Disaster weather power customer service optimization processing method

Abstract

The application discloses a method for optimizing processing of power customer service under disaster weather, and comprises the following steps: according to customer call information under disaster weather, customer group classification is performed through a customer group classification model, and corresponding customer group classification service is matched based on a preset customer group classification service strategy; according to call characteristics under disaster weather, extreme weather call law characteristic map is determined, call peak is predicted according to the extreme weather call law characteristic map, and agent resources are dynamically allocated according to an agent resource allocation strategy; according to customer call appeal text information under disaster weather, appeal classification and appeal analysis are performed, and appeal difference analysis is performed, high-frequency appeals are identified, and different services are matched based on high-frequency appeal service strategies. Through customer group division, extreme weather call law characteristic map analysis and user appeal difference analysis, different services are matched based on service strategies, and differentiated services are provided for users under disaster weather.

Description

Technical Field

[0001] This invention relates to the field of power service technology, and in particular to a method for optimizing power customer service during severe weather. Background Technology

[0002] Electricity supply is relevant to every household. As society develops, people's lives are becoming increasingly reliant on electricity, so the power supply from power companies is of paramount importance to every household.

[0003] In extreme weather events, such as typhoons, power supply may be disrupted, affecting people's lives. When power outages occur during extreme weather, people typically report the situation to the power company's customer service through various channels, such as calling customer service to report the area and location of the outage. During extreme weather, the number of calls from users or customers is enormous, and the needs and urgency of customers vary from region to region. For power customer service centers, processing such a massive volume of calls, classifying them according to customer needs, and providing timely responses to different users to address their needs is extremely difficult.

[0004] Therefore, developing an effective processing technology for power outages during extreme weather conditions, which allows for targeted resource allocation and different services based on the needs of different customers, is of great significance in resolving the contradiction between service demands and customer service. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings and defects of the prior art and provide a method for optimizing power customer service under severe weather conditions.

[0006] This invention is achieved through the following technical solution: A method for optimizing electricity customer service during severe weather is implemented based on a differentiated service response model, which performs the following steps: Based on customer call information during severe weather, customer groups are segmented using a customer segmentation model, and corresponding customer segmentation services are matched based on a preset customer segmentation service strategy. Based on the characteristics of incoming calls under severe weather, determine the pattern of incoming calls under extreme weather conditions, predict the peak of incoming calls based on the pattern of incoming calls under extreme weather conditions, and dynamically allocate agent resources based on the agent resource allocation strategy. We categorize and analyze customer calls during severe weather, identify high-frequency requests, and match differentiated services based on high-frequency request service strategies.

[0007] Preferably, the customer segmentation service strategy includes matching different service resource configuration standards, agent response priorities, and proactive service outreach strategies to different customer segments.

[0008] As a preferred approach, the customer segmentation model uses the severity of typhoon damage in the customer's region, the frequency of calls during extreme weather in the customer's history, the urgency of the call requests, the customer's star rating, historical service satisfaction scores, and the probability of repeat call complaints as input variables. The model uses the analytic hierarchy process (AHP) to assign weights to different input variables, quantifying the impact of different input variables on customer service pressure and the urgency of service requests. The model then uses the K-means unsupervised clustering algorithm to iteratively cluster all customer call samples, calibrating the cluster centers based on business scenarios, and finally outputting the customer segmentation results.

[0009] Preferably, the call characteristics of the severe weather include call timing distribution, call peak fluctuation, and call time period.

[0010] Preferably, the determination of the characteristic spectrum of extreme weather incoming calls includes: Based on historical typhoon extreme weather call time series data and normalized concurrent call baseline data, the ARIMA time series algorithm is used to fit the intraday hourly variation of call volume and the daily variation fluctuation curve within the disaster cycle. This allows for the mining of call volume tidal fluctuation patterns in four stages: before typhoon landfall, during the peak of typhoon impact, in the early post-disaster recovery period, and during the post-disaster stable period. The algorithm identifies the daily peak call times, peak call volume levels, and call volume decline cycles. The kernel density estimation algorithm is used to statistically analyze the call volume density distribution at different times. By comparing the differences in call volume patterns between extreme weather and normalized periods, as well as the differences in call volume fluctuations under different typhoon impact levels, a standardized extreme weather call volume pattern feature map is formed.

[0011] As a preferred option, the categories of requests include fault reporting, consultation and inquiry, complaints and suggestions, business processing, and assistance and support.

[0012] Preferably, the demand analysis includes statistical analysis of the proportion of different demands, the frequency of demands, and the probability of repeated demands in different time periods, different customer groups, and different disaster-stricken areas.

[0013] Preferably, the analysis of differences in demands includes: using statistical difference significance test algorithms to quantitatively compare the differences in demand structure, demand concentration, and service needs between different customer groups during typhoon periods and normal periods, during the post-disaster recovery period and during the typhoon peak period.

[0014] As a preferred approach, a service effectiveness evaluation model is constructed by using service strategy as the independent variable and service effectiveness as the dependent variable. Using the service effectiveness evaluation model, based on regression analysis algorithms, the correlation and influence coefficient between different service strategies and service effectiveness are analyzed to accurately identify the service strategy most suitable for extreme weather scenarios. The differentiated service response model is continuously optimized to form a closed-loop optimization mechanism of data analysis, strategy implementation, effect evaluation, and model iteration.

[0015] Preferably, the service strategy includes a customer segmentation service strategy, a seat resource allocation strategy, and a high-frequency demand service strategy. The service effectiveness includes call connection rate, work order completion time, repeat call rate, customer satisfaction score, and complaint rate.

[0016] The present invention provides an optimized power customer service method under severe weather conditions. Based on a differentiated service response model, the method performs customer segmentation, extreme weather call pattern feature analysis, and user demand difference analysis. Different services are matched based on preset service strategies, thereby providing differentiated services to users under severe weather conditions and meeting their needs. Attached Figure Description

[0017] Figure 1 This is a flowchart of the power customer service optimization method under severe weather conditions according to the present invention. Detailed Implementation

[0018] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] See Figure 1 As shown in the exemplary embodiment of this application, the method for optimizing electricity customer service under severe weather conditions is implemented according to a differentiated service response model, which performs the following steps: S1. Based on customer call information during severe weather, classify customers using a customer segmentation model and match corresponding customer segmentation services based on a preset customer segmentation service strategy. S2. Based on the characteristics of incoming calls under severe weather, determine the pattern of incoming calls under extreme weather, predict the peak value of incoming calls based on the pattern of incoming calls under extreme weather, and dynamically allocate agent resources according to the agent resource allocation strategy; S3. Classify and analyze customer call requests based on text information during severe weather, perform request difference analysis, identify high-frequency requests, and proactively match preset services based on high-frequency request service strategies.

[0020] According to an embodiment of this application, the customer segmentation service strategy includes matching different service resource configuration standards, agent response priorities, and proactive service outreach strategies to different customer segments.

[0021] According to an embodiment of this application, the customer segmentation model uses the severity of typhoon damage in the customer's area, the frequency of calls during extreme weather in the customer's history, the urgency of the call requests, the customer's star rating, historical service satisfaction scores, and the probability of repeat call complaints as input variables. The analytic hierarchy process (AHP) is used to assign weights to different input variables, quantifying the impact of different input variables on customer service pressure and the urgency of service needs. The K-means unsupervised clustering algorithm is used to iteratively cluster all customer call samples, and the cluster centers are calibrated in combination with business scenarios to finally output the customer segmentation results.

[0022] Specifically, in step S1 of this application, in order to achieve the core objective of classifying and categorizing customer groups with service needs under extreme weather conditions and implementing differentiated service policies, a precise hierarchical clustering algorithm for customer groups is used. The K-means clustering algorithm and the Analytic Hierarchy Process (AHP) combined modeling method are adopted to accurately classify customer callers. This abandons the simple single-dimensional classification model and realizes multi-factor comprehensive quantification of customer group risk level and service demand level.

[0023] The aforementioned customer group precise hierarchical clustering algorithm or model selects six core feature factors as the core input variables for customer group clustering modeling: the severity of typhoon damage in the customer's area, the frequency of calls during extreme weather in the past, the urgency of the call requests, the customer's star rating, historical service satisfaction scores, and the probability of repeat call complaints. First, the Analytic Hierarchy Process (AHP) is used to assign weights to the six feature factors, accurately quantifying the impact of different feature factors on customer service pressure and the urgency of service requests. Then, the K-means unsupervised clustering algorithm iteratively clusters the entire customer sample, calibrating the cluster centers based on actual business scenarios. Finally, all customer groups are divided into three differentiated levels, such as core priority customer groups, general routine service customer groups, and low-impact stable customer groups. Then, for each customer group, corresponding service resource configuration standards, agent response priorities, and proactive service outreach strategies are precisely matched to achieve refined classification and policy implementation for customer groups under extreme weather conditions.

[0024] According to embodiments of this application, the call characteristics of severe weather include call timing distribution, call peak fluctuations, and call time periods.

[0025] According to an embodiment of this application, the determination of the extreme weather call pattern characteristic spectrum includes: Based on historical typhoon extreme weather call time series data and normalized concurrent call baseline data, the ARIMA time series algorithm is used to fit the intraday hourly variation of call volume and the daily variation fluctuation curve within the disaster cycle. This allows for the mining of call volume tidal fluctuation patterns in four stages: before typhoon landfall, during the peak of typhoon impact, in the early post-disaster recovery period, and during the post-disaster stable period. The algorithm identifies the daily peak call times, peak call volume levels, and call volume decline cycles. The kernel density estimation algorithm is used to statistically analyze the call volume density distribution at different times. By comparing the differences in call volume patterns between extreme weather and normalized periods, as well as the differences in call volume fluctuations under different typhoon impact levels, a standardized extreme weather call volume pattern feature map is formed.

[0026] In this application, specifically, based on the call pattern time series mining and trend prediction algorithm, the application adopts the time series ARIMA time series prediction algorithm + kernel density statistical analysis method to carry out modeling analysis for the needs of analyzing the time series distribution of callers, peak call fluctuations, and concentrated call time periods during and after typhoons. On the one hand, based on historical typhoon extreme weather call time series data and normalized call baseline data, the ARIMA time series algorithm is used to fit the intraday hourly variation of call volume and the daily variation fluctuation curve within the disaster cycle. This accurately reveals the tidal fluctuation pattern of call volume in four stages: before typhoon landfall, during the peak period of typhoon impact, in the early stage of post-disaster recovery, and during the post-disaster stable period. It also accurately identifies key nodes such as daily peak call times, peak call volume levels, and call volume decline cycles. On the other hand, the kernel density estimation algorithm is used to statistically analyze the call density distribution at different times. By comparing the differences in call volume patterns between extreme weather and normal periods, and the differences in call volume fluctuations under different typhoon impact levels, a standardized extreme weather call volume pattern feature map is formed. This supports the customer service center in predicting peak call volume in advance and dynamically allocating agent manpower, achieving rapid response with pre-event manpower reserves and peak capacity backup during the event.

[0027] According to embodiments of this application, the categories of requests include fault reporting, consultation and inquiry, complaints and suggestions, business processing, and assistance and support.

[0028] According to the embodiments of this application, the demand analysis includes statistically analyzing the proportion of various demands, the frequency of demand occurrence, and the probability of repeated demands in different time periods, different customer groups, and different disaster-stricken areas.

[0029] According to an embodiment of this application, the analysis of differences in demands includes: using a statistical significance test algorithm to quantitatively compare the differences in demand structure, demand concentration, and service needs between different customer groups during typhoon periods and normal periods, during the post-disaster recovery period and during the typhoon peak period.

[0030] Specifically, in this application, based on caller ID text mining and difference comparison analysis algorithms or models, focusing on customer calls during typhoons and the post-disaster recovery period, a combination of Natural Language Processing (NLP) text mining, word frequency topic clustering, and difference significance testing algorithms is used to decompose the core caller ID requests, compare the differences in request structures, and identify high-frequency essential requests. This accurately identifies high-frequency essential requests specific to extreme weather and service shortcomings, providing data support for targeted optimization of service processes, proactive service pushes, and intelligent and quick script configuration.

[0031] In specific processing, the call recording transcripts and work order request notes are first processed through word segmentation, stop word filtering, and keyword extraction. Then, the massive amount of request text is clustered using an LDA topic model, automatically categorizing core requests such as fault reporting, inquiries, complaints, suggestions, business processing, and assistance / support into major and minor subcategories. Simultaneously, the proportion, frequency, and probability of repeated requests for each type of request are statistically analyzed across different time periods, customer groups, and disaster-stricken areas. Furthermore, a statistical significance test algorithm is used to quantify and compare the differences in request structure, concentration, and service needs between typhoon periods and normal times, post-disaster recovery periods and typhoon peak periods, and between key customer groups and ordinary customer groups. This allows for accurate identification of high-frequency, essential requests specific to extreme weather and service shortcomings.

[0032] According to the embodiments of this application, a service effectiveness evaluation model is constructed by taking service strategy as the independent variable and service effectiveness as the dependent variable. Using the service effectiveness evaluation model, based on regression analysis algorithm, the correlation and influence coefficient between different service strategies and service effectiveness are analyzed to accurately locate the service strategy most suitable for extreme weather scenarios, continuously optimize the differentiated service response model, and form a closed-loop optimization mechanism of data analysis, strategy implementation, effect evaluation, and model iteration to continuously enhance the initiative, personalization, and intelligence level of extreme weather customer service.

[0033] According to embodiments of this application, the service strategy includes a customer segmentation service strategy, a seat resource allocation strategy, and a high-frequency demand service strategy. The service effectiveness includes call connection rate, work order completion time, repeat call rate, customer satisfaction score, and complaint rate.

[0034] It should be noted that in this application, the raw data of the power customer service is cleaned and standardized in advance to solve problems such as messy format, data redundancy, missing and abnormal data, inconsistent standards, mismatched time sequence, and interference from invalid and dirty data in all the collected raw multi-source data. Through standardized and normalized big data processing, data format is unified and fields are standardized and regulated. After routine data processing such as data completion and outlier correction, the data is stored in layers. This completes the end-to-end governance of data from raw collection to high-quality modeling and analysis. The entire process can adopt a processing mode that combines offline batch processing and real-time incremental synchronization, which takes into account both the accuracy of historical review analysis and the timeliness of emergency real-time response.

[0035] In implementing this application, to ensure the comprehensiveness, accuracy, and timeliness of customer service analysis during severe weather, core data sources are integrated and aggregated to achieve full connectivity of call data, meteorological data, user profile data, and historical disaster service data, thus eliminating the problem of biased analysis caused by data silos. First, all call data from the customer service center and customer service call data, including all call records, outbound call records, agent handling work orders, work order completion, customer complaints, and missed call overflow records during typhoon extreme weather and post-disaster recovery periods; second, meteorological and disaster impact-themed data, covering meteorological monitoring data such as the typhoon-affected area and the duration of disasters; third, customer basic attributes and historical service profile data, including static and dynamic profile data such as basic customer identity information, the area of ​​the calling station, and past complaint and repair history; fourth, routine benchmark comparison data, selecting customer service call and service operation baseline data from non-extreme weather periods and similar typhoon impact periods in previous years, providing a benchmark reference for subsequent quantitative comparison of call patterns and differences in demands.

[0036] The power customer service optimization method under severe weather conditions according to this invention is implemented based on a differentiated service response model. It involves customer segmentation, analysis of call pattern characteristics under extreme weather conditions, and analysis of differences in user demands. Different services are matched based on preset service strategies, thereby providing differentiated services to users under severe weather conditions and meeting their needs.

[0037] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, the embodiments should be regarded as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of the equivalents of the claims be included within the invention.

[0038] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for optimizing power customer service during severe weather, characterized in that: According to the differentiated service response model, the differentiated service response model performs the following steps: Based on customer call information during severe weather, customer groups are segmented using a customer segmentation model, and corresponding customer segmentation services are matched based on a preset customer segmentation service strategy. Based on the characteristics of incoming calls under severe weather, determine the pattern of incoming calls under extreme weather conditions, predict the peak of incoming calls based on the pattern of incoming calls under extreme weather conditions, and dynamically allocate agent resources based on the agent resource allocation strategy. We categorize and analyze customer calls during severe weather, identify high-frequency requests, and match differentiated services based on high-frequency request service strategies.

2. The method for optimizing electricity customer service under severe weather conditions according to claim 1, characterized in that, The customer segmentation service strategy includes matching different service resource configuration standards, agent response priorities, and proactive service outreach strategies to different customer segments.

3. The method for optimizing power customer service under severe weather conditions according to claim 1, characterized in that, The customer segmentation model uses the severity of typhoon damage in the customer's area, the frequency of calls during extreme weather in the past, the urgency of the call requests, the customer's star rating, historical service satisfaction scores, and the probability of repeat complaints as input variables. The analytic hierarchy process (AHP) is used to assign weights to different input variables, quantifying the impact of different input variables on customer service pressure and the urgency of service needs. The K-means unsupervised clustering algorithm is used to iteratively cluster all customer call samples, and the cluster centers are calibrated in combination with business scenarios to finally output the customer segmentation results.

4. The method for optimizing power customer service under severe weather conditions according to claim 1, characterized in that, The characteristics of incoming calls during severe weather include the timing distribution of incoming calls, peak fluctuations in incoming calls, and the time period of incoming calls.

5. The method for optimizing power customer service under severe weather conditions according to claim 1, characterized in that, The determination of the characteristic spectrum of incoming calls during extreme weather includes: Based on historical typhoon extreme weather call time series data and normalized concurrent call baseline data, the ARIMA time series algorithm is used to fit the intraday hourly variation of call volume and the daily variation fluctuation curve within the disaster cycle. This allows for the mining of call volume tidal fluctuation patterns in four stages: before typhoon landfall, during the peak of typhoon impact, in the early post-disaster recovery period, and during the post-disaster stable period. The algorithm identifies the daily peak call times, peak call volume levels, and call volume decline cycles. The kernel density estimation algorithm is used to statistically analyze the call volume density distribution at different times. By comparing the differences in call volume patterns between extreme weather and normalized periods, as well as the differences in call volume fluctuations under different typhoon impact levels, a standardized extreme weather call volume pattern feature map is formed.

6. The method for optimizing electricity customer service under severe weather conditions according to claim 1, characterized in that, The categories of requests include fault reporting, consultation and inquiry, complaints and suggestions, business processing, and assistance and support.

7. The method for optimizing power customer service under severe weather conditions according to claim 1, characterized in that, The demand analysis includes statistics on the proportion of different demands, frequency of demands, and probability of repeated demands in different time periods, different customer groups, and different disaster-stricken areas.

8. The method for optimizing power customer service under severe weather conditions according to claim 1, characterized in that, The aforementioned analysis of differences in demands includes: using statistical significance testing algorithms to quantitatively compare differences in demand structure, demand concentration, and service needs between different customer groups during typhoon periods and normal times, during the post-disaster recovery period and during typhoon peak periods.

9. The method for optimizing electricity customer service under severe weather conditions according to any one of claims 1-8, characterized in that, By using service strategy as the independent variable and service effectiveness as the dependent variable, a service effectiveness evaluation model is constructed. Using the service effectiveness evaluation model and based on regression analysis algorithms, the correlation and influence coefficient between different service strategies and service effectiveness are analyzed to accurately identify the service strategy most suitable for extreme weather scenarios. The differentiated service response model is continuously optimized to form a closed-loop optimization mechanism of data analysis, strategy implementation, effect evaluation, and model iteration.

10. The method for optimizing electricity customer service under severe weather conditions according to claim 9, characterized in that, The service strategies include customer segmentation service strategies, seat resource allocation strategies, and high-frequency demand service strategies. The service effectiveness includes call connection rate, work order completion time, repeat call rate, customer satisfaction score, and complaint rate.

Images