Imagine if you could identify your business’s most profitable customers, craft a better marketing strategy to communicate with them, and inspire them to buy more? Well now you can. And the best part is that you can do it using the data you already have.
Today, everything we do creates data, and the volumes are enormous. Virtually every time someone views something online, enters search on Google, or even surfs the web on a smart phone, another chunk gets added – in real time - to the multibillion gigabyte (and growing) trove of data that can help us better understand and predict consumer behavior. We no longer need expertise in math or statistics or even expensive modeling software to get the most out of all these revealing consumer insights. A revolution in data analysis is underway, and the methods and tools for aggregating and analyzing this “data deluge” are suddenly far simpler, less expensive, and more precise than they were.
In this book – the first of its kind – Dimitri Maex, Managing Director of global advertising agency OgilvyOne New York and the engine behind the agency’s global analytics practice, reveals how to turn your data - those sexy little numbers that can mean more profit for your business – into actionable strategies that drive real growth and revenues. And he can show you how to do it at virtually no cost. In his clear, easy-to-understand style, he explains how to:
--Identify which customers are most valuable, which have the most potential to be valuable, which are most likely to buy more in the future, and which are not worth targeting.
-- Allocate your marketing assets in the best possible way and pinpoint the outlays that will generate the highest possible returns.
--Figure out precisely which communication or media brought a customer to your company’s web site and what that customer will do once she arrives.
--Predict which products or services customers will want in the future.
--Learn which customers are preparing to defect to the competition and how to stop them. -- Determine which customers buy your product because it is perfect for their needs, which ones purchase because they liked your ad, which ones chose you because of an appealing price, and which ones came to you through word-of-mouth…or some combination of all these factors.
-- Drill your geographic targeting down to the regional, zip code, and even neighborhood level.
--Optimize your web presence to get the maximum return from search.
A must read for marketers striving to get the biggest ROI on their advertising dollars, small business owners eager to grow faster, researchers needing a consumer in mind for whom to create new products or services, those in finance responsible for growing the bottom line, and even creatives looking for feedback to help them improve their output, Sexy Little Numbers is THE essential tool not just for math nerds and number crunchers, but for anyone wishing to use the data at their fingertips to grow their business and increase their profits dramatically.
|Publisher:||The Crown Publishing Group|
|Sold by:||Random House|
|File size:||6 MB|
About the Author
PAUL B. BROWN, a long-time contributor to The New York Times, is a bestselling author who has collaborated on numerous business classics including Customers for Life (with Carl Sewell) and Your Marketing Sucks (with Mark Stevens).
Read an Excerpt
How This Book Will Help You Grow Your Business
I work in an industry known for reducing complicated ideas to a single thought. These tag lines—“Merrill Lynch is bullish on America,” “Finger lickin’ good,” and “Don’t Leave Home Without It”—were all created by my colleagues at Ogilvy & Mather.
And in my corner of the ad agency—I run Ogilvy’s analytics team—we operate on a single premise: The most successful companies today are those that are able to convert the “data deluge” we all face into insights that drive real growth.
Our job is to help our clients uncover those insights. To do that, we need to explain what we discover within their data in simple, straightforward terms. (While the CFO is probably comfortable when I talk about “logistic regressions,” the rest of the people in the C suite—the CEO, the chief marketing officer (CMO), and the others we present to—usually don’t speak math shorthand, and their eyes glaze over after ten or fifteen seconds when I do. So, I have learned to present my ideas the way the “creatives” do, in simple, hopefully memorable images such as “at sixty miles an hour, the loudest noise in this Rolls-Royce comes from the electric clock.” (That was another one of ours.) And I will try as much as possible to use this type of language in this book.
Simple language only enhances this powerful fact: There is now a proven way to increase dramatically both your company’s sales and its ROI using data you probably already have but may not be aware of.
Well, if you think about it, there are really only two sides to every business: supply and demand. The supply side, how a company is going to fill orders—i.e., how they will fulfill their customers’ needs—is the one that a business controls. The company heads know, for example, how much productivity will increase if they buy a new machine.
The supply side is where most left-brainers—the logical, financial types—feel comfortable. For decades, they have increased the efficiencies of supply chains, streamlined processes, and developed measurements to track progress.
The demand side, on the other hand, is something companies don’t control; consumers do. Sure, you can try all kinds of things to reach customers, but ultimately it is the customer/consumer/client who decides if he is interested in what you have to offer. The demand side is the fuzzy place where cause and effect are not always clear. Did he buy because the product was perfect for his needs, because he liked your ad, because your price was appealing, because of word-of-mouth—or was it some combination of those factors and a hundred more?
Here finding out what happens when you turn the dials and push the buttons is a messy business. The customer bought immediately after clicking on your Internet ad; but was the banner ad the reason she bought? Figuring it all out is what I do on a daily basis. As you will see, I use the clean, tried-and-tested tools from the supply side and apply them to the messy demand side of the business.
These tools can help you, in the words of the book’s subtitle, grow your business in a way that increases both your sales and profits.
This is not only vitally important to those of you in the C suite—after all, the shareholders ultimately will judge you on how effectively you deploy their money—but to employees at all levels of the company. Marketers and people who run business units need to know which are the most profitable customers to target; researchers must have a (profitable) consumer in mind as they set off to create new products or services; those in customer service want to pay the most attention to the firm’s most valuable users/buyers; and, of course, the people in finance will always ask whether the company is going to make any money on its latest undertaking. By employing the ideas we are about to talk about, the return on your investment can be huge.
How huge? Here are two quick examples using the techniques I will be sharing with you:
• Caesars improved their return on online advertising spending by 15 percent to 30 percent by analyzing data generated from customer reviews about the hotels the company owned. There are software programs that not only search out every comment customers make on the web, but automatically sort those comments into scores of categories, and the company used those findings to change their offers and the language in their ads. For example, customers raved about the views from the hotel, and now those views are featured prominently in Caesars ads, while the price of the room is given less prominence.
• TD Ameritrade increased new account openings by 14 percent from the company website just by making very small changes to the copy, design, and images on the site, based on an incredibly thorough examination of its home page. Our team at Ogilvy tested every single word, color, and design element with customers to see what could be improved. It turned out that simply changing the signup language from “Apply online now” to “Get started” and altering the color of the button customers clicked from orange to green made a dramatic difference in the number of people who opened accounts.
As these examples show, the material in this book is not theoretical. It is already being used by companies to increase demand for their products. I am going to show you how you can do the same thing.
By looking differently at the existing data you have about your customers, you can improve:
Your strategy. You’ll learn how to fine-tune your overarching approach to both your customers and the competition based on the insights the numbers about your business can provide. For example, you will discover who your most profitable customers are, who is most likely to buy from you, and which customer segments are not worth targeting.
The tactics you use to carry out your strategy. Your data, when viewed correctly, will tell you how to approach and sell to your most profitable customers and the best ways to reach those who are likely to buy more from you.
The execution of your tactics. Your data will help you pinpoint where you will get the biggest returns and when would be the best time to implement your tactics.
There are two simple reasons why these improvements are possible: First, remarkable breakthroughs in technology allow us to sort through all the data about customer behavior to find discernible—and predictable—buying patterns. We have always had the data; but until now, companies could use it only in the crudest terms. Second, just about everything we do today generates data, giving us a much more complete picture of the people who do business with us, and the potential revenue companies are missing, as the following story shows.
On a recent business trip, I woke up in the Canary Wharf section of London and checked out of the Hilton. I took the subway to Paddington Station. From there, I hopped on the Heathrow Express, supposedly the most expensive train in the world, but still cheaper and faster than a cab (and it doesn’t make me carsick).
I checked in for my British Airways flight to JFK airport. Before boarding, I stopped at Boots, the largest drugstore in the UK, to get a four-pack of cucumber wet wipes. My wife, who is British, tells me they are the best in the world and she cannot find them in the United States (like Marmite and Cadbury Creme Eggs, this is another strange product British expats miss when living abroad). I also browsed the perfume section, where a saleswoman complimented me on buying Gucci’s latest fragrance, Flora. Three hours after waking up, I boarded my plane home.
In this short period of time, I left behind a rich trail of data. Hilton, if it knew where to look, would have seen it was my third stay in that hotel within six months. They also could have discovered I like a glass of wine before I go to bed and that I prefer the continental breakfast despite a promotion for the full English one. The London Transport Authority, if it wanted to, could see I was in town for a week—I had purchased a seven-day pass—and that I had crisscrossed the city during the day, always to go back to Canary Wharf in the evening. They might also have noticed that seven years ago, when I lived in London, I did the same thing every day.
The people who run the very expensive Heathrow Express now have buried somewhere in their records that I used their services for the third time in six months. British Airways would have noticed the same. Boots could have figured out that I was probably another British expat (well, Belgian with a British wife, actually) hamstering their fabulous cucumber wet wipes. And Gucci, had it been paying attention, would have noticed I bought the fragrance in a store with a couple of big video-screen monitors advertising it nonstop.
All this data was being gathered without my even going online to surf the web. If I had, then companies would have been able to track my every click, even if I didn’t purchase from them.
The point is not to tell you how much I travel for business, but to give you a tiny example of the volumes of data that are being collected and hardly used. Sure, Hilton knows (if they look) that I have a frequent-stayer account (Hilton HHonors), but I have yet to receive a targeted email or letter that says, “The next time you are London, Mr. Maex, may we suggest . . .” (some hotels have started to do these kinds of mailings). Boots has never mailed a catalog to our Brooklyn apartment, and I have never received a solicitation from Gucci, which is probably a good thing, given how much my wife likes their products.
While I was traveling, millions of others were generating similar volumes of data that same morning. They, like me, do this through interacting with websites, social networks, mobile devices, cash registers, etc.
Companies haven’t been using the vast majority of information we generate because—up until now—it has been too difficult to get at it in an useful way. I sympathize. The volume of data gathered every day is staggering. To put the amount into perspective: Imagine a database holding all words ever spoken by human beings since the beginning of time. Now, if you were to take 200 of those databases, you would have just about enough storage to hold all the data that will exist by the end of 2012. That’s a lot of data and the numbers will only increase dramatically in the years ahead.
But companies no longer have the excuse that the data is too hard to sort through. The tools invented in the last few years make it remarkably easy.
Let’s see how. Here’s a real-life example involving strategy. The story begins right after my wedding.
Cisco Systems: A Case Study
Katherine and I chose the most romantic destination for our honeymoon: Silicon Valley. It wasn’t exactly what we had planned.
A couple of days before we got married, in 2004, I got a call about a job opening in San Jose. While I love my native land of Belgium, I’d always wanted to work in the United States. You need big volumes of information to make my job fun. There are only ten million people in Belgium, so the databases are small. This was a great opportunity to work where the markets have bigger scale and are more sophisticated.
So, right after Katherine and I got married in Antwerp’s beautiful sixteenth-century town square, we found ourselves in the center of Silicon Valley with all of our stuff in boxes. My job would be working with Cisco Systems, the forty-billion-dollar technology company.
Cisco’s new head of demand generation had asked Ogilvy for help in setting up what they were calling an “advanced analytics” group. The goal of this new unit would be to figure out precisely to whom Cisco should be marketing and how much they should invest to reach those people. Up until this point at Cisco—and maybe at your company as well—these decisions were made by gut feel backed by some, often anecdotal, data.
That might be fine for a start-up company, but when you are an established firm, one spending a lot of money on marketing—hundreds of millions of dollars a year, in Cisco’s case—you need to do better than hunches and one-size-fits-all rules of thumb, such as you should spend 5 percent of revenues on marketing.
So Ogilvy sent me to Silicon Valley to see if I could help Cisco. Apart from their head of demand generation, nobody else at the company was necessarily waiting for the creation of an advanced analytics group. Marketers in general weren’t particularly interested in analytics back then. Most people didn’t go into marketing because they liked math; quite the opposite. So I had some convincing to do. Especially since the person who asked me to set this up, the only advocate I could count on, had left Cisco to join Oracle by the time I landed in San Jose!
So there I was in my little gray cubicle (the concept of a cubicle was foreign to me; I had seen it only in movies like Clerks, where the people who inhabited them always seemed to have dead-end jobs), on my first day at a new job in a new country. No one was asking for my help. No one really understood what advanced analytics was for. Heck, they didn’t even know what the term meant.
Given all this, the first thing I did was to look for buddies, like-minded people who understood the potential power of data and analytics in marketing. One of them was Mike Foley, who was in charge of Cisco’s marketing database. He could tell me all about the information Cisco had on their customers and prospects. This was great. If I was going to set up an advanced analytics function, I would need data. Data is the raw material for everything I do.
Mike and I teamed up, and I started to delve into the data, familiarizing myself with everything Cisco knew about its customers—which was a lot. For every company that had ever bought a Cisco product, there was data on when the companies bought, what they bought, how much they spent, and how often they bought from Cisco. (You probably have—or can get your hands on—this kind of data, too. Someone, or some department, in your company is sending out invoices. The data is probably inside their computers.)
Table of Contents
Introduction: Why numbers have always been sexy 1
Chapter 1 How this book will help you grow your business 7
Chapter 2 Targeting: Who should you talk to? 30
Chapter 3 Discover: What should you talk to customers about? 64
Chapter 4 Locate: How do you find them? 90
Chapter 5 Budget: "How much should we spend?" 122
Chapter 6 Yardsticks: How do you measure what works and what doesn't? 155
Chapter 7 Optimize: How do you do more of what works and less of what doesn't? 192
Chapter 8 The Future 217