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Sales Ex Machina 1 Sales Ex Machina How Artificial Intelligence is Changing the World of Selling Victor Antonio, BSEE, MBA James Glenn-Anderson, PhD 2 Sales Ex Machina 3 Published by Sellinger Group Copyright © 2018 by Victor Antonio All Rights Reserved. No part of this publication may be produced in any form or by any means, mechanical or electronic, including photocopy and recording, or by any information storage and retrieval system, without the permission in writing from the author or publisher; exceptions are made for brief excerpts used in published reviews. This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold with the understanding that the publisher is not engaged in rendering legal, accounting, or other professional services. If legal advice or other expert assistance is required, the services of a competent professional should be sought. This Sellinger Group Publication Edition is published by Victor Antonio of the Sellinger Group. Contact Information: PO Box 4342, Alpharetta, GA 30023 www.SellingerGroup.com Printed in the United States of America First Printing: January, 2018 Library of Congress Cataloging in Publication Data Antonio, Victor and Glenn-Anderson, James Sales Ex Machina, How Artificial Intelligence is Transforming the World of Selling ISBN 978-0-578-19139-3 (U.S.A.) 1. Business 2. Sales 4 Sales Ex Machina 5 Table of Contents Introduction Part 1: Science of AI Chapter 0: What Is This AI Thing? Chapter 1: Data Exhaust – Big Data Is Small Data Chapter 2: Cognitive Calling – How AI Is Impacting Telemarketing Chapter 3: Data Analytics Chapter 4: Predictive Modeling Part 2: AI Applications Chapter 5: Sales & Singularity Chapter 6: Salesforce.com Chapter 7: Sherlocking Social Media Chapter 8: Amazon’s Renaissance Chapter 9: EI – Email Intelligence Chapter 10: Hire Smarter (Contribution by Camille Gonzalez) Chapter 11: Lead Prioritization & Engagement Chapter 12: My VSA – Amelia Epilogue Glossary 6 Sales Ex Machina 7 INTRODUCTION: Sales Ex Machina Machines are getting smarter with each passing week, month, and year. So, where does that leave us? Where is the world of technology taking us, and how is it changing the way we do business and interact with others? What are the unforeseen implications or unintended consequences for selling and business? How much adaptation will be required for a salesperson to remain relevant? What will we have to know to still be of value in our various business pursuits? And most importantly, where do we fit as sales professionals in this artificially intelligent future? The world of selling is changing…again! But this time, it’s different. We are about to experience the equivalent of a major tectonic shift where the functional plates of sales, marketing, and technology will shear and, in some cases, smash against another. 8 Sales Ex Machina Functions that were once the domain of salespeople will be transformed, subsumed, or obliterated. Does that sound too dramatic? It shouldn’t! It’s begun to happen already, and far too many executives, managers, business owners, and sales professionals are either ignorant of the fact or are simply too busy trying to achieve their revenue quota to even notice what’s happening. I can only imagine that millions of years ago, a dinosaur looked upward and saw a meteor entering the earth’s atmosphere, stared at it for a moment, and then turned away without a second thought—never realizing that the meteor was the harbinger of its own extinction and the world from that moment on would be changed forever. Artificial intelligence (AI) is that meteor. It’s that digital anomaly that has entered our ecosystem, and its impact will be felt globally thanks to the help of various enabling technologies along with a global telecommunication infrastructure. In the computational sciences, we are seeing an exponential growth in processing power that calls into question, nullifies, and otherwise obviates Moore’s Law. For those unfamiliar with the term, Moore’s Law is an empirical technology growth model first cited by Gordon Moore, co- founder of Fairchild Semiconductor, who predicted a doubling of the number of transistors in integrated circuit chips every 12 to 18 months. By several more or less reasonable leaps of logic, this model has also been regarded as a harbinger of increased sophistication in technologies dependent on those integrated circuits, such as computers. This model has held approximately true for quite some time, but now with the advent of intelligent systems and allied technologies, there is evidence we’re actually beating the prediction. 9 In any event, with this increase in computational power, we are also seeing an inverse proportional relationship to cost. That is, the machines are getting more powerful AND cheaper. These new computers have allowed scientists and technologists to create programs exhibiting a combination of complexity, sophistication, and speed exceeding all that has come before. Some have claimed this computational power and sophistication are beginning to mimic and even rival the human brain in the areas of adaptation and creativity. This is certainly true in some respects. The fact is computers are working harder, better, faster, and getting stronger. Daft Punk1 captured the electronic ethos of AI: Work it harder Make it better Do it faster Makes us stronger More than ever Hour after hour Work is never over Add to this exponential growth in processing power a broadband infrastructure that allows any individual to be connected to any other individual or machine, anywhere in the world, and you can begin to get a glimpse of what this means to businesses, to consumers, and more specifically to sales professionals. 1 Daft Punk is an electronic music duo known for their music but more so for wearing helmets and gloves to assume robot personas. One of their most popular songs is “Harder Better Faster Stronger” on the album Discovery, released in 2001 under the Virgin label. 10 Sales Ex Machina Figure 1: NCTA.com Technology companies have continued to develop systems with the surprising ability to push more and more information through the air (wireless) and glass (fiber optics). Companies are also finding new ways to compress large amounts of data and increase in bandwidth capacity to levels that years ago would’ve seemed inconceivable. This is all significant and even breathtaking to consider. The term “information superhighway” is beginning to seem like an antiquated analogy; it only references intersystem speed and capacity while ignoring an exponentially increasing trend in embedded machine intelligence. GO AI In 1997 IBM's computer Deep Blue beat world chess champion Garry Kasparov, leading to the prediction of the rise of AI and how it would soon take over the world. That prediction was a bit premature, but a machine agent beating a human at tactics and 11 strategy? This had previously been considered a domain reserved to humans alone. Thinking people everywhere took note of this development as “handwriting upon the wall.” The question of whether Man or Machine was stronger in terms of brute strength was settled during the Industrial Revolution. Machines were stronger, but still needed humans to operate, design, and maintain. Watching a machine beat a human at strategy and creativity woke the world up to the possibility that our dominance in the realm of intelligence had come to an end. Though a hasty assumption, the coexistence of man and machine has become more and more prominent. Machines like Deep Blue are programmed with rules that guide its “thinking.” So while the machine did win, this victory didn’t mean it was necessarily smarter; it could still only perform what was programmed into it. Its “intelligence” did not translate into any other task or process. While impressed with the Deep Blue’s performance, we still proclaimed, “It won, but it’s still a machine that can only do what we tell it to do; it can’t think or learn on its own.” We found solace in this little fact and clung to it for reassurance that we hadn’t lost our cognitive dominance to a machine. In March 2016, we saw another milestone in artificial intelligence. An AI computer program called AlphaGo2 beat South Korean Lee Sedol in a five-game series of Go, a game somewhat akin to chess but, as some have claimed, even more difficult. This was the first time a computer Go program had beaten a 9-dan professional. AlphaGo won the first three games and lost to Sedol in the fourth game. Sedol resigned the final 2 AlphaGo is a narrow AI computer program that plays the board game Go. It was developed by Alphabet, Inc.'s Google DeepMind in 2015. 12 Sales Ex Machina game, giving AlphaGo a 4-1 win. It was the first Computer Go program to beat a human professional Go player. Just like chess, Go is also a game of strategy, but that’s where the direct comparison ends. Go has many more moving pieces and an incalculable amount of strategic moves and outcomes. An inherent “combinatorial explosion” renders the game much more difficult to master and by default, that more difficult for a machine to master. Not satisfied with a 4-1 win, the developers improved AlphaGo’s algorithm, which had previously used a Monte Carlo algorithm (i.e., one based on statistical trials), to determine what its next move should be. Next they employed a Deep Learning methodology to calculate a next step based on what the program had “learned” from previous moves. In every sense, from philosophical to mathematical to logical, this was much more “up close and personal.” AlphaGo was then ready to take on the next human expert.