Patent Description:
IP networks deployed by telecom service providers provide access to a data network, such as the Internet, to subscribing IP-capable nodes, thereby allowing subscribers to connect with, for example, the World Wide Web. As reliance of subscribers on services provided by the World Wide Web is ever growing, and as complexity of the networks increases through the omnipresence of IP-capable devices, management of telecom provider networks becomes more demanding.

Many telecom service provider networks employ semiautomatic network management systems using rule-based systems. When, for example, a network node reaches a certain load level, an action is triggered automatically, e.g. alarming network administrators.

To improve the complex task of network management, tools of automatic networking have been developed which allow networks to be self-managing with minimal dependencies on human administrators. Because data networks are increasingly employed for critical operations and are relevant for public safety, methods to acquire high quality observation data related to quality of the service on service provider networks have been proposed.

Solutions of the prior art also employ machine learning to analyze network data to learn patterns and create automatic processes for handling events matching the pattern. However, to train machine learning predictors to the highest performance, tagged data sets are required, which are expensive to obtain. Moreover, when problems in the network appear that are not reflected in the training data, the trained predictor cannot be re-trained in an easy manner since training data for the current problems are not yet available.

An object of the present disclosure is to provide a method and a system whereby a telecom subscriber network can manage itself by acquiring high-quality training data to obtain predictors that can be employed to take the best action to improve service quality of the IP network.

This object is solved by the subject-matter of the independent claims.

<CIT> describes a processing platform configured to identify metrics that influence user satisfaction with communication services provided by a communication network, and to generate at least one model that relates the identified metrics to user satisfaction scores. The processing platform may be further configured to generate at least one user satisfaction score for a plurality of users of the communication services given specified values of the identified metrics for only a subset of those users.

<CIT> describes a method for monitoring a DSL network executed by a monitoring device comprising obtaining operational data describing the functioning of a plurality of DSL ports, Service Level Agreement, SLA, data specifying technical requirements associated with respective DSL ports, and ticketing data specifying user complains associated with respective DSL ports; determining quality estimators in function of the operational data and the SLA data; and training a monitoring model in function of the operational data, SLA data, quality estimators and ticketing data. <CIT> describes using
machine learning to perform a classification based on a pattern in collected monitoring data and configuration data of an information technology system associated with an onset of an issue, the monitoring data to be collected during an operation of the IT system, and the configuration data representing an architecture of the IT system; predicting, based on the classification, the issue before the issue occurs or before the issue is detected or reported; and generate an indication of the predicted issue. <CIT> describes evaluating the stability of communication over a telecommunication line comprising ascertaining physical line data characterizing a physical characteristic of each line of first subscriber lines; collecting service quality information describing a quality of a service provided over each line of second subscriber lines; associating the service quality information with the physical line data related to the same particular line; and determining a decision criterion from the physical line data with associated service quality information, wherein the decision criterion is determined for deciding to which stability class the digital subscriber line belongs.

The present disclosure provides for a system and a method for generating predictions for improving the service quality of a telecom service provider network. Network conditions data that indicate an objective connection quality of subscribers with a data network via an IP network of the telecom service provider network are correlated with tags extracted from individual, subjective subscriber feedback data received at the telecom service provider network. The subscriber feedback data are obtained based on interactions of subscribers with a Business Support System, BSS, of the telecom service provider network. The tagged network conditions data are employed to train one or more predictors for the service quality of the telecom service provider network.

The trained predictor is employed to predict the service quality of the telecom service provider network based on current network conditions data and is employed to propose actions to improve the service quality. Predictions provided by the predictors may therefore allow identifying and resolving problems in the IP network before subscribers experience a degradation of service quality.

The present invention provides for a computer-implemented method of training and employing predictors according to appended independent claim <NUM>.

According to an embodiment, training of the one or more predictors comprises training a first predictor according to a supervised machine learning algorithm.

According to another embodiment, the training of the one or more predictors comprises training a plurality of models and selecting a model based on a performance metric, wherein the performance metric is based on at least one of an f1 -score, a ROC curve, and an AuC.

The plurality of models may be selected from model families including XGBoost models, Random-Forest models, convolutional neural network models, and recurrent neural network models.

According to another aspect, training of the one or more predictors may also comprise training a second predictor according to an unsupervised machine-learning algorithm disregarding the tags of the tagged network conditions.

According to a further embodiment, the computer-implemented method further comprises employing the rule-based engine to trigger actions, based on the predictions, to improve the service quality of the telecom service provider network.

According to an aspect, the actions may comprise at least one of actions on an operations support system of the telecom service provider network or actions on elements of the data network. According to another aspect, employing the rule-based engine to trigger the actions may comprise applying an automatic root cause analysis.

According to an embodiment, training of the one or more predictors can be performed periodically, or is performed concurrently to generating predictions.

The method may further comprise extracting an explanation from the one or more trained predictors allowing an interpretation of the predictions, wherein extracting the explanation comprises analyzing top impacting factors.

According to another embodiment, the predictions may comprise at least one of predictions of network outages and predictions of network traffic anomalies.

According to an aspect, the network conditions data may comprise at least one of data from data probes, data from voice probes, RAN metrics, RAN traces, metrics of a packet core network of the IP network, network slices metrics, data from Performance Management/Fault Management systems, logs and counters from network elements of the IP network, and service-based architecture messages, wherein the metrics of the packet core network comprise at least one of data on latencies, data on packet drops, data on throughputs, and data on a streaming quality.

The present invention further provides for a system for generating predictions for improving the service quality of a telecom service provider network according to appended independent claim <NUM>.

According to an aspect, the system may also comprise an actuator manager configured to trigger actions, based on predictions of the one or more predictors, to improve the service quality of the telecom service provider network.

According to yet another aspect, the network conditions collection component may be configured for massaging the network conditions data and for coalescing the network conditions data in events supplied to the predictor model manager, and the subscriber touchpoints collection component is configured for massaging the subscriber feedback data and for coalescing the subscriber feedback data in events supplied to the predictor model manager.

The accompanying drawings explain the principles of the solution. Further features and advantages will become apparent from the following and, more particularly, from the description as illustrated in the accompanying drawing wherein.

Described herein are systems and methods for generating predictions for the service quality of a telecom service provider network using one or more predictors that have been trained using supervised or unsupervised machine learning algorithms employing network conditions data and subscriber feedback data. For purposes of explanation, numerous examples and specific details are set forth in order to provide a thorough understanding of the described embodiments. The embodiments as defined by the claims may include some or all of the features in these examples alone or in combination with other features described below and may further include modifications and equivalents of the features and concepts described herein. Where an embodiment is a method, steps and elements of the method may be combinable in parallel for a sequential execution.

<FIG> illustrates a block diagram of a system <NUM> for the training path of one or more predictors <NUM> for the service quality of a telecom service provider network. A plurality of subscribers <NUM> is connected to IP network <NUM> of a telecom service provider network. IP network <NUM> may allow users to access a data network, such as the Internet, by routing data traffic to and from the subscribers <NUM> to the data network, so as to allow subscribers <NUM> to interact with the World Wide Web, or employ other protocols of communication with nodes of the data network.

IP network <NUM> comprises an access network comprising access nodes that subscribers can connect to, for example by radio networks for mobile phones, or DSL and cable networks for landline services. IP network <NUM> further comprises a core network such as an Evolved Packet Core network, a <NUM> core network, or an internet service provider core network. The core network is typically made up of fewer nodes that support larger bandwidths and greater numbers of services than the access network nodes. The core network domain of IP network <NUM> handles routing of data between networks, between different communication service providers and between geographical regions. Subscribers today have a plurality of IP capable devices in their user domain such as smartphones and routers that connect to the IP network's access network.

The telecom service provider employs one or more Operation Support Systems (OSS) <NUM> to manage operation of the IP network <NUM>. OSS <NUM> is typically a combination of hardware and software used to monitor and administer IP network <NUM>. OSS <NUM> monitors devices and services to ensure they are delivering the capabilities they were configured to deliver. A highly important function of OSS <NUM> is tracking problems in the IP network <NUM> by receiving fault messages from network elements of the IP network <NUM>. OSS <NUM> may include tools for diagnosing and mitigating a fault.

Traditionally, IP networks, such as IP network <NUM>, comprise individual single-purpose network devices such as servers and routers dedicated to a specific role, so that changing a network element required hardware upgrades of a device. Modern data networks move towards a virtualization of the network layer, so that network elements are programmable elements. Under Network Functions Virtualization (NFV), network nodes are implemented as virtual devices and are hence directly programmable. In Software Defined Networking (SDN), network control, e.g. making routing decisions, is separated from data forwarding. As SDN allows connections across the network to be defined and reconfigured by software, SDN and/or NFV makes it possible to realize updates to the network architecture in real time. The implementation of SDN and NFV technologies in IP network <NUM> makes a wide range of possible actions on network elements of IP network <NUM> available to OSS <NUM>.

OSS <NUM> is typically configured to collect statistics and traces from the network to aid understanding of how well data is flowing. In modern networks, performance management has become as critical to operations as fault management. Network conditions data available from OSS <NUM> measure the objective customer experience from a technical point of view.

As displayed in <FIG>, network conditions collection component <NUM> is configured to collect network conditions data indicating a connection quality of the connection of subscribers <NUM> with IP network <NUM> and/or a data network via the IP network <NUM>. Network conditions collection component <NUM> may be configured for correlating, massaging, and enriching the network conditions data, and finally, for coalescing them into events suitable to be provided to predictor model manager <NUM>.

Network conditions data may comprise data from network probes that retrieve data by polling from network elements of IP network <NUM>, data from voice probes relating to the quality of voice over IP over IP network <NUM>, or data from video probes relating to the quality of video streamed over IP network <NUM>. Network conditions data available from OSS <NUM> may also include radio access network (RAN) metrics, RAN traces, data from different network slices (<NUM> networks), and data from a core network of IP network <NUM> such as data on latencies, data on packet drops, data on throughputs, or data on a streaming quality. Network conditions may further be collected from Performance Management/Fault Management systems, logs or counters on network elements of the IP network <NUM>, or be inferred from service-based architecture messages. The network conditions data may also allow inferring a geographical location of a subscriber <NUM>.

OSS <NUM> may be configured to act on network elements of the IP network to avert degradation of service to subscribers. If service performance degrades for a customer, the impact for them can be as disruptive as a physical fault because more and more critical operations depend on data networks. In addition, telecom service providers often enter service level agreements that define that a set of specific services with specified quality parameters according to the needs of a given customer be guaranteed, implying the need to technically ensure that the specified service quality is maintained. For example, service level agreements may imply that the telecom service provider must ensure certain throughputs, jitter, data rates, or that a mean time between failures and mean time to repair are above certain thresholds.

The telecom service provider network comprises one or more Business Support Systems (BSS) <NUM> that are employed to manage subscriber touchpoints, such as front office processes supporting commercial, revenue, and customer-relationship activities. BSS <NUM> provides applications for customer-facing jobs like capturing order details, starting the billing process, and sending customer notifications regarding cost. Therefore, BSS <NUM> stores a wealth of subscriber touchpoints data. Subscriber touchpoints data provided by BSS <NUM> may thus include detailed customer orders comprising data such as service type, optional service parameters, a service level agreement, an average revenue per user (ARPU) of a subscriber, a subscriber ID, and an origin and termination of the service. Additionally, BSS <NUM> may be employed to plan timescales and engineer visits.

BSS <NUM> typically also includes services for a customer care hotline or a complaint service that, for example, receives tickets on problems encountered by subscribers. Subscriber touchpoints data provided by BSS <NUM> may thus include subscriber feedback data, such as complaints and tickets relating to problems encountered by subscribers. Subscriber touchpoints data provided by BSS <NUM> may also include crowd-sourced data like speed tests.

As illustrated in <FIG>, subscriber touchpoints collection component <NUM> may be configured to collect subscriber touchpoints data from BSS <NUM>. Subscriber touchpoints collection component <NUM> may be equipped to collect and correlate all data coming from the one or more BSS <NUM>. Subscriber touchpoints collection component <NUM> may further massage and enrich the subscriber touchpoints data from BSS <NUM>. Finally, subscriber touchpoints collection component <NUM> may coalesce the data into events suitable to be provided to predictor model manager <NUM>. Subscriber touchpoints collection component <NUM> may in particular be configured for collecting subscriber feedback data based on interactions of subscribers with BSS <NUM> of the telecom service provider network.

Subscriber touchpoints collection component <NUM> may be configured to extract tags from the received subscriber feedback data, the tags scoring the subscriber perceived service quality of the telecom network. In embodiments, tags with scores of least good, bad or fair are employed for tagging data from OSS <NUM>. Tags may involve other natural-language hints for explanation, such as a description of subscriber experience or a description of problems subscribers are facing. Subscriber feedback data provided by BSS <NUM> may specifically comprise a net promoter score, which is typically a number from <NUM> to <NUM> or <NUM> to <NUM>. The net promoter score may be employed by subscriber touchpoints collection component <NUM> to generate a tag with a corresponding score.

Subscriber feedback data collected by subscriber touchpoints collection component <NUM> may further comprise data on complaints by the subscriber. Complaints by the subscribers are employed to tag network conditions with a very low score. Subscriber feedback data may further comprise data collected from surveys, such as NPS surveys, which may also be employed to generate corresponding tags.

In embodiments, predictor model manager <NUM> may be configured to correlate the tags of perceived service quality of the telecom service provider network, as provided by subscriber touchpoints collection component <NUM>, with network conditions data provided by network conditions collection component <NUM> to generate tagged network conditions data. Correlating the tags with the network conditions data of the telecom service provider network comprises tagging data relating to a connection quality of a subscriber with a tag of the particular subscriber that score his or her perceived level of quality of service, if such a tag is available. Because touchpoints of the subscribers with BSS <NUM> are rare, only a small subset of the network conditions data can be tagged.

Predictor model manager <NUM> is configured for evaluating machine learning models to match network conditions data with subscriber touchpoints data, to generate predictions, explanations of the predictions, and proposals of next-best actions to improve the current service quality.

Predictions generated by predictors <NUM> may comprise predictions for a degradation of perceptual customer experience and the technical reasons for the degradation. An output of the predictors <NUM> may include a list of subscribers that are encountering network problems. In embodiments, predictions formed by predictors <NUM> further comprise predictions for network outages, network anomalies, subscriber problems with customer experience, subscriber churn, or a likelihood of a subscriber to call the customer service. Predictions of network anomalies may be employed to detect security issues and fraud related to subscribers <NUM>.

In more detail, predictor model manager <NUM> employs the tagged network conditions data as training data for a machine-learning algorithm. Machine learning tools have achieved considerable successes in recent years and an ever-growing number of disciplines rely on it. In machine learning, a goal is to employ training data that comprise data and corresponding known output to infer a function that best maps the data to the output. This function can then be applied to novel data to generate a prediction.

Predictors <NUM> may be trained based on various algorithms of machine learning to best infer a quality of service of the telecom service provider network based on received network conditions data. Specifically, predictor model manager <NUM> may be configured to employ a supervised machine-learning algorithm to train a predictor using the tagged network conditions data to yield a predictor for the service quality of the telecom service provider network. Training of a machine-learning model based on the tagged network conditions data usually comprises splitting the tagged network conditions data in a training set employed to train the model and a validation set used to tune hyperparameters of the machine learning model.

Predictor model manager <NUM> may additionally or alternatively employ an unsupervised machine-learning algorithm that employs data from network conditions collection component <NUM>. By unsupervised learning algorithms a predictor is trained employing training data that do not contain tags corresponding to a known output. Unsupervised learning algorithms infer a prediction by identifying previously unknown patterns in data sets. Predictions generated by a predictor <NUM> trained with unsupervised methods may comprise predictions for network anomalies, predictions for node behavior, or predictions for network behavior.

In embodiments, predictions may further involve providing predictions for individual subscribers from both a predictor trained with a supervised machine-learning algorithm and from a predictor trained with an unsupervised machine-learning algorithm. In these embodiments, the system may further identify common characteristics of the subscribers, for example, that subscribers employ the same serving nodes or access the same over-the-top content applications. The system may then group such subscribers and identify network or service problems of the IP network <NUM> based on the predictions, the common features, and the grouping of the subscribers.

In other embodiments, learning a predictor of the plurality of predictors <NUM> may be based on a semi-supervised learning algorithm.

In further embodiments, predictor model manager <NUM> may employ an automated machine learning approach that involves analyzing multiple different machine learning techniques and candidate models before selecting one or more of the candidate models as predictors <NUM> from the candidate models that achieve the best performance. The predictor model manager may furthermore automatically select and construct appropriate features, optimize hyperparameters of chosen machine learning models by training with training data from network conditions collection component <NUM> and subscriber touchpoints collection component <NUM>, and may include steps of post-processing of the machine learning models. Specifically, Predictor model manager <NUM> may be configured for selecting candidate models from among XGBoost models, Random Forest models, convolutional neural networks, recurrent neural networks, and other comparable models. Performance of a predictor may be measured by metrics like an f1-score, or may be based on a ROC (Receiver Operating Characteristics) curve that shows the performance of a classification model at all classification thresholds. Selecting models for optimal performance may also be based on an AuC (Area under Curve) which provides a score of performance across all possible classification thresholds.

The models yielded from selecting models achieving the best performance by predictor model manager <NUM> may also yield an ensemble of models that includes several models that in combination yield the best performance. The one or more predictors <NUM> may thus comprise an ensemble of models trained with respective supervised machine learning models employing the tagged network conditions data, and a second ensemble of models trained with respective unsupervised machine learning models disregarding the tags of the tagged network conditions data. Employing the unsupervised predictors in addition to the supervised predictors allows uncovering additional insight on problems and issues in the IP network <NUM>. The ensembles of models may be stacked ensembles trained by a super learner algorithm.

The predictor model manager may further be configured for providing explanations of the model predictions, such that the predictions can be understood and trusted by human supervisors of the network. For this purpose, the prediction model manager may use techniques to infer top impacting factors, generating Shapley additive explanations (SHAP) values or a local interpretable model-agnostic explanation (LIME). The SHAP value shows how each data point contributes either positively or negatively to the prediction based on a marginal contribution of the data point to the prediction. LIME provides a local predictive model that can be used to explain predictions of the machine learning models under the current circumstances. According to the LIME approach, an interpretable model is built that fits the predictions formed by a predictor <NUM> locally.

Training of the models according to the principles explained with respect to <FIG> may be performed periodically. In this case, technical data from the network are collected by network conditions collection component <NUM> over a period of time, subscriber feedback data may be collected by subscriber touchpoints collection component <NUM> over the same period of time, and predictors <NUM> may be re-trained with tagged network conditions data yielded from correlating the technical data from the network and the subscriber feedback data of the period of time.

Alternatively, predictors <NUM> may be trained concurrently to application of the models as predictors, as will be explained below with reference to <FIG>.

<FIG> illustrates an evaluation path of the trained system. After predictors <NUM> have been trained according to the principles explained above, current network conditions data arising from the connection of subscribers <NUM> with a data network via the IP network <NUM> are provided from OSS <NUM> to network conditions collection component <NUM>. Network conditions collection component <NUM> is configured to massage and enrich the network conditions data and to provide them to predictors <NUM>, which generate prediction <NUM> for the current service quality of the telecom service provider network.

Prediction <NUM> is provided to predictor model manager <NUM> that also receives subscriber touchpoints data from subscriber touchpoints collection component <NUM>. Subscriber touchpoints data such as data on demographics, a current price plan, an ARPU level may be employed by the predictor model manager together with prediction <NUM> to propose actions suitable for the particular subscriber. In an embodiment, prediction <NUM> and subscriber touchpoints data is fed to a rule-based engine comprising a rule base and an inference engine. The rule-based engine infers information on issues of the telecom subscriber network and proposes an action on OSS <NUM>, IP network <NUM>, or BSS <NUM> to improve the service quality.

Predictors as described above may predict network behavior, for example, anomalies in network traffic or a major network outage in a particular geographical region. Predictors may also comprise predictive models for subscriber behavior, for example, a likelihood to call customer service or a likelihood to make a complaint.

Proposals on suitable actions are provided from the predictor model manager <NUM> to actuator manager <NUM>. Based on the proposed actions and on a business logic defined by the network operator, actuator manager <NUM> may act <NUM> on elements after IP network <NUM>, or may act <NUM> on OSS <NUM>, or may act <NUM> on BSS <NUM> to mitigate problems in the network to improve the service quality provided to subscribers.

Actions of actuator manager <NUM> on OSS <NUM> and the IP network <NUM> may include upgrading a capacity of network elements by changing a configuration of network nodes. In embodiments, data network <NUM> comprises virtual network devices and the capacity of network elements may be updated by adjusting configuration settings of the virtual network. Actions on the network may further comprise upgrading of links between nodes, for example, by changing configuration settings of virtual network devices, routing tables, or flow tables of an SDN network In embodiments, links between nodes may be upgraded to improve a latency of connection between geographical regions.

Similarly, actions of actuator manager <NUM> may comprise a proposal to update a number of cells in an area of a cellular radio access network. Additionally, different policies may be applied to customers in dependence on an ARPU level. Specific valuable subscribers may be profiled to give them better experience. Actions of actuator manager <NUM> on OSS <NUM> may also comprise throttling user traffic or give precedence to user traffic for particular subscribers, for example to improve overall service quality of the telecom service provider network.

Furthermore, actions of actuator manager <NUM> may involve initiating campaigns to subscribers suggesting upgrading their own hardware devices. With upgraded hardware devices, perceived quality of service of the network is improved. In particular, perceived quality of service after a hardware upgrade at the subscriber end will better reflect network conditions as provided by IP network <NUM>, so that future training data will be of better quality.

<FIG> illustrates a method <NUM> of training predictors for improving the service quality of a telecom service provider as explained with reference to <FIG> above. Method <NUM> comprises receiving <NUM> network conditions data, receiving <NUM> subscriber feedback data, and correlating <NUM> the network conditions data with the subscriber feedback data, which may comprise tagging network conditions data relating to measures of connection quality of a particular subscriber with subscriber feedback data received from the particular subscriber.

Method <NUM> further comprises training <NUM> of predictors. As explained with reference to <FIG>, training predictors may comprise training a plurality of models and selecting a model or a combination of models achieving the highest performance. After training is finished, the parameters of the trained models are stored on computer memory for later access.

<FIG> illustrates a process flow for a method <NUM> of generating predictions for the service quality of a telecom service provider network. Method <NUM> comprises receiving <NUM> network conditions data. Method <NUM> may optionally also comprise receiving <NUM> subscriber touchpoint data.

Based on the network conditions data, predictions for the current level of service quality are generated <NUM> by one or more trained predictors <NUM>. Generating predictions may involve evaluating predictor models and detecting anomalies in the telecom service provider network.

Based on the generated predictions, and, optionally, on subscriber touchpoint data, actions on OSS <NUM>, IP network <NUM>, and/or BSS <NUM> may be triggered <NUM> to improve the service quality, as explained above with reference to <FIG>. Triggered actions may be based on a next best action suggestion, and/or on an automatic root cause analysis.

Triggered actions on OSS <NUM> may comprise actions on a policy server of the IP network <NUM>. Based on the predictions, the policy server may be configured to throttle subscriber traffic or give precedence to subscriber traffic, apply different policies to special very important customers in terms of ARPU, or initiate sending of campaigns to subscribers suggesting to upgrade their handsets due to their poor performance.

Triggered actions on the IP network <NUM> may involve changing a configuration of network elements of IP network <NUM> to improve their performance, changing configurations of routing of data packets, updates to connect new nodes to IP network <NUM>, and changes to devices' software or firmware. Triggered actions on the IP network <NUM> may specifically comprise actions on a packet gateway, or a virtual packet gateway of IP network <NUM>.

Method <NUM> may further comprise generating an explanation of the predictions as explained above, and notifying human operators of the explanation for the level of service quality of the telecom service provider network.

Training of models according to method <NUM> may be performed periodically as explained above. Alternatively, methods <NUM> and <NUM> may be performed concurrently in real-time to yield new instances of predictors concurrently to previous predictors being applied. Concurrently training new predictors is advantageous in a case when predefined predictors identify a case of severe degradation of service quality. In this case, new instances of predictors may be generated that are specifically trained to the current situation of a severed degradation of service quality and thus are better fitted to isolating probable causes for the severe degradation.

Concurrent training of the predictors may also comprise re-training existing model by considering new arriving data as training data and applying stochastic gradient descent to the weights of the predictors.

Embodiments of the disclosed invention thus allow self-management of a telecom service provider network. The present disclosure proposes employing subscriber feedback data from business support systems of a telecom service provider network to generate training data for machine learning algorithms.

In particular, employing subscriber feedback data overcomes the problem that it is expensive to collect high quality data on relevant measures of the service quality of a telecom service provider network. In particular, because subscribers are geographically distributed and have direct access to communication end points of the IP network, data collected from subscriber feedback data allows generating high quality training data that can be used to train predictors to high performance.

Claim 1:
A computer-implemented method (<NUM>) of training and employing predictors for improving the service quality of a telecom service provider network, the telecom service provider network providing subscribers (<NUM>) access to a data network (<NUM>), the method (<NUM>) comprising:
obtaining (<NUM>) network conditions data indicating a connection quality of the subscribers (<NUM>) with the data network (<NUM>);
obtaining (<NUM>) subscriber feedback data based on interactions of subscribers (<NUM>) with a set of business support systems (<NUM>) of the telecom service provider network;
correlating (<NUM>) the network conditions data with tags extracted from the subscriber feedback data to obtain tagged network conditions data;
training (<NUM>) one or more predictors (<NUM>) for the service quality of the telecom service provider network, the training of the predictors employing the tagged network conditions data,
further comprising:
obtaining (<NUM>) second network conditions data indicating a current connection quality of the subscribers (<NUM>) with the data network (<NUM>);
generating (<NUM>) predictions (<NUM>) for the service quality of the telecom service provider network by providing the second network conditions data to a trained predictor of the one or more predictors (<NUM>); and
employing a rule-based engine to, based on the predictions (<NUM>), propose actions to improve the service quality of the telecom service provider network and
further comprising extracting an explanation from the one or more trained predictors (<NUM>) allowing an interpretation of the predictions (<NUM>), wherein extracting the explanation comprises analyzing top impacting factors that impact the predictions generated by the one or more predictors.