ML used to manipulate large data sets, diagnose issues, predict and prescript actions. There is a mixture of more “traditional” activities such as network monitoring and capacity management which will become part of the closed loop of SDN/NFV (self-configuration, self-optimizing and self-healing).
ML used in revenue assurance exercises, the prediction of issues in bill payment processing and collections, and customer debt collection.
ML used to manage large data sets, diagnose issues, predict future issues and prescribe solutions.
Use of ML to undertake pattern and anomaly detection in both fraud and security use cases.
Identification of issues and diagnostic routines on large data sets (often from network) to help agents better understand a customer issue and provide resolution.
Use of ML to manage data and find patterns, anomalies and segmentations. Including text, speech analysis and other perceptual classification techniques. Used in various ways including voice of the customer and customer journey mapping
Use of ML to find patterns, anomalies and segmentations. Including text, speech analysis and other perceptual classification techniques. Used in various ways within marketing – including segmentation, background understanding for marketing team and reacting to trends
Use of ML to find insight in structured and unstructured data from across the internet for tasks such as monitoring brand reputation.
Intelligent automation used to automatically raise tickets, improve the routing of each ticket and find areas for process improvement.
RPA and more intelligent automations to create, check and provision orders. ML used to prescribe resolutions to orders that are flagged as having issues.
Categorization of the high volume of alarms generated by the telco network to identify those which are critical; plus predictive capabilities to identify best resolution to each alarm.
Automation of process flows around customer cases generated in the contact centre or cross-channel. Processes reach across to other departments such as the creation of tickets for field services.
ML, optical character recognition and RPA used to create a single repository of searchable documents, to extract or index data, create searches and analyse across the repository.
Inclusion of automation within the procure-to-pay and sourcing processes.
Addition of ML, optical character recognition and RPA in a range of routine activities – for example, accounts receivable or expense management
Use of RPA in processes such as onboarding, ML for creation of job descriptions and ML in planning and optimization tools
Use of RPA and employee chatbots in processes such as user support and services. ML used in orchestration and problem management.
Some limited use of ML when agents are assist customers. For example, optical character recognition and text analysis in managing email communications. Or sentiment analysis to automatically tag posts on social media with predicted intent.
ML to personalize content on website and other digital channels – also provision of next best action for the customer
Personalization of offer, experience or content using ML to increase likelihood of purchase or customer experience.
Used particular in IVR containment where speech analytics understands intent and then prediction/prescribes routing and messages
Tools for customer or agent (assisting the customer). May be installed on the customer device or in the OSS. Offer diagnostics, guided and proactive fixes. Mostly using rules-based analytics today but some use of ML to understand potential future customer issues and prescriptive, proactive resolution.
ML to manage data, create segmentation and prescribe next actions in programs such as churn management, loyalty and social marketing. Also the creation of outbound campaigns via SMS and other channels.
ML used in a variety of customer-facing systems (e.g. care, marketing, retail and sales teams). It manages the customer data in order to understand need and can also be used recommend the best content for an interaction. AI needed to manage unstructured data = for example, assembling the best permutations of imagery and text for a sales interaction.
ML used to manage data and to simulate and optimize possible network designs
ML used to suggest learning topics and material, to design new content and understand emotion/sentiment of those being trained and coached.ML used to manage data and to simulate and optimize possible network designs
ML to manage list of available network elements for the service build in real time – and undertake optimization and prescriptive next actions.
ML used to improve scheduling and dispatch of field technicians. Also, a set of non-personnel related use cases such as fuel optimization and predictive maintenance of field vehicles.
Some use of machine learning in the forecasting and planning of headcount; also in making real-time scheduling adjustments
Analytics to plan/budget merchandise and assortment in store or on e-commerce site, also to manage inventory
Some options for adding ML predictive and prescriptive capabilities into store functionality such as store performance dashboards
Some use of machine learning in the forecasting and planning of headcount; also in making real-time scheduling adjustments and auto assignment when coaching new store staff
Use of ML to manage data, find patterns and anomalies, and forecast/optimize plans. Used in competitive planning, device and other portfolio management tasks, pricing analysis and marketing resource management.
Predictive models using ML to identify leads based on fit and intent. ML manages data, segment and predictive scoring. Can also be used in account-based marketing to identify best leads to target within a single account.
Planning for prospecting and sales contact. Includes predicting likelihood to convert and creation of prescriptive next best offer/action. CPQ may also use optimization algorithms and expert systems to improve process.
ML used to optimize planning and forecasting cycle.
ML used in demand forecasting, to optimization the supply chain and in performance/risk management
Use of ML in business cases and forecasts.
Possible use of ML in planning models for strategic teams.
Today, these are fairly simple messaging services between an employee and a telco system. Providing answers to request for information or diagnostic insight – for example, a field service engineer requesting explanation of a red light on equipment while on-site
Chatbots that interact with customers in a variety of digital channels. Use natural language processing for text and speech. Plus potential perceptive techniques such as sentiment analysis to better understand the customer’s requirements.
Guidance tools for agents using natural language processing and text analytics to offer next-best actions such as the next step in the process or the best resolution to an issue
Chatbots which, typically, reach out to warm sales leads to try and set up appointments that will enable a sales person to close the deal.
ML used to prescribe a next best action which will provide an optimal answer to the question “What do I do next with this deal?”
A variety of future uses for employee chatbots, real-time guidance and background automation. For example, management of meetings – where the chatbot creates meeting minutes, organizes future meetings and chases actions.
Use of airborne cameras to inspect equipment on cell-towers, both for routine maintenance and in emergency scenarios where vehicular access to remote towers may be suddenly limited
Provision of AR solutions to enable field service technicians to see instructions and enabling remote experts to give instructions by seeing what the technician can see. Also, prescriptive suggestions to field staff while on site.
Immersive tech for training – Use of AR to provide real-time simulations and information for the routine training of customer facing staff
Robotic and VR solutions for the entertainment of customers coming in store. Solutions may also provide help or information to customers.
Perceptual classification for understanding customers and employees – Use of a range of AI techniques include speech, text, emotion and sentiment analysis throughout the telco, but particularly in customer-facing channels where information about customers and employees can be used to improve experience
Use of machine learning to automatically create collateral which is personalized based on understanding of the customer
Creation of standard documents such as financial reports and accounts using data from one or more systems