Brown Paul B. - Sexy little numbers: how to grow your business using the data you already have

Copyright 2012 by Ogilvy & Mather
All rights reserved.

Published in the United States by Crown Business, an imprint of the Crown Publishing Group, a division of Random House, Inc., New York. www.crownpublishing.com

CROWN BUSINESS is a trademark and CROWN and the Rising Sun colophon are registered trademarks of Random House, Inc.

Library of Congress Cataloging-in-Publication Data
Maex, Dimitri.
Sexy little numbers : how to grow your business using the data you already have / Dimitri Maex with Paul B. Brown.1st ed.
p. cm.
Includes index.
1. Consumer behaviorResearch. 2. Marketing Research. 3. AdvertisingResearch. 4. Data mining. I. Brown, Paul B. II. Title.
HF5415.32.M34 2012
658.83dc23 2011043275

eISBN: 978-0-307-88836-5

Illustrations by Chris Udemezue
Jacket design by Whitney Cookman

v3.1

F OR K ATHERINE , R AY, AND B RUCE

Ads that follow shoppers around as they go from one website to another; precise, real-time updates on the effectiveness of your marketing campaigns; and the ability to target the absolutely most profitable individuals within your customer basethese are just three of the thousands of things possible today, thanks to analytics, the shorthand description of the art and science of analyzing all the data that is out there.

So, you might think numbers have just suddenly become sexy; but thats far from the case.

Its probably fair to assume that our sexy little numbers started to pop up in marketing after the invention of the first direct-response campaigns. Aaron Montgomery Ward invented the mail-order catalog in 1872, and Richard Sears and Alvah Roebuck copied it in 1886. While there is no real evidence of how these early catalog pioneers measured their success and optimized operations, they had the ability to do so; and the fact that both catalogs thrived for more than one hundred years (to be replaced by their online equivalents) suggests they probably did a good job at it.

Indeed, Claude Hopkinss Scientific Advertising (1923), a forerunner of this book, states: The time has come when advertising has in some hands reached the status of a science. It is based on fixed principles and is reasonably exact. The causes and effects have been analyzed until they are well understood. The correct methods of procedure have been proved and established. We know what is most effective, and we act on basic laws.

Hopkins and later John Caples, in Tested Advertising Methods (1932), wrote mainly about mail-order and other direct-response vehicles. It was easy for them. They knew to whom they sent their catalogs, or direct mail pieces; all they had to do was track whether those people actually ended up buying. That worked fine until mass mediaradio and then televisioncame along. There was no simple way to tell who, precisely, heard or watched a broadcast. New techniques would be required for these new media to maintain the same levels of marketing accountability.

The first applications of more advanced mathematical techniques began in the 1950s, when operations research and management science models that had become popular in production and manufacturing following World War II were applied to marketing for the first time. Although extremely primitive by todays standardswhen we know exactly how long someone looked at an online ad, which parts he clicked on, and what actions, if any, were taken as a resultthe models began to provide an understanding of the marketing and media mix on outcomes such as brand awareness, consideration, and, ultimately, sales and profit.

The third era in the evolution of analytics happened during the 1990s, when Lifetime value models predicted what a customer would be worth over the long haul, and anti-attrition models were built to predict an individuals likelihood of ceasing to be an active consumer. The CRM revolution expanded marketing effectiveness tools and techniques considerably, and an analysts ability to sort through vast quantities of data was soon tested on customer-centric data derived from digital media.

With digital communicationsanything sent over the webeverything is measurable. Everything generates data, and the volumes are enormous. Googles digital database, which is probably the largest, captures more than 1 billion searches per day worldwide. This huge quantity of data can give companies unprecedented visibility into how customers engage with brands and how that engagement ultimately leads to revenue.

E-commerce environments provide us with a closed-loop system, which, in marketing effectiveness terms, gets us close to nirvana. We know which media individuals have been exposed to, how they came to a specific website, and what they do once they arrive there. We can observe these individuals entire shopping behavior, all the way to their actual conversion to a sale.

Digital data is also available in real time. We no longer have to wait weeks or months to learn the impact of our marketing activities. We can get a read almost instantaneously, allowing for immediate optimization. Digital has put the marketing math revolution on steroids.

Sam Palmisano, CEO of IBM at the time, explained the new world better than anyone during a speech to the Council on Foreign Relations in 2008 in which he pointed out that all things are becoming intelligent: New computing models can handle the proliferation of end-user devices, sensors, and actuators and connect them with back-end systems. Combined with advanced analytics, those supercomputers can turn mountains of data into intelligence that can be translated into action, making our systems, processes, and infrastructures more efficient, more productive and responsivein a word, smarter.

Take Zara, the flagship brand of the Spanish retail group Inditex SA, as an example. By integrating analytics into every single part of its business, it has done what was thought to be impossible in the clothing industrygoing from the design table to stores in a matter of weeks. It is not uncommon for other retailers to be working nine to twelve months out on their line of clothing for Christmas.

What does all this mean for you and me personally? As a result of analytics and the interconnectivity that Palmisano was talking about, picture your refrigerator in the very near future: Not only will it tell you when you are running out of milk and butter, but it will be able to draw up a shopping list and actually send the order to your local supermarket, which will then deliver it to you.

All of this is pretty wonderful, but it comes with a price. Some people are concerned about marketers being able to collect all this data about what they buy and where they go online.

I am going to talk a lot about privacy in the last chapter, but let me touch on it here, and my position may surprise you. I believe people are rebelling for valid reasons against widespread data collection for commercial purposes. Data collection is largely unregulated and hidden. And that is wrong.

But things are changing. In the absence of other standards, and because consumers have told us, quite rightly, that they have concerns, the advertising and marketing industries have begun to regulate themselves. We have pledged that we will not collect personal identifiable information (PII) without the consent of the consumer, and we give the consumer the opportunity to opt out of any data collection, but we will also educate consumers about our reasons for collecting this information in the first place.