wherein the calculation unit further calculates similarities between doctors having a desired range of usage or prescription probability based on the doctor's attributes using cosine similarity analysis, classifies the doctors based on the calculated similarities, and utilizes them to list the names of doctors within the desired similarity range.

[0197] <Supplementary Note 30> The computer according to any one of Supplementary Notes 24 to 29, wherein the sales promotion activity includes calculation of the type of sales activity and the amount of increase or decrease.

[0198] <Supplementary Note 31> The computer according to any one of Supplementary Notes 24 to 30, wherein the output unit can write out an electronic file or display it on a screen.

[0199] <Supplementary Note 32> The computer according to any one of Supplementary Notes 24 to 30, wherein the output unit is further capable of writing out an electronic file including recommended tactics or recommended actions for each sales activity or displaying it on a screen.

[0200] <Supplementary Note 33> The computer according to any one of Supplementary Notes 24 to 32, wherein the medical sales are sales of pharmaceuticals.

[0201] <Supplementary Note 34> The computer according to any one of Supplementary Notes 24 to 32, wherein the medical sales are sales of medical equipment.

[0202] <Supplementary Note 35> A computer for medical sales support is caused to acquire data including the attributes of each doctor, the number of sales promotion activities including face-to-face visits to doctors, web interviews, telephone interviews, holding seminars, conducting hands-on sessions, sending materials, email distribution, web seminars, or sending email newsletters, the opening of emails, viewing of web lectures, logging into a doctor portal site, playing back web detailing for medical professionals, or browsing history of case introduction content, schedules, results of sales promotion activities, or the attributes or evaluations of sales representatives toward doctors, store the program and data, and call up the program and data. a statistical method for supporting medical products, comprising: calculating a correlation coefficient between the sales of each facility and only those sales promotion activities for which the realized value p of the probability when a null hypothesis of a hypothesis test is established in relation to at least the sales is determined to be at a significant level and is less than a predetermined threshold, in order to reflect the differences or diversity of the prescription activities of multiple doctors within the facility; normalizing the correlation coefficient to calculate a relative influence; calculating a score by multiplying each correlation coefficient for each sales promotion activity by a weighting factor for the amount of activity for each doctor; and calculating a sales apportionment value of the amount of facility sales based on the score; constructing a multivariate logistic regression model using multiple explanatory variables included in the data using a statistical method including penalized logistic regression or logistic regression analysis, fixing explanatory variables related to the attributes of the doctors among the correlation coefficients, and assuming that at least one of the explanatory variables related to the sales promotion activities or the explanatory variables related to the sales representatives is an adjustable variable, thereby calculating an optimal combination of adjustable variables required to achieve a predetermined target prescription probability, thereby calculating, for each doctor, the minimum amount of sales promotion activities or the target level of the explanatory variables related to the sales representatives required to achieve the target prescription probability; and outputting the calculation results.

[0203] <Supplementary Note 36> The medical sales support method according to Supplementary Note 35, wherein the threshold value is 0.05.

[0204] <Supplementary Note 37> The medical sales support method according to Supplementary Note 35 or 36, wherein the statistical method includes the use of a machine learning model.

[0205] <Supplementary Note 38> The medical sales support method according to any one of Supplementary Notes 35 to 37, wherein the calculation of the sales promotion activity is performed using a linear or non-linear prediction model.

[0206] <Supplementary Note 39> The medical sales support method according to any one of Supplementary Notes 35 to 38, further comprising the step of performing sales promotion activities for doctors and data-based sales promotion activities using ROI analysis, which quantifies the relationship between investment amount and results, applies either a statistical method, a machine learning model, or a regression analysis method, calculates a β value for an increase in sales for each sales promotion activity by regression analysis, calculates a relative impact by summing the individual β values, and calculates an ROI by apportioning the increase.

[0207] <Appendix 40> The medical sales support method described in any of Appendices 35 to 39 further includes a step of calculating similarities between doctors using cosine similarity analysis for doctors who have a usage or prescription probability in a desired range based on the doctor's attributes, classifying the doctors based on the calculated similarities, and utilizing the calculated similarities to list the names of doctors in the desired similarity range.

[0208] <Supplementary Note 41> The medical sales support method according to any one of Supplementary Notes 35 to 40, wherein the sales promotion activity includes calculating the type of sales activity and the amount of increase or decrease.

[0209] <Supplementary Note 42> The medical sales support method according to any one of Supplementary Notes 35 to 41, including the step of writing out or displaying an electronic file on a screen.

[0210] <Supplementary Note 43> The medical sales support method according to any one of Supplementary Notes 35 to 41, including a step of writing out or displaying on a screen an electronic file including recommended tactics or recommended actions for each sales activity.

[0211] <Supplementary Note 44> The medical sales support method according to any one of Supplementary Notes 35 to 43, wherein the medical sales are sales of pharmaceuticals.

[0212] <Supplementary Note 45> The medical sales support method according to any one of Supplementary Notes 35 to 43, wherein the medical sales are sales of medical equipment.

[0213] <Supplementary Note 46> In order to support medical sales, a processor stores in a memory data including the attributes of each doctor, the number of sales promotion activities including face-to-face visits to doctors, web interviews, telephone interviews, holding seminars, conducting hands-on sessions, sending materials, email distribution, web seminars, or sending email newsletters, the opening of emails, viewing of web lectures, logging into a doctor portal site, playing back web detailing for medical professionals, or browsing history of case introduction content, schedules, results of sales promotion activities, or the attributes or evaluations of sales representatives toward doctors, the processor, after inputting the data, calls up the program and data, and calculates a correlation coefficient between the sales of each facility and only the sales promotion activities for which the p-value of the realized probability when a null hypothesis of a hypothesis test is made in relation to at least the sales is judged to be at a significant level below a predetermined threshold in order to reflect the differences or diversity of activities among the prescription activities of multiple doctors within the facility, normalizes the correlation coefficient to calculate the relative influence, calculates a score by multiplying each correlation coefficient for each sales promotion activity by the weight of the activity amount for each doctor, and calculates the sales apportionment value of the amount of sales of the facility based on the score; the processor uses a statistical method including penalized logistic regression or logistic regression analysis to construct a multivariate logistic regression model using multiple explanatory variables included in the data, fixes explanatory variables related to the attributes of doctors, and assumes that at least one of the explanatory variables related to the sales promotion activities or the explanatory variables related to the sales representatives is an adjustable variable, calculates an optimal combination of adjustable variables necessary to achieve a predetermined target prescription probability, and calculates, for each doctor, the target level of the minimum amount of sales promotion activities or the explanatory variables related to the sales representatives necessary to achieve the target prescription probability, or outputs the calculation results. One or more non-transitory readable media storing configured instructions.

[0214] As a result of the above, the present invention can support pharmaceutical companies in realizing efficient sales activities, thereby enabling them to reduce sales costs and optimize pharmaceutical costs, thereby contributing to curbing price increases in pharmaceuticals and other products, and contributing to improving the social situation in which lives that could be saved are not being saved.

[0215] Although the embodiments of the present invention have been disclosed above, the present invention is not limited to these and can be modified as appropriate within the scope of the technical idea of ​​the invention.

[0216] This invention can be applied not only to sales activities in the pharmaceutical industry, but also to insurance sales, medical equipment sales, and other industries that require face-to-face sales, and can be used as technology to support the formulation of optimal sales strategies based on data.

[0217] REFERENCE SIGNS LIST 1 Medical sales support information processing device 10 Server 15 Management information processing terminal 20 Customer information processing terminal 30 Customer information processing terminal

Claims

1. An acquisition unit that acquires data including the attributes of each physician, the number of sales promotion activities including face-to-face visits, web interviews, telephone interviews, seminars, hands-on training, material distribution, email distribution, web seminars, or email newsletters sent to the said physician, history and schedule of email opens, viewing of web lectures, logins to physician portal sites, playback of web detailing for healthcare professionals, or viewing of case study content, the results of the said sales promotion activities, or the attributes or evaluations of sales representatives to the said physician, A storage unit for storing the program and the aforementioned data, The program and the data are called, In order to reflect the differences or diversity of prescribing activities by multiple physicians within a facility, the correlation coefficients for each of the sales promotion activities that are judged to be at a significance level with respect to the sales of each facility and the p-value of the realized probability when the null hypothesis of the hypothesis test is formulated are calculated, the relative impact is calculated by normalizing the correlation coefficients for each sales promotion activity, a score is calculated by multiplying each of the correlation coefficients for each of the sales promotion activities by the weight of the amount of activity for each of the physicians, and the sales apportionment value of the facility sales amount is calculated based on the score. A calculation unit that constructs a multivariate logistic regression model using multiple explanatory variables included in the data, employing statistical methods including penalized logistic regression or logistic regression analysis, fixing the explanatory variable relating to the physician's attributes, and assuming that at least one of the explanatory variables relating to sales promotion activities or sales representatives is an adjustable variable, calculates the optimal combination of the adjustable variables necessary to achieve a pre-set target prescription probability, and calculates for each physician the minimum amount of sales promotion activity or target level of the explanatory variable relating to sales representatives necessary to achieve the target prescription probability; An output unit that outputs the calculation result, Medical sales support information processing device, including

2. The medical sales support information processing device according to claim 1, wherein the threshold is 0.

05.

3. The medical sales support information processing device according to claim 1, wherein the statistical method includes the use of a machine learning model.

4. The medical sales support information processing device according to claim 1, wherein the calculation of the sales promotion activities is performed using a linear or nonlinear predictive model.

5. The medical sales support information processing device according to claim 1, wherein the calculation unit further uses ROI analysis to calculate ROI for the sales promotion activities for physicians and the sales promotion activities based on the data, by quantifying the relationship between investment amount and results, applying either a statistical method, a machine learning model, or a regression analysis method, calculating a β value for the increase in sales for each of the sales promotion activities by regression analysis, summing the individual β values ​​to obtain a relative impact, and apportioning the increase.

6. The medical sales support information processing device according to claim 1, wherein the calculation unit further calculates the similarity between physicians who have a desired range of usage or prescription probability based on the attributes of the physicians using cosine similarity analysis, classifies the physicians based on the calculated similarity, and utilizes this for listing physician names within the desired similarity range.

7. The medical sales support information processing device according to claim 1, wherein the sales promotion activities include calculating the types and increases / decreases of sales activities.

8. The medical sales support information processing device according to claim 1, wherein the output unit can write out or display an electronic file on a screen.

9. The medical sales support information processing device according to claim 1, wherein the output unit can further write out or display on a screen an electronic file containing recommended tactics or recommended actions for each sales activity.

10. The medical sales support information processing device according to claim 1, wherein the medical sales are sales of pharmaceuticals.

11. The medical sales support information processing device according to claim 1, wherein the medical sales are sales of medical devices.

12. A computer program that causes the acquisition unit, storage unit, calculation unit, or output unit described in any one of claims 1 to 11 to perform its operation.

13. Steps to acquire data including the attributes of each physician, the number of sales promotion activities conducted with the said physician, including face-to-face visits, web interviews, telephone interviews, seminars, hands-on training, material distribution, email distribution, web seminars, or email newsletters, history and schedule of email opens, viewing of web lectures, logins to physician portal sites, playback of web detailing for healthcare professionals, or viewing of case study content, the results of the said sales promotion activities, or the attributes or evaluations of sales representatives with respect to the said physicians, A step of storing the program and the data, The program and the data are called, In order to reflect the differences or diversity of prescribing activities by multiple physicians within a facility, the correlation coefficients for each of the sales promotion activities that are judged to be at a significance level with respect to the sales of each facility and the p-value of the realized probability when the null hypothesis of the hypothesis test is formulated are calculated, the relative impact is calculated by normalizing the correlation coefficients for each sales promotion activity, a score is calculated by multiplying each of the correlation coefficients for each of the sales promotion activities by the weight of the amount of activity for each of the physicians, and the sales apportionment value of the facility sales amount is calculated based on the score. The steps include: constructing a multivariate logistic regression model using multiple explanatory variables included in the data, employing statistical methods including penalized logistic regression or logistic regression analysis; fixing the explanatory variable relating to the physician's attributes; assuming that at least one of the explanatory variables relating to sales promotion activities or sales representatives is an adjustable variable; calculating the optimal combination of the adjustable variables necessary to achieve a pre-set target prescription probability; and calculating the minimum amount of sales promotion activity or target level of the explanatory variable relating to sales representatives necessary to achieve the target prescription probability for each physician; The steps include outputting the calculation result, Medical sales support methods including

14. The medical sales support method according to claim 13, wherein the threshold is 0.

05.

15. The medical sales support method according to claim 13, wherein the statistical method includes the use of a machine learning model.

16. The medical sales support method according to claim 13, wherein the calculation of the sales promotion activities is performed using a linear or nonlinear predictive model.

17. Furthermore, the medical sales support method according to claim 13, further comprising the step of using ROI analysis to quantify the relationship between investment amount and results, apply one of the following to the sales promotion activities for physicians and the sales promotion activities based on the data, calculate the ROI for each of the sales promotion activities by regression analysis, calculate the β value for the increase in sales by the increase in sales, sum up the individual β values ​​to obtain the relative impact, and calculate the ROI by apportioning the increase.

18. Furthermore, the medical sales support method according to claim 13, further comprising the steps of calculating the similarity between physicians using cosine similarity analysis for physicians who have a desired range of usage or prescription probability based on the attributes of the physicians, classifying the physicians based on the calculated similarity, and utilizing this for listing the names of physicians within the desired similarity range.

19. The medical sales support method according to claim 13, wherein the sales promotion activities include calculating the types and increases / decreases of sales activities.

20. A medical sales support method according to claim 13, comprising the step of exporting or displaying an electronic file on a screen.

21. The medical sales support method according to claim 13, further comprising the step of exporting or displaying an electronic file containing recommended tactics or recommended actions for each of the aforementioned sales activities.

22. The medical sales support method according to any one of claims 13 to 21, wherein the medical sales are sales of pharmaceuticals.

23. The medical sales support method according to any one of claims 13 to 21, wherein the medical sales are sales of medical devices.

24. A computer comprising memory, which is a non-temporary computer-readable medium for storing computer programs, and a processor for executing said computer programs, The processor stores in memory data including the attributes of each physician, the number of sales promotion activities conducted with the physician, such as face-to-face visits, web interviews, telephone interviews, seminars, hands-on training, document distribution, email distribution, web seminars, or email newsletters, the history and schedule of email opens, viewing of web lectures, logins to physician portal sites, playback of web detailing for healthcare professionals, or viewing of case study content, the results of the sales promotion activities, or the attributes or evaluations of sales representatives with respect to the physicians. After the data is input, the processor retrieves the program and the data, and in order to reflect the differences or diversity of prescribing activities by multiple physicians within the facility, it calculates the correlation coefficient for each of the sales promotion activities between each facility and at least the sales of the facility, and calculates the relative impact by normalizing the correlation coefficient and calculating the relative impact by normalizing the correlation coefficient for each sales promotion activity multiplied by the weight of the activity amount for each physician, and calculates the sales apportionment value of the facility sales amount based on the score. The processor constructs a multivariate logistic regression model using multiple explanatory variables included in the data, employing statistical methods including penaltyed logistic regression or logistic regression analysis, fixing the explanatory variable relating to the physician's attributes, and assuming that at least one of the explanatory variables relating to sales promotion activities or sales representatives is an adjustable variable, calculates the optimal combination of the adjustable variables necessary to achieve a pre-set target prescription probability, and for each physician, calculates the minimum amount of sales promotion activity or target level of the explanatory variable relating to sales representatives necessary to achieve the target prescription probability. A computer that outputs the calculated recommended sales activities.

25. The computer according to claim 24, characterized in that the threshold value is 0.

05.

26. The computer according to claim 24, characterized in that the statistical method includes the use of a machine learning model.

27. The computer according to claim 24, characterized in that it performs the calculation of the sales promotion activities using a linear or nonlinear prediction model.

28. The computer according to claim 24, further comprising the calculation unit, which quantifies the relationship between investment amount and results, applies either a statistical method, a machine learning model, or a regression analysis method to the sales promotion activities for physicians and the sales promotion activities based on the data, calculates a β value for the increase in sales for each of the sales promotion activities by regression analysis, sums the individual β values ​​to determine the relative impact, and calculates ROI by apportioning the increase.

29. The computer according to claim 24, further characterized in that the calculation unit calculates the similarity between physicians using cosine similarity analysis for physicians who have a desired range of usage or prescription probability based on the attributes of the physicians, classifies the physicians based on the calculated similarity, and utilizes this for listing physician names within the desired similarity range.

30. The computer according to claim 24, characterized in that the aforementioned sales promotion activities include calculating the types and increases / decreases of sales activities.

31. The computer according to claim 24, wherein the output unit can write out or display electronic files on a screen.

32. The computer according to claim 24, wherein the output unit can further write or display an electronic file containing recommended tactics or recommended actions for each sales activity.

33. The computer according to any one of claims 24 to 32, wherein the aforementioned medical sale is a sale of pharmaceuticals.

34. The computer according to any one of claims 24 to 32, wherein the aforementioned medical sale is a sale of medical devices.

35. A computer used to support medical sales acquires data including the attributes of each physician, the number of sales promotion activities conducted with the physician (including face-to-face visits, web interviews, telephone interviews, seminars, hands-on sessions, document distribution, email distribution, web seminars, or email newsletters), email open rates, web lecture viewing rates, physician portal site login rates, playback of web detailing for healthcare professionals, or viewing rates of case study content, the results of the aforementioned sales promotion activities, or the attributes or evaluations of sales representatives to the physicians. The program and the aforementioned data are stored, The program and the data are called, In order to reflect the differences or diversity of prescribing activities by multiple physicians within a facility, the correlation coefficients for each of the sales promotion activities that are judged to be at a significance level with respect to the sales of each facility and the p-value of the realized probability when the null hypothesis of the hypothesis test is formulated are calculated, the relative impact is calculated by normalizing the correlation coefficients for each of the sales promotion activities and multiplying them by the weight of the amount of activity for each of the physicians is calculated, and the sales apportionment value of the facility's sales amount is calculated based on the score. Using statistical methods including penalized logistic regression or logistic regression analysis, a multivariate logistic regression model is constructed using multiple explanatory variables included in the data, the explanatory variable relating to the physician's attributes is fixed, and at least one of the explanatory variables relating to sales promotion activities or sales representatives is assumed to be an adjustable variable. By calculating the optimal combination of the adjustable variables necessary to achieve a pre-set target prescription probability, the minimum amount of sales promotion activity or target level of the explanatory variable relating to sales representatives necessary to achieve the target prescription probability is calculated for each physician. A medical sales support method that includes outputting the aforementioned calculation results.

36. The medical sales support method according to claim 35, wherein the threshold is 0.

05.

37. The medical sales support method according to claim 35, wherein the statistical method includes the use of a machine learning model.

38. The medical sales support method according to claim 35, wherein the calculation of the sales promotion activities is performed using a linear or nonlinear predictive model.

39. Furthermore, the medical sales support method according to claim 35, further comprising the step of using ROI analysis to quantify the relationship between investment amount and results, apply either a statistical method, a machine learning model, or a regression analysis method to calculate a β value for the increase in sales for each of the sales promotion activities by regression analysis, sum up the individual β values ​​to determine the relative impact, and calculate ROI by apportioning the increase.

40. Furthermore, the medical sales support method according to claim 35, further comprising the steps of calculating the similarity between physicians using cosine similarity analysis for physicians who have a desired range of usage or prescription probability based on the attributes of the physicians, classifying the physicians based on the calculated similarity, and utilizing this for listing the names of physicians within the desired similarity range.

41. The medical sales support method according to claim 35, wherein the sales promotion activities include calculating the types and increases / decreases of sales activities.

42. A medical sales support method according to claim 35, comprising the step of exporting or displaying an electronic file on a screen.

43. The medical sales support method according to claim 35, further comprising the step of exporting or displaying an electronic file containing recommended tactics or recommended actions for each of the aforementioned sales activities.

44. The medical sales support method according to any one of claims 35 to 43, wherein the medical sales are sales of pharmaceuticals.

45. The medical sales support method according to any one of claims 35 to 43, wherein the medical sales are sales of medical devices.

46. To support medical sales, the processor stores in memory data including the attributes of each physician, the number of sales promotion activities conducted with the physician (including face-to-face visits, web interviews, telephone interviews, seminars, hands-on sessions, document distribution, email distribution, web seminars, or email newsletters), email open history, viewing history of web lectures, logins to physician portal sites, playback of web detailing for healthcare professionals, or viewing of case study content, the results of the sales promotion activities, or the attributes or evaluations of sales representatives to the physicians. After the data is input, the processor retrieves the program and the data, and in order to reflect the differences or diversity of prescribing activities by multiple physicians within the facility, it calculates the correlation coefficient for each of the sales promotion activities between each facility and at least the sales of the facility, and calculates the relative impact by normalizing the correlation coefficient and calculating the relative impact by normalizing the correlation coefficient for each sales promotion activity and calculating a score by multiplying each correlation coefficient by the weight of the activity amount for each physician, and calculates the sales apportionment value of the facility sales amount based on the score. The processor constructs a multivariate logistic regression model using multiple explanatory variables included in the data, employing statistical methods including penaltyed logistic regression or logistic regression analysis, fixing the explanatory variable relating to the physician's attributes, and assuming that at least one of the explanatory variables relating to sales promotion activities or sales representatives is an adjustable variable, calculates the optimal combination of the adjustable variables necessary to achieve a pre-set target prescription probability, calculates the minimum amount of sales promotion activity or target level of the explanatory variable relating to sales representatives necessary to achieve the target prescription probability for each physician, or outputs the calculation results. One or more non-temporary readable media for storing configured instructions.