The system has the ability to understand text, written even in jargon or expert language, which in the data finds legality and similar requirements and the recognition of requests proposes the most optimal solution.
In organizations or departments where specific or technical knowledges are required, through resolving customers requirements, knowledge base is build up – it enables to even less experienced employees to resolve requirements for which expert was needed before. In order to make data base usable, it is required for all employees to describe in detail customer requirements as well as resolution process, thus achieving quality. This resolution describing process is supported by asw:auxilium system that precisely determines if suggested resolution is well or badly described/recorded. In case of inadequate description, system warns/signals and helps in that way quality building up of knowledge base.
Following are subsystems:
Subsystem for tracking similar records/requests enables finding already resolved requirements for the same problem that customer needs to resolve, if such problems exist, as well as their solutions.
Tracking similar requests
Module for tracking similar requests is designed for clients requests search and tracking those with same meaning as one that client is resolving. Text of requirement is source for semantic information that enables tracking of same content but differently written requests.
Tracking similar records
Module for tracking similar records is used to search for employee records and find those that are with same meaning as records or parts of records of clients. Semantic information could be extracted out of the texts that enables tracking of same content but differently written records.
Subsystem for optimization
Subsystem for optimization enables effectiveness improvement of customer support services by combining precise predictions on scope and type of work with intelligent distribution of resources for specific types of jobs.
This subsystem have following characteristics and modules:
Estimating human resourced required in the next period
This module aims, based on continual tracking of customer support department activities, to assess number of needed human resources in those departments. Early foreseeing of work growth in the customer support team enables better time distribution and organization of intense periods of work during the year. Similarly, early foreseeing of required work hours decrease enables employees to do other activities.
Module could be configured by following parameters:
Also, installed are reportng and warning/signalling in cases when system identifies that processes are not flowing as expected.
Job distribution among employees based on previous resolutions samples
Module for job distribution is aimed for automatic determination of what team member in customer support team should receive customer requirement. The system identifies most suitable and most competent employee out of all based on following parameters:
Subsystem for quality control of records
Subsystem for quality control of records allows follow up and efficiency improvement in customer support departments through historic reports on quality of records and suggestions for records quality improvement.
Modules are as follows:
Historic analysis
Historic analysis module provides reports on records quality based on existing data from previous periods. It allows to follow up quality of records through time and identify those activities where potential steps to improve quality of work are possible.
By this process gaining information on quality of records in defined period of time, with additional filters, is possible.
Parameters of reporting are:
Current evaluation
Current evaluation module provides quality assessment of records at the moment of recording and can offer suggestions for recording improvement. The objective is continual improvement of quality of records and efficiency of customer support.
Instant information on quality of records is enabled.
Also, information on possible steps for improvement of records is possible.
Subsystem for automatic resolution of requirements
Subsystem for automatic resolution of requirements allows improvement of customer support services by automatic resolving and response on simple and frequent requests, and form ore complex ones can suggest activities for resolution.
Automatic resolution of frequent requests
Module for automatic resolution of frequent requests automatize resolving of regular/routine/simple questions to customer support. Algorithm of machine learning firstly understand meaning of questions/requirements, and then selects appropriate answer pattern, fills it up automatically in answer form and sends it. Also it sends instructions to other systems that lead to requests resolution.
Suggesting resolutions
Module for suggesting activities for resolution serves for automatic suggestions of answers on questions received by customer support. The system by machine learning uses data base of all previously resolved requests to find those answers that match specific solutions, and then suggests it. Intelligent search is of data base and best solution suggestion is allowed.
