Intelligent sales process optimization method and system integrated with customer relationship management

Abstract

The invention relates to the technical field of intelligent sales, and provides an intelligent sales process optimization method and system integrated with customer relationship management, in the aspect of data processing, a multi-source data acquisition and integration module breaks through the limitation of data acquisition, data can be acquired from multiple channels and effectively cleaned and integrated, and compared with a traditional single data source, the data processing efficiency is greatly improved. A more comprehensive and accurate data basis is provided for subsequent analysis; the intelligent sales prediction model construction and optimization module adopts a hybrid prediction model and a real-time update optimization mechanism, is more accurate than a traditional prediction model and can adapt to market changes in time, and the ability of enterprises to grasp sales trends is improved; the sales decision support and feedback module provides a scientific basis for sales decision by means of various optimization algorithms, and is more reasonable than artificial experience decision; and a feedback mechanism can reuse actual sales and promotion feedback for model training to form a closed-loop optimization system, so that the efficiency and benefits of the sales process are continuously improved, and the competitiveness of enterprises in the market is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of intelligent sales technology, and specifically to an intelligent sales process optimization method and system integrated with customer relationship management. Background Art

[0002] In today's digital business environment, enterprise sales face unprecedented challenges and opportunities, which form the technical background of this system. On the one hand, market competition is becoming increasingly fierce, and consumer demands are becoming more diverse and personalized. Enterprises need to more accurately grasp market dynamics and customer needs to enhance product competitiveness and market share. On the other hand, the rapid development of information technology has led to an explosive growth in data. Internal ERP and CRM systems have accumulated a large amount of business data, and the internet also contains a vast amount of market-related information. How to effectively utilize this data has become a key issue.

[0003] However, existing technologies present numerous challenges. Traditional sales forecasting methods rely primarily on manual experience or simple statistical models, making it difficult to accurately capture complex trends and hidden patterns in sales data. This results in low forecast accuracy and an inability to adapt to rapidly changing market environments. Sales decisions lack a scientific and systematic approach, and product pricing, channel allocation, and marketing promotion often rely on subjective judgment, leading to irrational resource allocation and poor marketing effectiveness. Furthermore, data is scattered across different systems and platforms, with diverse formats and varying quality. This makes data collection and integration difficult, preventing it from providing a comprehensive and reliable basis for sales decisions.

[0004] Therefore, an intelligent sales process optimization method and system integrating customer relationship management is proposed. Through multi-source data collection and integration, comprehensive and accurate data is provided; an intelligent sales forecasting model is constructed to improve forecasting accuracy; scientific algorithms are used to assist sales decision-making and achieve rational allocation of resources. Continuous optimization is carried out through feedback mechanisms to enhance corporate sales efficiency and competitiveness. Summary of the Invention

[0005] Technical problems solved

[0006] In view of the deficiencies of the existing technology, the present invention provides an intelligent sales process optimization method and system integrating customer relationship management.

[0007] Technical Solution

[0008] To achieve the above-mentioned solution, the present invention provides the following technical solution: an intelligent sales process optimization method integrating customer relationship management, comprising the following steps:

[0009] S1: Multi-source data collection and integration: Extract historical sales order data to obtain detailed customer information; use web crawlers to set keywords for mainstream media and industry platforms to capture data, and use interfaces to collect consumer feedback; monitor competitor information and obtain macroeconomic and policy and regulatory data.

[0010] S2: Construction and optimization of intelligent sales forecasting model: Use the deep learning framework to build an LSTM network to model historical sales data. Use the attention mechanism formula to calculate scores and weighted representations to incorporate external factors for sales forecasting. Use the time series analysis library to build an ARIMA model and combine it with the LSTM model results to improve forecast accuracy.

[0011] S3: Sales decision support and feedback: In product pricing, market dynamics, sales forecasts and cost data are collected, substituted into the revised profit function, and the optimization algorithm is used to solve the optimal pricing; when allocating sales channels, based on sales forecasts for different regions and customer groups, with the goal of maximizing sales, considering constraints such as channel costs and efficiency, and using linear programming methods to determine resource allocation plans; in terms of marketing, based on sales forecasts and market dynamics, the optimal promotion timing, channels and content are determined through a comprehensive evaluation function.

[0012] Preferably, in step S1, the date format of market dynamic data is unified using Python's datetime module, text encoding problems are processed through the chardet and iconv libraries, and data such as sales orders are deduplicated based on a hash algorithm; with customer ID as the core, SQL statements are used to create views in a relational database to associate and integrate data from different data sources.

[0013] Preferably, in step S2, a time interval is set to compare the forecast results and actual sales data, an error index is calculated, and optimization is triggered when the error exceeds a threshold; new data is input into the model to update parameters, and the model structure is regularly evaluated and adjusted.

[0014] Preferably, in step S3, the sales team records the difference between actual sales and forecasts through the CRM system, and the marketing department uses research tools to collect feedback on the effectiveness of promotion activities; the feedback information is organized into structured data, re-entered into the multi-source data acquisition and integration module, and the model is retrained, parameters and structure are adjusted in the intelligent sales forecasting model module to form a closed-loop optimization system.

[0015] The system integrates customer relationship management with intelligent sales process optimization methods, including the following modules:

[0016] S1: Multi-source data collection and integration module:

[0017] Multi-channel data collection unit: Use ETL tools to extract historical sales order data from the company's internal ERP system and obtain detailed customer information from the CRM system; use a web crawler program written in Python based on the Scrapy framework to capture market dynamics data from mainstream news media websites and professional industry information platforms, and collect consumer data through social media platform API interfaces; use professional competitive intelligence collection tools to monitor competitor data; and cooperate with well-known market research institutions to obtain macroeconomic environment data.

[0018] Data Cleansing and Integration Unit: Use Python's datetime module to unify the date format of market dynamic data, utilize the chardet and iconv libraries to handle text encoding issues, and use a hash algorithm-based deduplication method to handle duplicate data; build an association system centered on customer ID, use SQL statements to create views in the relational database, and associate data from different data sources.

[0019] S2: Intelligent sales forecasting model construction and optimization module:

[0020] Hybrid forecasting model construction unit: This unit combines deep learning algorithms and time series analysis algorithms to build a hybrid forecasting model. It uses the long short-term memory (LSTM) network to model historical sales data. It introduces an attention mechanism to enable the model to focus on the impact of external factors on sales data, while also combining the ARIMA model with the LSTM model.

[0021] Model real-time update and optimization unit: Establish a real-time model monitoring mechanism to compare and analyze the forecast results with actual sales data. When the forecast error exceeds the set threshold, use the online learning algorithm to input the newly collected data into the model in real time to adjust the parameters. Regularly evaluate and adjust the model structure according to business conditions.

[0022] S3: Sales decision support and feedback module:

[0023] Sales Decision Support Unit: This unit uses a profit maximization formula and price elasticity coefficients to determine product pricing, combining market dynamics, sales forecasts, and costs. It optimizes sales channel resource allocation through linear programming based on sales forecasts for different regions and customer groups, taking into account constraints such as channel costs and efficiency. It also uses a comprehensive evaluation function to determine the optimal timing, channels, and content for marketing promotions, based on sales forecasts and market dynamics analysis.

[0024] Feedback mechanism unit: The sales team uses the CRM system to record the difference between actual sales and forecast results. The marketing department uses market research tools to provide feedback on the effectiveness of marketing activities. The feedback information is organized into structured data and re-entered into the multi-source data collection and integration module for retraining, parameter adjustment, and structure adjustment of the intelligent sales forecasting model.

[0025] Preferably, in the multi-channel data collection unit, the data extracted from the enterprise's internal ERP system includes but is not limited to order number, product type, sales quantity, sales amount, and customer ID information; the customer details obtained from the CRM system include but are not limited to basic customer information, purchase preferences, communication records, and complaint feedback; the web crawler program targets mainstream news media websites and professional industry information platforms, and sets keywords such as the name of the industry to which the product belongs, the product name, and industry hot events to capture data; the data collected from social media platforms include but are not limited to consumers' discussion of the product, word-of-mouth evaluation, and user experience sharing; with the help of professional competitive intelligence collection tools, competitors' product information including product features, functional upgrades, and appearance design is monitored; price strategies are tracked including recording price adjustment time, range, and price discounts during promotional activities; market promotion activity data is collected including but not limited to advertising channels, advertising creative content, promotion activity forms, and time nodes; macroeconomic indicator data obtained in cooperation with well-known market research institutions include but are not limited to GDP growth rate, interest rate, inflation rate, and unemployment rate; and the information on changes in industry policies and regulations collected is targeted at different industries.

[0026] Preferably, in the data cleaning and integration unit, when deduplication is performed based on the hash algorithm, the key information of the sales order data is combined into a character string to calculate the hash value, and the records with the same hash value are further compared and deleted. When creating a view, the customer ID is used as the association core, and the customer purchase history data, product attention data in market dynamics, and the market performance data of similar products of competitors are associated.

[0027] Preferably, in the hybrid forecasting model construction unit, the ARIMA (p, d, q) model The autoregressive moving average model used to describe time series data, where is the autoregressive part, p is the autoregressive order, is the autoregressive coefficient, B is the backshift operator (By t =y t-1 ); is the d-order difference operator, used to make the time series stationary; is the moving average part, q is the moving average order, θ i is the moving average coefficient; t is a white noise sequence.

[0028] Preferably, in the real-time model updating and optimization unit, the online learning algorithm is: let the model parameter be θ, the loss function be L(θ), and for the new data (x new ,y new ), parameter update formula Among them, α is the learning rate, which controls the step size of each parameter update; is the loss function with respect to the parameter θ in the new data (x new ,y new ) on the gradient.

[0029] Preferably, in the sales decision support unit, product pricing adopts the profit function Introducing the price elasticity coefficient ò, it is modified to The P value that maximizes profit is found through optimization algorithm; the sales channel allocation aims to maximize total sales, that is, Determine the optimal resource allocation plan through linear programming; market promotion through comprehensive evaluation function F=w1E ch +w2A con Determine the optimal promotion strategy, where w1 and w2 are weights, determined through historical data and market research.

[0030] Beneficial effects

[0031] Compared with the existing technology, the present invention provides an intelligent sales process optimization method and system integrating customer relationship management, which has the following beneficial effects:

[0032] 1. This intelligent sales process optimization method that integrates customer relationship management uses multi-source data processing to obtain more comprehensive and accurate data, providing strong support for decision-making. The intelligent sales forecasting model integrates multiple technologies, is more accurate than traditional models, and can be updated in real time to adapt to market changes. Sales decision support uses optimization algorithms to be more scientific and reasonable in pricing, channel allocation, and promotion. The feedback mechanism uses actual sales and promotion feedback for model optimization, forming a closed loop, continuously improving sales process efficiency and corporate competitiveness, and effectively overcoming the limitations of existing technologies.

[0033] 2. This intelligent sales process optimization system integrates customer relationship management. The multi-source data collection and integration module broadens data channels, cleans and integrates to improve data quality, and provides a more comprehensive and accurate basis for sales decisions. The intelligent sales forecast model construction and optimization module integrates multiple algorithms and can be updated in real time. Its accuracy far exceeds that of traditional models and can quickly adapt to market changes. The sales decision support and feedback module uses algorithms to scientifically price, allocate channel resources and formulate promotion strategies. The feedback mechanism can continuously optimize the model based on actual conditions, forming a closed-loop system, comprehensively improving sales efficiency and benefits, and enhancing the company's market competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 Schematic diagram of the method flow of the present invention;

[0035] Figure 2 Schematic diagram of the system framework of the present invention. DETAILED DESCRIPTION

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0037] See also Figures 1 and 2 The present invention proposes an intelligent sales process optimization method and system integrating customer relationship management, including the following contents:

[0038] 1. Intelligent Sales Process Optimization Method with Integrated Customer Relationship Management

[0039] 1. Implementation method of multi-source data collection and integration module

[0040] Multi-channel data collection: Using ETL tools, we regularly extract historical sales order data from the ERP system and obtain detailed customer information from the CRM system. We develop web crawlers using the Scrapy framework in Python, targeting keywords from mainstream media and industry platforms to capture data, and leverage social media APIs to gather consumer feedback. We use specialized tools to monitor competitor information and collaborate with research institutions to obtain macroeconomic, policy, and regulatory data.

[0041] Data cleaning and integration: We used Python's datetime module to standardize the date format of market dynamics data, handled text encoding issues with the chardet and iconv libraries, and used a hashing algorithm to deduplicate sales order data. We used SQL statements to create views in a relational database, centering on customer IDs, to link and integrate data from different sources.

[0042] 2. Intelligent sales forecasting model construction and optimization module implementation method

[0043] Hybrid forecasting model construction: Use a deep learning framework (such as TensorFlow or PyTorch) to build an LSTM network and model historical sales data. Use the attention mechanism formula to calculate scores and weighted representations, incorporating external factors into sales forecasts. Use a time series analysis library (such as statsmodels) to build an ARIMA model and combine it with the LSTM model results to improve forecast accuracy.

[0044] Real-time model updates and optimization: Compare forecasts against actual sales data at set intervals (e.g., daily), calculate error metrics (e.g., MAE, RMSE), and trigger optimization when the error exceeds a threshold. For example, using stochastic gradient descent, new data is fed into the model to update parameters, and the model structure is regularly evaluated and adjusted (e.g., increasing or decreasing the number of LSTM layers, adjusting the weights of the attention mechanism).

[0045] 3. Implementation method of sales decision support and feedback module

[0046] Sales Decision Support: For product pricing, we collect market trends, sales forecasts, and cost data, plug them into a modified profit function, and use optimization algorithms such as gradient descent to determine the optimal price. Regarding sales channel allocation, we use linear programming to determine resource allocation based on sales forecasts for different regions and customer groups, with the goal of maximizing sales, while considering constraints such as channel costs and efficiency. For marketing promotions, we use comprehensive evaluation functions based on sales forecasts and market trends to determine the optimal promotion timing, channels, and content.

[0047] Feedback Mechanism: The sales team uses the CRM system to record discrepancies between actual sales and forecasts, while the marketing department utilizes research tools to collect feedback on promotional effectiveness. This feedback is organized into structured data and re-entered into the multi-source data collection and integration module. The intelligent sales forecasting model module then retrains the model, adjusting parameters and structure to form a closed-loop optimization system.

[0048] 2. Intelligent sales process optimization system integrated with customer relationship management

[0049] 1. Multi-source data collection and integration module

[0050] 1.1 Multi-channel data collection

[0051] Internal enterprise data: Using ETL (Extract, Transform, Load) tools, we regularly extract historical sales order data from the enterprise resource planning (ERP) system, including order number, product type, sales quantity, sales amount, customer ID, and other information. We also obtain detailed customer information from the customer relationship management (CRM) system, including basic customer information, purchasing preferences, communication records, and complaint feedback, providing a foundation for subsequent analysis of customer behavior and needs.

[0052] Market dynamics data: A web crawler program written in Python, using the Scrapy framework, targets mainstream news media websites (such as Sina Technology and Tencent Finance) and professional industry information platforms (such as Autohome and 36Kr). By setting keywords (such as the product's industry name, product name, and industry hot topics), the program regularly crawls relevant news reports, industry trend analysis, market research reports, and other content. For example, in the automotive industry, news on changes in new energy vehicle policies and the impact of chip shortages on production can be obtained. Using open APIs on social media platforms (such as Weibo, TikTok, and Facebook), data on consumer discussion, word-of-mouth reviews, and user experience sharing can be collected based on product-related hashtags and keywords. For example, searching for user comments on Weibo related to a specific car brand can analyze their sentiment and focus.

[0053] Competitor data: Utilize professional competitive intelligence gathering tools (such as SimilarWeb and SEMrush) to regularly monitor competitor product information, including product features, feature upgrades, and design. Track pricing strategies, recording the timing and magnitude of price adjustments, as well as price discounts during promotional events. Collect marketing activity data, such as advertising channels (online advertising platforms, offline billboard placements, etc.), creative content, promotional activity formats, and timing. For example, in the cosmetics industry, this can provide insights into competitors' new product releases, online advertising platforms, and promotional discounts.

[0054] Macroeconomic Data: Establish long-term partnerships with renowned market research organizations (such as iResearch and Gartner) to regularly obtain data on macroeconomic indicators closely related to the industry, such as GDP growth, interest rates, inflation rates, and unemployment rates. Also, gather information on changes in industry policies and regulations, such as the impact of environmental protection policies on the chemical industry and tax adjustments on the manufacturing industry. For the real estate industry, monitor the impact of monetary and land policies on the market.

[0055] 1.2 Data Cleansing and Integration

[0056] Data cleaning: For the collected market dynamic data, if the date format is inconsistent (such as "2023-01-01" and "01 / 01 / 2023"), use Python's datetime module to unify the format. For text encoding issues, use the chardet library to automatically detect the encoding format, and use the iconv library to convert different encodings (such as UTF-8, GB2312) into a unified encoding. A deduplication method based on a hash algorithm is used to process duplicate data records. Taking sales order data as an example, the key information of each order record (such as order number, customer ID, product ID) is combined into a string, and its hash value is calculated. Records with the same hash value are further compared, and duplicates are deleted if they are exactly the same.

[0057] Data Integration: Build a correlation system centered around the customer ID. Using SQL statements, create views in a relational database (such as MySQL) to correlate customer purchase history data (stored in the sales database), product attention data from market trends (obtained through text analysis to extract keyword frequency), and competitor performance data on similar products (such as market share and sales volume changes). For example, create a view named "integrated_sales_data" to integrate data from different data sources using the customer ID, providing a comprehensive and structured data foundation for subsequent sales forecasts.

[0058] 2. Intelligent sales forecasting model construction and optimization module

[0059] 2.1. Construction of hybrid prediction model

[0060] A hybrid forecasting model was constructed by combining deep learning algorithms with time series analysis algorithms. First, a long short-term memory (LSTM) network was used to model historical sales data. LSTM can effectively handle long-term dependencies in time series data and capture complex trends in sales data. For example, by analyzing product sales data from different quarters and months over the past few years, the overall sales trend for the next period of time can be predicted.

[0061] LSTM unit calculation formula:

[0062] Forget Gate f t =σ(W f ·[h t-1 ,x t ]+b f ): determines how much cell state information of the previous moment is retained. Among them, f t is the output of the forget gate, σ is the sigmoid function, which is used to compress the value to between 0 and 1; W f is the forget gate weight matrix; [h t-1 ,x t ] means to hide the state h at the previous moment t-1 With the current input x t splicing; b f is the forget gate bias vector.

[0063] Input gate i t =σ(W i ·[h t-1 ,x t ]+b i ): Controls the extent to which the current input enters the cell state. i t is the input gate output, W i is the input gate weight matrix, b i is the input gate bias vector.

[0064] Output gate o t =σ(W o ·[h t-1 ,x t ]+b o ): Determines how much current cell state information to output as hidden state. t is the output gate output, W o is the output gate weight matrix, b o is the output gate bias vector.

[0065] Cell status update Generate candidate cell states. is the candidate cell state, W c is the cell state weight matrix, b c is the cell state bias vector, and tanh is the hyperbolic tangent function.

[0066] New cell state Combine the forget gate, input gate and candidate cell state to get the new cell state. t is the new cell state, and ⊙ represents element-by-element multiplication.

[0067] Hidden state h t =o t ⊙tanh(c t ): Get the hidden state based on the output gate and the new cell state. t is the hidden state output.

[0068] The introduction of the attention mechanism enables the model to focus on the impact of external factors such as market dynamics, competitor information, and the macroeconomic environment on sales data. For example, when a new competitive product emerges on the market, the attention mechanism can focus the model's attention on the relevant data of this competing product and analyze its potential impact on the company's product sales.

[0069] Attention score Measures the degree of correlation between the i-th sales data feature and the j-th external factor data. ij is the attention score; W1 and W2 are weight matrices; h i is the feature of the i-th sales data after LSTM processing; e j is the jth external factor data; N is the total number of external factor data.

[0070] Attention-weighted representation Get the weighted representation of external factors related to the i-th sales data. i Weighted results for attention.

[0071] At the same time, the ARIMA model in time series analysis is combined with the LSTM model, and the ARIMA model is used to predict the short-term fluctuations of sales data to further improve the accuracy of the prediction.

[0072] ARIMA(p,d,q) model Used to describe the autoregressive moving average model of time series data. is the autoregressive part, p is the autoregressive order, is the autoregressive coefficient, B is the backshift operator (By t =y t-1 ); is the d-order difference operator, used to make the time series stationary; is the moving average part, q is the moving average order, θ i is the moving average coefficient; t is a white noise sequence.

[0073] 2.2 Real-time model update and optimization

[0074] Establish a real-time model monitoring mechanism to regularly compare and analyze the model's prediction results with actual sales data. When the prediction error exceeds the set threshold, the model optimization process is triggered.

[0075] Leveraging online learning algorithms, newly collected sales data, market dynamics, and competitor data are fed into the model in real time to adjust and optimize model parameters. For example, when a major market event causes a shift in the industry landscape, the model can promptly learn from the new data, enabling it to quickly adapt to the changing business environment and improve forecast accuracy. Furthermore, the model's structure is regularly evaluated and adjusted based on actual business conditions, such as increasing or decreasing the number of LSTM network layers and adjusting the weight distribution of the attention mechanism, to better capture complex patterns and trends in sales data.

[0076] Online learning algorithm (taking stochastic gradient descent as an example): Let the model parameter be θ, the loss function be L(θ), and for new data (x new ,y new ), parameter update formula Among them, α is the learning rate, which controls the step size of each parameter update; is the loss function with respect to the parameter θ in the new data (x new ,y new ) on the gradient.

[0077] 3. Sales decision support and feedback module

[0078] 3.1 Sales Decision Support

[0079] Product pricing: Combined with market dynamics (such as competitor prices P comp ), sales forecast results (estimated sales volume ) and cost C, use the profit maximization formula to determine the product price P. Let the profit function be Considering factors such as market competition and consumer price sensitivity, the price elasticity coefficient ò is introduced to modify the profit function to Where ΔP is the price change. Using an optimization algorithm (such as gradient descent), we find the value of P that maximizes profit. For example, in the electronics market, we can determine a price that is competitive and profitable based on the prices of similar products from competitors and our own product costs and sales forecasts.

[0080] Sales channel allocation: According to the sales forecast of different regions (set region as r) and different customer groups (set customer groups as g) (forecast sales volume ), optimize the allocation of sales channel resources. Let the channel resources be R, and the resources allocated to customer group g in region r be R rg , with the goal of maximizing total sales, that is Considering the channel cost C rg and channel efficiency E rg The optimal resource allocation plan is determined through methods such as linear programming based on constraints such as sales volume. For example, in the fast-moving consumer goods industry, resources in online e-commerce platforms, offline supermarkets, convenience stores, and other channels are rationally allocated based on the sales potential of different regions and customer groups.

[0081] Marketing: Based on sales forecast results and market dynamics analysis, determine the best marketing timing (such as product sales season, the initial stage of new product launch, etc.), promotion channels (different channel effect evaluation indicators are E ch , such as ad click-through rate, conversion rate, etc.) and promotional content (content attractiveness index is A con , such as the number of likes and shares of the ad copy). Through the comprehensive evaluation function F = w1E ch +w2A con (w1 and w2 are weights, determined through historical data and market research) Determine the optimal promotion strategy. For example, in the beauty industry, based on product sales forecasts and market trends, choose to launch attractive advertising content on social media platforms, fashion magazines, and other channels during peak consumption periods such as holidays.

[0082] 3.2 Feedback Mechanism

[0083] Information collection: Sales team records actual sales through CRM system (actual sales volume Q act ) and the predicted results, including sales volume, sales amount, changes in customer purchasing behavior, etc. The marketing department uses market research tools (such as questionnaires, user interviews, etc.) to feedback the effectiveness of marketing activities, such as actual customer participation P part Differences from expectations, customer feedback on advertising content, etc.

[0084] Model Optimization Feedback: Feedback information is organized into structured data and fed into the Multi-Source Data Collection and Integration Module for reprocessing and integration. In the Intelligent Sales Forecasting Model Construction and Optimization Module, the model is retrained based on the new data, adjusting model parameters and structure. For example, if a marketing campaign is ineffective, relevant market dynamics and customer feedback are analyzed to adjust the weights of marketing factors in the model. This continuously optimizes the sales forecast model, improving forecast accuracy and the scientific nature of sales decisions, forming a closed-loop optimization system.

[0085] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent sales process optimization method integrating customer relationship management, characterized by: The following steps are involved: S1: Multi-source data collection and integration: Extract historical sales order data and obtain detailed customer information; use web crawlers to capture data by setting keywords for mainstream media and industry platforms, and use interfaces to collect consumer feedback; monitor competitor information and obtain macroeconomic and policy and regulatory data; S2: Intelligent Sales Forecasting Model Construction and Optimization: We used a deep learning framework to build an LSTM network to model historical sales data. We used the attention mechanism formula to calculate scores and weighted representations to incorporate external factors into sales forecasts. We used a time series analysis library to build an ARIMA model and combined it with the LSTM model results to improve forecast accuracy. S3: Sales decision support and feedback: In product pricing, market dynamics, sales forecasts and cost data are collected, substituted into the revised profit function, and the optimization algorithm is used to solve the optimal pricing; when allocating sales channels, based on sales forecasts for different regions and customer groups, with the goal of maximizing sales, considering constraints such as channel costs and efficiency, and using linear programming methods to determine resource allocation plans; in terms of marketing, based on sales forecasts and market dynamics, the optimal promotion timing, channels and content are determined through a comprehensive evaluation function.

2. The intelligent sales process optimization method integrating customer relationship management according to claim 1, characterized in that: In step S1, the date format of market dynamic data is unified using Python's datetime module, text encoding issues are processed using the chardet and iconv libraries, and duplicate sales order data are removed based on a hash algorithm; With customer ID as the core, use SQL statements to create views in the relational database to associate and integrate data from different data sources.

3. The intelligent sales process optimization method integrated with customer relationship management according to claim 1, characterized in that: In step S2, a time interval is set to compare the forecast results and actual sales data, and an error index is calculated. When the error exceeds a threshold, optimization is triggered; new data is input into the model to update parameters, and the model structure is regularly evaluated and adjusted.

4. The intelligent sales process optimization method integrated with customer relationship management according to claim 1, characterized in that: In step S3, the sales team records the difference between actual sales and forecasts through the CRM system, and the marketing department uses research tools to collect feedback on the effectiveness of promotion activities; the feedback information is organized into structured data and re-entered into the multi-source data acquisition and integration module, and the model is retrained, parameters and structure are adjusted in the intelligent sales forecasting model module to form a closed-loop optimization system.

5. Intelligent sales process optimization system integrated with customer relationship management, characterized by: The system is applied to the intelligent sales process optimization method for integrated customer relationship management according to any one of claims 1 to 4 above, and the system includes the following modules: S1: Multi-source data collection and integration module: S1.1: Multi-channel Data Collection Unit: Utilize ETL tools to extract historical sales order data from the company's internal ERP system and obtain detailed customer information from the CRM system; use a Python-based web crawler program based on the Scrapy framework to capture market dynamics data from mainstream news media websites and professional industry information platforms, and collect consumer data through social media platform APIs; utilize professional competitive intelligence collection tools to monitor competitor data; and collaborate with well-known market research institutions to obtain macroeconomic environment data. S1.2: Data Cleansing and Integration Unit: Use Python's datetime module to unify the date format of market dynamic data, utilize the chardet and iconv libraries to handle text encoding issues, and implement a hash-based deduplication method to handle duplicate data. Build a correlation system centered around customer IDs, use SQL statements to create views in a relational database, and correlate data from different data sources. S2: Intelligent sales forecasting model construction and optimization module: S2.1: Hybrid Forecasting Model Construction Unit: This unit combines deep learning algorithms and time series analysis algorithms to build a hybrid forecasting model. Long short-term memory (LSTM) networks are used to model historical sales data. An attention mechanism is introduced to enable the model to focus on the impact of external factors on sales data. The ARIMA model is also combined with the LSTM model. S2.2: Real-time Model Update and Optimization Unit: Establish a real-time model monitoring mechanism to compare and analyze forecast results with actual sales data. When the forecast error exceeds the set threshold, use online learning algorithms to input newly collected data into the model in real time to adjust parameters. Regularly evaluate and adjust the model structure based on business conditions. S3: Sales decision support and feedback module: S3.1: Sales Decision Support Unit: This unit uses a profit maximization formula and price elasticity coefficients to determine product pricing, combining market dynamics, sales forecasts, and costs. Based on sales forecasts for different regions and customer groups, and taking into account constraints such as channel costs and efficiency, it optimizes sales channel resource allocation through linear programming. Based on sales forecasts and market dynamics analysis, it uses a comprehensive evaluation function to determine the optimal marketing timing, channels, and content. S3.2: Feedback mechanism unit: The sales team records the difference between actual sales and forecast results through the CRM system. The marketing department uses market research tools to provide feedback on the effectiveness of marketing activities. The feedback information is organized into structured data and re-entered into the multi-source data collection and integration module for retraining, parameter adjustment and structure adjustment of the intelligent sales forecasting model.

6. The intelligent sales process optimization system integrated with customer relationship management according to claim 5, characterized in that: In the multi-channel data collection unit, the data extracted from the enterprise's internal ERP system includes but is not limited to order number, product type, sales quantity, sales amount, and customer ID information. The customer details obtained from the CRM system include but are not limited to basic customer information, purchase preferences, communication records, and complaint feedback; the web crawler program targets mainstream news media websites and professional industry information platforms, and sets keywords such as the name of the industry to which the product belongs, the product name, and industry hot events to capture data. The data collected from social media platforms include but are not limited to consumers' discussion of the product, word-of-mouth reviews, and user experience sharing; with the help of professional competitive intelligence collection tools, competitors' product information including product features, functional upgrades, and appearance design is monitored, and price strategies are tracked including recording price adjustment time, range, and price discounts during promotional activities. Market promotion activity data is collected including but not limited to advertising channels, advertising creative content, promotion activity forms and time nodes; macroeconomic indicator data obtained in cooperation with well-known market research institutions include but are not limited to GDP growth rate, interest rate, inflation rate, and unemployment rate. The information on changes in industry policies and regulations collected is targeted at different industries.

7. The intelligent sales process optimization system integrated with customer relationship management according to claim 5, characterized in that: In the data cleaning and integration unit, when deduplication is performed based on the hash algorithm, the key information of the sales order data is combined into a string to calculate the hash value, and the records with the same hash value are further compared and deleted. When creating a view, the customer ID is used as the association core to associate the customer's purchase history data, product attention data in market dynamics, and market performance data of similar products of competitors.

8. The intelligent sales process optimization system integrated with customer relationship management according to claim 5, characterized in that: In the hybrid forecasting model construction unit, the ARIMA (p, d, q) model The autoregressive moving average model used to describe time series data, where is the autoregressive part, p is the autoregressive order, is the autoregressive coefficient, B is the backshift operator (By t =y t-1 ); is the d-order difference operator, used to make the time series stationary; is the moving average part, q is the moving average order, θ i is the moving average coefficient; t is a white noise sequence.

9. The intelligent sales process optimization system integrated with customer relationship management according to claim 5, characterized in that: In the real-time updating and optimization unit of the model, the online learning algorithm is: let the model parameter be θ, the loss function be L(θ), and for the new data (x new ,y new ), parameter update formula Among them, α is the learning rate, which controls the step size of each parameter update; is the loss function with respect to the parameter θ in the new data (x new ,y new ) on the gradient.

10. The intelligent sales process optimization system integrated with customer relationship management according to claim 5, characterized in that: In the sales decision support unit, product pricing adopts profit function Introducing the price elasticity coefficient ò, it is modified to The P value that maximizes profit is found through optimization algorithm; the sales channel allocation aims to maximize total sales, that is, Determine the optimal resource allocation plan through linear programming; market promotion through comprehensive evaluation function F=w1E ch +w2A con Determine the optimal promotion strategy, where w1 and w2 are weights, determined through historical data and market research.

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