Disclosure of Invention
In view of the foregoing, the present disclosure provides a service policy generation method, apparatus, device, medium, and program product.
In one aspect of the present disclosure, a service policy generation method is provided, including:
obtaining logistics service experience data of a plurality of clients from a database, wherein the logistics service experience data comprises a plurality of service experience index data;
Respectively carrying out aggregation treatment on a plurality of service experience index data of each client to obtain comprehensive scores of each client on logistics service respectively;
Clustering a plurality of comprehensive scores associated with a plurality of clients by using a preset clustering algorithm, and dividing the clients into a plurality of client groups belonging to different priorities based on the clustering result;
and generating logistics service strategies corresponding to the client groups according to the priority information of the client groups.
According to an embodiment of the present disclosure, aggregating a plurality of service experience index data of each customer to obtain a composite score of each customer for a logistic service includes:
Based on the service experience index data, respectively calculating first variation coefficients of the service experience indexes, wherein the first variation coefficients are used for representing the degree of distinction of the service experience indexes;
Calculating the weight of each service experience index according to the first variation coefficient;
and calculating to obtain comprehensive scores of the clients on the logistics service respectively according to the service experience index data of the clients and the weights of the service experience indexes of the clients.
According to an embodiment of the present disclosure, calculating, according to a plurality of service experience index data of each client and weights of a plurality of service experience indexes of each client, a composite score of each client for a logistic service includes:
According to the service experience index data of each client and the weight of the service experience indexes of each client, calculating to obtain the service experience sub-dimension scores of each client on the logistics service respectively;
based on the scores of the service experience sub-dimensionalities, respectively calculating second variation coefficients of the service experience sub-dimensionalities;
Calculating the weight of each service experience sub-dimension according to the second variation coefficient;
And calculating to obtain a plurality of service experience main dimension scores of each client for logistics service respectively according to the service experience sub-dimension scores of each client and the weights of the service experience sub-dimensions of each client, and taking the service experience main dimension scores as comprehensive scores.
According to an embodiment of the present disclosure, calculating the first coefficient of variation of each service experience index based on each service experience index data includes:
based on the service experience index data, calculating to obtain the mean value and standard deviation of the service experience indexes;
And calculating to obtain a first variation coefficient of each service experience index according to the mean value and the standard deviation of each service experience index.
According to the embodiment of the disclosure, the logistics service experience data of each client respectively comprises M data sets, each data set respectively comprises N data subsets, the M data sets respectively relate to M different service experience main dimensions, the N data subsets respectively relate to N different service experience sub dimensions, and M, N is a positive integer;
Respectively carrying out aggregation processing on a plurality of service experience index data of each customer to obtain comprehensive scores of each customer on logistics service respectively, wherein the steps comprise:
for each data set, carrying out first aggregation processing on service experience index data in N data subsets respectively to obtain N service experience sub-dimension scores;
And carrying out second aggregation processing on the N service experience sub-dimension scores respectively associated with each data set to obtain M service experience main dimension scores as comprehensive scores.
According to an embodiment of the present disclosure, clustering a plurality of composite scores associated with a plurality of clients using a preset clustering algorithm includes:
Determining a predetermined number of target composite scores from the plurality of composite scores as a cluster center;
And clustering the multiple comprehensive scores based on the clustering center.
According to an embodiment of the present disclosure, wherein determining a predetermined number of target composite scores from the plurality of composite scores as a cluster center comprises:
Calculating the winning probability of the plurality of current sub-candidate comprehensive scores, wherein the winning probability is used for representing the probability that the candidate comprehensive scores are determined to be the next cluster center, and the plurality of current sub-candidate comprehensive scores are as follows: among the plurality of comprehensive scores, the rest of the plurality of comprehensive scores except the current cluster center;
Determining a next clustering center from the multiple current sub-candidate comprehensive scores according to the winning probabilities of the multiple current sub-candidate comprehensive scores;
and iteratively executing the operation of calculating the winning probability of the plurality of next-time-to-be-selected comprehensive scores and determining the next cluster center from the plurality of next-time-to-be-selected comprehensive scores according to the winning probability of the plurality of next-time-to-be-selected comprehensive scores until the number of cluster centers reaches the preset number.
According to an embodiment of the present disclosure, calculating the winning probability of the plurality of current sub-candidate composite scores includes:
And calculating the winning probability of the multiple current sub-candidate comprehensive scores according to the difference degree between each calculated current sub-candidate comprehensive score and the current clustering center.
Another aspect of the present disclosure provides a service policy generating device, which includes an acquisition module, an aggregation module, a clustering module, and a generating module.
The system comprises an acquisition module, a data processing module and a data processing module, wherein the acquisition module is used for acquiring logistics service experience data of a plurality of clients from a database, and the logistics service experience data comprises a plurality of service experience index data.
And the aggregation module is used for respectively carrying out aggregation treatment on the plurality of service experience index data of each client so as to obtain comprehensive scores of each client on the logistics service.
And the clustering module is used for carrying out clustering processing on a plurality of comprehensive scores associated with a plurality of clients by utilizing a preset clustering algorithm, and dividing the clients into a plurality of client groups belonging to different priorities based on the clustering processing result.
And the generation module is used for generating logistics service strategies corresponding to the client groups according to the priority information of the client groups.
According to an embodiment of the disclosure, the aggregation module includes a first calculation sub-module, a second calculation sub-module, and a third calculation sub-module.
The first computing sub-module is used for respectively computing first variation coefficients of the service experience indexes based on the service experience index data, and the first variation coefficients are used for representing the degree of distinction of the service experience indexes; the second computing sub-module is used for computing the weight of each service experience index according to the first variation coefficient; and the third calculation sub-module is used for calculating and obtaining comprehensive scores of the clients on the logistics service respectively according to the service experience index data of the clients and the weights of the service experience indexes of the clients.
According to an embodiment of the disclosure, the third computing sub-module includes a first computing unit, a second computing unit, a third computing unit, and a fourth computing unit.
The first calculation unit is used for calculating to obtain a plurality of service experience sub-dimension scores of each client for logistics service according to the service experience index data of each client and the weight of the service experience indexes of each client; the second calculation unit is used for calculating second variation coefficients of all the service experience sub-dimensions based on the scores of all the service experience sub-dimensions; the third calculation unit is used for calculating the weight of each service experience sub-dimension according to the second variation coefficient; and the fourth calculation unit is used for calculating and obtaining a plurality of service experience main dimension scores of each client for logistics service respectively as comprehensive scores according to the service experience sub-dimension scores of each client and the weights of the service experience sub-dimensions of each client.
According to an embodiment of the disclosure, the first computing submodule includes a fifth computing unit and a sixth computing unit.
The fifth calculation unit is used for calculating the mean value and standard deviation of each service experience index based on the data of each service experience index; and the sixth calculation unit is used for calculating and obtaining the first variation coefficient of each service experience index according to the mean value and standard deviation of each service experience index.
According to the embodiment of the disclosure, the logistics service experience data of each client respectively comprises M data sets, each data set respectively comprises N data subsets, the M data sets respectively relate to M different service experience main dimensions, the N data subsets respectively relate to N different service experience sub dimensions, and M, N is a positive integer.
The aggregation module comprises a first aggregation sub-module and a second aggregation sub-module.
The first aggregation sub-module is used for respectively carrying out first aggregation processing on the service experience index data in the N data subsets aiming at each data set so as to obtain N service experience sub-dimension scores. And the second aggregation sub-module is used for carrying out second aggregation processing on the N service experience sub-dimension scores respectively associated with each data set so as to obtain M service experience main dimension scores as comprehensive scores.
According to an embodiment of the disclosure, the clustering module comprises a determining sub-module, a clustering sub-module.
The determining submodule is used for determining a preset number of target comprehensive scores from the plurality of comprehensive scores to serve as a clustering center; and the clustering sub-module is used for carrying out clustering processing on the multiple comprehensive scores based on the clustering center.
According to an embodiment of the present disclosure, wherein determining the sub-module comprises:
A seventh calculating unit, configured to calculate winning probabilities of a plurality of current sub-candidate comprehensive scores, where the winning probabilities are used to characterize a probability that the candidate comprehensive score is determined to be a next cluster center, and the plurality of current sub-candidate comprehensive scores are: among the plurality of comprehensive scores, the rest of the plurality of comprehensive scores except the current cluster center;
The determining unit is used for determining the next clustering center from the multiple current sub-candidate comprehensive scores according to the winning probabilities of the multiple current sub-candidate comprehensive scores;
and the iteration unit is used for iteratively executing the operation of calculating the winning probability of the plurality of next-time to-be-selected comprehensive scores and determining the next cluster center from the plurality of next-time to-be-selected comprehensive scores according to the winning probability of the plurality of next-time to-be-selected comprehensive scores until the number of the cluster centers reaches the preset number.
According to the embodiment of the disclosure, the seventh calculating unit includes a calculating subunit, configured to calculate, according to the calculated degree of difference between each current sub-candidate comprehensive score and the current cluster center, the winning probability of the plurality of current sub-candidate comprehensive scores.
Another aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the service policy generation method described above.
Another aspect of the present disclosure also provides a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the service policy generation method described above.
Another aspect of the present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the above-described service policy generation method.
According to the embodiment of the disclosure, the logistics service strategy is generated after the logistics service experience data of a plurality of clients are obtained from the database and the data is processed by executing the method of the embodiment of the disclosure, so that the automatic generation of the logistics service strategy is realized, the problems of time and labor waste and high labor cost in manual investigation in the related art are solved, and the production efficiency is improved.
In addition, the logistic service experience data of the client is objective expression of the service experience of the client, comprehensive scores of the client on the logistic service can be obtained through aggregation processing of a plurality of service experience index data of the client, comprehensive evaluation of service quality of various services provided by the client can be represented, and the problem that a real and objective evaluation result cannot be obtained due to subjective prejudice of client answers in a manual interview mode is avoided. Based on the fact that similar clients have certain aggregation and similarity to comprehensive scores of different service experiences, the embodiment of the disclosure can further realize client grading by clustering the comprehensive scores of a plurality of clients, and can facilitate making different coping strategies according to different priorities by clustering the clients into groups with different priorities, so that the client experience can be improved to a greater extent.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a convention should be interpreted in accordance with the meaning of one of skill in the art having generally understood the convention (e.g., "a system having at least one of A, B and C" would include, but not be limited to, systems having a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
In the logistics field, logistics customer experience guarantee is an important link, is commonly used in evaluation indexes of logistics customer service related departments, and an investigation approach is commonly cooperated with a user research department, so that customers using logistics related products are randomly extracted through a regular NPS telephone visit mode, and then a large-disc conclusion is presumed through a representative sample; or the satisfaction condition of the logistics service is judged by manually reading records visited by the client manager by periodically concentrating part of manpower.
Based on NPS phone call mode, customer coordination is typically low, interview quality is irregular, and by way of phone interview mode, there is a lack of a customer's look, limb assistance to judge the authenticity of the answer, overall the customer remains in a passive mode, there is a possibility of rendering unrealistic assessment for ending the interview as soon as possible. In addition, the mode interview has single content, lacks design of flow experience, lacks direct feedback on improvement of product page functions and the like, and is a mode of regularly reading visit records of a client manager by concentrated manpower, and has the advantages of long time consumption, large manpower investment, limited involved area and incapability of realizing short-period and rapid investigation reaction.
Therefore, the method for acquiring satisfaction degree of logistics service in the related technology is time-consuming and labor-consuming, high in labor cost investment, and unreasonable in follow-up customer service problem solving strategies and influences customer experience because of a certain subjective bias of customer answers, so that a real and objective evaluation result cannot be obtained.
In view of this, an embodiment of the present disclosure provides a service policy generation method, including:
obtaining logistics service experience data of a plurality of clients from a database, wherein the logistics service experience data comprises a plurality of service experience index data;
Respectively carrying out aggregation treatment on a plurality of service experience index data of each client to obtain comprehensive scores of each client on logistics service respectively;
Clustering a plurality of comprehensive scores associated with a plurality of clients by using a preset clustering algorithm, and dividing the clients into a plurality of client groups belonging to different priorities based on the clustering result;
and generating logistics service strategies corresponding to the client groups according to the priority information of the client groups.
According to the embodiment of the disclosure, the logistics service strategy is generated after the logistics service experience data of a plurality of clients are obtained from the database and the data is processed by executing the method of the embodiment of the disclosure, so that the automatic generation of the logistics service strategy is realized, the problems of time and labor waste and high labor cost in manual investigation in the related art are solved, and the production efficiency is improved.
In addition, the logistic service experience data of the client is objective expression of the service experience of the client, comprehensive scores of the client on the logistic service can be obtained through aggregation processing of a plurality of service experience index data of the client, comprehensive evaluation of service quality of various services provided by the client can be represented, and the problem that a real and objective evaluation result cannot be obtained due to subjective prejudice of client answers in a manual interview mode is avoided. Based on the fact that similar clients have certain aggregation and similarity to comprehensive scores of different service experiences, the embodiment of the disclosure can further realize client grading by clustering the comprehensive scores of a plurality of clients, and can facilitate making different coping strategies according to different priorities by clustering the clients into groups with different priorities, so that the client experience can be improved to a greater extent.
Fig. 1 schematically illustrates an application scenario diagram of a service policy generation method, apparatus, device, medium and program product according to an embodiment of the present disclosure.
As shown in fig. 1, an application scenario 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104, a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as shopping class applications, web browser applications, search class applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only) may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be a variety of electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background management server (by way of example only) providing support for websites browsed by users using the terminal devices 101, 102, 103. The background management server may analyze and process the received data such as the user request, and feed back the processing result (e.g., the web page, information, or data obtained or generated according to the user request) to the terminal device.
In an application scenario of the embodiment of the present disclosure, a service person may initiate a request by using the terminal device 101, 102, 103, for obtaining a logistics service policy. In response to a user request, the server 105 may be configured to execute the service policy generating method according to the embodiments of the present disclosure, for example, may obtain logistics service experience data of a plurality of clients from a database, aggregate the plurality of service experience index data of each client to obtain comprehensive scores of the logistics service of each client, cluster the plurality of comprehensive scores associated with the plurality of clients, divide the plurality of clients into a plurality of client groups with different priorities according to a result of the clustering process, generate a logistics service policy according to priority information of the plurality of client groups, and after generating the logistics service policy, the server 105 may display a policy result to the user through the terminal devices 101, 102, 103.
It should be noted that, the service policy generating method provided by the embodiments of the present disclosure may be generally executed by the server 105. Accordingly, the service policy generation apparatus provided by the embodiments of the present disclosure may be generally disposed in the server 105. The service policy generation method provided by the embodiments of the present disclosure may also be performed by a server or a server cluster that is different from the server 105 and is capable of communicating with the terminal devices 101, 102, 103 and/or the server 105. Accordingly, the service policy generating apparatus provided by the embodiments of the present disclosure may also be provided in a server or a server cluster that is different from the server 105 and is capable of communicating with the terminal devices 101, 102, 103 and/or the server 105.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
In the technical scheme of the disclosure, the related processes of collecting, storing, using, processing, transmitting, providing, disclosing and applying personal information of the user all conform to the regulations of related laws and regulations, necessary security measures are adopted, and the public welcome is not violated.
In the technical scheme of the disclosure, the authorization or consent of the user is obtained before the personal information of the user is obtained or acquired.
The service policy generation method of the disclosed embodiment will be described in detail below with reference to fig. 2 to 6 based on the scenario described in fig. 1.
Fig. 2 schematically illustrates a flowchart of a service policy generation method according to an embodiment of the present disclosure.
As shown in fig. 2, the service policy generation method of this embodiment includes operations S201 to S204.
In operation S201, obtaining logistics service experience data of a plurality of clients from a database, wherein the logistics service experience data includes a plurality of service experience index data;
In operation S202, aggregating the multiple service experience index data of each customer to obtain comprehensive scores of each customer for the logistic service;
In operation S203, clustering a plurality of composite scores associated with a plurality of clients using a preset clustering algorithm, and dividing the plurality of clients into a plurality of client groups belonging to different priorities based on a result of the clustering;
In operation S204, a logistics service policy corresponding to each customer segment is generated according to the priority information of the plurality of customer segments.
According to the embodiment of the disclosure, the logistics service experience data of the client can be obtained by analyzing the historical purchasing log data of the client, and the portrait information of the client can be obtained by analyzing the log, such as basic information (region, user year, etc.), consumption behavior information (ordering frequency, ordering time, ordering amount, B-end or C-end, etc.).
The log data may include evaluation log data of a customer for a certain logistics service in a history period, log data for monitoring logistics nodes of a plurality of logistics services in a history period, log data for monitoring customer complaints of a plurality of logistics services in a history period, return log data for returning a plurality of logistics services in a history period, and the like. The logistics service experience data may include a plurality of service experience index data, which may be respectively based on index data in different dimensions.
For example, the primary dimension of the service that the logistics service experience data can encompass can include reliability, timeliness, personnel services, flexibility, information quality, economy, and the like.
Further, based on reliability dimension, the coverage of online stores is required to be considered, and the factors such as remote areas and villages, goods returning guarantee, complete package without secondary packaging, fresh freshness and the like are involved, and the related indexes comprise goods returning coverage, package breakage rate, goods returning yield, goods returning times, cold chain coverage, defrosting rate and the like.
Based on timeliness dimension, several links in the dispatch process need to be considered: the order is sent to the merchant to send out goods, the goods are sent to the merchant to be received from the goods, the goods are received in peak period and normal period, the goods are returned and exchanged, and the related indexes comprise the time interval of receiving, the delivery time, the delay time of peak period, the time of exchanging and the like.
Based on personnel service dimension, the performances of couriers and customer service, including the professional property of dressing, language notification, delivery, missed delivery, problem answering and the like, need to be considered, and the related indexes comprise complaint rate, complaint work order content, problem communication times and the like.
Based on flexibility dimension, payment mode, delivery mode and the like need to be considered, and related indexes comprise payment time, payment selectivity degree and the like.
Based on the information quality dimension, information such as a supplier known by a consumer, a mode of acquiring the commodity, a commodity transportation condition and the like needs to be considered, and related indexes include a supplier true rate, a quality guarantee rate, a brand coverage, a commodity transportation information update delay time and the like.
Based on economical dimension, packaging, transportation and reverse logistics cost are considered, and enterprise logistics cost is required to be minimized while customer experience is met, and related indexes comprise single average packaging cost, excessive packaging rate, unrealistic oil charge reimbursement, return risk coverage rate, reimbursement cost, reimbursement rate and the like.
It should be noted that, in the embodiment of the present disclosure, the consent or the authorization of the user may be obtained before the service experience index data of the user is obtained. For example, before operation S201, a request to acquire user information may be issued to the user. In case the user agrees or authorizes that the user information can be acquired, the operation S201 is performed.
According to the embodiment of the disclosure, for each customer, the service experience index data of the customer may reflect the various service guarantee conditions provided for the customer and the customer experience to a certain extent, and the service quality of the various services provided for the customer may be different, and in operation S202, the comprehensive score of the customer on the logistics service may be obtained by performing aggregation processing on the multiple service experience index data of the customer, and the comprehensive condition of the service quality of the various services provided for the customer may be represented.
According to the embodiment of the disclosure, for different clients, the index data may have different characteristic performances, for example, for high-quality clients, the degree of service guarantee provided for the clients is higher, and the experience of the clients is better, so that the comprehensive score of the logistics service for the clients is higher; in contrast, for customers with lower priority, the degree of service guarantee provided for the customers is poor, the customer experience is also poor, and the comprehensive score for the logistics service of the customers is also low.
According to an embodiment of the present disclosure, based on the fact that the composite scores of the same kind of clients for different service experiences have a certain aggregation and similarity, in operation S203, the plurality of clients may be divided into a plurality of client groups belonging to different priorities by performing a clustering process on the plurality of composite scores associated with the plurality of clients.
According to an embodiment of the present disclosure, in operation S204, a logistics service policy corresponding to each customer group is generated according to priority information of a plurality of customer groups, and different logistics service policies may be generated for customer groups of different priorities, respectively. For example, for a customer group with higher priority, in a logistics service policy generated by the acquired business rule, service response time is shorter, active return visit times are more, and the like. Aiming at the customer groups with lower priorities, in the generated logistics service strategy, the service response time is longer, the initiative return visit times are fewer, and the like.
According to the embodiment of the disclosure, the logistics service strategy is generated after the logistics service experience data of a plurality of clients are obtained from the database and the data is processed by executing the method of the embodiment of the disclosure, so that the automatic generation of the logistics service strategy is realized, the problems of time and labor waste and high labor cost in manual investigation in the related art are solved, and the production efficiency is improved.
In addition, the logistic service experience data of the client is objective expression of the service experience of the client, comprehensive scores of the client on the logistic service can be obtained through aggregation processing of a plurality of service experience index data of the client, comprehensive evaluation of service quality of various services provided by the client can be represented, and the problem that a real and objective evaluation result cannot be obtained due to subjective prejudice of client answers in a manual interview mode is avoided. Based on the fact that similar clients have certain aggregation and similarity to comprehensive scores of different service experiences, the embodiment of the disclosure can further realize client grading by clustering the comprehensive scores of a plurality of clients, and can facilitate making different coping strategies according to different priorities by clustering the clients into groups with different priorities, so that the client experience can be improved to a greater extent.
In summary, the method of the embodiment of the disclosure changes the existing customer experience research mode of visiting records and regular NPS by means of manpower reading statistics, has timeliness, pertinence and insight in practice, solves the customer satisfaction measurement problems of intensive labor force, long time consumption, incapability of quick response and subjective answer bias, is more comprehensive and objective in index statistics, is quicker in response period, can release manpower on more important guidance and department cooperativity work, and can achieve the purpose of improving the customer satisfaction more accurately and efficiently through classification in statistical sense.
According to the embodiment of the disclosure, before data aggregation, each index can be subjected to standardized processing, so that the influence of dimension is eliminated, and the comparability among indexes is enhanced. For example, a z-score normalization method can be adopted, and the original value of each index is converted into a normal distribution with the average value of 0 and the standard deviation of 1 by introducing the average value and the standard deviation of each index.
The conversion method is shown in the following formula (1):
Wherein xz is a post-conversion index, x is a pre-conversion index, mean is the mean of the indexes, and std is the standard deviation of the indexes.
For example, if breakage index a11 indicates breakage of the day-to-day dosing in the past 30 days, breakage values of the day-to-day dosing in 30 days are expressed as (a111,a112,a113,...,a1130) in an array
Calculating the average value of the index a11 in the past 30 daysThe following formula (2):
calculating standard deviation of breakage rate of index a11 in the past 30 daysThe following formula (3):
then the normalized a11 becomes
According to an embodiment of the present disclosure, in the foregoing method, aggregating a plurality of service experience index data of each customer to obtain a composite score of each customer for a logistic service includes:
First, based on each service experience index data, first variation coefficients of each service experience index are calculated respectively, wherein the first variation coefficients are used for representing the degree of distinction of the service experience indexes.
According to an embodiment of the present disclosure, calculating the coefficient of variation may include first calculating, based on each service experience index data, a mean value and a standard deviation of each service experience index, and then calculating, based on the mean value and the standard deviation of each service experience index, a first coefficient of variation of each service experience index.
For a certain index, the variation coefficient is the ratio of the standard deviation to the average value of the index, and the following formula (4):
Wherein cv is a coefficient of variation that is an indicator of a service experience; sigma is the standard deviation of the service experience index; μ is the mean of the service experience metrics.
Then, calculating the weight of each service experience index according to the first variation coefficient; for example, the ratio of each index variation coefficient to the sum of all index variation coefficients in the same dimension may be calculated to obtain the weight of each service experience index in the same dimension.
And then, according to the service experience index data of each client and the weight of the service experience indexes of each client, calculating to obtain the comprehensive score of each client on the logistics service. For example, a weighted sum of the multiple service experience index data may be calculated according to weights of multiple service experience indexes of each client, so as to obtain comprehensive scores of each client on the logistics service.
According to embodiments of the present disclosure, the coefficient of variation, which is an index for measuring data differences in statistics, may be used to characterize the degree of distinction between indexes. In the similar index clusters, the larger the variation coefficient is, the stronger the information resolution capability of the capability is, the higher the interpretation is, and the larger the effect on comprehensive evaluation is. According to the aggregation method disclosed by the embodiment of the disclosure, each index is aggregated into the data sets with multiple dimensions through the variation coefficient, and the data sets are used as comprehensive scores of customers on logistics services, and because the variation coefficient capable of representing the degree of distinction between the indexes is introduced in the data aggregation process, the index with higher degree of distinction can be configured with larger weight, so that the comprehensive scores obtained by weighting and summing the multiple indexes are higher in interpretation and higher in objectivity and referenceability.
Fig. 3 schematically illustrates a method schematic diagram for aggregating multiple service experience metrics data of individual clients according to an embodiment of the disclosure. The method of data aggregation processing according to the embodiments of the present disclosure is further described below with reference to fig. 3.
According to an embodiment of the present disclosure, as shown in fig. 3, the logistic service experience data may include a plurality of different main dimension data, and may further include a plurality of different sub-dimension data based on each main dimension, and further may further include a plurality of different service experience index data based on each sub-dimension.
For example, the primary dimension of the service that the logistics service experience data can encompass can include reliability, timeliness, personnel services, flexibility, information quality, economy, and the like. Further, based on the reliability main dimension, the sub-dimension that can be involved can include, for example, coverage of online stores can involve remote areas and villages, refund guarantee, package integrity, no secondary packaging, fresh freshness, etc., further, under each sub-dimension, a plurality of indexes can be further included, for example, based on the refund guarantee sub-dimension, the indexes involved include refund commodity coverage, package breakage rate, on-line yield, refund number of times, etc., based on the fresh freshness sub-dimension, the indexes involved include cold chain coverage, defrosting rate, etc.
According to an embodiment of the present disclosure, based on the above three levels of data partitioning, the logistics service experience data of each client includes M data sets, each data set includes N data subsets, each data set is associated with M different service experience main dimensions, each data subset is associated with N different service experience sub dimensions, and M, N is a positive integer.
Based on the above, aggregation processing is performed on the multiple service experience index data of each customer, so that the comprehensive score of each customer on the logistics service respectively comprises two aggregation processes.
Wherein, the first polymerization is: and aiming at each data set, carrying out first aggregation processing on the service experience index data in the N data subsets respectively to obtain N service experience sub-dimension scores.
The second polymerization is as follows: and carrying out second aggregation processing on the N service experience sub-dimension scores respectively associated with each data set to obtain M service experience main dimension scores as comprehensive scores.
Further, in the first aggregation, for each data set (each main dimension), the first aggregation processing is performed on the service experience index data under each sub-dimension corresponding to the N data subsets, so as to obtain N service experience sub-dimension scores, which specifically may include the following operations:
First, based on the data of each service experience index in each data subset, a first variation coefficient of each service experience index is calculated, and a weight of each service experience index is calculated according to the first variation coefficient, for example, a ratio of each index variation coefficient to a sum of all index variation coefficients in the same sub-dimension may be calculated, so as to obtain the weight of each service experience index in the same sub-dimension.
Then, according to the service experience index data of each client and the weight of the service experience indexes of each client, calculating to obtain the service experience sub-dimension scores of each client on the logistics service respectively; for example, for each customer, a weighted sum of the multiple service experience index data in each sub-dimension can be calculated according to the weight of each service experience index in each sub-dimension, so as to obtain multiple service experience sub-dimension scores of each customer for the logistics service.
For example, a coefficient of variation c11、c12、c13 of the series index a11、a12、a13 of the client i is calculated according to equation (4);
And then respectively calculating the weight indexes of the index a11、a12、a13 as follows (5) - (7):
And similarly, wa21、wa22、wa31、wa32 … … can be obtained, and the weighted sum of the service experience index data in the sub-dimension is calculated to obtain a service experience sub-dimension score A1：a11·wa11+a12·wa12+a13·wa13 in the sub-dimension.
Further, in the second aggregation, performing a second aggregation process on N service experience sub-dimension scores respectively associated with each data set, so as to obtain M service experience main dimension scores, where the M service experience main dimension scores as a comprehensive score include:
Firstly, based on the scores of all the service experience sub-dimensions corresponding to each main dimension, respectively calculating a second variation coefficient of each service experience sub-dimension, and calculating the weight of each service experience sub-dimension according to the second variation coefficient, for example, calculating the ratio of the variation coefficient of each sub-dimension to the sum of the variation coefficients of all the sub-dimensions in the same main dimension to obtain the weight of each service experience sub-dimension in the same main dimension.
For example, a coefficient of variation cA3 of the coefficient of variation cA2,A3 of the coefficient of variation cA1,A2 of a1 is calculated according to formula (4);
And then respectively calculating the weights of the index A1、A2、A3 as shown in the following formulas (8) - (10):
and then, calculating to obtain a plurality of service experience main dimension scores of each client for logistics service respectively according to the service experience sub-dimension scores of each client and the weights of the service experience sub-dimensions of each client, and taking the service experience main dimension scores as comprehensive scores.
For example, a weighted sum of multiple service experience sub-dimension scores under the main dimension is calculated respectively, so as to obtain a service experience main dimension score A of the main dimension: a1·za1+A2·za2+A3·za3.
According to the same calculation mode, other main dimension scores of the client can be calculated: b, C, D, E, F), then for client i, his multiple dimension experience scores as an array: (Ai,Bi,Ci,Di,Ei,Fi).
According to the embodiment of the disclosure, since massive indexes of different clients have the characteristics of high complexity and possibly strong correlation (i.e. multiple indexes have the same effect on the same dimension) redundancy, evaluation conclusion cannot be obtained only by means of correlation analysis. Therefore, data aggregation is carried out according to each sub-dimension in multiple dimensions, and each class can be ensured to cover the whole aspect of the customer experience evaluation index system. Through twice aggregation, on the basis of comprehensively obtaining experience scores of specific crowds on different logistics service dimensions by various indexes, the influence of the index with higher differentiation degree can be further strengthened, the influence of the index with lower differentiation degree is weakened, the obtained comprehensive score has stronger referenceability, the subsequent real-time monitoring of the experience difference and change effect of different crowds is facilitated, the dimensions and specific indexes of the probes are automatically lowered, the problem is located, and a targeted improvement strategy is provided.
According to the embodiment of the disclosure, based on the fact that similar clients have certain aggregation and similarity to the comprehensive scores of different service experiences, the embodiment of the disclosure can further realize client grading by carrying out clustering processing on the comprehensive scores of a plurality of clients, and the clients are clustered into groups with different priority levels, so that different coping strategies can be formulated according to the different priority levels conveniently, and the client experience can be improved to a large extent. Wherein, utilizing a preset clustering algorithm, clustering a plurality of comprehensive scores associated with a plurality of clients comprises: determining a predetermined number of target composite scores from the plurality of composite scores as a cluster center; and clustering the multiple comprehensive scores based on the clustering center.
According to embodiments of the present disclosure, the clustering algorithm may employ an optimization algorithm based on K-means clustering. The algorithm clusters according to the similarity between the data, namely clusters according to the distance between the data, wherein the larger the distance is, the lower the similarity is, the smaller the distance is, and the higher the similarity between indexes is.
According to the embodiment of the present disclosure, unlike the method of randomly designating a cluster center by the conventional K-means clustering algorithm, in the clustering method of the embodiment of the present disclosure, the cluster center is determined by the probability that each data point is selected as the cluster center.
Specifically, determining a predetermined number of target composite scores from the plurality of composite scores as a cluster center includes:
Calculating the winning probability of the plurality of current sub-candidate comprehensive scores, wherein the winning probability is used for representing the probability that the candidate comprehensive scores are determined to be the next cluster center, and the plurality of current sub-candidate comprehensive scores are as follows: among the plurality of comprehensive scores, the rest of the plurality of comprehensive scores except the current cluster center; specifically, the winning probability of the multiple current sub-candidate comprehensive scores is calculated according to the difference degree between each calculated current sub-candidate comprehensive score and the current clustering center. For example, it may be calculated separately: the ratio of the difference degree of each current sub-candidate comprehensive score and the sum of the difference degrees of all current sub-candidate comprehensive scores.
Determining a next clustering center from the multiple current sub-candidate comprehensive scores according to the winning probabilities of the multiple current sub-candidate comprehensive scores; and iteratively executing the operation of calculating the winning probability of the plurality of next-time-to-be-selected comprehensive scores and determining the next cluster center from the plurality of next-time-to-be-selected comprehensive scores according to the winning probability of the plurality of next-time-to-be-selected comprehensive scores until the number of cluster centers reaches the preset number.
For example, after the comprehensive scores of j clients are obtained by the above two aggregation, the method for performing the clustering process may be:
Operation 1, randomly selecting a six-dimensional experience score of a certain client as a first index value after initial standardization, for example, selecting an experience score (Ap,Bp,Cp,Dp,Ep,Fp) of a client p as an initial point.
Operation 2, calculating the difference D (x) between the score of each other client and the score of the client p.
For example, customer q has an experience score (Aq,Bq,Cq,Dq,Eq,Fq), then the difference between customer q and the original particle customer p is represented by the following equation (11)
D1(x)＝(Aq-Ap)2+(Bq-Bp)2+(Cq-Cp)2+(Dq-Dp)2+(Eq-Ep)2+(Fq-Fp)2 (11)
Based on the above calculation method, the difference between j-1 customers and the initial particle customer p is D1(x)、D2(x)、.....、Dj-1 (x).
Operation 3, calculating the winning probability of the other j-1 customers selected as the first cluster center according to the difference degree between the comprehensive scores of the other j-1 customers and the initial particle customer p, wherein the calculation method is as shown in the following formula (12). The larger the selection probability value, the greater the probability that the composite score for the customer is selected as the next cluster center.
If the probability of client j is the greatest, the first cluster center is the experience score to which client j belongs (Aj,Bj,Cj,Dj,Ej,Fj).
The operations 2 and 3 are executed, the difference between the score of each other client and the score of the client j is calculated, the winning probability of the other j-1 clients selected as the first clustering center is calculated, and the largest probability is selected as the second clustering center; and iteratively executing the operations 2 and 3 until K initial index values are selected, wherein the K initial index values represent K classes of clients.
After K clustering centers are selected, the difference value between each customer score and the K clustering center objects is calculated, and customers with smaller difference values from the clustering centers are classified as one type. Thus, a plurality of client groups with different priorities are obtained through clustering.
After the priority information of a plurality of customer groups is obtained, whether all the categories need to be concerned or not can be considered based on cost benefit in the process of generating the logistics service strategy according to the priority information, and because the categories of the customers are many, but not all the group categories are worth paying attention, for example, in a certain extreme crowd condition, only a few people can be involved in millions of samples. Therefore, the customer numbers of each customer group can be calculated, the customers are ranked according to the number of the customers, and the part of the customer groups ranked at the front are selected as the important attention group.
Fig. 4 illustrates an experience score radar graph for different customer groups according to an embodiment of the present disclosure.
As shown in fig. 4, the distribution of index scores in six dimensions is shown for three classes of customers with different priorities. For example, for class I customers, the A, B, C dimension score is higher, the D, E, F dimension score is less than ideal, and for class III customers, only the F dimension, such as reliability, meets better criteria, but none of the A, B, C, D, E dimension performs satisfactorily.
After the fact that certain specific dimensions are poor in scoring of specific crowds is obtained, dimension decoupling can be performed, for example, experience is insufficient on economy of dimension A for class III clients, average value differences and changes of sub-dimension A1、A2、A3 in dimension A are detected, specific index a11、a12、a13 in the sub-dimension is further detected to check the differences and changes, specific problems are located, and a targeted improvement strategy is given to the crowds.
Based on the service policy generation method, the disclosure further provides a service policy generation device. The device will be described in detail below in connection with fig. 5.
Fig. 5 schematically shows a block diagram of a service policy generation device according to an embodiment of the present disclosure.
As shown in fig. 5, the service policy generating device 500 of this embodiment includes an acquisition module 501, an aggregation module 502, a clustering module 503, and a generating module 504.
The obtaining module 501 is configured to obtain, from a database, logistic service experience data of a plurality of clients, where the logistic service experience data includes a plurality of service experience index data.
And the aggregation module 502 is configured to aggregate the multiple service experience index data of each customer to obtain a comprehensive score of each customer for the logistics service.
The clustering module 503 is configured to perform clustering processing on a plurality of comprehensive scores associated with a plurality of clients by using a preset clustering algorithm, and divide the plurality of clients into a plurality of client groups belonging to different priorities based on a result of the clustering processing.
And the generating module 504 is configured to generate a logistics service policy corresponding to each customer group according to the priority information of the plurality of customer groups.
According to the embodiment of the disclosure, the acquisition module 501 acquires the logistics service experience data of a plurality of clients from the database, and the aggregation module 502, the clustering module 503 and the generation module 504 execute the method of the embodiment of the disclosure to generate the logistics service strategy after processing the data, so that the automatic generation of the logistics service strategy is realized, the problems of time and labor waste and high labor cost in the related art through manual investigation are solved, and the production efficiency is improved.
In addition, the logistic service experience data of the client is objective expression of the service experience of the client, and the aggregate module 502 is used for aggregating the plurality of service experience index data of the client, so that comprehensive scores of the client on the logistic service can be obtained, comprehensive evaluation of service quality of various services provided by the client can be represented, and the problem that a real and objective evaluation result cannot be obtained due to subjective prejudice of client answers in a manual interview mode is avoided. Based on the fact that similar clients have certain aggregation and similarity to the comprehensive scores of different service experiences, the embodiment of the disclosure performs clustering processing on the comprehensive scores of a plurality of clients through the clustering module 503, client grading can be further achieved, and clients are clustered into groups with different priority levels, so that different coping strategies can be formulated according to different priorities conveniently, and client experiences can be improved to a large extent.
According to an embodiment of the present disclosure, the aggregation module 502 includes a first calculation sub-module, a second calculation sub-module, and a third calculation sub-module.
The first computing sub-module is used for respectively computing first variation coefficients of the service experience indexes based on the service experience index data, and the first variation coefficients are used for representing the degree of distinction of the service experience indexes; the second computing sub-module is used for computing the weight of each service experience index according to the first variation coefficient; and the third calculation sub-module is used for calculating and obtaining comprehensive scores of the clients on the logistics service respectively according to the service experience index data of the clients and the weights of the service experience indexes of the clients.
According to an embodiment of the disclosure, the third computing sub-module includes a first computing unit, a second computing unit, a third computing unit, and a fourth computing unit.
The first calculation unit is used for calculating to obtain a plurality of service experience sub-dimension scores of each client for logistics service according to the service experience index data of each client and the weight of the service experience indexes of each client; the second calculation unit is used for calculating second variation coefficients of all the service experience sub-dimensions based on the scores of all the service experience sub-dimensions; the third calculation unit is used for calculating the weight of each service experience sub-dimension according to the second variation coefficient; and the fourth calculation unit is used for calculating and obtaining a plurality of service experience main dimension scores of each client for logistics service respectively as comprehensive scores according to the service experience sub-dimension scores of each client and the weights of the service experience sub-dimensions of each client.
According to an embodiment of the disclosure, the first computing submodule includes a fifth computing unit and a sixth computing unit.
The fifth calculation unit is used for calculating the mean value and standard deviation of each service experience index based on the data of each service experience index; and the sixth calculation unit is used for calculating and obtaining the first variation coefficient of each service experience index according to the mean value and standard deviation of each service experience index.
According to the embodiment of the disclosure, the logistics service experience data of each client respectively comprises M data sets, each data set respectively comprises N data subsets, the M data sets respectively relate to M different service experience main dimensions, the N data subsets respectively relate to N different service experience sub dimensions, and M, N is a positive integer.
The aggregation module 502 includes a first aggregation sub-module, a second aggregation sub-module.
The first aggregation sub-module is used for respectively carrying out first aggregation processing on the service experience index data in the N data subsets aiming at each data set so as to obtain N service experience sub-dimension scores. And the second aggregation sub-module is used for carrying out second aggregation processing on the N service experience sub-dimension scores respectively associated with each data set so as to obtain M service experience main dimension scores as comprehensive scores.
According to an embodiment of the present disclosure, the clustering module 503 includes a determining sub-module, a clustering sub-module.
The determining submodule is used for determining a preset number of target comprehensive scores from the plurality of comprehensive scores to serve as a clustering center; and the clustering sub-module is used for carrying out clustering processing on the multiple comprehensive scores based on the clustering center.
According to an embodiment of the present disclosure, wherein determining the sub-module comprises:
A seventh calculating unit, configured to calculate winning probabilities of a plurality of current sub-candidate comprehensive scores, where the winning probabilities are used to characterize a probability that the candidate comprehensive score is determined to be a next cluster center, and the plurality of current sub-candidate comprehensive scores are: among the plurality of comprehensive scores, the rest of the plurality of comprehensive scores except the current cluster center;
The determining unit is used for determining the next clustering center from the multiple current sub-candidate comprehensive scores according to the winning probabilities of the multiple current sub-candidate comprehensive scores;
and the iteration unit is used for iteratively executing the operation of calculating the winning probability of the plurality of next-time to-be-selected comprehensive scores and determining the next cluster center from the plurality of next-time to-be-selected comprehensive scores according to the winning probability of the plurality of next-time to-be-selected comprehensive scores until the number of the cluster centers reaches the preset number.
According to the embodiment of the disclosure, the seventh calculating unit includes a calculating subunit, configured to calculate, according to the calculated degree of difference between each current sub-candidate comprehensive score and the current cluster center, the winning probability of the plurality of current sub-candidate comprehensive scores.
Any of the acquisition module 501, aggregation module 502, clustering module 503, generation module 504 may be combined in one module to be implemented, or any of the modules may be split into multiple modules, according to embodiments of the present disclosure. Or at least some of the functionality of one or more of the modules may be combined with, and implemented in, at least some of the functionality of other modules. At least one of the acquisition module 501, aggregation module 502, clustering module 503, generation module 504 may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), programmable Logic Array (PLA), system on a chip, system on a substrate, system on a package, application Specific Integrated Circuit (ASIC), or by hardware or firmware, such as any other reasonable way of integrating or packaging the circuits, or any one of or a suitable combination of three of software, hardware, and firmware, according to embodiments of the present disclosure. Or at least one of the acquisition module 501, the aggregation module 502, the clustering module 503, the generation module 504 may be at least partly implemented as a computer program module which, when run, may perform the respective functions.
Fig. 6 schematically illustrates a block diagram of an electronic device adapted to implement a service policy generation method according to an embodiment of the disclosure.
As shown in fig. 6, an electronic device 600 according to an embodiment of the present disclosure includes a processor 601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. The processor 601 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or an associated chipset and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. Processor 601 may also include on-board memory for caching purposes. The processor 601 may comprise a single processing unit or a plurality of processing units for performing different actions of the method flows according to embodiments of the disclosure.
In the RAM 603, various programs and data necessary for the operation of the electronic apparatus 600 are stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. The processor 601 performs various operations of the method flow according to the embodiments of the present disclosure by executing programs in the ROM 602 and/or the RAM 603. Note that the program may be stored in one or more memories other than the ROM 602 and the RAM 603. The processor 601 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in the one or more memories.
According to an embodiment of the present disclosure, the electronic device 600 may also include an input/output (I/O) interface 605, the input/output (I/O) interface 605 also being connected to the bus 604. The electronic device 600 may also include one or more of the following components connected to the I/O interface 605: an input portion 606 including a keyboard, mouse, etc.; an output portion 607 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The drive 610 is also connected to the I/O interface 605 as needed. Removable media 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on drive 610 so that a computer program read therefrom is installed as needed into storage section 608.
The present disclosure also provides a computer-readable storage medium that may be embodied in the apparatus/device/system described in the above embodiments; or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs which, when executed, implement methods in accordance with embodiments of the present disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, the computer-readable storage medium may include ROM 602 and/or RAM 603 and/or one or more memories other than ROM 602 and RAM 603 described above.
Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowcharts. The program code, when executed in a computer system, causes the computer system to implement the service policy generation method provided by embodiments of the present disclosure.
The above-described functions defined in the system/apparatus of the embodiments of the present disclosure are performed when the computer program is executed by the processor 601. The systems, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.
In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed in the form of signals over a network medium, and downloaded and installed via the communication section 609, and/or installed from the removable medium 611. The computer program may include program code that may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
In such an embodiment, the computer program may be downloaded and installed from a network through the communication portion 609, and/or installed from the removable medium 611. The above-described functions defined in the system of the embodiments of the present disclosure are performed when the computer program is executed by the processor 601. The systems, devices, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.
According to embodiments of the present disclosure, program code for performing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, such computer programs may be implemented in high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. Programming languages include, but are not limited to, such as Java, c++, python, "C" or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that the features recited in the various embodiments of the disclosure and/or in the claims may be provided in a variety of combinations and/or combinations, even if such combinations or combinations are not explicitly recited in the disclosure. In particular, the features recited in the various embodiments of the present disclosure and/or the claims may be variously combined and/or combined without departing from the spirit and teachings of the present disclosure. All such combinations and/or combinations fall within the scope of the present disclosure.
The embodiments of the present disclosure are described above. These examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the embodiments cannot be used advantageously in combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be made by those skilled in the art without departing from the scope of the disclosure, and such alternatives and modifications are intended to fall within the scope of the disclosure.