Intelligent conversion and statistical method and system for business sales data

Abstract

The invention relates to the technical field of sales data statistics, in particular to an intelligent conversion and statistical method and system for business sales data, and the method comprises the steps: collecting original sales data in real time, carrying out the data preprocessing of the original sales data, generating processed sales data, and carrying out the statistical processing of the processed sales data. Associating the processed sales data to a corresponding salesman entity; based on the processed sales data, dynamically generating a sales combat containing key performance indicator visualization; according to a preset dynamic evaluation model, in combination with the associated processed sales data, performing comprehensive performance scoring and ranking on the salesman entities, and determining a red list and a black business list based on a ranking result; and comparing and analyzing the behavior data characteristics of the employee entities in the red list and the black list, and generating different sales strategy suggestions. According to the application, closed-loop management from data acquisition to strategy generation is realized, and the automation and intelligence level and decision-making efficiency of sales management are improved.

Description

Technical Field

[0001] This invention relates to the technical field of sales data statistics, and in particular to an intelligent conversion, statistical method and system for business sales data. Background Technology

[0002] In today's digital business environment, enterprise sales management faces unprecedented challenges and opportunities. On the one hand, market competition is becoming increasingly fierce, and consumer demands are becoming more diversified and personalized. Enterprises need to grasp market dynamics and customer needs more accurately to improve the sales team's combat capabilities and market share. On the other hand, the rapid development of information technology has led to an explosive growth in sales data. Enterprises' internal CRM (Customer Relationship Management) systems, ERP (Enterprise Resource Planning) systems, and the back-end systems of various e-commerce platforms have accumulated a large amount of business data. However, how to effectively utilize this scattered and heterogeneous data and transform it into practical sales strategies has become a core challenge in modern sales management.

[0003] Currently, existing technical solutions in the field of sales data analysis and performance management typically have the following limitations:

[0004] Insufficient data integration and processing capabilities: Sales data is typically scattered across multiple independent systems and platforms, such as the backends of different e-commerce platforms, CRM systems, and offline sales records, resulting in diverse data formats and inconsistent quality. Existing technologies lack efficient and unified data collection and cleaning mechanisms, making it difficult to automatically unify formats, remove duplicates, and imput missing values ​​from multi-source heterogeneous data. This leads to low data integration efficiency and fails to provide a comprehensive and reliable data foundation for subsequent analysis. Many companies still rely on manual methods to export data from different systems and integrate it into spreadsheets, a cumbersome and error-prone process.

[0005] Sales performance evaluation suffers from a lack of simplistic dimensions: Traditional sales performance management, especially sales report generation and red / black list assessments, relies heavily on simple key performance indicators (KPIs) statistics and static rankings. Existing technological solutions struggle to provide multi-dimensional, comprehensive, and quantitative evaluations of salesperson performance. For example, the lack of consideration for performance stability, growth trends, and customer follow-up quality leads to biased evaluation results that fail to accurately reflect a salesperson's overall capabilities and development potential. This results in a lack of scientific basis for sales team management and incentive measures, making it difficult to accurately identify top performers and those needing improvement.

[0006] A disconnect exists between data analysis and decision support: Many existing sales data analysis tools and reporting systems primarily focus on listing basic statistical indicators and visualizing data. While they can "present the status quo," they lack in-depth analytical capabilities and forward-looking guidance. In particular, current technologies struggle to automatically and intelligently pinpoint the behavioral root causes behind performance shortcomings based on comparative analysis using red and black lists, and to generate specific, actionable sales strategy improvement suggestions. The decision-making process still heavily relies on managers' personal experience, failing to form a closed loop from "data insight" to "strategy generation," thus limiting further improvements in sales management efficiency.

[0007] Therefore, there is an urgent need in this field for a solution that can achieve full-chain automation and intelligence, from multi-source sales data collection and intelligent cleaning and integration to multi-dimensional dynamic performance evaluation and intelligent strategy suggestion generation. Summary of the Invention

[0008] To address the aforementioned technical issues, this application provides an intelligent conversion and statistical method and system for business sales data.

[0009] The above-mentioned objective of this application is achieved through the following technical solution:

[0010] A method for intelligent transformation and statistical analysis of business sales data, comprising the following steps:

[0011] Real-time collection of raw sales data, data preprocessing of the raw sales data to generate processed sales data, and association of the processed sales data with the corresponding salesperson entity;

[0012] Based on the processed sales data, a sales report containing visualizations of key performance indicators is dynamically generated. The key performance indicators include at least sales revenue, conversion rate, and average order value.

[0013] Based on the preset dynamic evaluation model and the associated processed sales data, the salesperson entities are comprehensively rated and ranked, and the red list and black list are determined based on the ranking results.

[0014] By comparing and analyzing the behavioral data characteristics of salesperson entities in the red list and black list, differentiated sales strategy suggestions are generated.

[0015] The sales reports, blacklists, and sales strategy suggestions will be integrated, pushed out, and visualized.

[0016] By adopting the above technical solution, and through real-time collection of raw sales data from multiple heterogeneous data sources followed by cleaning, transformation, and semantic unification processing, the low integration efficiency caused by data fragmentation and inconsistent formats in existing technologies can be effectively solved. Standardized sales data records are generated and linked to salesperson entities, providing a high-quality data foundation for subsequent analysis. Dynamically generated sales reports with visualized key performance indicators enable managers to quickly and intuitively grasp overall sales performance. Salespersons are comprehensively evaluated and ranked based on a dynamic assessment model, establishing "red lists" and "black lists," achieving diversified and dynamic evaluation standards and avoiding the bias of single performance indicators. Finally, differentiated sales strategy suggestions are generated through comparative analysis, and integrated visualization results are pushed out, forming a closed-loop management system from data collection to decision support, significantly improving the precision and efficiency of sales management.

[0017] In a preferred embodiment, this application can be further configured as follows: the real-time collection of raw sales data, the preprocessing of the raw sales data to generate processed sales data, and the association of the processed sales data with the corresponding salesperson entity, specifically including:

[0018] The system receives raw sales data through connection interfaces from different data sources and adds timestamps to store it in a temporary data buffer.

[0019] Read the raw sales data from the temporary data buffer, normalize and map the data fields of the raw sales data to eliminate semantic ambiguity;

[0020] Identify and remove duplicate original sales data, and fill in the missing key fields appropriately to obtain cleaned original sales data;

[0021] The cleaned raw sales data is converted according to a preset standardized format to generate processed sales data. Based on the salesperson's unique identifier, the processed sales data is associated and bound with the corresponding salesperson entity profile.

[0022] By adopting the above technical solution, raw sales data is received from different data source interfaces, timestamp buffers are added, and normalization mapping, deduplication, and missing field imputation are performed to generate standardized processed data and associate it with salesperson entities. This preprocessing step effectively eliminates data heterogeneity and noise, ensures the consistency and reliability of data quality, provides a clean and structured data foundation for subsequent analysis, reduces decision-making bias caused by data errors, and improves the robustness and processing efficiency of the system.

[0023] In a preferred embodiment, this application can be further configured such that: the dynamic generation of sales reports containing visualizations of key performance indicators based on the processed sales data specifically includes:

[0024] Sales data associated with the salesperson entity are aggregated according to a preset time period to calculate the value of key performance indicators;

[0025] Compare and analyze the current period's key performance indicator values ​​with historical data from the same period and preset target values ​​to calculate the completion rate and growth rate;

[0026] Based on the values, completion rates, and growth rates of the key performance indicators, a visual sales report containing data tables, trend charts, and performance summaries is automatically generated.

[0027] By adopting the above technical solution, sales data is aggregated according to a preset cycle, key performance indicator values ​​are calculated, and the completion rate and growth rate are compared and analyzed with historical data and target values. A visual sales report containing tables, charts, and summaries is automatically generated. This step enables dynamic monitoring and visualization of performance data, allowing managers to intuitively grasp sales progress, adjust strategies in a timely manner, and enhance data readability and interactivity, thereby improving the responsiveness and scientific nature of sales management decisions.

[0028] In a preferred embodiment, this application can be further configured as follows: Based on a preset dynamic evaluation model and combined with associated processed sales data, a comprehensive performance score and ranking of sales personnel entities are determined, and a red list and black list are determined based on the ranking results. Specifically, this includes:

[0029] The dynamic evaluation model is constructed, which includes the dimensions of absolute performance, indicator stability, and growth trend, and weights are assigned to different dimensions.

[0030] Based on the processed sales data that has been associated, calculate the individual score for each salesperson entity under the dimensions of absolute performance, indicator stability, and growth trend.

[0031] Based on the weights of each dimension in the dynamic evaluation model, the individual scores of each salesperson entity are weighted and integrated to obtain their comprehensive performance score.

[0032] All sales representatives are ranked according to their overall performance scores. Sales representatives in the top first preset percentage are included in the red list, while those in the bottom second preset percentage or those whose key performance indicators have been continuously unsatisfactory are included in the black list.

[0033] By adopting the above technical solution, a dynamic evaluation model is constructed that includes dimensions of absolute performance, indicator stability, and growth trend. Individual scores for each dimension are calculated and weighted, and a red and black list is generated based on the overall score. This multi-dimensional evaluation mechanism avoids the bias of a single indicator, provides a fair and comprehensive assessment of salesperson performance, helps identify outstanding and under-improvement personnel, thereby incentivizing team competition, optimizing resource allocation, and improving the overall performance of the sales team.

[0034] In a preferred example, this application can be further configured as follows: Based on the processed sales data associated with the relationship, the individual score for each salesperson entity in the indicator stability dimension is calculated, specifically including:

[0035] Obtain the daily key performance indicator sequence of the salesperson entity within a preset historical period, calculate the ratio of the standard deviation to the mean of the daily key performance indicator sequence, and obtain the performance volatility coefficient.

[0036] Based on a preset mapping relationship between volatility coefficient and scoring, the performance volatility coefficient is converted into a first stability score. Anomalies that are continuously below a preset threshold in the daily key performance indicator sequence are detected. A second stability score is calculated based on the number and distribution of anomalies. The final indicator stability dimension score is obtained by combining the first stability score with the second stability score.

[0037] By employing the aforementioned technical solution, the historical daily key performance indicator (KPI) sequences of sales personnel are obtained, performance volatility coefficients are calculated, and stability scores are generated based on the volatility coefficients and outlier detection. This step enhances the granularity of performance evaluation by quantifying indicator volatility, helps identify stability issues in sales personnel performance, provides data support for targeted training and management, thereby reducing the risk of performance volatility and improving the sales team's sustained productivity.

[0038] In a preferred example, this application can be further configured as follows: Based on the processed sales data associated with the relationship, the calculation of a single score for each salesperson entity in the growth trend dimension specifically includes:

[0039] Based on the current time point, obtain the key performance indicator data of the salesperson entity for several consecutive time periods in the past;

[0040] The key performance indicator data were fitted using a linear regression algorithm to obtain the performance growth slope;

[0041] Calculate the month-on-month growth rate of the current period's key performance indicator data compared to the previous period's data. Based on the performance growth slope and the month-on-month growth rate, obtain the growth trend dimension score through weighted calculation.

[0042] By employing the aforementioned technical solution, a linear regression algorithm is used to fit historical key performance indicator data to obtain the growth slope and month-on-month growth rate, and a growth trend score is calculated. This step predicts the development potential of sales personnel through trend analysis, enabling managers to proactively formulate growth plans, encourage continuous improvement, optimize talent pipeline development, and thus promote the long-term healthy development of the sales team and enhance organizational competitiveness.

[0043] In a preferred embodiment, this application can be further configured as follows: the comparative analysis of behavioral data characteristics of salesperson entities in the red list and black list to generate differentiated sales strategy recommendations specifically includes:

[0044] Extract high-frequency and effective behavioral features of salesperson entities in the red list to form a set of excellent behavioral features, and identify the key performance indicator shortcomings and corresponding behavioral missing features of salesperson entities in the black list.

[0045] The missing behavioral features are matched with the set of excellent behavioral features, and targeted improvement strategies are mapped from a preset strategy knowledge base.

[0046] Based on the specific weaknesses of the salesperson entities in the blacklist, the improvement strategies are personalized and combined to generate the sales strategy recommendations.

[0047] By employing the aforementioned technical solution, the high-frequency, effective behavioral characteristics of sales representatives on the "red list" are extracted and compared with the weaker behaviors of those on the "blacklist." Improvement strategies are then mapped from the strategy knowledge base, and personalized sales strategy suggestions are generated. This step, through data-driven behavioral analysis, provides specific and actionable improvement solutions, helping sales representatives address their weaknesses, replicate successful experiences, and thereby enhance the targeting and effectiveness of sales strategies, accelerating the performance improvement process.

[0048] Secondly, the above-mentioned inventive objective of this application is achieved through the following technical solutions:

[0049] A smart conversion and statistical system for business sales data, the smart conversion and statistical system for business sales data comprising:

[0050] The sales data acquisition module is used to collect raw sales data in real time, preprocess the raw sales data, generate processed sales data, and associate the processed sales data with the corresponding salesperson entity.

[0051] The sales report generation module is used to dynamically generate sales reports containing visualizations of key performance indicators based on the processed sales data. The key performance indicators include at least sales revenue, conversion rate, and average order value.

[0052] The list generation module is used to score and rank the salesperson entities based on a preset dynamic evaluation model and the associated processed sales data, and to determine the red list and black list based on the ranking results.

[0053] The strategy generation module is used to compare and analyze the behavioral data characteristics of salesperson entities in the red list and black list, and generate differentiated sales strategy suggestions.

[0054] The visualization module is used to integrate, push, and visualize the sales reports, red and black lists, and sales strategy suggestions.

[0055] By adopting the above technical solution, and through real-time collection of raw sales data from multiple heterogeneous data sources followed by cleaning, transformation, and semantic unification processing, the low integration efficiency caused by data fragmentation and inconsistent formats in existing technologies can be effectively solved. Standardized sales data records are generated and linked to salesperson entities, providing a high-quality data foundation for subsequent analysis. Dynamically generated sales reports with visualized key performance indicators enable managers to quickly and intuitively grasp overall sales performance. Salespersons are comprehensively evaluated and ranked based on a dynamic assessment model, establishing "red lists" and "black lists," achieving diversified and dynamic evaluation standards and avoiding the bias of single performance indicators. Finally, differentiated sales strategy suggestions are generated through comparative analysis, and integrated visualization results are pushed out, forming a closed-loop management system from data collection to decision support, significantly improving the precision and efficiency of sales management.

[0056] Thirdly, the above-mentioned objectives of this application are achieved through the following technical solutions:

[0057] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the intelligent conversion and statistical method for the aforementioned business sales data.

[0058] Fourthly, the above-mentioned objectives of this application are achieved through the following technical solutions:

[0059] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the intelligent conversion and statistical methods for the aforementioned business sales data.

[0060] In summary, this application includes at least one of the following beneficial technical effects:

[0061] 1. By collecting raw sales data from multiple heterogeneous data sources in real time and performing cleaning, transformation, and semantic unification processing, this technology effectively solves the problem of low integration efficiency caused by data fragmentation and inconsistent formats in existing technologies. It generates standardized sales data records and links them to salesperson entities, providing a high-quality data foundation for subsequent analysis. Dynamically generated sales reports with visualized key performance indicators enable managers to quickly and intuitively grasp overall sales performance. Based on a dynamic evaluation model, salespersons are comprehensively scored and ranked, and red and black lists are established, achieving diversified and dynamic evaluation standards and avoiding the one-sidedness of single performance indicators. Finally, differentiated sales strategy suggestions are generated through comparative analysis, and integrated visualization results are pushed out, forming a closed-loop management system from data collection to decision support, significantly improving the precision and efficiency of sales management.

[0062] 2. Aggregate sales data according to a preset cycle, calculate key performance indicator values, and compare and analyze completion rates and growth rates with historical data and target values. Automatically generate a visual sales report containing tables, charts, and summaries. This step enables dynamic monitoring and visualization of performance data, allowing managers to intuitively grasp sales progress, adjust strategies promptly, and enhance data readability and interactivity, thereby improving the responsiveness and scientific basis of sales management.

[0063] 3. Construct a dynamic evaluation model that includes dimensions of absolute performance, indicator stability, and growth trend. Calculate individual scores for each dimension and weight them together. Generate a red and black list based on the overall score. This multi-dimensional evaluation mechanism avoids the one-sidedness of a single indicator, provides a fair and comprehensive assessment of salesperson performance, helps identify outstanding and under-improvement personnel, thereby incentivizing team competition, optimizing resource allocation, and improving the overall performance of the sales team.

[0064] 4. Extract high-frequency, effective behavioral characteristics from sales representatives on the "red list" and compare them with the weaker behaviors of those on the "black list." Map improvement strategies from the strategy knowledge base and generate personalized sales strategy suggestions. This step, through data-driven behavioral analysis, provides specific and actionable improvement solutions, helping sales representatives address their weaknesses, replicate successful experiences, and thus enhance the targeting and effectiveness of sales strategies, accelerating the performance improvement process. Attached Figure Description

[0065] Figure 1 This is a flowchart of an intelligent conversion and statistical method for business sales data in one embodiment of this application;

[0066] Figure 2 This is a flowchart illustrating the implementation of step S10 in the intelligent conversion and statistical method for business sales data in one embodiment of this application.

[0067] Figure 3This is a flowchart illustrating the implementation of step S20 in the intelligent conversion and statistical method for business sales data in one embodiment of this application.

[0068] Figure 4 This is a flowchart illustrating the implementation of step S30 in the intelligent conversion and statistical method for business sales data in one embodiment of this application.

[0069] Figure 5 This is a flowchart illustrating the implementation of step S32 in the intelligent conversion and statistical method for business sales data in one embodiment of this application.

[0070] Figure 6 This is another implementation flowchart of step S32 in the intelligent conversion and statistical method of business sales data in one embodiment of this application;

[0071] Figure 7 This is a flowchart illustrating the implementation of step S40 in the intelligent conversion and statistical method for business sales data in one embodiment of this application.

[0072] Figure 8 This is a principle block diagram of an intelligent conversion and statistical system for business sales data in one embodiment of this application;

[0073] Figure 9 This is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0074] The present application will be further described in detail below with reference to the accompanying drawings.

[0075] In one embodiment, such as Figure 1 As shown, this application discloses an intelligent conversion and statistical method for business sales data, which specifically includes the following steps:

[0076] S10: Collect raw sales data in real time, preprocess the raw sales data to generate processed sales data, and associate the processed sales data with the corresponding salesperson entity.

[0077] In this embodiment, raw sales data refers to unprocessed sales records obtained in real time from various sales channels (such as CRM systems, e-commerce platforms, and offline store terminals), including order information, customer interaction logs, transaction amounts, etc. Data preprocessing aims to eliminate data noise and inconsistencies, ensuring data quality. A salesperson entity refers to a salesperson profile stored in the system, containing attributes such as a unique identifier, job information, and historical performance.

[0078] Specifically, the system connects to different data sources via a distributed data acquisition interface to receive raw sales data streams in real time. The received data is timestamped and stored in a temporary data buffer to handle high-concurrency data inflows and prevent data loss. Subsequently, the data cleaning module reads the buffer data, normalizes data fields, and eliminates semantic ambiguity. Next, a deduplication algorithm identifies and removes duplicate records, and missing key fields are filled using interpolation or a prediction model based on historical data to obtain cleaned raw sales data. Finally, the cleaned data is converted according to a pre-defined JSON or XML standardized format to generate processed sales data, which is then associated with the salesperson's unique identifier (such as employee ID) and their entity profile, forming a complete salesperson-sales data mapping relationship.

[0079] S20: Based on the processed sales data, dynamically generate a sales report containing visualizations of key performance indicators (KPIs), which include at least sales revenue, conversion rate, and average order value.

[0080] In this embodiment, the sales report refers to a visual report that aggregates the performance of salespersons. Key performance indicators (KPIs) such as sales revenue (total transaction amount), conversion rate (number of transactions / number of visits) and average order value (sales revenue / number of transactions) are dynamically generated, meaning they are automatically updated based on real-time data.

[0081] Specifically, sales data is aggregated and linked according to a preset time period (e.g., daily or weekly), and SQL aggregation functions are used to calculate the KPI value for each salesperson. Then, the current period's KPI is compared with historical data from the same period last year (e.g., the same period last month) and preset target values ​​(e.g., quarterly targets) to calculate the completion rate (actual value / target value) and growth rate ((current value - historical value) / historical value). Finally, a battle report containing data tables, trend line charts, and performance summary text is automatically generated using a visualization library, supporting real-time push to the management terminal.

[0082] S30: Based on the preset dynamic evaluation model and the associated processed sales data, the salesperson entity is comprehensively rated and ranked, and the red list and black list are determined based on the ranking results.

[0083] In this embodiment, the dynamic evaluation model is a multi-dimensional scoring model. The red list recognizes outstanding salespersons, the black list identifies those who need improvement, and the ranking is based on weighted scores.

[0084] Specifically, the model building module defines three dimensions: absolute performance (e.g., sales revenue), indicator stability (e.g., volatility), and growth trend (e.g., growth rate). Weights are assigned to each dimension, and individual scores for each salesperson are calculated. These scores are then weighted and summed to obtain a comprehensive performance score. Finally, the scores are sorted in descending order, with the top 10% placed on a "red list," and the bottom 5% or those failing to meet targets for three consecutive periods placed on a "black list."

[0085] S40: Compare and analyze the behavioral data characteristics of the salesperson entities in the red list and black list to generate differentiated sales strategy suggestions.

[0086] In this embodiment, behavioral data features refer to the salesperson's work behavior (such as customer visit frequency and communication duration), and strategy recommendations are based on best practices and weakness analysis.

[0087] Specifically, the process involves extracting high-frequency, effective characteristics (such as frequent customer follow-ups) from sales representatives on the "red list" to form a high-performing set; and identifying the weaknesses (such as low conversion rates) and corresponding behavioral deficiencies (such as lack of follow-up) of sales representatives on the "black list." Then, a matching algorithm compares the missing features with the high-performing set, mapping improvement strategies (such as increased training) from a strategy knowledge base (such as a database storing best practices). Finally, personalized strategies are combined based on specific weaknesses to generate a recommendation report.

[0088] S50: Integrate and push out the sales reports, red and black lists, and sales strategy suggestions, and display them visually.

[0089] Specifically, integrated push refers to displaying and visually integrating battle reports, lists, and suggestions into a responsive UI through a unified platform, supporting drill-down queries and real-time updates, and pushing them to administrators via messaging services.

[0090] In this embodiment, by collecting raw sales data from multiple heterogeneous data sources in real time and performing cleaning, transformation, and semantic unification processing, the problem of low integration efficiency caused by data dispersion and inconsistent formats in existing technologies can be effectively solved. Standardized sales data records are generated and linked to salesperson entities, providing a high-quality data foundation for subsequent analysis. Sales reports with visualized key performance indicators are dynamically generated, enabling managers to quickly and intuitively grasp overall sales performance. Salespersons are comprehensively evaluated and ranked based on a dynamic assessment model, and red and black lists are established, achieving diversified and dynamic evaluation standards and avoiding the one-sidedness of single performance indicators. Finally, differentiated sales strategy suggestions are generated through comparative analysis, and integrated visualization results are pushed out, forming a closed-loop management system from data collection to decision support, significantly improving the precision and efficiency of sales management.

[0091] In one embodiment, such as Figure 2As shown, in step S10, the raw sales data is collected in real time, preprocessed, and processed to generate processed sales data. The processed sales data is then associated with the corresponding salesperson entity. Specifically, this includes:

[0092] S11: Receives raw sales data through connection interfaces based on different data sources, adds timestamps, and stores it in a temporary data buffer.

[0093] In this embodiment, the connection interfaces for different data sources refer to interface components that adapt to multiple data protocols (such as HTTP, FTP, and database connections) to ensure cross-platform data compatibility. Timestamps are used to record data arrival times, and temporary data buffers serve as an intermediate storage layer to enable asynchronous processing of data streams.

[0094] Specifically, during initialization, connection parameters for each data source are configured, and data is retrieved in real time through polling or event-driven mechanisms. After data is received, a timestamp accurate to milliseconds is added, and the data is stored in an in-memory temporary buffer. The buffer has a capacity threshold and an expiration policy to prevent memory overflow.

[0095] S12: Read the original sales data from the temporary data buffer, normalize the data fields of the original sales data, and eliminate semantic ambiguity.

[0096] In this embodiment, normalization mapping refers to mapping heterogeneous data fields to a unified data model. Semantic ambiguity elimination is achieved through a predefined mapping rule base to ensure data consistency.

[0097] Specifically, after reading data from the buffer, the mapping rule library is invoked to map the source fields to the target standard fields. Simultaneously, the enumerated values ​​are standardized to eliminate ambiguity caused by differences in data sources.

[0098] S13: Identify and remove duplicate original sales data, and fill in the missing key fields appropriately to obtain cleaned original sales data.

[0099] In this embodiment, duplicate data identification is based on hash value comparison of business primary keys (such as order numbers), and missing field filling is performed using statistical methods or machine learning models to ensure data integrity.

[0100] Specifically, the business primary key hash value of each record is calculated and compared with historical hash values ​​in the cache; if duplicates are found, the record is discarded. For missing fields, imputation is performed using the mean or pattern, or missing values ​​are predicted based on a regression model, generating a cleaned dataset.

[0101] S14: Convert the cleaned raw sales data according to a preset standardized format to generate processed sales data. Based on the salesperson's unique identifier, associate and bind the processed sales data with the corresponding salesperson entity profile.

[0102] In this embodiment, the salesperson entity profile refers to the salesperson metadata stored in the database.

[0103] Specifically, the cleaned data is serialized into a standard format and persisted to the data lake. The association module uses the salesperson's unique identifier (such as employee ID) as a foreign key to index and link the processed sales data with the salesperson profile table, establishing one-to-one or one-to-many associations.

[0104] In one embodiment, such as Figure 3 As shown, in step S20, a sales report containing visualizations of key performance indicators is dynamically generated based on the processed sales data. This specifically includes:

[0105] S21: Aggregate sales data associated with the salesperson entity according to a preset time period, and calculate the value of key performance indicators.

[0106] In this embodiment, the time period is configured by the system (e.g., by hour, day, or month), aggregation operations use grouped queries, and KPI calculations are based on mathematical formulas.

[0107] Specifically, the sales amount is summed and the number of transactions is counted by grouping salesperson ID and time window, and then the conversion rate and average order value are calculated. The results are stored in an aggregate table.

[0108] S22: Compare and analyze the current period's key performance indicator values ​​with historical data from the same period and preset target values ​​to calculate the completion rate and growth rate.

[0109] In this embodiment, the comparative analysis involves time series comparison, and the completion rate and growth rate are expressed as percentages for performance evaluation.

[0110] Specifically, the system queries historical databases to obtain data from the same period, compares this data with the current value, calculates the percentage difference, and marks abnormal fluctuations.

[0111] S23: Based on the values, completion rates, and growth rates of the key performance indicators, automatically generate a visualized sales report that includes data tables, trend charts, and performance summaries.

[0112] Specifically, KPI data is populated into an HTML template to generate a battle report containing tables, charts, and text summaries, which is then pushed via email or message queue.

[0113] In one embodiment, such as Figure 4As shown, in step S30, based on the preset dynamic evaluation model and the associated processed sales data, the salesperson entities are comprehensively evaluated and ranked, and a red list and black list are determined based on the ranking results. Specifically, this includes:

[0114] S31: Construct the dynamic evaluation model, which includes the dimensions of absolute performance, indicator stability, and growth trend, and assign weights to different dimensions.

[0115] In this embodiment, the model dimensions are designed based on business logic, and the weights are optimized through expert experience or machine learning.

[0116] Specifically, weights are set by business experts based on strategic objectives, either through the analytic hierarchy process (AHP) or directly. For example, in a specific configuration: absolute performance weight (W1) = 0.5, indicator stability weight (W2) = 0.3, and growth trend weight (W3) = 0.2. Weight configurations can be dynamically adjusted via the management backend to adapt to changes in emphasis during different assessment cycles.

[0117] S32: Based on the processed sales data that has been associated, calculate the individual score for each salesperson entity under the dimensions of absolute performance, indicator stability, and growth trend.

[0118] Specifically, for absolute performance scores, normalization is used to convert the original KPI values ​​(such as monthly sales) into a range of 0-100 points. For example, absolute performance score = (current sales - historical lowest sales) / (historical highest sales - historical lowest sales) * 100;

[0119] For the indicator stability score, calculate the coefficient of variation (CV, standard deviation / mean) of the salesperson's sales over the past N periods (e.g., 12 weeks). The smaller the CV value, the higher the stability. The score can be designed as Indicator Stability Score = 100 * (1 - CV), ensuring the result is within 0-100 points.

[0120] For growth trend scoring, linear regression is used to fit the sales time series of the past N periods to obtain the slope. The score can be based on the sign and magnitude of the slope. For example, growth trend score = 50 + (slope / absolute value of maximum slope) * 50, which quantifies the trend into a score.

[0121] S33: Based on the weights of each dimension in the dynamic evaluation model, the individual scores of each salesperson entity are weighted and integrated to obtain their comprehensive performance score.

[0122] In this embodiment, the weighted fusion is a linear weighted sum, ensuring fair scores.

[0123] Specifically, the score calculation module calculates the overall monetization score of the salesperson's entity using the weighted formula: Overall Score = (Absolute Value Score × 0.5) + (Stability Score × 0.3) + (Growth Trend Score × 0.2).

[0124] S34: Sort all salesperson entities according to the comprehensive performance score, and include the salesperson entities ranked in the first preset percentage in the red list, and include the salesperson entities ranked in the second preset percentage or those whose key performance indicators have been continuously unsatisfactory in the black list.

[0125] In this embodiment, the preset percentage is set by the administrator (e.g., the top 10% of the red list and the bottom 5% of the black list), and consecutive failure to meet the target refers to the KPI being lower than the threshold multiple times.

[0126] Specifically, after sorting, the system automatically updates the list database and triggers a notification mechanism.

[0127] In one embodiment, such as Figure 5 As shown, in step S32, based on the associated processed sales data, the individual score for each salesperson entity in the indicator stability dimension is calculated, specifically including:

[0128] S321: Obtain the daily key performance indicator sequence of the salesperson entity within a preset historical period, calculate the ratio of the standard deviation to the mean of the daily key performance indicator sequence, and obtain the performance volatility coefficient.

[0129] In this embodiment, the historical period, such as the most recent 30 days, is used. The volatility coefficient measures the stability of performance, and the smaller the ratio, the more stable the performance.

[0130] Specifically, the data query module obtains the daily KPI sequence, calculates the mean and standard deviation, and then calculates the ratio as the volatility coefficient.

[0131] S322: Based on the preset volatility coefficient and scoring mapping relationship, the performance volatility coefficient is converted into a first stability score, anomalies that are continuously below a preset threshold are detected in the daily key performance indicator sequence, a second stability score is calculated based on the number and distribution of anomalies, and the final indicator stability dimension score is obtained by combining the first stability score.

[0132] In this embodiment, the mapping relationship is like a linear mapping table, and outlier detection uses a sliding window algorithm, combined with scores to ensure comprehensive evaluation.

[0133] Specifically, the fluctuation coefficient is mapped to a score of 0-100 (the smaller the coefficient, the higher the score). At the same time, points that are continuously below the threshold (such as 80% of the average) are detected, and a second score is calculated based on the frequency of abnormality. The final score is a weighted average.

[0134] In one embodiment, such as Figure 6 As shown, in step S32, based on the associated processed sales data, the individual score of each salesperson entity in the growth trend dimension is calculated, specifically including:

[0135] S323: Based on the current time point, obtain the key performance indicator data of the salesperson entity for several consecutive time periods in the past.

[0136] In this embodiment, the time period is the past 6 months, and the data sequence is used for trend analysis.

[0137] Specifically, the system queries the historical aggregation table to obtain the monthly KPI data sequence.

[0138] S324: Use a linear regression algorithm to fit the key performance indicator data to obtain the performance growth slope.

[0139] In this embodiment, a linear regression is used to fit the trend line, and the slope represents the growth momentum.

[0140] Specifically, linear regression is used to fit the sales time series of the past N periods to obtain the slope. The score can be based on the sign and magnitude of the slope. For example, the growth trend score = 50 + (slope / absolute value of the maximum slope) * 50, which quantifies the trend into a score.

[0141] S325: Calculate the month-on-month growth rate of the current period's key performance indicator data compared to the previous period's data. Based on the performance growth slope and the month-on-month growth rate, obtain the growth trend dimension score through weighted calculation.

[0142] In this embodiment, the month-on-month growth rate reflects short-term changes, and the weighted calculation balances the long-term and short-term trends.

[0143] Specifically, the slope is mapped to the base score, and the month-on-month growth rate is used as an adjustment factor to obtain a weighted score of 0-100.

[0144] In one embodiment, such as Figure 7 As shown, in step S40, the behavioral data characteristics of the salesperson entities in the red list and black list are compared and analyzed to generate differentiated sales strategy suggestions, specifically including:

[0145] S41: Extract the high-frequency and effective behavioral features of the salesperson entities in the red list to form a set of excellent behavioral features, and identify the key performance indicator shortcomings and corresponding behavioral deficiencies of the salesperson entities in the black list.

[0146] In this embodiment, high-frequency effective features are identified through frequent pattern mining, and the weakness analysis is based on KPI deviation.

[0147] Specifically, the behavior logs of salespersons on the red list are analyzed to extract common patterns. The key performance indicators (KPIs) of salesperson entities on the blacklist are compared with preset industry benchmarks or the average values ​​of salesperson entities on the red list. One or more KPIs that are significantly lower than the benchmark or average are identified as critical weaknesses. Historical behavioral data associated with these critical weaknesses are extracted and matched with the typical behavioral patterns of salesperson entities on the red list to pinpoint specific behavioral deficiencies.

[0148] S42: Match the missing behavioral features with the set of excellent behavioral features, and map targeted improvement strategies from the preset strategy knowledge base.

[0149] In this embodiment, the strategy knowledge base pre-stores the correspondence between key performance indicator shortcomings, behavioral deficiencies and improvement strategy actions.

[0150] Specifically, the cosine similarity of behavioral features is calculated, and the key weakness and behavioral missing features are used as the joint index key to perform query matching in the strategy knowledge base. The matching strategy (such as "If customer follow-up is missing, a daily plan is recommended") is retrieved from the knowledge base.

[0151] S43: Based on the specific weaknesses of the salesperson entities in the blacklist, the improvement strategies are personalized and combined to generate the sales strategy recommendations.

[0152] Specifically, if one or more candidate strategies are matched, the process proceeds to the priority ranking step, where strategies are ranked according to the severity of the shortcomings and combined into a list of executable suggestions. If no direct match is found, a general strategy suggestion is generated through semantic analysis by analyzing the successful strategies of red-listed business entities in similar shortcomings scenarios.

[0153] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0154] In one embodiment, an intelligent conversion and statistical system for business sales data is provided, which corresponds one-to-one with the intelligent conversion and statistical methods for business sales data in the above embodiments. For example... Figure 8 As shown, the intelligent conversion and statistics system for this business sales data includes a sales data acquisition module, a sales report generation module, a list generation module, a strategy generation module, and a visualization module. Detailed descriptions of each functional module are as follows:

[0155] The sales data acquisition module is used to collect raw sales data in real time, preprocess the raw sales data, generate processed sales data, and associate the processed sales data with the corresponding salesperson entity.

[0156] The sales report generation module is used to dynamically generate sales reports containing visualizations of key performance indicators based on the processed sales data. The key performance indicators include at least sales revenue, conversion rate, and average order value.

[0157] The list generation module is used to score and rank the salesperson entities based on a preset dynamic evaluation model and the associated processed sales data, and to determine the red list and black list based on the ranking results.

[0158] The strategy generation module is used to compare and analyze the behavioral data characteristics of salesperson entities in the red list and black list, and generate differentiated sales strategy suggestions.

[0159] The visualization module is used to integrate, push, and visualize the sales reports, red and black lists, and sales strategy suggestions.

[0160] For specific limitations regarding the intelligent conversion and statistical system for business sales data, please refer to the limitations on the intelligent conversion and statistical methods for business sales data mentioned above, which will not be repeated here. Each module in the aforementioned intelligent conversion and statistical system for business sales data can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in an electronic device, or stored in the memory of an electronic device as software, so that the processor can call and execute the corresponding operations of each module.

[0161] In one embodiment, an electronic device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown, this electronic device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores sales data, sales reports, and blacklists / whitelists. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements an intelligent conversion and statistical method for business sales data.

[0162] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0163] Real-time collection of raw sales data, data preprocessing of the raw sales data to generate processed sales data, and association of the processed sales data with the corresponding salesperson entity;

[0164] Based on the processed sales data, a sales report containing visualizations of key performance indicators is dynamically generated. The key performance indicators include at least sales revenue, conversion rate, and average order value.

[0165] Based on the preset dynamic evaluation model and the associated processed sales data, the salesperson entities are comprehensively rated and ranked, and the red list and black list are determined based on the ranking results.

[0166] By comparing and analyzing the behavioral data characteristics of salesperson entities in the red list and black list, differentiated sales strategy suggestions are generated.

[0167] The sales reports, blacklists, and sales strategy suggestions will be integrated, pushed out, and visualized.

[0168] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0169] Real-time collection of raw sales data, data preprocessing of the raw sales data to generate processed sales data, and association of the processed sales data with the corresponding salesperson entity;

[0170] Based on the processed sales data, a sales report containing visualizations of key performance indicators is dynamically generated. The key performance indicators include at least sales revenue, conversion rate, and average order value.

[0171] Based on the preset dynamic evaluation model and the associated processed sales data, the salesperson entities are comprehensively rated and ranked, and the red list and black list are determined based on the ranking results.

[0172] By comparing and analyzing the behavioral data characteristics of salesperson entities in the red list and black list, differentiated sales strategy suggestions are generated.

[0173] The sales reports, blacklists, and sales strategy suggestions will be integrated, pushed out, and visualized.

[0174] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0175] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0176] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for intelligent conversion and statistical analysis of business sales data, characterized in that, The intelligent conversion and statistical methods for the business sales data include the following steps: Real-time collection of raw sales data, data preprocessing of the raw sales data to generate processed sales data, and association of the processed sales data with the corresponding salesperson entity; Based on the processed sales data, a sales report containing visualizations of key performance indicators is dynamically generated. The key performance indicators include at least sales revenue, conversion rate, and average order value. Based on the preset dynamic evaluation model and the associated processed sales data, the salesperson entities are comprehensively rated and ranked, and the red list and black list are determined based on the ranking results. By comparing and analyzing the behavioral data characteristics of the business entities in the red list and black list, differentiated sales strategy suggestions are generated. The sales reports, blacklists, and sales strategy suggestions will be integrated, pushed out, and visualized.

2. The intelligent conversion and statistical method for business sales data according to claim 1, characterized in that, The process of collecting raw sales data in real time, preprocessing the raw sales data to generate processed sales data, and associating the processed sales data with the corresponding salesperson entity specifically includes: The system receives raw sales data through connection interfaces from different data sources and adds timestamps to store it in a temporary data buffer. Read the raw sales data from the temporary data buffer, normalize and map the data fields of the raw sales data to eliminate semantic ambiguity; Identify and remove duplicate original sales data, and fill in the missing key fields appropriately to obtain cleaned original sales data; The cleaned raw sales data is converted according to a preset standardized format to generate processed sales data. Based on the salesperson's unique identifier, the processed sales data is associated and bound with the corresponding salesperson entity profile.

3. The intelligent conversion and statistical method for business sales data according to claim 1, characterized in that, The step of dynamically generating a sales report containing visualizations of key performance indicators based on the processed sales data specifically includes: Sales data associated with the salesperson entity are aggregated according to a preset time period to calculate the value of key performance indicators; Compare and analyze the current period's key performance indicator values ​​with historical data from the same period and preset target values ​​to calculate the completion rate and growth rate; Based on the values, completion rates, and growth rates of the key performance indicators, a visual sales report containing data tables, trend charts, and performance summaries is automatically generated.

4. The intelligent conversion and statistical method for business sales data according to claim 1, characterized in that, The process involves using a pre-set dynamic evaluation model, combined with processed and correlated sales data, to comprehensively score and rank the performance of sales representatives. Based on the ranking results, a red list and a black list are determined. Specifically, this includes: The dynamic evaluation model is constructed, which includes the dimensions of absolute performance, indicator stability, and growth trend, and weights are assigned to different dimensions. Based on the processed sales data that has been associated, calculate the individual score for each salesperson entity under the dimensions of absolute performance, indicator stability, and growth trend. Based on the weights of each dimension in the dynamic evaluation model, the individual scores of each salesperson entity are weighted and integrated to obtain their comprehensive performance score. All sales representatives are ranked according to their overall performance scores. Sales representatives in the top first preset percentage are included in the red list, while those in the bottom second preset percentage or those whose key performance indicators have been continuously unsatisfactory are included in the black list.

5. The intelligent conversion and statistical method for business sales data according to claim 4, characterized in that, Based on the processed sales data associated with the above association, a single score for each salesperson entity in the indicator stability dimension is calculated, specifically including: Obtain the daily key performance indicator sequence of the salesperson entity within a preset historical period, calculate the ratio of the standard deviation to the mean of the daily key performance indicator sequence, and obtain the performance volatility coefficient. Based on a preset mapping relationship between volatility coefficient and scoring, the performance volatility coefficient is converted into a first stability score. Anomalies that are continuously below a preset threshold in the daily key performance indicator sequence are detected. A second stability score is calculated based on the number and distribution of anomalies. The final indicator stability dimension score is obtained by combining the first stability score with the second stability score.

6. The intelligent conversion and statistical method for business sales data according to claim 4, characterized in that, Based on the processed sales data associated with the above association, a single score for each salesperson entity in the growth trend dimension is calculated, specifically including: Based on the current time point, obtain the key performance indicator data of the salesperson entity for several consecutive time periods in the past; The key performance indicator data were fitted using a linear regression algorithm to obtain the performance growth slope; Calculate the month-on-month growth rate of the current period's key performance indicator data compared to the previous period's data. Based on the performance growth slope and the month-on-month growth rate, obtain the growth trend dimension score through weighted calculation.

7. The intelligent conversion and statistical method for business sales data according to claim 1, characterized in that, The comparative analysis of the behavioral data characteristics of salesperson entities in the red list and black list generates differentiated sales strategy recommendations, specifically including: Extract high-frequency and effective behavioral features of salesperson entities in the red list to form a set of excellent behavioral features, and identify the key performance indicator shortcomings and corresponding behavioral missing features of salesperson entities in the black list. The missing behavioral features are matched with the set of excellent behavioral features, and targeted improvement strategies are mapped from a preset strategy knowledge base. Based on the specific weaknesses of the salesperson entities in the blacklist, the improvement strategies are personalized and combined to generate the sales strategy recommendations.

8. An intelligent conversion and statistical system for business sales data, characterized in that, The intelligent conversion and statistical system for the business sales data includes: The sales data acquisition module is used to collect raw sales data in real time, preprocess the raw sales data, generate processed sales data, and associate the processed sales data with the corresponding salesperson entity. The sales report generation module is used to dynamically generate sales reports containing visualizations of key performance indicators based on the processed sales data. The key performance indicators include at least sales revenue, conversion rate, and average order value. The list generation module is used to score and rank the salesperson entities based on a preset dynamic evaluation model and the associated processed sales data, and to determine the red list and black list based on the ranking results. The strategy generation module is used to compare and analyze the behavioral data characteristics of salesperson entities in the red list and black list, and generate differentiated sales strategy suggestions. The visualization module is used to integrate, push, and visualize the sales reports, red and black lists, and sales strategy suggestions.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the intelligent conversion and statistical method for business sales data as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the intelligent conversion and statistical method for business sales data as described in any one of claims 1 to 7.

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