Medical marketing management method and system and storage medium

Abstract

The invention provides a medicine marketing management method and system and a storage medium, and the method comprises the steps: collecting a medicine purchasing record of a customer, storing the medicine purchasing record of the customer in a relational database, synchronously refreshing pharmacy inventory data, and intelligently generating a medicine purchasing plan; and obtaining medicine attribute classification based on the medicine purchasing record of the customer, establishing a dynamic reminding mechanism according to the medicine attribute classification and in combination with a medicine metabolism cycle, and sending medicine purchasing reminding information to the customer according to a reminding date predicted by the dynamic reminding mechanism. Through the method and the corresponding system, medicine supplement can be accelerated, supply chain interruption is avoided, potential income is increased, a more accurate customer contact strategy is provided, personalized care is provided for medicine purchasing customers, the sense of trust is increased, the stickiness of the customers is enhanced, and the repeated purchasing rate of old customers is increased.

Description

Technical Field

[0001] The present invention provides a pharmaceutical marketing management method, system and storage medium, which relates to the technical field of drug management. Background Art

[0002] The current pharmaceutical marketing management methods show significant systematic defects in the actual application in small and medium-sized pharmacies. The traditional management architecture adopts a discrete information deployment mode, resulting in serious data barriers between the POS transaction system, the CRM customer relationship management module and the core ERP resource planning platform. The existing methods fail to build a complete customer digital portrait system. The CRM system can only capture less than half of the omnichannel interaction data (including scenarios such as offline drug purchase, O2O platform, and remote consultation). This data incompleteness directly leads to a low conversion rate of precision marketing and an extended member repurchase cycle. Summary of the Invention

[0003] The present invention provides a pharmaceutical marketing management method, system and storage medium to solve the above-mentioned problems:

[0004] A pharmaceutical marketing management method provided by the present invention, the method includes:

[0005] Collect the drug purchase records of customers, store the drug purchase records of the customers in a relational database, synchronously refresh the pharmacy inventory data and intelligently generate a drug procurement plan;

[0006] Obtain the drug attribute classification based on the drug purchase records of customers. According to the drug attribute classification, establish a dynamic reminder mechanism in combination with the drug metabolism cycle, and send drug purchase reminder information to customers according to the reminder date predicted by the dynamic reminder mechanism.

[0007] Further, collecting the drug purchase records of customers, storing the drug purchase records of the customers in a relational database, synchronously refreshing the pharmacy inventory data and intelligently generating a drug procurement plan includes:

[0008] Receive the drug purchase records of customers entered by offline cashiers and online platforms. The drug purchase records of customers include: customer basic identity information, name of purchased drug, category of purchased drug, drug specification, quantity of purchased drug, customer's dosage of the drug, customer's current inventory of the drug, and purpose of the customer's purchase of the drug;

[0009] Establish a relational database, update the relational database based on the drug purchase records of customers. The relational database includes: user table, order table, order details table, drug table and inventory table;

[0010] Obtain the refreshed inventory data of the pharmacy through the inventory table, and intelligently generate a drug procurement plan based on the refreshed inventory data of the pharmacy.

[0011] Further, obtain the refreshed inventory data of the pharmacy through the inventory table, and intelligently generate a drug procurement plan based on the refreshed inventory data of the pharmacy, including:

[0012] Obtain drug sales data based on a relational database. The drug sales data includes: drug ID, drug name, sales date, and sales quantity. Obtain the external factors affecting drug sales. The external factors include: weather data, holidays, promotional activities, and epidemic information. The weather data includes: temperature, humidity, and snowfall. The weather data is obtained through a weather API;

[0013] Remove the outliers in the obtained drug sales data and fill in the outliers. If there is missing sales data, fill it with the average sales volume of adjacent times;

[0014] Extract the features in the drug sales data and the external factors affecting drug sales. The features include: basic time features, periodic features, weather-related features, drug-related features, and epidemic-related features. The basic time features are "month" and "day of the week". The periodic features are "whether it is a holiday" and "drug promotion activity time". The weather-related features are "temperature", "whether it is raining or snowing", and "humidity". The drug-related features are "drug category", "drug price", "manufacturer", and "drug dosage form". Divide the extracted feature data into a training set and a test set;

[0015] Build an LSTM model, train the LSTM model with the training set, and evaluate the trained LSTM model with the validation set to obtain the trained LSTM model;

[0016] Obtain future feature data and input it into the trained LSTM model to obtain the predicted sales quantity of drugs;

[0017] According to the predicted sales volume, obtain the dynamic inventory threshold for each drug. The dynamic inventory threshold = predicted sales volume × safety factor. The value range of the safety factor is [1, 2];

[0018] Compare the current inventory in the inventory table with the dynamic inventory threshold to obtain the recommended drug procurement quantity. The replenishment quantity = dynamic inventory threshold - current inventory. If the current inventory is greater than the dynamic threshold, no procurement is required.

[0019] Further, obtain drug attribute classification based on customers' drug purchase records. According to the drug attribute classification, establish a dynamic reminder mechanism in combination with the drug metabolism cycle, and send drug purchase reminder information to customers according to the reminder date predicted by the dynamic reminder mechanism, including:

[0020] Obtain drug attribute classification based on customers' drug purchase records. The drug attribute classification includes: drugs for chronic diseases, drugs for acute diseases, health care drugs, and standing drugs;

[0021] Obtain the reminder dates for customers to repurchase the drug respectively through the models corresponding to the drug attribute classifications, and send reminder messages to the customers based on the reminder dates.

[0022] Furthermore, obtain the reminder dates for customers to repurchase the drug respectively through the models corresponding to the drug attribute classifications, and send reminder messages to the customers according to the reminder dates, including:

[0023] If the drug purchased by the customer is a drug for chronic diseases, calculate the reminder date for the customer to repurchase the drug through the prediction model for the repurchase time of drugs for chronic diseases.

[0024] Furthermore, obtain the reminder dates for customers to repurchase the drug respectively through the models corresponding to the drug attribute classifications, and send reminder messages to the customers according to the reminder dates, including:

[0025] If the drug purchased by the customer is a health care drug, calculate the reminder date for the customer to repurchase the drug through the prediction model for the repurchase time of health care drugs.

[0026] Furthermore, obtain the reminder dates for customers to repurchase the drug respectively through the models corresponding to the drug attribute classifications, and send reminder messages to the customers according to the reminder dates, including:

[0027] If the drug purchased by the customer is a frequently used drug, calculate the reminder date for the customer to repurchase the drug through the prediction model for the repurchase time of frequently used drugs.

[0028] A pharmaceutical marketing management system proposed by the present invention, the system includes:

[0029] A replenishment module, configured to collect the drug purchase records of customers, store the drug purchase records of the customers in a structured data warehouse, synchronously refresh the pharmacy inventory data and intelligently generate a drug procurement plan;

[0030] A reminder message sending module, configured to obtain the drug attribute classification based on the drug purchase records of customers, establish a dynamic reminder mechanism in combination with the drug metabolism cycle according to the drug attribute classification, and send drug purchase reminder messages to customers according to the reminder dates predicted by the dynamic reminder mechanism.

[0031] A pharmaceutical marketing management system proposed by the present invention, the system includes:

[0032] A memory, configured to store programs;

[0033] A processor, configured to load the program to execute the pharmaceutical marketing management method.

[0034] A computer-readable storage medium proposed by the present invention, on which a computer program is stored, and when the computer program is executed by a processor, the pharmaceutical marketing management method is implemented.

[0035] Advantages of the present invention: Inventory management is optimized, effectively maintaining drug inventory, avoiding out-of-stock impacts caused by too low inventory, and at the same time reducing the capital occupation brought by excessive inventory; Customer satisfaction is improved. Notifying customers in advance to purchase again can enhance the service experience, especially for patients who need to take medicine for a long time, providing convenience; Procurement efficiency is improved. Through the automatically generated replenishment plan, the workload of manual calculation and judgment can be reduced, and at the same time, the replenishment of drugs can be accelerated to avoid supply chain interruptions; Potential revenue is increased. A more precise customer contact strategy (such as reminders and regular purchases) can enhance customer stickiness and increase the repeat purchase rate of old customers; The system is intelligent. The use of a database and an automated reminder system realizes the intelligence of the management process, reduces human errors, and improves operation efficiency; By comprehensively using data processing, inventory management, and customer relationship management technologies, the automation and high efficiency of pharmacy information processing are realized, improving the business process. Brief Description of the Drawings

[0036] Figure 1 It is a schematic diagram of a pharmaceutical marketing management method described in the present invention. Detailed Embodiments

[0037] In order to more clearly understand the above objects, features, and advantages of the present invention, the present invention will be described in detail below with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0038] Many specific details are set forth in the following description in order to fully understand the present invention. The described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the description of the present invention herein are only for the purpose of describing specific embodiments, and are not intended to limit the present invention.

[0040] An embodiment of the present invention, a pharmaceutical marketing management method, the method includes:

[0041] Collect the drug purchase records of customers, store the drug purchase records of the customers in a relational database, synchronously refresh the pharmacy inventory data, and intelligently generate a drug procurement plan;

[0042] Obtain the drug attribute classification based on the customer's drug purchase records. According to the drug attribute classification, establish a dynamic reminder mechanism in combination with the drug metabolism cycle, and send drug purchase reminder information to customers according to the reminder date predicted by the dynamic reminder mechanism.

[0043] The working principle and effects of the above technical solution are as follows: Data entry and storage, receive the customer's drug purchase information entered by the user, enter it through the user interface or other means, and store the drug purchase information in a relational database, including customer information, drug information, purchase quantity and date, etc.; Update inventory data, query the current inventory of the corresponding drug in the database, and update the inventory data of the relevant drug according to the customer's purchase quantity; Generate a replenishment plan, check the updated inventory situation, compare it with the preset minimum inventory or safety inventory level, and for drugs below the threshold, generate a purchase order or a recommended replenishment plan, and notify the purchasing staff to replenish the stock; Set a reasonable re-purchase cycle according to the drug category, and calculate the customer's next purchase reminder date based on the purchase date and the purchase cycle; Send reminder information. When the reminder date arrives, send reminder information to the customer by means of text message, email or APP notification, etc. The reminder information includes the name of the purchased drug, the recommended purchase time and relevant preferential information, etc. Inventory management optimization, effectively maintain drug inventory, avoid out-of-stock impact caused by too low inventory, and at the same time reduce the capital occupation brought by excessive inventory; Improve customer satisfaction, advance notice of re-purchase to customers can enhance the service experience, especially for patients who need to take medicine for a long time, providing convenience; Improve procurement efficiency, through the automatically generated replenishment plan, the workload of manual calculation and judgment can be reduced, and at the same time, the replenishment of drugs can be accelerated to avoid supply chain interruption; Increase potential revenue, more accurate customer contact strategies (such as reminders and regular purchases) can enhance customer stickiness and increase the repeat purchase rate of old customers; System intelligence, using the database and automated reminder system to realize the intelligence of the management process, reduce human errors, and improve operation efficiency; Comprehensively utilize data processing, inventory management and customer relationship management technologies to realize the automation and high efficiency of pharmacy information processing and improve the business process.

[0044] In one embodiment of the present invention, collect the customer's drug purchase records, store the customer's drug purchase records in a relational database, synchronously refresh the pharmacy inventory data and intelligently generate a drug procurement plan, including:

[0045] Receive the customer's drug purchase records entered offline at the cashier and online platforms. The customer's drug purchase records include: customer basic identity information, name of the purchased drug, category of the purchased drug, drug specification, quantity of the purchased drug, customer's medication dosage, customer's current inventory of this drug, and the purpose of the customer's purchase of the drug;

[0046] Establish a relational database and update the relational database based on customer drug purchase records. The relational database includes: a user table, an order table, an order details table, a drug table, and an inventory table;

[0047] Obtain the refreshed inventory data of the pharmacy through the inventory table and intelligently generate a drug procurement plan based on the refreshed inventory data of the pharmacy.

[0048] The working principle and effects of the above technical solution are as follows: Data reception and entry, receiving and entering customer drug purchase information from the POS terminal and the online order terminal, including customer basic identity information, drug name, category, specification, quantity, dosage, current inventory, and usage; Relational database design: User table: Store the basic information of customers (such as name, contact information, address, etc.); Order table: Store the basic information of orders (such as order ID, order date, customer ID, etc.); Order details table: Store the specific drug purchase information (such as drug ID, quantity, dosage, usage, etc.); Drug table: Store the basic information of drugs (such as drug name, category, specification, price, etc.); Inventory table: Store the current inventory information of drugs (such as drug ID, current inventory quantity, minimum inventory quantity, etc.); Add the received purchase information to the corresponding database tables, update the order table and the order details table to reflect the new order information, and update the inventory quantity of the corresponding drugs in the inventory table. The specific method is to deduct the purchase quantity from the current inventory; Generate a replenishment plan, regularly or real-time scan the inventory table, check the inventory quantity of drugs, and add the drugs with inventory quantity lower than the preset minimum inventory quantity to the replenishment plan to generate a procurement recommendation or a replenishment order, and notify the procurement department to perform the replenishment operation. Efficient inventory management, real-time update of inventory information, improve drug management efficiency, and reduce the situations of out-of-stock and overstock; Precise customer management, detailed customer purchase records help analyze customer behavior and preferences, improve customer service and personalized sales strategies; Optimized procurement process, automated replenishment plan generation, reduce manual intervention and errors, and improve the speed and accuracy of the procurement process; Improve operational efficiency, an integrated data management platform, facilitate the rapid retrieval and analysis of information, and support decision-making; Enhance the customer experience, accurate and timely inventory management reduces customer waiting time and improves customer satisfaction; Through this solution, an integrated process from order reception, data entry, inventory management to replenishment generation is realized, improving the operational efficiency and service quality of pharmacies or drug supply chains.

[0049] In an embodiment of the present invention, obtaining the refreshed inventory data of the pharmacy through the inventory table and intelligently generating a drug procurement plan based on the refreshed inventory data of the pharmacy includes:

[0050] Obtain drug sales data based on a relational database. The drug sales data includes: drug ID, drug name, sales date, and sales quantity. Obtain the external factors affecting drug sales. The external factors include: weather data, holidays, promotional activities, and epidemic information. The weather data includes: temperature, humidity, and snowfall. The weather data is obtained through a weather API;

[0051] Remove the outliers in the obtained drug sales data and smooth the outliers. If there is missing sales data, fill it with the average sales volume at adjacent times;

[0052] Extract the features from the drug sales data and the external factors affecting drug sales. The features include: basic time features, periodic features, weather-related features, drug-related features, and epidemic-related features. The basic time features are "month" and "day of the week". The periodic features are "whether it is a holiday" and "drug promotion activity time". The weather-related features are "temperature", "whether it is raining or snowing", and "humidity". The drug-related features are "drug category", "drug price", "manufacturer", and "drug dosage form". Divide the extracted feature data into a training set and a test set;

[0053] Build an LSTM model, train the LSTM model with the training set, and evaluate the trained LSTM model with the validation set to obtain the trained LSTM model;

[0054] Obtain future feature data and input it into the trained LSTM model to obtain the predicted sales quantity of drugs;

[0055] According to the predicted sales volume, obtain the dynamic inventory threshold for each drug. The dynamic inventory threshold = predicted sales volume × safety factor. The value range of the safety factor is [1, 2];

[0056] Compare the current inventory quantity in the inventory table with the dynamic inventory threshold to obtain the recommended drug purchase quantity. The replenishment quantity = dynamic inventory threshold - current inventory quantity. If the current inventory quantity is greater than the dynamic threshold, no purchase is required.

[0057] The working principle and effects of the above technical solution are as follows: Data extraction and preprocessing, extract drug sales records, obtain the drug sales records in the past 12 months, including drug ID, name, sales date, and sales quantity; obtain external factor data, obtain weather-related data (temperature, humidity, snowfall) from the weather API, and collect information such as holidays, promotions, and epidemics; data cleaning and completion, remove outliers, use statistical methods or machine learning algorithms (such as box plot method, Z-Score) to identify and remove outliers, and complete the data. For missing sales data, use the average sales volume of adjacent dates to complete it; feature extraction, basic time features extract "month" and "day of the week", periodic features extract "whether it is a holiday" and "drug promotion time", weather-related features extract daily "temperature", "whether it rains or snows", and "humidity", drug-related features include "drug category", "drug price", "manufacturer", and "drug dosage form"; epidemic-related features extract relevant features according to epidemic information; divide the extracted feature data into training set and test set for model training and verification, and use the long short-term memory network (LSTM) model to capture the law of time series data; use the training set to train the LSTM model and adjust the parameters to obtain the best performance; use the validation set to evaluate the model performance to ensure the accuracy and reliability of its prediction; use the trained LSTM model to predict future drug sales; the dynamic inventory threshold is obtained by multiplying the predicted sales volume by a safety factor (ranging from [1, 2]); compare the current inventory in the inventory table with the dynamic inventory threshold. If the current inventory is less than the dynamic threshold, calculate "replenishment quantity = dynamic inventory threshold - current inventory quantity". Accurate sales prediction, using the LSTM model to capture the time series characteristics of sales volume and external influencing factors to achieve more accurate sales volume prediction; optimize inventory management, through the dynamic inventory threshold, make inventory management more flexible, can effectively cope with sales volume fluctuations, and improve inventory turnover; reduce costs and waste, through accurate prediction and replenishment quantity calculation, reduce the losses caused by overstocking and out-of-stock, and optimize logistics and warehousing costs; improve decision-making efficiency, data-driven prediction and decision-making improve the efficiency and reliability of the entire inventory management and replenishment plan; enhance market response ability, quickly respond to market demand changes, especially during the high-incidence period of epidemics or promotional activities, and enhance the competitiveness of pharmacies and customer satisfaction; achieve precise management of sales and inventory, by comprehensively considering various influencing factors, significantly improve the prediction ability of market demand and operational efficiency.

[0058] In one embodiment of the present invention, based on the customer's drug purchase records, obtain the drug attribute classification, and based on the drug attribute classification, establish a dynamic reminder mechanism in combination with the drug metabolism cycle, and send a drug purchase reminder message to the customer according to the reminder date predicted by the dynamic reminder mechanism, including:

[0059] Obtain the drug attribute classification based on the customer's drug purchase records. The drug attribute classification includes: chronic disease medications, acute disease medications, health care products, and standing medications;

[0060] Respectively obtain the reminder date for the customer to repurchase the drug through the models corresponding to the drug attribute classification, and send reminder information to the customer based on the reminder date.

[0061] The working principle and effects of the above technical solution are as follows: Drug category identification, analyze customer purchase information, and identify the drug category according to the drug information purchased by the customer through the drug ID or name; The drug categories are divided into chronic disease medications, acute disease medications, health care products, and standing medications; Establish a purchase cycle model for different drug categories, and each model contains the typical usage cycle and replenishment frequency of the drugs corresponding to that category; Send reminder information, formulate reminder strategies, and timely send reminders to customers through text messages, emails, or APP notifications based on the calculated reminder dates; Reminder content design: includes drug name, recommended purchase time, purchase channel reminder, and possible promotional offers. Improve customer satisfaction. Through timely and warm reminders, help customers avoid running out of medicine or missing the best medication time, and improve customer satisfaction and stickiness with the pharmacy service; Personalized service, provide personalized drug purchase suggestions based on the drug usage scenario, and achieve more targeted services; Increase sales and loyalty. Regular reminders increase the customer's repurchase rate, further increase drug sales, and enhance the trust and loyalty between customers and the pharmacy; Optimize inventory and supply chain management. Through systematic prediction of the drug purchase cycle, the pharmacy can arrange inventory more reasonably and reduce inventory shortages or surpluses caused by demand fluctuations; Enhance competitiveness. An efficient customer management method improves the pharmacy's market response ability and enhances its competitive advantage and market share; By analyzing customer drug purchase behavior and drug characteristics, automatically calculate and send reminder information, optimizing customer relationship management and service processes.

[0062] In an embodiment of the present invention, respectively obtain the reminder date for the customer to repurchase the drug through the models corresponding to the drug attribute classification, and send reminder information to the customer according to the reminder date, including:

[0063] If the drug purchased by the customer is a chronic disease medication, calculate the reminder date for the customer to repurchase the drug through the chronic disease medication repurchase time prediction model. Specifically, the chronic disease medication repurchase time prediction model is:

[0064]

[0065] Among them, T next represents the predicted time for the customer to repurchase the drug next time, T last represents the time point when the customer last purchased the chronic disease medication, T irepresents the interval time of the customer's historical purchase of the drug, ΔC represents the current price change of the drug, n represents the number of times the customer has historically purchased the drug, and W e represents the remaining expiration date weight of the drug, and R u represents the drug inventory consumption ratio of the user;

[0066]

[0067]

[0068] If the drug purchased by the customer is a health care drug, the reminder date for the customer to purchase the drug again is calculated through the health care drug repurchase time prediction model. Specifically, the health care drug repurchase time prediction model is:

[0069]

[0070] Among them, T s represents the next replenishment cycle of the health care drug, represents the historical average replenishment cycle of the customer for this drug, β represents the buffer factor, and σ T represents the standard deviation of the replenishment cycle of the customer for this drug;

[0071] If the drug purchased by the customer is a standing drug, the reminder date for the customer to purchase the drug again is calculated through the standing drug repurchase time prediction model. Specifically, the standing drug repurchase time prediction model is:

[0072]

[0073] Among them, T n represents the predicted time for the customer to repurchase this standing drug next time, and T c represents the time point when the customer last purchased this standing drug, and Q c represents the customer's current standing drug inventory, and Q m represents the minimum acceptable inventory of the standing drug for this customer, D represents the daily usage of this standing drug for this customer, k represents the adjustment coefficient, and T e represents the expiration time of the standing drug purchased by the customer, and T P represents the shelf life of the standing drug purchased by the customer, and s f represents the seasonal adjustment factor.

[0074]

[0075] Among them, α represents the sensitivity coefficient, and the value range is [0.1, 0.3], and U s represents the historical usage of the standing drug for the current season of this customer, represents the annual average usage of the standing drug for this customer.

[0076] The working principle and effects of the above technical solution are as follows: By constructing a repurchase time prediction model for different drug categories, the next purchase time of customers can be accurately predicted with different calculation formulas, ensuring that pharmacies can remind customers to purchase drugs at an appropriate time, reducing the risk of out-of-stock, and improving customer experience and loyalty; The repurchase time prediction model for chronic disease medications comprehensively considers the customer's historical purchase behavior (calculated by the average purchase interval), price changes, remaining shelf life of drugs, and inventory consumption rate to dynamically adjust the purchase time prediction; The repurchase time prediction model for health care drugs calculates the next purchase reminder through the average replenishment cycle and cycle standard deviation, considering the customer's historical regular replenishment behavior; The repurchase time prediction model for essential drugs considers inventory, daily consumption, shelf life, and seasonal factors to predict the purchase time of essential drugs and keep the inventory within a reasonable range; Personalized reminder: Personalized calculation is performed according to drug type and user habits, improving user experience and reminder accuracy; Accurate repurchase time prediction helps pharmacies optimize inventory management, avoid out-of-stock or overstock; Provide accurate drug purchase suggestions and reminders, increase customer stickiness, enhance customer satisfaction and loyalty; By monitoring the expiration time and inventory consumption, reduce the risks brought by expired drugs or inventory pressure; By actively reminding customers to purchase again, increase the sales opportunities of pharmacies, improve customer experience, provide personalized care for customers, increase trust, and enhance brand loyalty; Provide intelligent data support for pharmacies, help achieve refined management and improve overall operation efficiency.

[0077] An embodiment of the present invention is a pharmaceutical marketing management system, which includes:

[0078] A replenishment module, which is used to receive the customer's drug purchase information entered by the user, store the customer's drug purchase information in a relational database, update the inventory data of relevant drugs in the pharmacy, and generate a drug replenishment plan based on the updated inventory data;

[0079] A reminder information sending module, which is used to obtain the drug category according to the customer's drug purchase information, calculate the reminder date for the customer to repurchase the drug based on the drug category, and send a reminder information to the customer according to the reminder date.

[0080] The working principle and effects of the above technical solution are as follows: Data entry and storage, receiving the customer's purchased drug information entered by the user through the user interface or other means, and storing the drug purchase information in a relational database, including customer information, drug information, purchase quantity, and date, etc.; Updating inventory data, querying the current inventory of the corresponding drug in the database, and updating the inventory data of the relevant drug according to the customer's purchase quantity; Generating a replenishment plan, checking the updated inventory situation, comparing it with the preset minimum inventory or safety inventory level, and generating a purchase order or a recommended replenishment plan for drugs below the threshold to notify the purchasing staff to replenish the stock; Setting a reasonable re-purchase cycle according to the drug category, calculating the customer's next purchase reminder date based on the purchase date and the purchase cycle; Sending reminder messages, when it reaches the reminder date, sending reminder messages to the customer by means of text messages, emails, or APP notifications, etc. The reminder messages include the name of the purchased drug, the recommended purchase time, and relevant preferential information, etc. Optimization of inventory management, effectively maintaining drug inventory, avoiding out-of-stock impacts caused by too low inventory, and at the same time reducing the capital occupation brought by excessive inventory; Improvement of customer satisfaction, notifying the customer in advance of the re-purchase can enhance the service experience, especially for patients who need to take medicine for a long time, providing convenience; Improvement of procurement efficiency, through the automatically generated replenishment plan, the workload of manual calculation and judgment can be reduced, and at the same time, the drug replenishment can be accelerated to avoid supply chain disruptions; Increase in potential revenue, more precise customer contact strategies (such as reminders and regular purchases) can enhance customer stickiness and increase the repeat purchase rate of old customers; System intelligence, using the database and the automated reminder system to realize the intelligence of the management process, reducing human errors and improving operation efficiency; Comprehensively utilizing data processing, inventory management, and customer relationship management technologies to realize the automation and high efficiency of pharmacy information processing and improve the business process.

[0081] An embodiment of the present invention, a pharmaceutical marketing management system, includes:

[0082] A memory for storing programs;

[0083] A processor for loading the program to execute the pharmaceutical marketing management method.

[0084] An embodiment of the present invention, a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the pharmaceutical marketing management method is implemented.

[0085] The user data and other data involved in this application have all been obtained with full consent and authorization, and the collection, use, and processing of relevant information comply with the relevant laws, regulations, and standards of the relevant countries and regions.

[0086] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A pharmaceutical marketing management method, characterized in that, The method includes: Collecting the customer's drug purchase records, storing the customer's drug purchase records in a relational database, synchronously refreshing the pharmacy inventory data, and intelligently generating a drug procurement plan; Obtaining the drug attribute classification based on the customer's drug purchase records, establishing a dynamic reminder mechanism according to the drug attribute classification and in combination with the drug metabolism cycle, and sending a drug purchase reminder message to the customer according to the reminder date predicted by the dynamic reminder mechanism.

2. The pharmaceutical marketing management method according to claim 1, characterized in that Collecting the customer's drug purchase records, storing the customer's drug purchase records in a relational database, synchronously refreshing the pharmacy inventory data, and intelligently generating a drug procurement plan, including: Receiving the customer's drug purchase records entered offline at the cashier and online on the platform, where the customer's drug purchase records include: customer basic identity information, name of the purchased drug, category of the purchased drug, drug specification, quantity of the purchased drug, customer's dosage of the drug, customer's current inventory of the drug, and purpose of the customer's purchase of the drug; Establishing a relational database and updating the relational database based on the customer's drug purchase records. The relational database includes: user table, order table, order details table, drug table, and inventory table; Obtaining the refreshed inventory data of the pharmacy through the inventory table and intelligently generating a drug procurement plan based on the refreshed inventory data of the pharmacy.

3. The pharmaceutical marketing management method according to claim 2, wherein Obtaining the refreshed inventory data of the pharmacy through the inventory table and intelligently generating a drug procurement plan based on the refreshed inventory data of the pharmacy, including: Obtaining the drug sales data based on the relational database, where the drug sales data includes: drug ID, drug name, sales date, and sales quantity, obtaining the external factors affecting the drug sales, where the external factors include: weather data, holidays, promotional activities, and epidemic information, and the weather data includes: temperature, humidity, and snowfall, and the weather data is obtained through a weather API; Removing the outliers in the obtained drug sales data and filling in the outliers. If there is missing sales data, it is filled in with the average sales volume at a neighboring time; Extracting the features from the drug sales data and the external factors affecting drug sales, where the features include: basic time features, periodic features, weather-related features, drug-related features, and epidemic-related features. The basic time features are "month" and "day of the week", the periodic features are "whether it is a holiday" and "drug promotion activity time", the weather-related features are "temperature", "whether it is raining or snowing", and "humidity", the drug-related features are "drug category", "drug price", "manufacturer", and "drug dosage form", and dividing the extracted feature data into a training set and a test set; Constructing an LSTM model, training the LSTM model with the training set, evaluating the trained LSTM model with the validation set, and obtaining the trained LSTM model; Obtaining the future feature data and inputting it into the trained LSTM model to obtain the predicted sales quantity of the drug; According to the predicted sales volume, obtaining the dynamic inventory threshold for each drug, where the dynamic inventory threshold = predicted sales volume × safety factor, and the value range of the safety factor is [1, 2]; Compare the current inventory quantity in the inventory list with the dynamic inventory threshold to obtain the recommended purchase quantity of drugs. The replenishment quantity = dynamic inventory threshold - current inventory quantity. If the current inventory quantity is greater than the dynamic threshold, no purchase is required.

4. The pharmaceutical marketing management method according to claim 1, characterized in that Obtain the drug attribute classification based on the customer's drug purchase records. According to the drug attribute classification, establish a dynamic reminder mechanism in combination with the drug metabolism cycle, and send drug purchase reminder information to customers according to the reminder date predicted by the dynamic reminder mechanism, including: Obtain the drug attribute classification based on the customer's drug purchase records. The drug attribute classification includes: drugs for chronic diseases, drugs for acute diseases, health care drugs, and standing drugs; Respectively obtain the reminder date for the customer to repurchase the drug through the model corresponding to the drug attribute classification, and send reminder information to the customer based on the reminder date.

5. The pharmaceutical marketing management method according to claim 4, characterized in that, Respectively obtain the reminder date for the customer to repurchase the drug through the model corresponding to the drug attribute classification, and send reminder information to the customer according to the reminder date, including: If the drug purchased by the customer is a drug for chronic diseases, calculate the reminder date for the customer to repurchase the drug through the chronic disease drug repurchase time prediction model.

6. The pharmaceutical marketing management method according to claim 4, wherein Respectively obtain the reminder date for the customer to repurchase the drug through the model corresponding to the drug attribute classification, and send reminder information to the customer according to the reminder date, including: If the drug purchased by the customer is a health care drug, calculate the reminder date for the customer to repurchase the drug through the health care drug repurchase time prediction model.

7. The pharmaceutical marketing management method according to claim 4, characterized in that, Respectively obtain the reminder date for the customer to repurchase the drug through the model corresponding to the drug attribute classification, and send reminder information to the customer according to the reminder date, including: If the drug purchased by the customer is a standing drug, calculate the reminder date for the customer to repurchase the drug through the standing drug repurchase time prediction model.

8. A pharmaceutical marketing management system, characterized in that, The system includes: A replenishment module, which is used to collect the customer's drug purchase records, store the customer's drug purchase records in a structured data warehouse, synchronously refresh the pharmacy inventory data, and intelligently generate a drug purchase plan; A module for sending reminder information, which is used to obtain the drug attribute classification based on the customer's drug purchase records, establish a dynamic reminder mechanism according to the drug attribute classification in combination with the drug metabolism cycle, and send drug purchase reminder information to customers according to the reminder date predicted by the dynamic reminder mechanism.

9. A pharmaceutical marketing management system, characterized in that, Including: A memory for storing programs; A processor for loading the program to execute the pharmaceutical marketing management method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the pharmaceutical marketing management method according to any one of claims 1-7.

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