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Omer Artun

I am a scientist by training; I am an entrepreneur at heart, driven by curiosity of knowledge and challenging status quo. In elementary school, I saw the opportunity to make a profit collecting fruit from mulberry trees from our school backyard and selling it on the street, enlisting my schoolmates to help me run this small business. With some prodding from my engineer parents, I followed in my older brother's footsteps to enter a PhD program in physics at Brown University, studying under Leon Cooper at The Institute for Brain and Neural Systems. Dr. Cooper has received the Nobel Prize in Physics for his work on superconductivity and later decided that the next big problem to solve was in neuroscience, decoding how we learn and adapt. He is a pioneer in learning theory since the early 70s, using both experimental neuroscience as a base as well as statistical techniques for understanding and creating learning systems, now popularly called machine learning. I worked on both biological mechanisms that underlie learning and memory storage as well as construction of artificial neural networks, networks that can learn, associate, and reproduce such higher level cognitive acts as abstraction, computation, and language acquisition. Although these tasks are carried out easily by humans, they have not been easy to embody as conventional computer program.

As I was getting close to graduating from the PhD program at Brown University around 1998, I noticed that the business world was mostly running on simple spreadsheets, and I wanted to apply a data science and machine-learning approach to business. This goal led me to work for McKinsey & Co., the premier strategy consulting firm that helps large companies formulate strategies based on a fact-based problem solving approach.

When I joined McKinsey & Co. in 1999, I was able to test drive some of this data scientific approach in a few studies. My first project was to help a large technology company improve sales coverage, scientifically matching the sales team with the customers based on customer needs, sales team's skill, and experience. The CEO was impressed with the results on paper, but was unable to operationalize the results in real life, in a repeatable way. This is what I call the last mile problem of analytics. I realized that this is a big problem to solve. Analytics is an important enabler in improving commercial efficiency, but can only create value if it becomes part of the day-to-day execution workflow. I saw this theme repeat over and over again in many areas of business, pricing, supply chain, marketing, and sales. Most McKinsey projects I have been part of ended up on a slide deck which had all the right answers but very rarely created any real value. Equipped with McKinsey training, I joined one of my clients, Micro Warehouse as VP of Marketing, in 2002, with the goal to bring data science to everyday operations. I was lucky to be empowered by the CEO Jerry York and President Kirby Myers. Jerry was the most analytically driven person I ever knew in business, still to this day. He was previously CFO of IBM during Gerstner years, and CFO of Chrysler before that. He encouraged me to use data science to help him run the business better.

I knew I had to architect my approach in a way that married data science with execution to solve the last mile problem. I had two important recruits, Dr. Michel Nahon, a brilliant Yale-trained applied mathematician who helped me with machine-learning algorithms, and the hacker extraordinaire Glen Demeraski, who helped me with everything database and application related. I created approaches and systems that used data to more efficiently allocate resources, reduce marketing costs, and uncover new revenue sources. We had significant impact on marketing efficiency, pricing, and discounting patterns as well as salesforce effectiveness. In early 2003 we had real-time systems alerting purchase, pricing, and customer acquisition patterns of the sales team compared to moving averages to take immediate action by the sales leadership. After Micro Warehouse, from 2004 to 2006, I joined Best Buy as Senior Director of Business-to-Business marketing of its newly founded Best Buy for Business division. Best Buy at the time also struggled with the same exact last mile problem, lots of internal resources, tools, many high-flying consultants talking about customer segmentation, and analytics, but when you walked into a store, none of that had any impact at the customer level. This is the true test of analytics; does it impact the customers in a positive way that they can experience it? If not, then you have the wrong setup. Making progress at Best Buy was much more difficult, which I will touch on in Chapter 1.

While working at Micro Warehouse and Best Buy, I was also a regular guest lecturer at Columbia University and NYU Stern MBA programs Relationship Marketing and Pricing courses that Dr. Hitendra Wadhwa taught. I also became an Adjunct Professor at NYU Stern for Spring 2006, teaching the MBA level Relationship Marketing program. During this period, talking to students, doing market research, talking to colleagues at different companies, I postulated that data-driven predictive marketing would become the new paradigm for the next 10 years. The value of predictive marketing was already clear to me, but its importance has accelerated due to digital transformation of commerce, increase in customer touch-points, and exponential increase in the size, variety, and velocity of data (which is now popularly called “big data”).

If you ask me what is the one important thing I learned from Dr. Cooper, I would say that it is breaking the problem down to its core and solving it at a fundamental level. He always said the idea behind the solution to any problem has to be clean and very simple. This is how I thought about the marketer's problem. Marketing was easy in the days of the old corner store. People knew our name, our likes and dislikes, and treated us on a one-to-one basis. Marketers lost touch with their customers in the era of one-size-fits-all mass optimization. Customers became survey responders and focus group participants; it was all about products and channels. However, the need for customer-centric marketing has always been there, it just wasn't practical and cost effective to practice. Digital transformation including web, email, mobile, social, location technologies combined with technologies to store, process, and extract information has significantly changed what is practical and cost effective.

Predictive marketing is the approach that restores that personal touch by bringing that human sensibility into our digital and offline lives, by focusing on the consumers individually to understand what they did and what they will do next. Predictive analytics, based on machine-learning algorithms, offers enormous leverage to marketers trying to make sense of these actions. Rather than replacing human decision making, machine learning and complex algorithms could help people amplify their intelligence and deal with problems on a much larger scale, something like giving a bulldozer to people used to digging with a shovel.

I saw the opportunity to solve a problem that a growing number of companies were struggling with, and I decided to disrupt the status quo and solve this problem. In 2006, I founded AgilOne, to bring the power of big data and predictive analytics to everyday marketers with an easy-to-use, yet powerful, cloud-based software platform.

AgilOne was initially bootstrapped for the first 5 years, then backed by top tier VC firms including Sequoia Capital, Mayfield Fund, Tenaya Capital, and Next World Capital. We are helping more than 150 brands in retail, B2B, Internet, media, publishing, and education deliver relevant experiences across channels. Through complete and accurate customer profiles, predictive insights, and built-in life cycle marketing campaigns, marketers boost customer loyalty and increase customer lifetime value.

In my spare time, I claim to be an accomplished potter of 28 years, having studied at Rhode Island School of Design under Lawrence Bush during my years at Brown. A native of Turkey, I now live in Los Gatos with my wife Burcak and two daughters, Ayse and Leyla. As I write this introduction, my daughter Ayse, who is a freshman at Castilleja School in Palo Alto, is reading an article about predictive marketing for her math class, which shows how predictive marketing will become mainstream for the next generation.

Dominique Levin

I credit my education, a combination of engineering school, design school, and business school for my left-brain–right-brain approach to marketing: I have a master's of science (Cum Laude) in industrial design engineering from Delft University in The Netherlands and a master's of business administration (with Distinction) from Harvard University. I recommend all marketers to marry human creativity with technology learning in order to deliver value to customers. Over the past 20 years I have run marketing at companies large and small, on four different continents, targeting businesses and consumers. Above all, I was an early convert to the importance of customer data.

In 1994 I took my first marketing job: a summer internship in Cusco, Peru. I drove around in a pickup truck to visit local farmers and tally how many would join a local cooperative to process fruits into marmalades and liquors. For my next job, at Philips Consumer Electronics, I was asked to find a way to sell more electronics to girls and women. I mingled with teenagers at local high schools to collect data. Philips launched a product called KidCom, an electronic organizer for girls, and proto-typed TeenCom, a two-way paging device for teenagers. My boss on this project was Tony Fadell, who later became the father of the iPod and iPhone, and who went on to found NEST. In 1997, I relocated to Tokyo, Japan, to work for Nippon Telegraph and Telephone (NTT). All employees at NTT, whether in product or finance, worked one weekend in the company store to meet and serve customers. I recommend such “meet the customer” program to any company as no numbers can totally replace meeting customers face to face.

In 2000, I moved to Silicon Valley and ran marketing for my first big data company, LogLogic – later acquired by TIBCO Software. For the first time I had access to lots of customer data in digital form. Log files are like the digital video cameras of the Internet. At LogLogic we used this log data to monitor security, but it also opened my eyes to the possibilities of using similar data to better understand and serve customers.

I went on to work for several other technology companies, including Fundly and Totango, focusing on building highly data-driven marketing organizations. Fundly helps non-profits use social media to raise money. We used data to automate the process from self-service sign-up to fundraising success. Totango offered a predictive marketing solution that monitors customer behavior to identify both promising and struggling customers. In both cases data and predictions helped to accelerate customer acquisition and increase customer lifetime value, while lowering the cost of sales.

I met Omer in my role as CMO at Agilone, where I got to work with thousands of marketers just like you to figure out how they can best use customer data to delight customers. Omer and I are united in our data-driven and customer-centric approach to marketing. Data and humanistic experiences go hand-in-hand. Our passion for customers has led us to this book.

In my spare time, I love to travel with my husband and three children and experience people, places, and cultures around the world. I play ice hockey to blow off steam and was once a member of the Dutch national team. I love to work with entrepreneurs and help them make their dreams a reality.