AI in Marketing, Sales and Service_ How Marketers without a Data Science Degree can use AI, Big Data - AI in MARKETING SALES and SERVICE How Marketers

AI in Marketing, Sales and Service_ How Marketers without a Data Science Degree can use AI, Big Data

This preview shows page 1 out of 280 pages.

You've reached the end of your free preview.

Want to read all 280 pages?

Unformatted text preview: AI in MARKETING, SALES and SERVICE How Marketers without a Data Science Degree can use AI, Big Data and Bots Peter Gentsch AI in Marketing, Sales and Service Peter Gentsch AI in Marketing, Sales and Service How Marketers without a Data Science Degree can use AI, Big Data and Bots Peter Gentsch Frankfurt, Germany ISBN 978-3-319-89956-5 ISBN 978-3-319-89957-2  (eBook) Library of Congress Control Number: 2018951046 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2019 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover illustration: Andrey Suslov/iStock/Getty Cover design by Tom Howey This Palgrave Macmillan imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Contents Part I  AI 101 1 AI Eats the World 3 1.1 AI and the Fourth Industrial Revolution 3 1.2 AI Development: Hyper, Hyper… 5 1.3 AI as a Game Changer 6 1.4 AI for Business Practice 8 Reference 9 2 A Bluffer’s Guide to AI, Algorithmics and Big Data 11 2.1 Big Data—More Than “Big” 11 2.1.1 Big Data—What Is Not New 12 2.1.2 Big Data—What Is New 12 2.1.3 Definition of Big Data 12 2.2 Algorithms—The New Marketers? 14 2.3 The Power of Algorithms 15 2.4 AI the Eternal Talent Is Growing Up 17 2.4.1 AI—An Attempt at a Definition 17 2.4.2 Historical Development of AI 18 2.4.3 Why AI Is Not Really Intelligent—And Why That Does Not Matter Either 22 References 24 v vi     Contents Part II  AI Business: Framework and Maturity Model 3 AI Business: Framework and Maturity Model 27 3.1 Methods and Technologies 27 3.1.1 Symbolic AI 27 3.1.2 Natural Language Processing (NLP) 28 3.1.3 Rule-Based Expert Systems 28 3.1.4 Sub-symbolic AI 29 3.1.5 Machine Learning 31 3.1.6 Computer Vision and Machine Vision 33 3.1.7 Robotics 34 3.2 Framework and Maturity Model 34 3.3 AI Framework—The 360° Perspective 34 3.3.1 Motivation and Benefit 34 3.3.2 The Layers of the AI Framework 35 3.3.3 AI Use Cases 36 3.3.4 Automated Customer Service 36 3.3.5 Content Creation 36 3.3.6 Conversational Commerce, Chatbots and Personal Assistants 37 3.3.7 Customer Insights 37 3.3.8 Fake and Fraud Detection 38 3.3.9 Lead Prediction and Profiling 38 3.3.10 Media Planning 39 3.3.11 Pricing 39 3.3.12 Process Automation 40 3.3.13 Product/Content Recommendation 40 3.3.14 Sales Volume Prediction 41 3.4 AI Maturity Model: Process Model with Roadmap 41 3.4.1 Degrees of Maturity and Phases 41 3.4.2 Benefit and Purpose 48 3.5 Algorithmic Business—On the Way Towards Self-Driven Companies 49 3.5.1 Classical Company Areas 50 3.5.2 Inbound Logistics 50 3.5.3 Production 53 3.5.4 Controlling 53 3.5.5 Fulfilment 53 3.5.6 Management 54 3.5.7 Sales/CRM and Marketing 54 Contents     vii 3.5.8 Outbound Logistics 54 Algorithmic Marketing 56 3.6.1 AI Marketing Matrix 57 3.6.2 The Advantages of Algorithmic Marketing 59 3.6.3 Data Protection and Data Integrity 60 3.6.4 Algorithms in the Marketing Process 61 3.6.5 Practical Examples 63 3.6.6 The Right Use of Algorithms in Marketing 66 3.7 Algorithmic Market Research 67 3.7.1 Man Versus Machine 67 3.7.2 Liberalisation of Market Research 68 3.7.3 New Challenges for Market Researchers 69 3.8 New Business Models Through Algorithmics and AI 71 3.9 Who’s in Charge 72 3.9.1 Motivation and Rationale 73 3.9.2 Fields of Activity and Qualifications of a CAIO 75 3.9.3 Role in the Scope of Digital Transformation 76 3.9.4 Pros and Cons 76 3.10 Conclusion 77 References 78 3.6 Part III Conversational AI: How (Chat)Bots Will Reshape the Digital Experience 4 Conversational AI: How (Chat)Bots Will Reshape the Digital Experience 81 4.1 Bots as a New Customer Interface and Operating System 81 4.1.1 (Chat)Bots: Not a New Subject—What Is New? 81 4.1.2 Imitation of Human Conversation 82 4.1.3 Interfaces for Companies 83 4.1.4 Bots Meet AI—How Intelligent Are Bots Really? 84 4.1.5 Mitsuku as Best Practice AI-Based Bot 87 4.1.6 Possible Limitations of AI-Based Bots 88 4.1.7 Twitter Bot Tay by Microsoft 88 4.2 Conversational Commerce 89 4.2.1 Motivation and Development 89 4.2.2 Messaging-Based Communication Is Exploding 90 4.2.3 Subject-Matter and Areas 91 4.2.4 Trends That Benefit Conversational Commerce 92 viii     Contents 4.2.5 4.2.6 4.2.7 Examples of Conversational Commerce 93 Challenges for Conversational Commerce 94 Advantages and Disadvantages of Conversational Commerce 95 4.3 Conversational Office 95 4.3.1 Potential Approaches and Benefits 95 4.3.2 Digital Colleagues 96 4.4 Conversational Home 97 4.4.1 The Butler Economy—Convenience Beats Branding 97 4.4.2 Development of the Personal Assistant 99 4.5 Conversational Commerce and AI in the GAFA Platform Economy 110 4.6 Bots in the Scope of the CRM Systems of Companies 113 4.6.1 “Spooky Bots”—Personalised Dialogues with the Deceased 114 4.7 Maturity Levels and Examples of Bots and AI Systems 115 4.7.1 Maturity Model 115 4.8 Conversational AI Playbook 116 4.8.1 Roadmap for Conversational AI 116 4.8.2 Platforms and Checklist 118 4.9 Conclusion and Outlook 121 4.9.1 E-commerce—The Deck Is Being Reshuffled: The Fight for the New E-commerce Eco System 121 4.9.2 Markets Are Becoming Conversations at Last 122 References 124 Part IV  AI Best and Next Practices 5 AI Best and Next Practices 129 5.1 Sales and Marketing Reloaded—Deep Learning Facilitates New Ways of Winning Customers and Markets 129 5.1.1 Sales and Marketing 2017 129 5.1.2 Analogy of the Dating Platform 130 5.1.3 Profiling Companies 131 5.1.4 Firmographics 131 5.1.5 Topical Relevance 132 5.1.6 Digitality of Companies 133 5.1.7 Economic Key Indicators 133 Contents     ix 5.2 5.3 5.4 5.1.8 Lead Prediction 134 5.1.9 Prediction Per Deep Learning 135 5.1.10 Random Forest Classifier 136 5.1.11 Timing the Addressing 137 5.1.12 Alerting 137 5.1.13 Real-World Use Cases 138 Digital Labor and What Needs to Be Considered from a Costumer Perspective 139 5.2.1 Acceptance of Digital Labor 143 5.2.2 Trust Is the Key 143 5.2.3 Customer Service Based on Digital Labor Must Be Fun 144 5.2.4 Personal Conversations on Every Channel or Device 144 5.2.5 Utility Is a Key Success Factor 145 5.2.6 Messaging Is Not the Reason to Interact with Digital Labor 145 5.2.7 Digital Labor Platform Blueprint 145 Artificial Intelligence and Big Data in Customer Service 148 5.3.1 Modified Parameters in Customer Service 148 5.3.2 Voice Identification and Voice Analytics 150 5.3.3 Chatbots and Conversational UI 152 5.3.4 Predictive Maintenance and the Avoidance of Service Issues 155 5.3.5 Conclusion: Developments in Customer Service Based on Big Data and AI 157 Customer Engagement with Chatbots and Collaboration Bots: Methods, Chances and Risks of the Use of Bots in Service and Marketing 157 5.4.1 Relevance and Potential of Bots for Customer Engagement 157 5.4.2 Overview and Systemisation of Fields of Use 158 5.4.3 Abilities and Stages of Development of Bots 159 5.4.4 Some Examples of Bots That Were Already Used at the End of 2016 161 5.4.5 Proactive Engagement Through a Combination of Listening and Bots 162 5.4.6 Cooperation Between Man and Machine 164 5.4.7 Planning and Rollout of Bots in Marketing and Customer Service 165 x     Contents 5.5 5.6 5.4.8 Factors of Success for the Introduction of Bots 168 5.4.9 Usability and Ability to Automate 168 5.4.10 Monitoring and Intervention 169 5.4.11 Brand and Target Group 169 5.4.12 Conclusion 169 The Bot Revolution Is Changing Content Marketing— Algorithms and AI for Generating and Distributing Content 170 5.5.1 Robot Journalism Is Becoming Creative 171 5.5.2 More Relevance in Content Marketing Through AI 172 5.5.3 Is a Journalist’s Job Disappearing? 172 5.5.4 The Messengers Take Over the Content 173 5.5.5 The Bot Revolution Has Announced Itself 174 5.5.6 A Huge Amount of Content Will Be Produced 175 5.5.7 Brands Have to Offer Their Content on the Platforms 176 5.5.8 Platforms Are Replacing the Free Internet 177 5.5.9 Forget Apps—The Bots Are Coming! 177 5.5.10 Competition Around the User’s Attention Is High 178 5.5.11 Bots Are Replacing Apps in Many Ways 178 5.5.12 Companies and Customers Will Face Each Other in the Messenger in the Future 178 5.5.13 How Bots Change Content Marketing 179 5.5.14 Examples of News Bots 180 5.5.15 Acceptance of Chat Bots Is Still Controversial 181 5.5.16 Alexa and Google Assistant: Voice Content Will Assert Itself 183 5.5.17 Content Marketing Always Has to Align with Something New 184 5.5.18 Content Marketing Officers Should Thus Today Prepare Themselves for a World in Which … 185 Chatbots: Testing New Grounds with a Pinch of Pixie Dust? 185 5.6.1 Rogue One: A Star Wars Story—Creating an Immersive Experience 185 5.6.2 Xmas Shopping: Providing Service and Comfort to Shoppers with Disney Fun 186 5.6.3 Do You See Us? 187 Contents     xi 5.6.4 Customer Services, Faster Ways to Answer Consumers’ Request 187 5.6.5 A Promising Future 188 5.6.6 Three Takeaways to Work on When Creating Your Chatbot 188 5.7 Alexa Becomes Relaxa at an Insurance Company 189 5.7.1 Introduction: The Health Care Market—The Next Victim of Disruption? 189 5.7.2 The New Way of Digital Communication: Speaking 190 5.7.3 Choice of the Channel for a First Case 192 5.7.4 The Development of the Skill “TK Smart Relax” 193 5.7.5 Communication of the Skill 199 5.7.6 Target Achievement 200 5.7.7 Factors of Success and Learnings 201 5.8 The Future of Media Planning 202 5.8.1 Current Situation 202 5.8.2 Software Eats the World 203 5.8.3 New Possibilities for Strategic Media Planning 205 5.8.4 Media Mix Modelling Approach 206 5.8.5 Giant Leap in Modelling 206 5.8.6 Conclusion 209 5.9 Corporate Security: Social Listening, Disinformation and Fake News 211 5.9.1 Introduction: Developments in the Process of Early Recognition 211 5.9.2 The New Threat: The Use of Bots for Purposes of Disinformation 212 5.9.3 The Challenge: “Unkown Unknowns” 215 5.9.4 The Solution Approach: GALAXY—Grasping the Power of Weak Signals 216 5.10 Next Best Action—Recommender Systems Next Level 221 5.10.1 Real-Time Analytics in Retail 221 5.10.2 Recommender Systems 223 5.10.3 Reinforcement Learning 228 5.10.4 Reinforcement Learning for Recommendations 231 5.10.5 Summary 233 xii     Contents 5.11 How Artificial Intelligence and Chatbots Impact the Music Industry and Change Consumer Interaction with Artists and Music Labels 233 5.11.1 The Music Industry 233 5.11.2 Conversational Marketing and Commerce 236 5.11.3 Data Protection in the Music Industry 238 5.11.4 Outlook into the Future 244 References 245 Part V Conclusion and Outlook: Algorithmic Business—Quo Vadis? 6 Conclusion and Outlook: Algorithmic Business—Quo Vadis? 251 6.1 Super Intelligence: Computers Are Taking Over—Realistic Scenario or Science Fiction? 251 6.1.1 Will Systems Someday Reach or Even Surmount the Level of Human Intelligence? 251 6.2 AI: The Top 11 Trends of 2018 and Beyond 256 6.3 Implications for Companies and Society 261 Index 267 Notes on Contributors Alex Dogariu has over 10 years of experience in customer management, corporate strategy and disruptive technologies (e.g. artificial intelligence, RPA, blockchain) in e-commerce, banking services and automotive OEMs. Alex began his career at Accenture, driving CRM and sales strategy innovations. He then moved on to be managing director at logicsale AG, revolutionizing e-commerce through dynamic repricing. In 2015, he joined Mercedes-Benz Consulting, leading the customer management strategy and innovation department. He was recently awarded twice the 1st place in the Best of Consulting competition hosted by WirtschaftsWoche in the categories Digitization as well as Sales and Marketing. Klaus Eck  is a blogger, speaker, author and founder of the content marketing agency d.Tales. Prof. Dr. rer. pol. Nils Hafner  is an international expert in building consistently profitable customer relations. He is professor for customer relationship management at the Lucerne University of Applied Sciences and Arts and heads a program for customer relations management. Prof. Dr. Hafner studied economics, psychology, philosophy and modern history in Kiel and Rostock (Germany). He earned his Ph.D. in innovation management/marketing with a dissertation on KPIs of call center services. After his engagement as a practice leader CRM in one of the largest business consulting firms, he established from 2002 to 2006 the first CRM Master program in the German-speaking countries. At present, he advises the management of medium-sized and major enterprises in Germany, Switzerland and Europe in matters of CRM. In his blog xiii xiv     Notes on Contributors “Hafner on CRM”, he is trying to emphasize the informative, delightful, awkward, tragic and funny aspects of the subject. Since 2006, he publishes the “Top 5 CRM Trends of the Year” and speaks about these trends in over 80 Speeches per year for international top companies. Bruno Kollhorst works as Head of advertising and HR-marketing at Techniker Krankenkasse (TK), Germanys biggest public health insurance company. He is also member of the Social Media Expert Board at BVDW. The media and marketing-specialist works also as lecturer at University of Applied Sciences in Lübeck and is a freelance author. Beneath advertising, content marketing and its digitalization, he is also an expert in the sectors brand cooperation and games/e-sports. Jens Scholz  studied mathematics at the TU Chemnitz with specialization in statistics. After this, he worked as managing director of die WDI media agentur GmbH. He is one of the founders of the prudsys AG. Since 2003 he was responsible for marketing and later sales at prudsys. Since 2006 he is the CEO of the company. Andreas Schwabe in his role as Managing Director of Blackwood Seven Germany, he revolutionizes media planning through artificial intelligence and machine learning. With a specifically developed platform, the software company calculates for each customer the “Media Affect Formula”, which enables an attribution of all online channels such as Search, YouTube and Facebook along with offline such as TV, radio broadcast, print and OOH. This simulates the ideal media mix for the customers. Blackwood Seven has 175 employees in Munich, Copenhagen, Barcelona, New York and Los Angeles. Dr. Michael Thess  studied mathematics in Chemnitz und St. Petersburg. He specialized in numerical analysis and received the Ph.D. at the TU Chemnitz. As one of the founders of the prudsys AG, he was responsible for research and development. Since 2017 he manages the Signal Cruncher GmbH, a daughter company of prudsys. Dr. Thomas Wilde  is an entrepreneur and lecturer at LMU Munich. His area of expertise lies in digital transformation, especially in software solutions for marketing and service in social media, e-commerce, messaging platforms and communities. Prior to that, he worked as an entrepreneur, consultant and manager in strategic business development. He studied economics and did his doctor’s degree in business informatics and new media at the Ludwig-Maximilian University in Munich. List of Figures Fig. 1.1 Fig. 2.1 Fig. 2.2 Fig. 2.3 Fig. 2.4 Fig. 2.5 Fig. 3.1 Fig. 3.2 Fig. 3.3 Fig. 3.4 Fig. 3.5 Fig. 3.6 Fig. 3.7 Fig. 3.8 Fig. 3.9 Fig. 3.10 Fig. 3.11 Fig. 3.12 Fig. 3.13 Fig. 4.1 Fig. 4.2 Fig. 4.3 The speed of digital hyper innovation 5 Big data layer (Gentsch) 12 Correlation of algorithmics and artificial intelligence (Gentsch) 16 Historical development of AI 19 Steps of evolution towards artificial intelligence 23 Classification of images: AI systems have overtaken humans 23 Business AI framework (Gentsch) 30 Use cases for the AI business framework (Gentsch) 36 Algorithmic maturity model (Gentsch) 42 Non-algorithmic enterprise (Gentsch) 43 Semi-automated enterprise (Gentsch) 44 Automated enterprise (Gentsch) 45 Super intelligence enterprise (Gentsch) 46 Maturity model for Amazon (Gentsch) 47 The benefit of the algorithmic business maturity model (Gentsch) 49 The business layer for the AI business framework (Gentsch) 50 AI marketing matrix (Gentsch) 58 AI enabled businesses: Different levels of impact (Gentsch) 72 List of questions to determine the potential of data for expanded and new business models (Gentsch) 73 Bots are the next apps (Gentsch) 84 Communication explosion over time (Van Doorn 2016) 91 Total score of the digital assistants including summary in comparison (Gentsch) 106 xv xvi     List of Figures Fig. 4.4 Fig. 4.5 Fig. 4.6 Fig. 4.7 Fig. 4.8 Fig. 4.9 Fig. 4.10 Fig. 4.11 Fig. 5.1 Fig. 5.2 Fig. 5.3 Fig. 5.4 Fig. 5.5 Fig. 5.6 Fig. 5.7 Fig. 5.8 Fig. 5.9 Fig. 5.10 Fig. 5.11 Fig. 5.12 Fig. 5.13 Fig. 5.14 Fig. 5.15 Fig. 5.16 Fig. 5.17 Fig. 5.18 Fig. 5.19 Fig. 5.20 Fig. 5.21 Fig. 5.22 Fig. 5.23 Fig. 5.24 The strengths of the assistants in the various question categories (Gentsch) The best assistants according to categories (Gentsch) AI, big data and bot-based platform of Amazon Maturity levels of bot and AI systems Digital transformation in e-commerce: Maturity road to Conversational Commerce (Gentsch 2017 based on Mücke Sturm & Company, 2016) Determination of the Conversational Commerce level of maturity based on an integrated touchpoint analysis (Gentsch) Involvement of benefits, costs and risks of automation (Gentsch) Derivation of individual recommendations for action on the basis of the Conversational Commerce analysis (Gentsch) Analogy to dating platforms Automatic profiling of companies on the basis of big data Digital index—dimensions Phases and sources of AI-supported lead prediction Lead prediction: Automatic generation of lookalike companies Fat head long tail (Source Author adapted from Mathur 2017) Solution for a modular process (Source Author adapted from Accenture (2016)) Digital Labor Platform Blueprint Virtual service desk Value Irritant Matrix (Source Price and Jaffe 2008) Savings potential by digitalisation and automation in service Digital virtual assistants in Germany, Splendid Research, 2017 Digital virtual assistants 2017, Statista/Norstat Use of functions by owners of smart speakers in the USA, Statista/Comscore, 2017 TK-Schlafstudie, Die Techniker, 2017 Daytime-related occasions in the “communicative reception hall”, own illustration How Alexa works, simplified, t3n 360° Communication about Alexa skill Statistics on the use of “TK Smart Relax”, screenshot Amazon Developer Console Blackwood Seven illustration of “Giant leap in modelling” Blackwood Seven illustration of standard variables in the marketing mix modelling Blackwood Seven illustration of the hierarchy of variables with cross-media connections for an online retailer Triangle of disinformation Screenshot: GALAXY emergent terms 107 108 111 115 117 118 119 119 131 132 134 135 136 140 141 147 148 149 160 191 193 194 195 196 198 200 201 207 208 209 212 218...
View Full Document

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern

Stuck? We have tutors online 24/7 who can help you get unstuck.
A+ icon
Ask Expert Tutors You can ask You can ask You can ask (will expire )
Answers in as fast as 15 minutes