Marketing activity generation method and device based on sales data analysis

Abstract

The invention discloses a marketing activity generation method and device based on sales data analysis, and the method comprises the steps: obtaining sales data of all drugstores in a target region, disease maps corresponding to all medicines in the drugstores, and population health data and environmental climate monitoring data in the target region; establishing a regional health assessment index set according to the population health data and the environmental climate monitoring data, and calculating a health situation index of the target region based on the regional health assessment index set; extracting feature information of the first data to obtain a feature data set; performing clustering analysis on the feature data set to generate a category label of each pharmacy; generating a customer portrait according to the category label and the feature data set; and generating a marketing activity scheme corresponding to each category label through a preset model according to the category labels, the customer portraits and inventory data in the sales data. The method can attract the target customer group, thereby improving the rate of return of marketing investment.

Description

Technical Field

[0001] The present application relates to the field of data analysis, and specifically to a method and device for generating marketing activities based on sales data analysis. Background Art

[0002] With the development of big data and cloud computing technologies, companies can gain a deeper understanding of consumer behavior patterns by analyzing historical data, thereby providing guidance for business decisions. This data analysis method can help companies more accurately grasp market demand and consumer behavior characteristics, and then formulate more effective marketing strategies. Through this refined data-driven approach, companies not only improve marketing efficiency, but also significantly increase consumer satisfaction and loyalty.

[0003] At present, in order to increase drug sales in the pharmaceutical industry, various means are usually used to attract customers. For example, some pharmacies regularly carry out promotional activities, such as discounts, buy-one-get-one-free activities, etc.; some pharmacies provide long-term customers with benefits such as point rewards and exclusive discounts through membership management. These traditional marketing methods can increase sales to a certain extent.

[0004] However, existing traditional marketing methods set unified promotion standards based on national average data, which makes marketing activities less targeted and difficult to effectively attract customer groups, thus affecting the conversion rate of marketing investment. Summary of the invention

[0005] The present application provides a method and device for generating a marketing campaign based on sales data analysis, which is used to attract target customer groups and thus improve the return on marketing investment.

[0006] In a first aspect of the present application, a method for generating a marketing campaign based on sales data analysis is provided, which is applied to a server, and the method comprises: Obtain sales data of all pharmacies in the target area, disease maps corresponding to all drugs in the pharmacies, and population health data and environmental climate monitoring data in the target area; establish a regional health assessment indicator set based on the population health data and environmental climate monitoring data, and calculate the health status index of the target area based on the regional health assessment indicator set; extract feature information of the first data to obtain a feature data set, the feature data set characterizes the sales characteristics, customer needs and health needs of the pharmacy, the first data includes sales data, disease maps and health status index; perform cluster analysis on the feature data set to generate category labels for each pharmacy, the category labels include the scale level and target customer group of the pharmacy; generate customer portraits based on the category labels and feature data sets, the customer portraits include customer behavior preferences, disease demand characteristics and drug purchasing tendencies; generate marketing activity plans corresponding to each category label through a preset model based on the category labels, customer portraits and inventory data in the sales data.

[0007] Optionally, a regional health assessment indicator set is established based on population health data and environmental climate monitoring data, and the health status index of the target area is calculated based on the regional health assessment indicator set, specifically including: Based on a preset statistical analysis method, health factors are extracted from population health data and environmental climate monitoring data; the health factors are stratified according to multiple time periods to obtain a health factor set, and the time periods include a first time period, a second time period, and a third time period, wherein the time range of the second time period is greater than the time range of the first time period, and the time range of the third time period is greater than the time range of the second time period; a health assessment indicator set is constructed based on the health factor set, and the health assessment indicator set includes multiple health assessment indicators for each time period; multiple health assessment indicators for each time period are aggregated and calculated to obtain a health situation index in the target area.

[0008] Optionally, a health assessment indicator set is constructed based on the health factor set, including: The health factors of each time period are feature extracted according to the target dimensions to obtain the basic feature vector, and the target dimensions include the disease risk dimension, the environmental pressure dimension and the population health vulnerability dimension; the basic feature vector is subjected to time series correlation analysis to obtain the time series correlation feature; the basic feature vector is feature fused with the time series correlation feature to obtain the time series health feature vector, and the time series health feature vector includes feature values ​​corresponding to multiple features; the feature values ​​are ranked by importance through a preset importance evaluation algorithm to obtain the importance ranking result; multiple features in the importance ranking result whose importance scores exceed the preset threshold are determined as health assessment indicators to obtain a health assessment indicator set.

[0009] Optionally, perform cluster analysis on the feature dataset to generate category labels for each pharmacy, including: Based on the disease map, the disease demand characteristic data of the target area is extracted according to the regional health situation index; the clustering parameters are determined based on the preset clustering evaluation indicators, and the sales data and the disease demand characteristic data are clustered according to the clustering parameters to obtain the clustering results; the clustering number of each pharmacy is determined according to the clustering results; the characteristic distribution statistics of the pharmacies corresponding to the same cluster number in the preset dimensions are counted; the category label corresponding to each cluster number is determined according to the characteristic distribution statistics.

[0010] Optionally, after determining the cluster number of each pharmacy according to the clustering results, the method further includes: calculating a clustering effectiveness index of the clustering results; if the clustering effectiveness index does not meet a preset condition, redetermining the clustering parameters.

[0011] Optionally, after generating a promotion activity plan for a category corresponding to each category label by a preset model according to the category label, the customer portrait, and the inventory data in the sales data, the method further includes: Calculate the similarity of the customer portraits of the first pharmacy and generate a similarity matrix, where the first pharmacy is the pharmacy corresponding to the same category label; calculate the difference value of the customer portraits of the first pharmacy based on the similarity matrix; extract the difference features of the target customer portraits, where the target customer portraits are customer portraits whose customer portrait difference values ​​are greater than a preset threshold; divide the first pharmacy into second pharmacies corresponding to multiple subcategory labels based on the difference features; adjust the promotion activity plan based on the sales data and customer portraits of each second pharmacy.

[0012] Optionally, a method for generating a marketing campaign based on sales data analysis is applied to a server, and the method further includes: Obtain the digital twin model of the pharmacy corresponding to each category label; based on sales data and customer portraits, use the digital twin model to simulate and evaluate the sales of the pharmacy under the promotion plan to obtain the simulation evaluation results; adjust the promotion plan based on the simulation evaluation results.

[0013] In a second aspect of the present application, a marketing activity generation system based on sales data analysis is provided, comprising: The acquisition module is used to obtain the sales data of all pharmacies in the target area, the disease maps corresponding to all drugs in the pharmacies, and the population health data and environmental climate monitoring data in the target area; A calculation module is used to establish a regional health assessment indicator set based on population health data and environmental climate monitoring data, and calculate the health situation index of the target area based on the regional health assessment indicator set; An extraction module is used to extract feature information of the first data to obtain a feature data set, wherein the feature data set represents the sales characteristics, customer needs and health needs of the pharmacy, and the first data includes sales data, disease maps and health status index; The analysis module is used to perform cluster analysis on the feature data set and generate a category label for each pharmacy. The category label includes the scale level and target customer group of the pharmacy. The first generation module is used to generate a customer profile based on the category label and feature data set. The customer profile includes customer behavior preferences, disease demand characteristics, and drug purchase tendencies; The second generation module is used to generate a marketing activity plan corresponding to each category label through a preset model based on the category label, customer portrait and inventory data in the sales data.

[0014] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any one of the methods described above.

[0015] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any of the methods described above is executed.

[0016] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. This solution integrates sales data, disease maps, population health data, and environmental climate monitoring data to establish a regional health assessment indicator set and calculate the health trend index to accurately identify the health needs of the target area; extracts feature data sets and generates drugstore category labels through cluster analysis to clarify the size of drugstores and customer groups; combines category labels to build customer portraits, gaining in-depth insights into customer behavior preferences, disease needs, and purchasing tendencies; and combines inventory data to design marketing activities and optimize resource allocation. Through the full process analysis from health needs to customer portraits to inventory dynamics, different marketing activity plans are matched for different customer groups, which enhances the pertinence of marketing activities and improves the conversion rate of marketing investment.

[0017] 2. Health factors are extracted based on population health data and environmental climate monitoring data. The dynamic changing trends of health factors in the short, medium and long term are captured through stratified processing of multiple time periods. Combined with time series correlation analysis and feature importance ranking, the ability to mine complex correlations between health factors is enhanced, ensuring the scientificity and accuracy of health assessment indicators. Through the calculation of the health status index, the health status and its changing trends of the target area can be comprehensively and dynamically reflected, providing efficient and reliable data support for the accurate assessment of regional health needs and the formulation of subsequent marketing strategies.

[0018] 3. By integrating disease demand characteristic data and sales data, it can accurately capture the multi-dimensional characteristics of drugstores in terms of disease demand and sales characteristics; through the optimization of clustering parameters and clustering operations, it can scientifically group drugstores according to their characteristics to ensure the rationality and accuracy of clustering results; through statistical analysis of the characteristic distribution of clustering numbers, the category labels of drugstores are accurately matched with information such as their scale level, target customer groups or business directions, and the classification of drugstores is further refined. This technical method realizes the accurate identification of drugstore characteristics and the clear classification of business characteristics through cluster analysis, which provides an important foundation for the construction of subsequent customer portraits and the formulation of marketing plans, significantly improves the efficiency and accuracy of drugstore classification and analysis, and effectively supports the implementation of precision marketing strategies.

[0019] 4. Through in-depth analysis of the similarities and differences of pharmacy customer portraits under the same category label, the pharmacy classification is refined, and the potential differences in customer needs in the same category of pharmacies are further captured; by extracting the difference characteristics of the target customer portraits, a basis is provided for the precise promotion strategy of sub-category pharmacies; at the same time, through the dynamic adjustment of the promotion activity plan, it ensures that the marketing activities are highly matched with the customer needs of each sub-category pharmacy. This technical method not only realizes the refined adjustment and optimization of promotion activities, significantly improves the accuracy and effectiveness of marketing plans, but also effectively covers the diverse customer needs through differentiation strategies, further enhancing the market competitiveness and customer satisfaction of pharmacies.

[0020] 5. By introducing digital twin technology, the actual operating characteristics of drugstores are digitized and modeled, and the effects of promotional activities can be predicted and verified in a virtual environment, thereby reducing the trial and error costs of promotional activities in reality; using simulation evaluation results, potential problems in promotional plans can be accurately identified and adjusted in a timely manner, so that promotional activities are closer to the actual operating needs and customer portrait characteristics of drugstores; in addition, through the high simulation capabilities of digital twin models, the dynamic sales performance of drugstores under different promotional strategies can be captured, thereby providing data support and deep insights for the optimization of marketing plans. This technical method significantly improves the scientificity and accuracy of promotional activity design, reduces the waste of marketing resources, enhances the intelligence level of marketing decisions, and at the same time improves the profitability and market competitiveness of drugstores in actual operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flowchart of a method for generating a marketing activity based on sales data analysis in an embodiment of the present application; Figure 2 It is a structural diagram of a marketing activity generation system based on sales data analysis in an embodiment of the present application; Figure 3 It is a schematic diagram of the structure of an electronic device in an embodiment of the present application.

[0022] Explanation of the accompanying drawings: 201, acquisition module; 202, calculation module; 203, extraction module; 204, analysis module; 205, first generation module; 206, second generation module; 207, adjustment module; 208, simulation module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0023] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0024] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0025] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0026] Figure 1 It is a flow chart of a method for generating a marketing activity based on sales data analysis in an embodiment of the present application.

[0027] See also Figure 1 In an embodiment of the present application, a method for generating a marketing activity based on sales data analysis is applied to a server, and the method includes: S101, obtaining sales data of all pharmacies in the target area, disease maps corresponding to all drugs in the pharmacies, and population health data and environmental climate monitoring data in the target area; The system can obtain sales data of the target area through the sales system of the pharmacy or the database of the chain pharmaceutical company. The sales data may include drug sales volume, sales time, customer purchase frequency and inventory data, etc., which can reflect the operating conditions of the pharmacy and drug demand.

[0028] Disease maps are established networks of relationships between drugs and diseases, which can be directly obtained from the drug database of the State Drug Administration, official information from drug manufacturers, or medical literature. The maps provide a clear basis for analyzing the scope of application of drugs, such as whether a certain type of antibiotic is suitable for treating a specific infectious disease.

[0029] For population health data, the public health system and the medical insurance system can be connected to the data system of the present invention to obtain population health data, or the health survey data of the target area can be used to collect population health data. Population health data can include chronic disease prevalence, disease distribution, and health behavior characteristics (such as smoking and drinking rates) to assess the overall health status of regional residents.

[0030] In addition, the system can obtain real-time and historical environmental climate monitoring data from the meteorological monitoring platform through the API interface, and can also be sorted and extracted through publicly released environmental quality reports. Environmental climate monitoring data involves environmental factors such as air quality (such as PM2.5 concentration), temperature and humidity, and precipitation. These data are closely related to health conditions. For example, air pollution may lead to a high incidence of respiratory diseases.

[0031] To ensure that sales data, disease maps, population health data, and environmental climate data can be interrelated, they need to be standardized in a unified manner, including the unification of formats, identifiers, time and space dimensions, units, and indicators. First, convert data from different sources into a unified structured format, such as a database table or a standardized file format, and use unified identifiers (such as drug ID, region ID, time) to match sales data, disease maps, population health data, and environmental climate data to ensure that drugs, regions, and time are represented consistently in each data set. Second, align the time and space dimensions of the data, such as aggregating hourly climate data to the daily level to keep it consistent with the time dimension of sales data and health data, and correspond the pharmacy addresses to administrative divisions to make the spatial range consistent. Units and indicators also need to be standardized, such as unifying the currency into RMB and converting health indicators into the same statistical units (such as percentages or per thousand people).

[0032] S102. Extract health factors from population health data and environmental climate monitoring data based on preset statistical analysis methods; extract key factors that affect health status from population health data and environmental climate monitoring data through data mining and statistical analysis (such as multivariate regression analysis, factor analysis, etc.). These key factors are called health factors. Population health data may include the prevalence of people of different age groups in the region, the incidence of chronic diseases, the spread of infectious diseases, etc.; environmental climate monitoring data may include temperature, humidity, air quality (such as PM2.5, PM10), tap water quality, and seasonal climate changes. For example, in winter, temperature and air humidity may lead to a high incidence of respiratory diseases, so they are identified as health factors, while in summer, tap water quality and temperature may be closely related to the spread of intestinal diseases, so they are identified as health factors.

[0033] S103, hierarchically processing the health factors according to multiple time periods to obtain a health factor set, where the time periods include a first time period, a second time period, and a third time period, wherein a time range of the second time period is greater than a time range of the first time period, and a time range of the third time period is greater than a time range of the second time period; The extracted health factors are layered according to multiple time periods to generate a set of health factors. The time period is divided into three levels: the first time period (short-term, such as weeks or months), the second time period (medium-term, such as quarters or half years), and the third time period (long-term, such as annual or multi-year trends). The first time period focuses on reflecting short-term changes in health factors, such as the impact of air quality fluctuations on respiratory diseases within a week. The second time period reflects medium-term trends, such as the correlation between the spread of seasonal influenza and temperature fluctuations within a quarter. The third time period is used to capture long-term impacts, such as the impact of annual air pollution levels on the incidence of chronic diseases (such as asthma or lung disease). Through layered processing, health factors are integrated into different time scales, which can more comprehensively reflect the temporal dynamic characteristics of health factors and their potential impact on health trends.

[0034] S104, constructing a health assessment indicator set according to the health factor set, where the health assessment indicator set includes multiple health assessment indicators for each time period; Specifically, the health factors of each time period are feature extracted according to the target dimensions to obtain a basic feature vector, and the target dimensions include the disease risk dimension, the environmental pressure dimension and the population health vulnerability dimension; the basic feature vector is subjected to time series correlation analysis to obtain time series correlation features; the basic feature vector is feature fused with the time series correlation features to obtain a time series health feature vector, and the time series health feature vector includes feature values ​​corresponding to multiple features; the feature values ​​are ranked by importance using a preset importance evaluation algorithm to obtain an importance ranking result; multiple features in the importance ranking result whose importance scores exceed a preset threshold are determined as health assessment indicators to obtain a health assessment indicator set.

[0035] Among them, the construction of the health assessment indicator set is completed by feature extraction, analysis and screening of health factors in each time period. Specifically, first of all, the core features reflecting the regional health status can be extracted from the health factor set through statistical methods, machine learning techniques and other methods, and the health situation of the region can be described from aspects such as disease risk, environmental pressure and population health status (population health vulnerability). For example, by analyzing data such as disease incidence, air quality index, temperature fluctuations and the proportion of the elderly population in a certain time period, the basic features that can quantify the regional health level are extracted and constructed into a basic feature vector. The basic feature vector is a static description of the health factors in different time periods, which is used to characterize the direct effect of the health factors on regional health.

[0036] On this basis, it is necessary to further analyze the dynamic change characteristics of health factors in the time dimension. Through time series correlation analysis, the mutual relationship and potential time dependence of health factors in different time periods can be obtained. For example, temperature fluctuations may have a certain lag correlation with the incidence of influenza, and changes in air quality may have a periodic impact on the spread trend of respiratory diseases. Through the analysis of these correlations, the dynamic characteristics of health factors in the time dimension can be extracted, and combined with the basic feature vector, a time series health feature vector can be generated. The time series health feature vector contains both the static characteristics of the health factors themselves and the dynamic correlation characteristics between health factors.

[0037] In order to further screen out the features that have the most significant impact on the health status of the target area, it is necessary to sort the importance of the feature values ​​in the time series health feature vector. Each feature value can be quantitatively scored by a preset importance evaluation algorithm to evaluate its impact on the regional health status. For example, by analyzing features such as disease incidence, air quality index, and the proportion of the elderly population, the importance score of the feature to regional health is calculated based on a preset importance evaluation algorithm to identify the key features in the health factor. Among them, the importance evaluation algorithm can be based on the statistical characteristics of the data, combined with the domain experience of experts, or feature importance evaluation based on machine learning models. Quantitatively score each feature value. Finally, by sorting the importance scores of all features, the features with scores exceeding the preset threshold are screened out, determined as health assessment indicators, and a health assessment indicator set is constructed. At the same time, each screened health assessment indicator is matched with its specific data value (i.e., feature indicator value) in the target time period. The feature indicator value is obtained by extracting, aggregating, or time-aligning population health data and environmental climate monitoring data, and can accurately reflect the feature status in the target time period. For example, the characteristic index value of the air quality index can be the daily average or monthly average value in the time period, the characteristic index value of the influenza incidence rate can be the weekly number of cases per thousand people, and the characteristic index value of the proportion of the elderly population can be directly derived from statistical data. For example, in a certain target area and time period, the characteristics obtained through analysis include air quality index (AQI), influenza incidence rate, proportion of elderly population, temperature fluctuation range, etc. After importance evaluation, the importance score of the air quality index is 0.85, the influenza incidence rate is 0.78, the proportion of elderly population is 0.65, and the temperature fluctuation range is 0.45. If the preset threshold is 0.5, the air quality index, influenza incidence rate and proportion of elderly population are selected as health assessment indicators. At this time, the health assessment indicator set will contain these indicators and their corresponding characteristic index values, such as the air quality index is 120 (indicating mild pollution), the influenza incidence rate is 15‰ (that is, 15 people out of every thousand people are infected with influenza), and the proportion of the elderly population is 18% (that is, the proportion of the elderly population in the area is 18%).

[0038] S105: Aggregate and calculate multiple health assessment indicators in each time period to obtain a health status index in the target area.

[0039] The health assessment index set constructed in step S104 includes health assessment indicators and their characteristic index values ​​in the target area and the corresponding time period. Health assessment indicators are important features that have been screened, such as the air quality index (AQI), influenza incidence, proportion of the elderly population, and temperature fluctuation range; and the characteristic index values ​​are the specific data of these features in the time period. For example, in a certain time period, the air quality index is 120, the influenza incidence is 15‰, and the proportion of the elderly population is 18%. These characteristic index values ​​will be used as input data for subsequent standardization processing and aggregation calculations.

[0040] Since different health assessment indicators have different dimensions and value ranges (for example, the air quality index may be 0-500, while the influenza incidence rate is 0-100‰), directly using these characteristic indicator values ​​for calculations may result in the results being biased towards indicators with a larger value range. Therefore, it is necessary to standardize the values ​​of each characteristic indicator so that they are within the same scale range. The standardization formula is: Among them, Z i is the characteristic index value after standardization of the i-th health assessment index, x i is the characteristic index value of the ith health assessment index, max(x) and min(x) are the upper and lower limits of the value range of the ith health assessment index, respectively. The standardized characteristic index value Z i Will fall within the range of [0,1]. For example, if the air quality index ranges from 0 to 500 and the air quality index is 120 during a certain period of time, then the standardized value is: Through standardization, all health assessment indicator values ​​are adjusted to a uniform range to facilitate subsequent calculations.

[0041] Different health assessment indicators have different degrees of impact on the overall health situation, so it is necessary to assign weights to each indicator. The allocation of weights can be based on expert experience or data analysis results, such as assigning weights through expert scoring, or quantifying the importance of each indicator through statistical analysis methods (such as regression analysis). Suppose the weight allocation results for a certain time period are: the weight of the air quality index (AQI) is 0.4, the weight of the influenza incidence is 0.35, and the weight of the proportion of the elderly population is 0.25. This means that air quality has the greatest impact on the regional health situation, followed by the influenza incidence, and the impact of the proportion of the elderly population is relatively small. The sum of the weights must meet the following constraints: Among them, ω i is the weight of the i-th health assessment indicator, and n is the total number of health assessment indicators.

[0042] The health status index in the target area is calculated by the aggregation formula, which is: Among them, H is the health status index of the target area; Z i is the characteristic index value after standardization of the i-th health assessment index; ω i is the weight of the ith health assessment indicator, reflecting the impact of a single indicator on health status; f(Z i ) is a nonlinear function used to perform nonlinear transformation (e.g., exponential, logarithmic or other forms) on the effect of a single health assessment indicator; ω ij is the interaction weight between the i-th and j-th health assessment indicators, reflecting the impact of the interaction between the indicators on the health situation; g(Z i ,Z j ) is the interaction effect function, which is used to describe the interaction relationship between two indicators; α is the overall contribution adjustment coefficient of a single health assessment indicator; β is the overall contribution adjustment coefficient of the interaction effect; γ is the adjustment coefficient of the bias term; Bias is the bias term, which is usually a constant or set according to prior knowledge in a specific field.

[0043] is the contribution of a single health assessment indicator to the health status index H, f(Z i ) The standardized index value Z i Perform nonlinear transformations; The contribution of the interaction effects between indicators to the health status index was calculated.

[0044] S106. Perform cluster analysis on the feature data set to generate a category label for each pharmacy, where the category label includes the scale level and target customer group of the pharmacy; Specifically, based on the disease map, the disease demand characteristic data of the target area is extracted according to the regional health situation index; the clustering parameters are determined based on the preset clustering evaluation indicators, and the sales data and the disease demand characteristic data are clustered according to the clustering parameters to obtain the clustering results; the clustering number of each pharmacy is determined according to the clustering results; the characteristic distribution statistics of the pharmacies corresponding to the same cluster number in the preset dimensions are counted; and the category label corresponding to each cluster number is determined according to the characteristic distribution statistics.

[0045] Among them, the disease atlas is the association information between drugs and corresponding diseases, describing the therapeutic or preventive relationship between drugs and diseases. For example, a certain antiviral drug is mainly used to treat influenza, and a certain antihypertensive drug is mainly for patients with hypertension. Extracting disease demand characteristic data in the target area is completed by combining the regional health situation index and the disease atlas. First, according to the disease risk analysis in the regional health situation index, the main health problems in the target area, such as influenza, respiratory infections or chronic diseases, are identified. These risks can be directly reflected by specific indicators of the health situation index (such as influenza risk index, chronic disease risk index or respiratory disease risk index). Then, using the disease atlas, the disease risks identified in the health situation index are associated with the corresponding drugs or treatment plans. For example, when the health situation index shows that the risk of influenza is high, the demand for drugs related to influenza (such as antiviral drugs, antipyretics, cough suppressants, etc.) can be matched through the disease atlas. Then, these disease risks are compared and analyzed with the actual drug sales data to verify the matching degree between the health situation index and the drug demand of pharmacies and ensure the accuracy of the extraction results. For example, if the drug sales data shows that the sales of antiviral drugs increase significantly in winter, it is further confirmed that influenza is the main disease demand feature in the region. Finally, by comprehensively analyzing the health trend index, disease map and sales data, the disease demand feature data of the target area is extracted, such as "the high incidence of influenza in winter, the demand for antiviral drugs is large" or "chronic diseases dominate for a long time, and the demand for antihypertensive and hypoglycemic drugs is stable".

[0046] After extracting the disease demand characteristic data, it is necessary to perform cluster analysis on the pharmacies in the target area so as to divide the pharmacies into different categories according to sales characteristics and customer needs. Before performing the clustering operation, it is necessary to first determine the parameters of the clustering analysis, such as selecting a suitable clustering algorithm and setting the number of clusters (K value). Common clustering algorithms include K-Means, DBSCAN, and hierarchical clustering. The determination of the number of clusters K value can be completed by the preset clustering evaluation index. Common methods include determining the K value by finding the inflection point of the sum of squared errors within the cluster or directly setting the initial value based on field experience. For example, in the target area, if it is known that pharmacies can be roughly divided into three categories: large comprehensive pharmacies, small community pharmacies, and specialist pharmacies, K=3 can be initially set.

[0047] After the parameters are determined, cluster analysis is performed based on the pharmacy's sales data and disease demand feature data. Sales data includes information such as the pharmacy's sales volume, sales revenue, and inventory levels, while disease demand feature data reflects the main demand of customers in the target area for certain disease-related drugs. Clustering operations divide pharmacies into different categories by analyzing these multidimensional feature data. For example, in the target area, pharmacies may be divided into the following categories through cluster analysis: Category A: Large comprehensive pharmacies with a wide range of medicines. The target customers are mainly patients with chronic diseases. The sales characteristics are reflected in the high proportion of chronic disease drugs.

[0048] Category B: Small community pharmacies, targeting the daily medication needs of ordinary residents. Their sales characteristics are reflected in the high sales of common medicines such as cold medicines and anti-inflammatory drugs.

[0049] Category C: Specialized pharmacies, targeting specific disease groups (such as diabetic patients), with sales characteristics reflected in the sales volume of specific drugs accounting for a significantly higher proportion than other categories of pharmacies.

[0050] After completing the clustering operation, it is necessary to perform feature distribution statistics on the pharmacies corresponding to each cluster number to further analyze the common characteristics of each category of pharmacies. Feature distribution statistics include key indicators such as sales mean, inventory level, and drug category coverage. For example, for a certain cluster number (such as category A), you can count the average monthly sales of pharmacies in this category, the average number of drug categories covered, and the sales share of chronic disease drugs.

[0051] By analyzing the statistical values ​​of the feature distribution, a category label can be generated for each cluster number. The category label is a brief description of the characteristics of the pharmacy, including the scale level (such as large pharmacies or small pharmacies) and the characteristics of the target customer group (such as chronic disease patients, ordinary residents, etc.). For example, the category label of category A can be described as "a large-scale comprehensive pharmacy, the target customer group is the middle-aged and elderly people, and the demand for chronic disease drugs is high."

[0052] In order to ensure the accuracy and rationality of the clustering results, the clustering effectiveness index needs to be calculated after determining the cluster number of each pharmacy according to the clustering results.

[0053] Specifically, a clustering effectiveness index of the clustering result is calculated; if the clustering effectiveness index does not meet a preset condition, the clustering parameters are re-determined.

[0054] Among them, the clustering effectiveness index is a quantitative evaluation of the quality of clustering results. Common effectiveness indicators include silhouette coefficient, Calinski-Harabasz index and Davies-Bouldin index. Assuming that the silhouette coefficient of the current clustering result is 0.4, which is lower than the preset threshold of 0.5, it means that there is a problem with the clustering result and the clustering parameters need to be further optimized. The clustering operation can be repeated by increasing or decreasing the K value. For example, adjusting the initial K value from 3 to 5 may make the clustering results more in line with the actual situation; or the key features can be screened or weighted to make the impact of the clustering results more significant. For example, increasing the weight of the feature of drug type coverage may make the clustering results more accurate. After adjusting the parameters, the clustering operation needs to be repeated and the clustering effectiveness index is calculated again until the index meets the preset conditions.

[0055] S107. Generate a customer profile based on the category label and feature data set, where the customer profile includes customer behavior preferences, disease demand characteristics, and drug purchase tendencies; The category labels are derived through cluster analysis and include the scale of the pharmacy (such as large pharmacies, small community pharmacies) and its main target customer groups (such as the elderly, patients with chronic diseases or young customers). Combined with the category labels, the characteristics of the pharmacy's customer base can be preliminarily determined. For example, a small community pharmacy may mainly serve surrounding residents, while a large pharmacy may cover a wider customer base with more diverse needs.

[0056] By analyzing the feature data set, customer behavior preferences, disease demand characteristics, and drug purchase tendencies are generated. The analysis of customer behavior preferences is based on the sales data of pharmacies, identifying customers' purchasing habits, such as purchase frequency, time preferences (such as whether they are concentrated in the morning and evening peaks), and purchase channels (online or offline). For example, customers of large pharmacies may be more inclined to make large one-time purchases, while customers of small pharmacies pay more attention to daily convenience and small consumption. Disease demand characteristics are obtained by combining disease maps and regional health trend index analysis. The disease map provides the association between drugs and diseases, while the health trend index reflects the health risk characteristics of the target area, such as influenza risk, chronic disease risk, etc. Through these data, the main disease needs of the customer group are identified. For example, the high proportion of demand for antihypertensive drugs indicates that the customer base of the pharmacy is mainly chronic disease patients, while the high sales of cold medicines and antiviral drugs may indicate that the proportion of young customers is large. Finally, by analyzing drug sales data and inventory data, customers' drug purchase tendencies are extracted. This includes customer preferences for drug brands (such as whether they prefer well-known brand drugs), sensitivity to drug prices (such as the sales distribution of low-priced drugs and high-priced drugs), and the tendency to repeatedly purchase specific drugs. For example, during the peak flu season, customers’ purchase inclination to purchase antiviral drugs will significantly increase, while middle-aged and elderly customers may be more inclined to purchase chronic disease management drugs. Ultimately, a customer profile is formed that includes customer behavior preferences, disease demand characteristics, and drug purchase inclinations.

[0057] S108, generating a marketing campaign plan corresponding to each category label through a preset model according to the category label, customer portrait, and inventory data in the sales data; In step S108, the inventory data is first analyzed to determine the best-selling and slow-selling drugs in the pharmacy. For example, for a certain category of pharmacies, the inventory of antihypertensive drugs is sufficient and the sales volume is high, while the inventory of vitamin drugs is overstocked. At this time, it is necessary to design corresponding promotional activities based on the sales characteristics and inventory status of the drugs to improve inventory turnover and sales efficiency. At the same time, inventory analysis also needs to consider potential disease needs. For example, according to the health trend index, it is predicted that the influenza season is about to begin, but the inventory of antiviral drugs is insufficient, so it is necessary to formulate a replenishment plan in advance and design related promotional activities.

[0058] Secondly, match customer profiles with inventory data to determine the key drugs and target customers for marketing activities. For example, for pharmacies whose customer profiles show that they are mainly patients with chronic diseases, they can focus on promoting chronic disease drugs such as antihypertensive drugs and hypoglycemic drugs, and design targeted promotional activities, such as "discounts for chronic disease drugs" or "free health testing services". For slow-selling drugs or seasonal drugs, special inventory clearance activities are designed, such as "buy antiviral drugs and get free antipyretics" or "discounts on vitamins".

[0059] Then, a specific marketing campaign plan is generated through the preset model. The preset model can be based on a rule-based decision model or a machine learning model, combined with the behavioral preferences, disease demand characteristics, and drug purchase tendencies in the customer portrait, to recommend the most suitable promotional activities for customers. For example, for customer groups that tend to buy online, online coupon activities can be designed; for customers who prefer high-end brand drugs, high-end drug combination discount activities can be designed. In addition, the marketing plan should also include health service activities, such as providing free health lectures or blood sugar and blood pressure testing services for patients with chronic diseases, or promoting vaccination discounts during the peak influenza season.

[0060] Finally, we comprehensively consider the time and channel preferences of customer behavior preferences and optimize the time and form of the activity content. For example, we can intensively promote promotional information during the peak period of customer purchases and reach target customers through multiple channels such as WeChat groups and APP notifications. Through these steps, we can finally generate a marketing activity plan corresponding to each category label to ensure that the activity can accurately match customer needs.

[0061] Optional, in Figure 1 After step S108 of the illustrated embodiment, the following steps may be performed: Calculate the similarity of the customer portraits of the first pharmacy and generate a similarity matrix, where the first pharmacy is the pharmacy corresponding to the same category label; calculate the difference value of the customer portraits of the first pharmacy based on the similarity matrix; extract the difference features of the target customer portraits, where the target customer portraits are customer portraits whose customer portrait difference values ​​are greater than a preset threshold; divide the first pharmacy into second pharmacies corresponding to multiple subcategory labels based on the difference features; adjust the promotion activity plan based on the sales data and customer portraits of each second pharmacy.

[0062] Among them, for pharmacies belonging to the same category label (i.e., the first pharmacy), based on their customer portraits (including behavioral preferences, disease demand characteristics, drug purchase tendencies, etc.), the similarity of customer portraits between pharmacies is calculated to generate a similarity matrix. The similarity matrix quantifies the degree of difference between them by measuring the similarity of customer portraits of different pharmacies. The customer portrait difference value of each pharmacy is calculated based on the similarity matrix, and the target customer portraits whose customer portrait difference value is greater than the preset threshold are identified. These portraits reflect significantly different customer needs or behavior patterns.

[0063] Extract the difference characteristics of the target customer portrait, such as the significant differences in purchasing preferences, disease needs or consumption habits of different pharmacy customer groups, and further divide the first pharmacy that originally belonged to the same category label into second pharmacies corresponding to multiple sub-category labels. This process can more accurately refine the pharmacy classification and identify subtle differences between pharmacies. For example, pharmacies in the same "small community pharmacy" category may be divided into two sub-categories: "pharmacy mainly for chronic disease patients" and "pharmacy mainly for seasonal needs of young people" due to different customer age groups or health needs.

[0064] Based on the sales data and customer profile of each second-tier pharmacy, the promotion plan is adjusted to more accurately match the characteristics of each sub-category of pharmacies. For example, for pharmacies that mainly serve patients with chronic diseases, promotional activities can focus on antihypertensive drugs, hypoglycemic drugs and other chronic disease drugs, and add health testing service discounts; while for pharmacies that mainly serve young customers, they can focus on promoting cold medicines and vitamins, and design promotional activities on online social platforms.

[0065] Optional, in Figure 1 In the illustrated embodiment, the method further comprises: Obtain the digital twin model of the pharmacy corresponding to each category label; based on sales data and customer portraits, use the digital twin model to simulate and evaluate the sales of the pharmacy under the promotion plan to obtain the simulation evaluation results; adjust the promotion plan based on the simulation evaluation results.

[0066] The system can obtain the digital twin model of the pharmacy corresponding to each category label from the industry database or the enterprise's internal technology platform. The digital twin model is pre-built based on the pharmacy's business data (such as sales records, inventory information, customer behavior) and market characteristics, and can accurately reflect the pharmacy's operating characteristics. After obtaining the digital twin model, the pharmacy's sales data, customer portraits, and current marketing campaign plans are used as inputs, and the digital twin model is used for simulation evaluation to predict the potential effects of promotional activities.

[0067] Based on the above input data, the digital twin model simulates the impact of promotional activities on pharmacy sales and may generate the following simulation results: Sales forecast: Predict sales trends during promotional activities, such as the increase in drug sales and the lasting impact after the activity ends.

[0068] Customer response analysis: Evaluate the degree of response of target customers to promotional activities, such as whether the discount is strong enough to attract the target customer group or whether there are potential customers that are not covered.

[0069] Inventory pressure assessment: simulate the impact of promotional activities on drug inventory, evaluate whether the inventory can meet the promotional demand, and whether there will be insufficient or excessive inventory problems.

[0070] According to the simulation evaluation results of the digital twin model, the activity plan is optimized and adjusted for the problems and deficiencies in the promotion activities. For example, if the simulation results show that the current discount is not enough to attract target customers, the discount can be appropriately increased or the threshold for full discount can be lowered; if the evaluation shows that the inventory of specific drugs may be insufficient, it is necessary to replenish them in advance; if some customer groups have a low response, the activity content can be adjusted to cover their needs more accurately. In addition, the activity time and promotion channels can be optimized according to customer behavior preferences, such as adding night coupons for online customers, or providing discount activities at specific time periods in the store for offline customers. Through these adjustments, promotional activities are more in line with customer needs and improve activity efficiency.

[0071] See also Figure 2 , is a schematic diagram of a marketing activity generation system based on sales data analysis provided in an embodiment of the present application. A marketing activity generation system based on sales data analysis 200 specifically includes: The acquisition module 201 is used to acquire the sales data of all drug stores in the target area, the disease maps corresponding to all drugs in the drug stores, and the population health data and environmental climate monitoring data in the target area; A calculation module 202 is used to establish a regional health assessment indicator set based on population health data and environmental climate monitoring data, and calculate the health situation index of the target area based on the regional health assessment indicator set; An extraction module 203 is used to extract feature information of the first data to obtain a feature data set, where the feature data set represents the sales characteristics, customer needs and health needs of the pharmacy, and the first data includes sales data, disease maps and health status index; An analysis module 204 is used to perform cluster analysis on the feature data set to generate a category label for each pharmacy, where the category label includes the scale level and target customer group of the pharmacy; The first generation module 205 is used to generate a customer profile based on the category label and feature data set, where the customer profile includes customer behavior preferences, disease demand characteristics, and drug purchase tendencies; The second generating module 206 is used to generate a marketing activity plan corresponding to each category tag through a preset model according to the category tag, customer portrait and inventory data in the sales data.

[0072] Optionally, the calculation module 202 is specifically configured to: Based on a preset statistical analysis method, health factors are extracted from population health data and environmental climate monitoring data; the health factors are stratified according to multiple time periods to obtain a health factor set, and the time periods include a first time period, a second time period, and a third time period, wherein the time range of the second time period is greater than the time range of the first time period, and the time range of the third time period is greater than the time range of the second time period; a health assessment indicator set is constructed based on the health factor set, and the health assessment indicator set includes multiple health assessment indicators for each time period; multiple health assessment indicators for each time period are aggregated and calculated to obtain a health situation index in the target area.

[0073] Optionally, the calculation module 202 is further specifically configured to: The health factors of each time period are feature extracted according to the target dimensions to obtain the basic feature vector, and the target dimensions include the disease risk dimension, the environmental pressure dimension and the population health vulnerability dimension; the basic feature vector is subjected to time series correlation analysis to obtain the time series correlation feature; the basic feature vector is feature fused with the time series correlation feature to obtain the time series health feature vector, and the time series health feature vector includes feature values ​​corresponding to multiple features; the feature values ​​are ranked by importance through a preset importance evaluation algorithm to obtain the importance ranking result; multiple features in the importance ranking result whose importance scores exceed the preset threshold are determined as health assessment indicators to obtain a health assessment indicator set.

[0074] Optionally, the analysis module 204 is specifically used for: Based on the disease map, the disease demand characteristic data of the target area is extracted according to the regional health situation index; the clustering parameters are determined based on the preset clustering evaluation indicators, and the sales data and the disease demand characteristic data are clustered according to the clustering parameters to obtain the clustering results; the clustering number of each pharmacy is determined according to the clustering results; the characteristic distribution statistics of the pharmacies corresponding to the same cluster number in the preset dimensions are counted; the category label corresponding to each cluster number is determined according to the characteristic distribution statistics.

[0075] Optionally, the analysis module 204 is further specifically configured to: Calculate clustering effectiveness indicators for clustering results; If the clustering effectiveness index does not meet the preset conditions, the clustering parameters are re-determined.

[0076] Optionally, the system further includes an adjustment module 207, which is specifically configured to: Calculate the similarity of the customer portraits of the first pharmacy and generate a similarity matrix, where the first pharmacy is the pharmacy corresponding to the same category label; calculate the difference value of the customer portraits of the first pharmacy based on the similarity matrix; extract the difference features of the target customer portraits, where the target customer portraits are customer portraits whose customer portrait difference values ​​are greater than a preset threshold; divide the first pharmacy into second pharmacies corresponding to multiple subcategory labels based on the difference features; adjust the promotion activity plan based on the sales data and customer portraits of each second pharmacy.

[0077] Optionally, the system further includes a simulation module 208, which is specifically used for: Obtain the digital twin model of the pharmacy corresponding to each category label; based on sales data and customer portraits, use the digital twin model to simulate and evaluate the sales of the pharmacy under the promotion plan to obtain the simulation evaluation results; adjust the promotion plan based on the simulation evaluation results.

[0078] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0079] This embodiment also discloses an electronic device, referring to Figure 3 The electronic device may include: at least one processor 301 , at least one communication bus 302 , a user interface 303 , a network interface 304 , and at least one memory 305 .

[0080] The communication bus 302 is used to realize the connection and communication between these components.

[0081] The user interface 303 may include a display screen (Display) and a camera (Camera). The optional user interface 303 may also include a standard wired interface and a wireless interface.

[0082] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0083] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0084] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally be at least one storage device located away from the aforementioned processor 301. As Figure 3 As shown, the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program for a method for generating a marketing campaign based on sales data analysis.

[0085] exist Figure 3 In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 301 can be used to call an application stored in the memory 305 for a marketing activity generation method based on sales data analysis. When executed by one or more processors 301, the electronic device executes one or more methods in the above-mentioned embodiments.

[0086] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0087] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0088] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0089] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0090] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0091] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory 305. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory 305 and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory 305 includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk.

[0092] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for generating marketing activities based on sales data analysis, characterized in that: Applied in a server, the method comprises: Obtaining sales data of all pharmacies in the target area, disease maps corresponding to all drugs in the pharmacies, and population health data and environmental climate monitoring data in the target area; Establishing a regional health assessment indicator set according to the population health data and environmental climate monitoring data, and calculating the health situation index of the target area based on the regional health assessment indicator set; Extracting feature information of the first data to obtain a feature data set, wherein the feature data set represents the sales characteristics, customer needs, and health needs of the pharmacy, and the first data includes the sales data, the disease map, and the health status index; Performing cluster analysis on the feature data set to generate a category label for each of the pharmacies, wherein the category label includes the scale level and target customer group of the pharmacy; Generate a customer profile based on the category label and the feature data set, wherein the customer profile includes customer behavior preferences, disease demand characteristics, and drug purchase tendencies; According to the category labels, the customer portraits and the inventory data in the sales data, a marketing activity plan corresponding to each category label is generated through a preset model.

2. The method according to claim 1, characterized in that The step of establishing a regional health assessment indicator set according to the population health data and the environmental climate monitoring data, and calculating the health status index of the target area based on the regional health assessment indicator set, specifically includes: Extracting health factors from the population health data and the environmental climate monitoring data based on a preset statistical analysis method; The health factors are layered according to a plurality of time periods to obtain a health factor set, wherein the time period includes a first time period, a second time period, and a third time period, wherein the time range of the second time period is greater than the time range of the first time period, and the time range of the third time period is greater than the time range of the second time period; Constructing a health assessment indicator set according to the health factor set, wherein the health assessment indicator set includes a plurality of health assessment indicators in each of the time periods; Aggregate calculation is performed on multiple health assessment indicators in each time period to obtain a health status index in the target area.

3. The method according to claim 2, characterized in that The step of constructing a health assessment indicator set according to the health factor set specifically includes: Extracting features of the health factors of each time period according to target dimensions to obtain a basic feature vector, wherein the target dimensions include disease risk dimensions, environmental pressure dimensions, and population health vulnerability dimensions; Performing time series correlation analysis on the basic feature vector to obtain a time series correlation feature; Performing feature fusion on the basic feature vector and the time series associated feature to obtain a time series health feature vector, wherein the time series health feature vector includes feature values ​​corresponding to multiple features; The importance of the characteristic values ​​is sorted by using a preset importance evaluation algorithm to obtain an importance sorting result; A plurality of the features whose importance scores in the importance ranking result exceed a preset threshold are determined as health assessment indicators to obtain a health assessment indicator set.

4. The method according to claim 1, characterized in that: The cluster analysis of the feature data set to generate a category label for each of the pharmacies specifically includes: Based on the disease map, extracting disease demand characteristic data of the target area according to the regional health situation index; Determine clustering parameters based on preset clustering evaluation indicators, and perform clustering operations on the sales data and the disease demand characteristic data according to the clustering parameters to obtain clustering results; Determine a cluster number for each of the pharmacies according to the clustering result; Counting the characteristic distribution statistics of the pharmacies corresponding to the same cluster number in the preset dimension; The category label corresponding to each of the cluster numbers is determined according to the feature distribution statistics.

5. The method according to claim 4, characterized in that After determining the cluster number of each of the pharmacies according to the clustering result, the method further includes: Calculating a clustering effectiveness index of the clustering result; If the clustering effectiveness index does not meet the preset conditions, the clustering parameters are re-determined.

6. The method according to claim 1, characterized in that After generating a promotion activity plan for each category corresponding to the category label by a preset model according to the category label, the customer portrait and the inventory data in the sales data, the method further includes: Calculate the similarity of customer portraits of the first pharmacy to generate a similarity matrix, where the first pharmacy is the pharmacy corresponding to the same category label; Calculating a customer portrait difference value of the first pharmacy according to the similarity matrix; Extracting difference features of the target customer portrait, wherein the target customer portrait is a customer portrait whose difference value of the customer portrait is greater than a preset threshold; Dividing the first pharmacy into second pharmacies corresponding to a plurality of subcategory labels according to the difference characteristics; The promotion plan is adjusted based on the sales data of each of the second pharmacies and the customer portrait.

7. The method according to claim 1, characterized in that The method further comprises: Obtain a digital twin model of the pharmacy corresponding to each of the category labels; Based on the sales data and the customer portrait, the sales situation of the pharmacy under the promotion activity plan is simulated and evaluated by the digital twin model to obtain a simulation evaluation result; The promotion activity plan is adjusted according to the simulation evaluation result.

8. A marketing activity generation system based on sales data analysis, characterized in that: include: An acquisition module, used to acquire the sales data of all pharmacies in the target area, the disease maps corresponding to all drugs in the pharmacies, and the population health data and environmental climate monitoring data in the target area; A calculation module, used to establish a regional health assessment indicator set according to the population health data and environmental climate monitoring data, and calculate the health situation index of the target area based on the regional health assessment indicator set; An extraction module, configured to extract feature information of the first data to obtain a feature data set, wherein the feature data set represents the sales characteristics, customer needs, and health needs of the pharmacy, and the first data includes the sales data, the disease map, and the health status index; An analysis module, configured to perform cluster analysis on the feature data set to generate a category label for each of the pharmacies, wherein the category label includes the scale level and target customer group of the pharmacy; A first generating module, configured to generate a customer profile according to the category label and the feature data set, wherein the customer profile includes customer behavior preferences, disease demand characteristics, and drug purchase tendency; The second generating module is used to generate a marketing activity plan corresponding to each category label through a preset model according to the category label, the customer portrait and the inventory data in the sales data.

9. A marketing activity generating device based on sales data analysis, characterized in that: include: one or more processors and memory; The memory is coupled to the one or more processors, and is used to store computer program codes, wherein the computer program codes include computer instructions, and the one or more processors call the computer instructions to enable the marketing activity generation device based on sales data analysis to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a device for generating a marketing campaign based on sales data analysis, the device for generating a marketing campaign based on sales data analysis is enabled to execute the method according to any one of claims 1 to 7.

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