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SAP Machine Learning for Telco

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Turn Thinking Into Doing - In this ebook you'll discover why machine learning is taking off in telco, trends to watch out for, and SAP use cases that show measureable results.

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SAP Machine Learning for Telco

  1. 1. SAP Machine Learning for Telco 1 SAP Machine Learning for Telco Turn Thinking into Doing
  2. 2. SAP Machine Learning for Telco 2 IN THIS E-BOOK, YOU’LL DISCOVER: Why Machine Learning is taking off in telco Trends in Machine Learning SAP use cases that show measurable results Introduction 3 7 9
  3. 3. SAP Machine Learning for Telco 3 MACHINE LEARNING DEFINED Artificial Intelligence (AI) is a broad technology that emulates the brain and can sense, reason, act, and adapt. Machine Learning (ML) is an application of AI based on guided algorithms that are focused on specific tasks.And Deep Learning (DL) is a subset of ML that can learn unsupervised from large amounts of data. CHAPTER ONE Machine Learning: Powering Tomorrow Artificial Learning • Intelligence exhibited by machines • Expanding and branching areas of research, development, and investment • Includes robotics, rule-based reasoning, natural language processing, knowledge representation techniques, and more Machine Learning • A subfield of AI which aims to teach computers the ability to do tasks with data, without explicit programming • Uses numerical and statistical approaches including artificial neural network techniques to encode learning • Models are built using training computation runs, can also through usage Deep Learning • A subfield of ML that uses specialized computational techniques, typically multi-layer artificial neural networks • Laying allows cascaded learning and abstraction levels • Computationally intensive enabled by clouds, GPUs, and increasingly more specialized HW like FPGA and new custom hardware Artificial Learning Machine Learning Deep Learning
  4. 4. SAP Machine Learning for Telco 4 A POWERFUL TECHNOLOGY The industry has evolved significantly in making these technologies more available. What was once mainly the work of computer science researchers is now readily accessible outside of research facilities; we can download ML libraries at home. When you combine the availability of big data technologies and the accessibility of Machine Learning, it’s becoming quite a powerful capability. Three points that make Machine Learning applicable and available for mainstream business adoption: Advancement on the Deep Learning algorithm The advancement of hardware, especially GPU, makes Deep Learning realistic Availability of massive amount of data, more data means better results for Deep Learning algorithm CHAPTER ONE MACHINE LEARNING: POWERING TOMORROW
  5. 5. SAP Machine Learning for Telco 5 A BRIEF HISTORY OF MACHINE LEARNING1 Alan Turing creates the “Turing Test” to determine if a computer has real intelligence. To pass the test, a computer must be able to fool a human into believing it is also human. The first computer learning program, checkers, and the “nearest neighbor” algorithm are written. These revolutionized how computers understood strategies and pattern recognition. Students at Stanford University invent the Stanford Cart which can navigate obstacles in a room on its own. Work on Machine Learning shifts from a knowledge-driven approach to a data-driven approach. Companies like IBM, Google,Amazon, Microsoft, and Facebook make major strides in Machine Learning with discoveries that shape how we use it today. Google’s AI algorithm beats a professional player at the Chinese board game, Go, which is considered the world’s most complex board game. 1950 1950s -1960s 1979 1990s 2016 Mid 2000s CHAPTER ONE MACHINE LEARNING: POWERING TOMORROW
  6. 6. SAP Machine Learning for Telco 6 WHY DOES MACHINE LEARNING MATTER? The challenges of growing volumes of data, slow information processing, and affordability have led businesses to become increasingly interested in Machine Learning. ML makes it possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a massive scale. And by building precise models, a business has a better chance of identifying profitable opportunities – or avoiding unknown risks. CHAPTER ONE MACHINE LEARNING: POWERING TOMORROW Machine Learning - A Game Changer INCREMENTALVALUE Known Knowns Known Unknowns Unknown Unknowns • Expertise • BI • Machine Learning • Big Data
  7. 7. SAP Machine Learning for Telco 7 CHAPTER TWO Trends & Capabilities in Machine Learning TOP TRENDS OF AI/ML IN TELCO Artificial Intelligence and Machine Learning have been forecasted to make a dramatic impact on businesses, namely telcos in the next decade. Here are the top trends to look out for: • View AI as one of the core technologies for digital transformation • Critical technology for the success of 5G, both from network perspective and from going beyond connectivity perspective • Some Tier-1 telcos either launched or planned to launch their own AI platform • Embed AI/ML technology in all business areas eg: customer experience, network automation, business process, and new digital services. • Telcos are among the first adopters of AI/ML technology among all industries • Main drivers for telco to adopt AI/ML technologies include operation cost reduction and customer experience optimization Early adopters of AI/ML
  8. 8. SAP Machine Learning for Telco 8 BUSINESS POTENTIAL Machine Learning has a vast variety of capabilities across industries, and businesses are currently exploring this as they introduce intelligence into their value chains. However, with only 12% of businesses actively piloting AI technology, a wide world of business opportunity awaits for companies of all sizes. of businesses are actively piloting AI technology. Four areas of Machine Learning Capabilities and Services: Text/ Document Services (e.g. Sentiment Analysis) Image/Video Services (e.g. Image classification) Speech & Audio Services (eg: Voice recognition) Tabular Services (eg: Time series analysis) 12% CHAPTER TWO TRENDS & CAPABILITIES IN MACHINE LEARNING
  9. 9. SAP Machine Learning for Telco 9 CHAPTER THREE Use Cases in Machine Learning With Machine Learning in place, this can reduce customer friction, personalize experiences, and enable businesses to achieve higher margins with greater efficiency. Read on to learn how Machine Learning can optimize and enhance communications service providers and customer experiences. of businesses believe AI/ML learning technology will be a game-changer in the Telco industry. 93%
  10. 10. SAP Machine Learning for Telco 10 CHAPTER THREE USE CASES IN MACHINE LEARNING SITUATION: CSPs (Communications service providers) these days are switching their focus from revenue growth towards margin protection and improvement. They need to not just look at corporate margin, but also obtain margin insights down to individual customer level, and come up with optimizing plan for each individual customer to improve the margin. CHALLENGE: • CSPs lack means to obtain timely individual margin, dealing with many heterogeneous data sources. • CSPs are mainly relying on business expert’s experience in margin optimization, lacking AI/ML powered method to come up with the best action recommendation. of telecommunications CEOs identify “customer insights”as the most critical investment area.2 83% USE CASE ONE: Maximize Customer Value – Proactively Defend and Improve Individual Margin
  11. 11. SAP Machine Learning for Telco 11 SOLUTION: • Leverage big data technology to combine data from BSS, OSS and ERP to create a joint view of multi dimensional and high granular margin on individual customer base • Apply ML algorithm to train next best action model with historic data eg: customer usage, purchasing pattern, social links. Come up with next best action for each individual customers, eg: recommend a change of plan to increase the margin, or try to let the customer go if he/she is a negative margin customer and there’s no way to change this. • Integrate with CRM system to make sales/service staff ready with recommendations in case of customers in store visits or contacting call center BENEFITS: • ML powered and self tuned recommendation to optimize margin for each individual customer • Personalized recommendation to suite each customer’s behavior, assisting sales representative’s interaction with customer • Industrialized way to generate recommendations on a massive level, instead of rule engines supported by business users CHAPTER THREE USE CASES IN MACHINE LEARNING
  12. 12. SAP Machine Learning for Telco 12 CHAPTER THREE USE CASES IN MACHINE LEARNING USE CASE TWO: Infrastructure Operation Optimization - Intelligent Telco Tower Surveillance SITUATION: Mobile CSPs possess a large number of mobile base stations. Although, in some cases, CSPs outsource the maintenance but still act as the assets owner. It is CSP’s core interest to reduce the maintenance effort and improve efficiency and asset utilization. CHALLENGE: • It requires frequent on-site inspection and tower climbing to make sure that all the infrastructures, including passive equipment such as power generator, air conditioner, are running ok. • In some areas, illegal intrusion of telco towers might happen to steal valuable equipment. It is difficult to prevent without continuous monitoring. • The location of the base stations spread widely, and they require continuous monitoring and regular on-site maintenance. By 2025 IoT could have an annual economic impact of $3.9 to $11.1 trillion.3 $11.1trillion
  13. 13. SAP Machine Learning for Telco 13 SOLUTION: • Apply AI empowered video & image analysis capability on top of the surveillance cameras surrounding the telco towers. Automatically detect and alert on irregular events eg: Intrusion, Fire, Smoke. • Apply Machine Learning algorithm on the analysis of IoT sensor data collected from the environmental sensors installed around telco towers, combining with the analysis of the surveillance camera data to provide 360 degree continuous monitoring and real-time alert of irregular events. • Integrate with workforce dispatching system and material ledger in case on-site maintenance and spare parts replacement is required. • Cross-check active network configuration information with assets management system to maximize network utilization. BENEFITS: • Realize 24/7 continuous and 360 degree monitoring, reduce downtime caused by irregular events and improve coverage • Greatly reduce the number of on-site inspections and therefore the required technician resources • Real-time alert and Integration with backend system to trigger swift response CHAPTER THREE USE CASES IN MACHINE LEARNING
  14. 14. SAP Machine Learning for Telco 14 USE CASE THREE: Customer Service Automation - 24/7 Interaction and Resolution SITUATION: CSPs receive many complaints about the connectivity of CPEs (Customer Premise Equipment – eg: Broadband Modem, IPTV Box) every day. And they need an efficient way to fix these problems quickly so as not to affect the customer experience and improve retention CHALLENGE: • Dispatching technicians for the CPE connectivity complaints generate huge number of truck rolls and cost, while in many cases, on-site visits are not necessary; problems could be solved with simple reset. • Customers are also not satisfied with the complaints processing speed in case a on-site maintenance is involved. CHAPTER THREE USE CASES IN MACHINE LEARNING The global chatbot market is expected to rise from a valuation of $113 to $994.5 million in 2024.4 $994.5million
  15. 15. SAP Machine Learning for Telco 15 SOLUTION: • Adopt customer service chatbot to realize 24/7 scalable customer interaction, provide instant response and resolution by implementing Machine Learning enabled automation in service ticketing and the relevant systems including fulfillment and assurance • Apply Machine Learning algorithm to train connectivity fault models based on historic data, including network log data and service ticket data. Detect and identify common solutions for connectivity issues. Identify failure patterns which do need technician dispatches and which do not. • Integrate with customer service system and OSS system, provide automatic fixing solution and instructions in case on-site maintenance is not needed. In many cases it would just need reset on the CPE side and on the telco’s side and it could be realized in a self-service way. BENEFITS: • Realize 24/7 CPE complaints processing in a fully automated and self-service mode and greatly improve customer experience. • Greatly reduce unnecessary technician dispatches and truck rolls, thus reducing cost. • Automatically detect root cause by learning from historical patterns, accelerating complaints processing. CHAPTER THREE USE CASES IN MACHINE LEARNING
  16. 16. SAP Machine Learning for Telco 16 Sources 1.“A Short History of Machine Learning -- Every Manager Should Read,” Forbes, February 2016 2.“IBM Sales and Distribution - White Paper Executive Summary,” IBM, May 2014 3.“What’s New with the Internet of Things?,” McKinsey & Company, May 2017 4.“Chatbot Market - Global Industry Analysis, Size, Share, Growth, Trends, and Forecast 2016 - 2024,”Transparency Market Research, December 2016 To learn more, visit www.sap.com/products/leonardo.html and www.sap.com/products/leonardo/machine-learning.html For SAP for Telecommunications, visit sap.com/solution/industry/telecommunications.html or join the discussion about Machine Learning at the-digital-future.com. To connect with innovators in your industry, visit sap.io or the SAP Innovation Center Network at icn.sap.com. © 2018 SAP SE or an SAP affiliate company. All rights reserved. No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP SE or an SAP affiliate company. SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries. Please see http://www.sap.com/corporate-en/legal/copyright/index. epx#trademark for additional trademark information and notices. Some software products marketed by SAP SE and its distributors contain proprietary software components of other software vendors. National product specifications may vary. These materials are provided by SAP SE or an SAP affiliate company for informational purposes only, without representation or warranty of any kind, and SAP SE or its affiliated companies shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP SE or SAP affiliate company products and services are those that are set forth in the express warranty statements accompanying such products and services, if any. Nothing herein should be construed as constituting an additional warranty. In particular, SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or any related presentation, or to develop or release any functionality mentioned therein. This document, or any related presentation, and SAP SE’s or its affiliated companies’ strategy and possible future developments, products, and/or platform directions and functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason without notice. The information in this document is not a commitment, promise, or legal obligation to deliver any material, code, or functionality. All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from expectations. Readers are cautioned not to place undue reliance on these forward-looking statements, which speak only as of their dates, and they should not be relied upon in making purchasing decisions. @SAP_Telco SAP for Telco

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