Customer relationship management system in SaaS mode

Abstract

The invention relates to the technical field of customer data analysis, in particular to a customer relationship management system in an SaaS mode, which comprises a customer behavior acquisition module, a customer behavior analysis module, a customer behavior analysis module, a customer behavior analysis module, a customer behavior analysis module, a customer behavior analysis module, a customer behavior analysis module and a customer behavior analysis module, wherein the customer behavior analysis module counts the customer purchase frequency, the purchase amount and the service request times according to time periods on the basis of customer purchase records and service request data; and extracting behavior characteristics of each time period, and generating a customer time sequence behavior characteristic analysis result. According to the method, time period statistics is performed on the customer purchase record and the service request data, and the behavior characteristics are extracted, so that the behavior change of the customer can be dynamically captured. Meanwhile, through cross matching of customer behavior data and channel activity performance, the behavior change of the customer before and after the activity can be accurately judged, so that the marketing strategy is optimized. In combination with behavior prediction of the customer at multiple time nodes, future purchase demands and behavior trends of the customer can be insighted in advance, and opportunity grasp in customer relationship management is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of customer data analysis, and in particular to a customer relationship management system under a SaaS model. Background Art

[0002] The field of customer data analysis technology uses data mining, machine learning, and statistical analysis to understand customer behavior, optimize customer experience, and drive sales growth. At the same time, by analyzing data such as purchase history, customer feedback, and social media interactions, companies can identify consumer preferences and develop more effective marketing strategies. In addition, analyzing customer data can also help companies improve customer satisfaction and loyalty and optimize products and services.

[0003] Among them, the customer relationship management system is a technical solution for managing the interaction between enterprises and their customers. The main functions include centralized storage of customer information, tracking customer interactions, and automating sales processes and marketing activities. By providing a detailed customer view, it helps enterprises to serve customers more effectively and enhance customer relationships, thereby improving sales efficiency and targeted marketing. In addition, the system can also assist enterprises in providing the right products and services to the right customers at the right time, thereby optimizing customer lifecycle management and improving customer maintenance efficiency.

[0004] In terms of processing and analyzing customer data, existing technologies focus more on the static storage of customer information and the management of customer interaction records, making it difficult to effectively capture customer behavior information that changes over time. This data processing method makes it difficult for companies to respond promptly and accurately to customer relationship maintenance. In addition, existing technologies for evaluating channel activities make it difficult to understand the actual impact of activities on customer behavior based on information about changes in customer behavior before and after the activities. In addition, in terms of customer churn risk management and the discovery of potential customer demand data, companies can only respond after customer behavior has changed significantly, which is not conducive to quickly capturing customer behavior trends and changes in potential demand. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a customer relationship management system under the SaaS model.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: A customer relationship management system under the SaaS model includes: The customer behavior collection module collects statistics on the customer's purchase frequency, purchase amount, and service request times according to time periods based on the customer's purchase records and service request data, extracts the behavior characteristics of each time period, and generates customer time series behavior characteristics analysis results; Based on the analysis results of the customer's temporal behavior characteristics, the channel activity analysis module cross-matches the duration and frequency of channel activities with customer behavior data through the multi-tenant architecture of SaaS, determines the changes in customer behavior before and after the channel activities, and generates activity performance analysis results; The customer behavior prediction module extracts the customer's purchase behavior and service request data at multiple time points based on the activity performance analysis results, predicts the customer's future purchase behavior and demand changes, and analyzes the customer churn probability based on the customer's purchase potential, generating customer behavior change analysis results; The customer relationship maintenance optimization module is based on the customer behavior change analysis results and activity performance analysis results. According to the customer behavior change and channel activity effect data, it matches the customer's purchasing behavior with the channel resource allocation, analyzes the key time periods for maintenance or follow-up services, and generates customer relationship maintenance optimization results.

[0007] As a further solution of the present invention, the step of extracting the behavior features of each time period is specifically as follows: Based on customer purchase records and service request data, daily purchase records are grouped into integrated data sets by time period. Customer behavior data is cleaned according to the integrated time period to obtain a time series analysis input set. Based on the time series analysis input set, the customer's purchase frequency, amount and behavior responsiveness data are aggregated, the purchase behavior frequency and amount in multiple time periods are integrated, and combined with the customer behavior responsiveness data, the formula is used: ; Compare the customer's behavior frequency and consumption amount data, and calculate the average statistical value of the customer's behavior frequency and responsiveness within the time period , obtain the customer's behavioral characteristic data; in, It is Customers in Purchase behavior frequency data for a period of time, It is Customers in The purchase amount in a certain period of time, is the behavioral responsiveness adjustment coefficient, It is Customers in Behavioral responsiveness in a time period, is the total number of days in the time period; Based on the customer's behavior characteristic data, the customer's behavior changes are counted, a customer behavior change curve is constructed, the curve is used to represent the changing trend of the customer's behavior, and the customer's time series behavior characteristic analysis results are obtained.

[0008] As a further solution of the present invention, the acquisition step of determining the behavior changes of customers before and after the channel activity is specifically as follows: Based on the analysis results of the customer's temporal behavior characteristics, the channel activity data is integrated through the multi-tenant architecture of SaaS, and the customer's participation frequency is cross-matched with the duration and frequency of channel activities to obtain a channel activity data set; Based on the channel activity data set, the formula is used: ; Calculate the average change in purchases made by a customer during their channel activity , generate analysis results of customer consumption changes during the activity; in, It is Customers in The amount of consumption in a time period, It is The amount of money spent by a customer in the previous period, is the number of time periods during which the channel is active; Based on the analysis results of changes in customer consumption during the event, evaluate changes in customer behavior before and after the channel event, analyze the impact of the event on customer behavior, and obtain information on changing trends in customer consumption behavior.

[0009] As a further solution of the present invention, the acquisition steps of comparing and analyzing multiple channel activities are specifically as follows: According to the change trend information of the customer consumption behavior, extract the ratio of the number of customer participation to the total number of customers, quantify the customer participation in the channel activity, and obtain the customer participation data of the activity; Based on the customer participation data of the activities and the historical data of other channel activities, the customer participation rates between multiple activities are compared, and combined with the purchase changes, the key activity types are identified to obtain the activity performance analysis results.

[0010] As a further solution of the present invention, the steps of obtaining the predicted future purchase behavior and demand changes of customers are specifically as follows: Based on the activity performance analysis results, extract the customer's purchase behavior and service request data at multiple time points, classify and organize them according to time periods, analyze the customer's purchase frequency and service request times, and generate a customer behavior data set; Based on the customer behavior data set, the formula is adopted: ; Calculate the customer from the stage To stage The conversion probability , generate customer behavior prediction results; in, Indicates the possibility that the customer will continue to behave in the future. It is a stage Total number of customer actions, It is a stage Total number of customer actions, is the weight coefficient, It is a stage Customer satisfaction, It is a stage of customer service calls.

[0011] As a further solution of the present invention, the step of obtaining the probability of analyzing customer churn is specifically as follows: Based on the customer behavior prediction results, the formula is adopted: ; Calculate the customer's future consumption behavior potential , generate customer purchase potential data; in, is the customer's purchase frequency, It is the customer’s cumulative purchase behavior. is the total spending power of the customer, is the customer loyalty weight; Extract relevant information from feedback data and transaction records, analyze customer behavior changes over multiple time periods, combine the customer purchase potential data, evaluate the customer's future consumption trends, and generate customer behavior change analysis results.

[0012] As a further solution of the present invention, the steps of matching the customer's purchase behavior with the channel resource allocation are specifically as follows: Based on the customer behavior change analysis results and channel activity effect analysis results, analyze the customer's consumption growth and participation behavior, and perform data processing to generate customer behavior and channel activity data sets; Based on the customer behavior and channel activity data set, compare the customer's purchase changes with the allocation of channel resources, evaluate whether the existing resources meet customer needs, analyze the rationality of resource allocation in combination with the customer's participation behavior, and generate adjusted resource configuration.

[0013] As a further solution of the present invention, the steps of obtaining the customer relationship maintenance optimization result are specifically as follows: According to the adjusted resource allocation, the formula is adopted: ; Calculate critical periods for maintenance or follow-up services , generate key period data; in, is the increase in customer demand, It is the allocation of channel resources. Indicates the frequency of customer interaction in the activity, is the key resource weight; Based on the key period data, the allocation priority of channel resources is adjusted, the rationality of resources is analyzed, and optimization is performed to generate customer relationship maintenance optimization results.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by performing time period statistics on customer purchase records and service request data and extracting behavioral features, customer behavior changes can be captured dynamically. At the same time, by cross-matching customer behavior data and channel activity performance, customer behavior changes before and after the activity can be accurately judged, thereby optimizing marketing strategies. Combined with customer behavior predictions at multiple time points, it is possible to gain insight into customers' future purchase needs and behavior trends in advance and optimize timing in customer relationship management. In addition, by intelligently matching customer purchase behavior with channel resources, customer maintenance efficiency is improved, ensuring that the company provides the most appropriate services or products at the best time. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a system flow chart of the present invention; Figure 2 A flowchart of the steps for extracting the behavior features of each time period in the present invention; Figure 3 A flowchart of the steps for obtaining the behavior changes of customers before and after channel activities according to the present invention; Figure 4 A flowchart of the acquisition steps for comparing and analyzing various channel activities of the present invention; Figure 5 A flowchart of the steps for predicting future purchasing behavior and demand changes of customers in the present invention; Figure 6 A flowchart of the steps for analyzing the probability of customer churn in the present invention; Figure 7 A flowchart of the steps for matching the purchasing behavior of customers with the allocation of channel resources in the present invention; Figure 8 This is a flow chart of the steps for obtaining the customer relationship maintenance optimization results of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0018] See also Figure 1 , a customer relationship management system under the SaaS model includes: The customer behavior collection module integrates customer behavior information in each time period from the cloud data source based on customer purchase records and service request data, and collects statistics on the customer's purchase frequency, purchase amount, and service request times according to the time period. Based on the data, it obtains the customer's behavior change curve in multiple periods, extracts the behavior characteristics of each time period, and generates customer time series behavior characteristic analysis results; The channel activity analysis module is based on the analysis results of customer temporal behavior characteristics. Through the multi-tenant architecture of SaaS, it integrates the channel activity time period and the changing trend of customer behavior, cross-matches the duration and frequency of channel activities with customer behavior data, calculates the change in customer purchases during the channel activity period, determines the changes in customer behavior before and after the channel activity, and then calculates the customer participation rate index of the activity. By comparing and analyzing multiple channel activity information, it generates activity performance analysis results; The customer behavior prediction module extracts the customer's purchase behavior and service request data at multiple time points based on the activity performance analysis results, calculates the probability of the customer's conversion from the current stage to the next stage, predicts the customer's future purchase behavior and demand changes, and analyzes the customer churn probability based on the customer's purchase potential, generating customer behavior change analysis results; The customer relationship maintenance optimization module is based on the analysis results of customer behavior changes and activity performance. According to customer behavior changes and channel activity effect data, it matches customer purchasing behavior with channel resource allocation, analyzes the key periods for maintenance or follow-up services, and generates customer relationship maintenance optimization results. The results of the customer's temporal behavior characteristics analysis include customer behavior frequency distribution, purchase amount changes and service request number statistics; the activity performance analysis results include activity response rate, purchase behavior change range and activity frequency impact; the customer behavior change analysis results include purchase intention prediction value, expected change in service requests and life cycle stage transition probability; the customer relationship maintenance optimization results include maintenance trigger time point, resource investment allocation standard and customer communication frequency recommendation.

[0019] See also Figure 2 ,The specific steps for extracting the behavioral features of each time period are: Based on customer purchase records and service request data, daily purchase records are grouped into integrated data sets by time period. Customer behavior data is cleaned according to the integrated time period to obtain a time series analysis input set. Through the cloud data source, the enterprise's database or external cloud platform is connected, and historical data related to customer behavior is obtained through API, SQL query or data interface. The data includes customer purchase records and service request information. Then, for the timestamp in each record, the daily purchase records and service request data are classified according to the timestamp according to the predetermined time period (such as every hour, every working day, etc.). This process requires the system to accurately match the timestamp according to the setting of each time period to ensure that each record is accurately classified into the corresponding time period. In the integrated data set, abnormal high or low values ​​are screened out by removing outliers in the data. During the cleaning process, data screening conditions will be set, such as setting a reasonable range of purchase amounts and service request times. Values ​​outside these ranges will be marked or eliminated, and the cleaned data will be processed according to unified standards within the same time period. To ensure data standardization, all data types will be converted to a consistent format, such as the purchase amount will be unified into the same currency unit, the time will be unified into a standard format, and data integrity and consistency will be ensured.

[0020] Based on the time series analysis input set, the customer's purchase frequency, amount and behavior responsiveness data are aggregated, the purchase behavior frequency and amount in multiple time periods are integrated, and combined with the customer behavior responsiveness data, the formula is used: ; Compare the customer's behavior frequency and consumption amount data, and calculate the average statistical value of the customer's behavior frequency and responsiveness within the time period , obtain the customer's behavioral characteristic data; in, Used to represent the comprehensive behavioral characteristics of customers in a specific period of time. It is Customers in The purchase behavior frequency data for a time period indicates the number of times a customer makes a purchase during that time period. It is obtained by counting the purchase records on a daily or hourly basis. It is Customers in The purchase amount in a time period is the consumption amount of the customer in the corresponding time period in the integrated data set, which comes from the customer's consumption record database. It is the behavioral responsiveness adjustment coefficient, which is set according to the weight of the impact of customer response behavior on the business. It is usually determined by the business characteristics of the enterprise. For example, if the response speed directly affects customer satisfaction, the coefficient can be estimated through customer service response records. It is Customers in The behavioral responsiveness of a time period indicates the customer's enthusiasm for responding to channel activities or service requests. It can be quantified by the customer's feedback speed or participation. It is the total number of days in the time period used to calculate the average value. It can be 7 days, 30 days or other time periods set by the enterprise.

[0021] Set the time period to 7 days, and count the behavior from the 1st to the 3rd day, and the purchase frequency 、Amount And responsiveness , followed by , , , and continue the same process until the third day, that is: ; Substitute the values ​​to complete the calculation: If the days in the time period are 5 purchases of 100 on the first day and the responsiveness is 0.8, 3 purchases of 75 on the second day and the responsiveness is 0.6, 4 purchases of 120 on the third day and the responsiveness is 0.7, and the responsiveness coefficient , then the calculation is as follows: ; ; ; The results show that the average behavior characteristic value of the customer during this time period is 436.67. If the average value of the customer's historical behavior characteristic is 400, compared with the current behavior characteristic value, it means that the customer's recent activities are higher than their historical behaviors. This analysis can help companies understand changes in customer behavior and identify customers who are more active during this time period.

[0022] Based on the customer's behavioral characteristic data, the customer's behavior changes are counted, and a customer behavior change curve is constructed. The curve is used to represent the changing trend of customer behavior, and the customer's time series behavior characteristic analysis results are obtained; Based on the characteristic data in each time period, the behavioral data of different time periods are integrated into curve graphs in turn to show the changing trend of customer behavior over time. The curves are further smoothed based on the characteristic values ​​of each time period, and the curves of different customer behavior patterns are summarized in sections. The change range of behavioral characteristics in each time period is compared, and finally the customer time series behavior characteristic analysis results are generated to obtain the overall trend curve of customer behavior over time.

[0023] See also Figure 3 , the specific steps to determine the changes in customer behavior before and after channel activities are: Based on the analysis results of customer temporal behavior characteristics, the channel activity data is integrated through the multi-tenant architecture of SaaS, and the customer's participation frequency is cross-matched with the duration and frequency of channel activities to obtain the channel activity data set; Based on the comprehensive behavioral characteristics of customers in a specific period of time Through the multi-tenant architecture of SaaS, in which different tenants share system resources by allocating isolated data tables and logical partitions, the channel activity data of each tenant is separated and managed in a unified manner in a distributed data management manner, integrating the changing trends of channel activity time periods and customer behavior, and summarizing the comprehensive behavioral characteristics of customers in a specific time period. As the analysis input; during the activity, obtain the duration and frequency data of each channel, first summarize the channel activity duration and frequency by time period, and then summarize the comprehensive behavioral characteristics of customers in a specific time period The data set is cross-matched, that is, the frequency of customer participation in a specific time period is matched with the duration and frequency of channel activities, and the data are grouped according to the time period distribution to count the customer participation in each time period. After the matching is completed, the customer participation results are compared with Correlate and compare the participation frequency and behavior feature values ​​in different time periods, sort the interaction frequency of customers during the activity and establish the distribution of the activity time period. Behavioral characteristics and participation frequency of customers, and analyze the differences in customer behavior in the same channel activities.

[0024] Based on the channel activity data set, the formula is used: ; Calculate the average change in purchases made by a customer during their channel activity , generate analysis results of customer consumption changes during the activity; in, Used to characterize the consumption growth brought about by customer participation activities. It is Customers in The consumption amount in each time period is obtained through the customer's purchase record data. The recorded amount directly reflects the customer's consumption behavior. It is customers in the previous time period ( The consumption amount of the period) is obtained by Similarly, the difference in amount can be obtained by recording the purchase behavior of customers in adjacent time periods. It is the number of time periods during channel activity that are averaged to quantify the average level of consumption growth.

[0025] If a channel activity lasts for 3 days, the daily consumption amounts of customers are , ,and ,but: ; ; The results show that the average daily consumption change of customers during the channel activity is 26.67. If the historical consumption value is 20 yuan, by comparing with the customer's historical consumption value of 20 yuan, it shows that the customer's consumption during the channel activity has increased significantly. This result reflects the customer's positive response to the activity and illustrates the role of the activity in increasing the frequency and amount of customer consumption, providing a useful quantitative basis for the analysis of the effect of channel activities.

[0026] Based on the analysis results of customer consumption changes during the event, evaluate the changes in customer behavior before and after the channel event, analyze the impact of the event on customer behavior, and obtain information on the changing trend of customer consumption behavior; According to the calculation results Determine the changes in customer behavior before and after channel activities, collect and divide customer behavior data according to the time periods before, during, and after the activity, and collect statistics on customer purchase behavior and participation in each stage. Compare the average purchase frequency and consumption amount of customers before the activity with those after the activity. Compare and identify the incremental changes in customer purchases during the event, further summarize the regular consumption level before the event, and compare the magnitude of changes during the event Together with regular data, analyze the impact of activities on customer behavior, identify the degree of fluctuation in customer purchasing behavior, and derive changing trends in customer consumption behavior during channel activities.

[0027] See also Figure 4 , the specific steps for comparing and analyzing various channel activities are as follows: Based on the changing trend information of customer consumption behavior, extract the ratio of the number of participating customers to the total number of customers, quantify the degree of customer participation in channel activities, and obtain the customer participation data of the activities; To extract the ratio of the number of customer engagements to the total number of customers, the formula is used: ; in, It is the customer participation rate, which indicates the proportion of customers participating in a channel activity. The customer participation rate is usually used to measure the ability of an activity to attract customers. It is the number of customers who actually participated in the activity during the channel activity. This data is obtained through customer participation records, such as customer click, purchase or interaction behavior data, obtained through activity monitoring. It is the total number of customers invited or targeted during the channel activity. The number of target customer groups is usually extracted through the customer database, such as the number of customers sent by campaign SMS or email.

[0028] If during the event, the target number of customers is 5,000, and the records show that 1,200 customers participated in the event, clicked, or completed a purchase, calculate the customer participation rate : ; The results show that the customer participation rate of the channel activity is 24%, that is, 24% of the target customers participated in this activity. If the average customer participation rate of historical activities is 20%, the participation rate of this activity is 4 percentage points higher than the historical value, indicating that this channel activity performs well in attracting customer participation.

[0029] Based on the customer participation data of the activities and the historical data of activities in other channels, compare the customer participation rates between various activities, and identify the key activity types in combination with the purchase changes to obtain the results of activity performance analysis; First, extract the historical customer participation data of each channel activity from the system, including the participation rate and purchase change of each activity, and classify and organize these data according to the type, duration, frequency and other dimensions of the activity; then, The value is compared item by item with the participation rate of other activities to analyze which activity types and settings can more effectively attract customer participation, so as to find activities with higher participation rates and greater purchase changes. Finally, through segmentation analysis, the correlation between participation rate and purchase changes can be identified to identify which type of channel activities is most helpful in increasing customer consumption.

[0030] See also Figure 5 , the specific steps to predict customers’ future purchasing behavior and demand changes are: Based on the results of activity performance analysis, extract the customer's purchase behavior and service request data at multiple time points, classify and organize them according to time periods, analyze the customer's purchase frequency and service request times, and generate a customer behavior data set; First, the customer's purchasing behavior and service request data in different time periods are obtained through the customer relationship management system (CRM), including the number of purchases, service requests and other interactive behavior data at each time node. These data are classified through the timestamp records in the CRM system, and then integrated and analyzed with the channel activity effect data to analyze the frequency of customer behavior, consumption amount, service request response time and other specific behavioral performances. By analyzing the customer's behavioral characteristics in different time periods, the customer's behavioral change trend can be identified. After the data is summarized by time period, the customer behavior of each node is associated to track the customer's purchasing tendencies and demand changes.

[0031] Based on the customer behavior data set, the formula is used: ; Calculate the customer from the stage To stage The conversion probability , generate customer behavior prediction results; in, Indicates the possibility that the customer will continue to behave in the future. It is a stage The total number of customer behaviors, including purchases and service requests, is obtained through the CRM system. It is a stage The total number of customer behaviors, reflecting the number of customer behaviors in the next stage, is obtained by the CRM system. is the weight coefficient, which reflects the importance of customer behavior. The weight is obtained through historical data analysis. Different behavior types (such as high-frequency purchase behavior) can be given higher weights. It is a stage Customer satisfaction, collected through customer feedback surveys or rating information, is used to reflect the quality of customer experience at that stage. It is a stage The number of customer service supports is counted through the service records of the CRM system. The number of services the customer receives during this stage is counted.

[0032] If in stage (Within 1 month) the total number of customer purchases and service requests is 800, The total number of actions (in the next month) is 600 times, and the weight coefficient ,stage The customer satisfaction score is 80 points, The number of service support times is 50 times, and the customer participation rate is 0.24, then the calculation is as follows: ; The results show that customers from stage Towards stage The conversion probability is 2.74, and a higher conversion probability indicates that the customer is very likely to continue to behave in the next stage. If the customer participation rate in the previous stage is 24% ( ), indicating that one quarter of the target customers actively participated in the marketing activities, which had a positive effect on the continuation of customer behavior, thus predicting a higher conversion probability in the subsequent stage. Combined with higher The value indicates that the customer not only has positive behavior in the current stage, but also has a high probability of participating in the future stage. This combination provides a more comprehensive perspective for customer behavior prediction, which can help companies formulate future marketing and customer maintenance strategies more accurately.

[0033] See also Figure 6 , the specific steps for analyzing the probability of customer churn are: Based on the customer behavior prediction results, the formula is used: ; Calculate the customer's future consumption behavior potential , generate customer purchase potential data; in, It is the customer's purchase frequency, which indicates the average number of purchases made by a customer in a specific period of time. This data is obtained through transaction records in the customer relationship management system (CRM). By analyzing the number of purchases made by a customer in the CRM system in the past specific period of time, the average purchase frequency is calculated. It is the customer's cumulative purchase behavior, indicating the total purchase amount of the customer in a specific period of time. The data is obtained through the sales records of the CRM system. By accumulating the customer's purchase amount, the total consumption behavior in the period is obtained. It is the total spending power of the customer, indicating the customer's disposable spending power, which is usually inferred from the customer's financial data or income. This data can be estimated through the customer's financial report, income survey or credit information system, reflecting the customer's overall financial status. It is the customer loyalty weight, which indicates the degree of customer loyalty to the brand. It is usually determined by analyzing factors such as the customer's repeated purchase behavior, satisfaction score, and years of brand interaction. It can be extracted through the customer's long-term purchase history and satisfaction feedback system, and given an appropriate weight value to reflect the customer's loyalty.

[0034] If the customer's purchase frequency times, cumulative purchases , total spending power of customers , customer loyalty weight , the customer purchase potential is calculated as follows: ; The result shows that the customer's purchase potential is 5.2. If the historical average purchase potential is 4.5, then the result of 5.2 for this customer indicates that his consumption potential is significantly higher than the average, which makes this customer worthy of special attention from the company. This means that this customer's future consumption behavior is very likely to continue, or even increase. The value of customer loyalty can not only identify which customers have a high theoretical purchasing potential, but also assess the likelihood of achieving this potential in practice. This helps companies make more data-backed decisions when formulating promotional activities, pricing strategies, and customer loyalty programs.

[0035] Extract relevant information from feedback data and transaction records, analyze customer behavior changes over multiple time periods, combine customer purchase potential data, evaluate customer future consumption trends, and generate customer behavior change analysis results; First, extract the customer's historical purchase data from the customer relationship management system, analyze the customer's purchase frequency and cumulative consumption amount in the past year, and organize these data by month. Then, combine the customer's satisfaction feedback and service request records to analyze the customer's satisfaction change trend, determine the customer's consumption behavior in different time periods, and further use these data to evaluate the customer's future consumption trend, combined with the calculated customer purchase potential , we can determine whether the customer's future consumption behavior will continue and the trend of the consumption amount. By analyzing the customer's purchase potential higher than the historical value, combined with the customer's satisfaction data, we can identify the customer with a low risk of churn and predict that he will continue to maintain a high consumption frequency in the future.

[0036] See also Figure 7 , the specific steps for matching customer purchasing behavior with channel resource allocation are: Analyze the customer's consumption growth and participation behavior based on the results of the customer behavior change analysis and channel activity effect analysis, and perform data processing to generate customer behavior and channel activity data sets; Combined with known purchase changes and participation rates, the consumption growth caused by the activity is quantified by comparing purchase data before and after the activity, while evaluating the response of different customer groups to the activity. Further data integration will include combining purchase change data and customer participation data with other relevant behavioral data (such as the number of service requests, customer feedback, etc.), using statistical and data mining techniques to identify patterns and trends in consumption and participation behavior, and finally integrating customer behavior changes with channel effect results.

[0037] Based on customer behavior and channel activity data sets, compare customer purchase changes with channel resource allocation, evaluate whether existing resources meet customer needs, analyze the rationality of resource allocation based on customer participation behavior, and generate adjusted resource configuration; By analyzing the changes in customer purchases , match it with the existing channel resource allocation, evaluate whether the resources of different channels reasonably meet the actual needs of customers, and then combine it with the customer's participation rate , analyze the usage of customers in each channel, compare and Data from the customer service department can be used to determine whether resources need to be reallocated. For example, some channels with high participation rates but insufficient resources may lead to poor customer experience and underutilized purchasing potential, which indicates an imbalance in resource allocation. Ensure that customers can obtain sufficient channel support during key periods and accurately match customer purchasing behavior with channel resources to improve resource utilization and enhance customer satisfaction.

[0038] See also Figure 8 , the specific steps for obtaining customer relationship maintenance optimization results are: According to the adjusted resource allocation, the formula is adopted: ; Calculate critical periods for maintenance or follow-up services , generate key period data; in, Indicates the best time for enterprises to maintain or follow up customer relationships. It is the increment of customer demand. Through the analysis of transaction records or service request logs in the customer relationship management system, the changes in customer demand in a specific period of time can be extracted. It is the channel resource allocation, which indicates the resource allocation to different channels. It can be obtained through the channel management system. Indicates the frequency of customer interaction in an activity. The CRM system is used to extract data such as the frequency of activity participation. It is the key resource weight, which indicates the importance of a specific resource to maintaining customer relationships, and is based on historical usage effect analysis.

[0039] If customer demand increases , channel resource allocation , customer engagement , key resource weight , then the calculation is as follows: ; The calculation results show that this period is the best time to maintain customer relationships or provide follow-up services. By comparing with historical data (if the benchmark value is 1.5), it can be seen that the customer demand increment in the current period is large and the resource allocation is reasonable, so customer relationship maintenance services need to be prioritized.

[0040] Based on key period data, adjust the allocation priority of channel resources, analyze the rationality of resources, and optimize them to generate customer relationship maintenance optimization results; First, extract customer behavior changes and channel resource data, including previous customer demand increments , participation rate and resource allocation , and then integrate these data into the customer relationship management system, by analyzing customer needs and channel resource allocation in different time periods, comparing whether resources can meet customer actual needs, and according to the key resource weights Adjust resource priorities and optimize resource allocation strategies. The final customer relationship maintenance optimization information can ensure that the company can reasonably allocate resources during critical periods to improve customer satisfaction and long-term relationship maintenance.

[0041] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A customer relationship management system under the SaaS model, characterized in that: The system comprises: The customer behavior collection module collects statistics on the customer's purchase frequency, purchase amount, and service request times according to time periods based on the customer's purchase records and service request data, extracts the behavior characteristics of each time period, and generates customer time series behavior characteristics analysis results; Based on the analysis results of the customer's temporal behavior characteristics, the channel activity analysis module cross-matches the duration and frequency of channel activities with customer behavior data through the multi-tenant architecture of SaaS, determines the changes in customer behavior before and after the channel activities, and generates activity performance analysis results; The customer behavior prediction module extracts the customer's purchase behavior and service request data at multiple time points based on the activity performance analysis results, predicts the customer's future purchase behavior and demand changes, and analyzes the customer churn probability based on the customer's purchase potential, generating customer behavior change analysis results; The customer relationship maintenance optimization module is based on the customer behavior change analysis results and activity performance analysis results. According to the customer behavior change and channel activity effect data, it matches the customer's purchasing behavior with the channel resource allocation, analyzes the key time periods for maintenance or follow-up services, and generates customer relationship maintenance optimization results.

2. The customer relationship management system under the SaaS model according to claim 1, characterized in that: The steps of extracting the behavior features of each time period are specifically as follows: Based on customer purchase records and service request data, daily purchase records are grouped into integrated data sets by time period. Customer behavior data is cleaned based on the integrated time period to obtain a time series analysis input set. Based on the time series analysis input set, the customer's purchase frequency, amount and behavior responsiveness data are aggregated, the purchase behavior frequency and amount in multiple time periods are integrated, and combined with the customer behavior responsiveness data, the formula is used: ； Compare the customer's behavior frequency and consumption amount data, and calculate the average statistical value of the customer's behavior frequency and responsiveness within the time period , obtain the customer's behavioral characteristic data; in, It is Customers in Purchase behavior frequency data for a period of time, It is Customers in The purchase amount in a certain period of time, is the behavioral responsiveness adjustment coefficient, It is Customers in Behavioral responsiveness in a time period, is the total number of days in the time period; Based on the customer's behavior characteristic data, the customer's behavior changes are counted, a customer behavior change curve is constructed, the curve is used to represent the changing trend of the customer's behavior, and the customer's time series behavior characteristic analysis results are obtained.

3. The customer relationship management system under the SaaS model according to claim 2 is characterized in that: The steps for determining the changes in customer behavior before and after the channel activity are specifically as follows: Based on the analysis results of the customer's temporal behavior characteristics, the channel activity data is integrated through the multi-tenant architecture of SaaS, and the customer's participation frequency is cross-matched with the duration and frequency of channel activities to obtain a channel activity data set; Based on the channel activity data set, the formula is used: ； Calculate the average change in purchases made by a customer during their channel activity , generate analysis results of customer consumption changes during the activity; in, It is Customers in The amount of consumption in a time period, It is The amount of money spent by a customer in the previous period, is the number of time periods during which the channel is active; Based on the analysis results of changes in customer consumption during the event, evaluate changes in customer behavior before and after the channel event, analyze the impact of the event on customer behavior, and obtain information on changing trends in customer consumption behavior.

4. The customer relationship management system under the SaaS model according to claim 3 is characterized in that: The specific steps for comparing and analyzing multiple channel activities are as follows: According to the change trend information of the customer consumption behavior, extract the ratio of the number of customer participation to the total number of customers, quantify the customer participation in the channel activity, and obtain the customer participation data of the activity; Based on the customer participation data of the activities and the historical data of other channel activities, the customer participation rates between multiple activities are compared, and combined with the purchase changes, the key activity types are identified to obtain the activity performance analysis results.

5. The customer relationship management system under the SaaS model according to claim 4 is characterized in that: The steps for obtaining the predicted future purchasing behavior and demand changes of customers are specifically as follows: Based on the activity performance analysis results, extract the customer's purchase behavior and service request data at multiple time points, classify and organize them according to time periods, analyze the customer's purchase frequency and service request times, and generate a customer behavior data set; Based on the customer behavior data set, the formula is adopted: ； Calculate the customer from the stage To stage The conversion probability , generate customer behavior prediction results; in, Indicates the possibility that the customer will continue to behave in the future. It is a stage Total number of customer actions, It is a stage Total number of customer actions, is the weight coefficient, It is a stage Customer satisfaction, It is a stage Customer service support calls.

6. The customer relationship management system under the SaaS model according to claim 5, characterized in that: The steps for obtaining the probability of analyzing customer churn are specifically as follows: Based on the customer behavior prediction results, the formula is adopted: ； Calculate the customer's future consumption behavior potential , generate customer purchase potential data; in, is the customer's purchase frequency, It is the customer’s cumulative purchase behavior. is the total spending power of the customer, is the customer loyalty weight; Extract relevant information from feedback data and transaction records, analyze customer behavior changes over multiple time periods, combine the customer purchase potential data, evaluate the customer's future consumption trends, and generate customer behavior change analysis results.

7. The customer relationship management system under the SaaS model according to claim 6, characterized in that: The steps for obtaining the matching of the customer's purchasing behavior and the channel resource allocation are specifically as follows: Based on the customer behavior change analysis results and channel activity effect analysis results, analyze the customer's consumption growth and participation behavior, and perform data processing to generate customer behavior and channel activity data sets; Based on the customer behavior and channel activity data set, compare the customer's purchase changes with the allocation of channel resources, evaluate whether the existing resources meet customer needs, analyze the rationality of resource allocation in combination with the customer's participation behavior, and generate adjusted resource configuration.

8. The customer relationship management system under the SaaS model according to claim 7, characterized in that: The steps for obtaining the customer relationship maintenance optimization result are specifically as follows: According to the adjusted resource allocation, the formula is adopted: ； Calculate critical periods for maintenance or follow-up services , generate key period data; in, is the increase in customer demand, It is the allocation of channel resources. Indicates the frequency of customer interaction in the activity, is the key resource weight; Based on the key period data, the allocation priority of channel resources is adjusted, the rationality of resources is analyzed, and optimization is performed to generate customer relationship maintenance optimization results.

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