Authors: Jeffrey Walton
Take the Fourth
Take the Fourth
Jeffrey Walton
AuthorHouse™
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© 2011 Jeffrey Walton. All rights reserved.
No part of this book may be reproduced, stored in a retrieval system, or transmitted by any means without the written permission of the author.
First published by AuthorHouse
ISBN: 978-1-4520-8928-7 (sc)
ISBN: 978-1-4520-8929-4 (hc)
ISBN: 978-1-4520-8930-0 (ebk)
Library of Congress Control Number: 2011900409
Printed in the United States of America
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and such images are being used for illustrative purposes only.
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Contents
T
here would be a few more trees left standing, one less book on the shelf, and a ton of ideas still bouncing around in my head like lotto balls, if it wasn’t for the daily doses of support and patience that I received from my loving wife Wendy. Through her encouragements and perseverance I took my first steps of placing keystrokes to liquid crystal displays and developing characters, plots, and subplots and eventually turning them into this novel before you. I thank her for that. I love her for that and for that I dedicate these bounded words to her, my Wendy. These words are as much hers as they are mine.
“The right of the people to be secure in their persons, houses, papers, and effects, against unreasonable searches and seizures, shall not be violated, and no warrants shall issue, but upon probable cause, supported by oath or affirmation, and particularly describing the place to be searched, and the persons or things to be seized.”
An excerpt from a thesis entitled “DATA”
A
visionary is one who can foresee the realization of technologies based upon innovations and inventions that do not exist today.
Joseph Woodland was a visionary. In 1949, while relaxing in his beach chair, he invented the barcode by placing Morse code in the sand with his fingers. There were no barcode readers or lasers at this time and computers were in their infancy stage. Over time the new technologies bloomed and the barcode made strides. Lasers became scanners, scanners became fingers, and the barcode became a language, a language spoken throughout the world, by computers throughout the world.
Look around today, barcodes, barcodes, barcodes, on milk, DVD’s, FedEx packages, assembly lines, luggage, bottled water, drugs, cars, furniture, greeting cards, t-shirts, passports, lumber, and even people. A series of lines in the sand becomes the universal methodology of tracking—tracking what we buy, where we are going, when we do so, how we do it, and who we are. Each time a barcode is read it becomes raw data—raw data analyzed in the most mundane way, raw data analyzed in the most personal way.
Think about it. At the grocery store, the shopping cart becomes a basket of information. Scan a bag of potato chips and the price automatically appears on the screen with the total. Scan a bag of potato chips and the computer will delete an entry out of its inventory. If the shelf inventory is low a message automatically appears on the LCD window display of the stock boy’s PDA. If store inventory falls below a predetermined amount it will automatically file an order for more potato chips with the purveyor. Information provided to the purveyor will be used in ranking the store’s profit margin based on its product of potato chips. This in turn will be used to determine delivery priorities. The more the store sells, the faster they receive their product. Scanning saves the store money, less time for error, no need to call in orders, no need to do inventory. Scan the bag of chips and handed with the receipt are coupons for future purchases. These coupons could be for the same product or its competitors, buy Coke and get a Pepsi coupon, buy Lays get a Herr’s coupon. Competitors use this information to gain insights to their market campaigns and insights into their rivalries.
Harmless information, 1’s and 0’s, are just sitting on a database in the middle of Kansas somewhere. Harmless information used by the store to help run its store. At any exact moment in time, inventory can be taken and supply statistical information on product sales and profit margins. Decisions can be made whether to increase shelf space for a particular product or remove a product altogether, find out if the in store bakery is churning out dough or stuck in dough, if fish sales actually increase on Fridays, whether or not to put those 40 cases of almost flat soda on sale based on its limitation of shelf life, or how many dozen eggs were sent back based on expiration date. They can predict the future, project inventory for next week, next month, even next year, all by analyzing their raw data.
Now scan a supersaver card, punch in a phone number, pay by debit/credit card, swipe a finger, have a retinal scan (not rectal mind you, though that would make checkout lines a little more interesting) and this bag of potato chips becomes linked to an individual along with the date and time. Raw data becomes information; information transformed into who bought what, where, and when.
Along with the Herr’s bag of potato chips, Marcy Peterson purchases Little Debbie Snack Cakes, 3 Lean Cuisine’s frozen dinners (meatloaf, lasagna, smoked turkey), two 2 liter diet Cokes, loaf of Wonder bread, 24oz jar of Skippy peanut butter, Dial soap, Kotex, bottle of Advil, mint flavored Scope, two pints of Ben & Jerry’s (Cherry Garcia, Fudge Brownie), 40 watt GE light bulbs 2-pack, string cheese, a Starr magazine, and a pack of Tic-Tacs. Marcy’s total $129.86 deducted from her Fidelity checking account on July 18, 2005, at 5:45p.m. in the Winn Dixie, Jacksonville, Florida, store #23345. Marcy uses one $1.00 coupon.
With this data, a store can pinpoint its customers’ demographics. What’s the gender majority? What’s the average age of the customers? What’s the average income? Who’s the best customer? What’s their average spending per visit? How much did they spend last year? Do they use coupons? Food stamps? Are they married? Have children? With this data, they can ask themselves—Should a pharmacy be installed, buy more carts with child seats? Should the fresh vegetables and fruits be upgraded? Sell a cheaper grade of hamburger? Add more family only parking? Charge a fee for bank withdrawals when they use a debit card? Add more types of shampoo? Change their magazine types at the checkout counters? Add a self-checkout?
Now grant access to the database with outside vendors, let them query to their hearts content—for a price of course, and here is where the scary part starts.
This same information, the same 1’s and 0’s sitting in the middle of Kansas can become personal without knowing it. That same Marcy applies for some health insurance. She calls on the phone and is connected to a salesperson. After taking some general information, the computer whirls, spins, chugs, and spits back a monthly dollar amount all within less than a minute. The processing power behind this monthly dollar amount is staggering. It used to be just a quick look-up in the actuary tables based on age and race, not anymore. It’s a very complex process, basically a computer program, broken into many parts. The first part of this massive thinking machine deals with the entered data—name, telephone number, social security number, date of birth. This data is used for search parameters, search parameters for databases; many databases, one of them being the Winn Dixie database in the middle of Kansas somewhere. The information stored there is harmless—who bought what, where, and when, but in this specific case the search parameters are: Marcy and all items bought within the last year. The raw data is returned—it’s just mindless information to the naked eye. Most people wouldn’t give it a second glance if they saw the information scattered on a piece of paper—just a bunch of numbers and names, no big deal. The second part of this complex process is the data scrubbing; turning this mundane information into something of use. From analyzing the data (another part of this computer program and the most complicated of them all) it can be determined that Marcy Peterson lives alone and buys food for roughly 3 days during each trip to the grocery store. Her average cost per shopping trip: $65.12. She rarely buys fresh fruits, vegetables, and never buys fish. She mainly purchases prepackaged meals and snacks with an average daily consumption of 3589 calories per day. Checking other databases and a search for any type of health club membership turns up empty. Last time she bought a pair of sneaks was 2 ½ years ago. Clearly Marcy is not health conscious and there is an 82.34% chance that she is overweight. Cha-ching—increase base insurance amount by 33%. Combined this information with her yearly driving distance and any traffic violations reported at the local DMV, and it is assumed she is a safe driver—deduct 1.5% off the base amount. Next, incorporate the type of work she does via her social security number, her Friday night bar bills or liquor bills taken from her Master Card, any doctor visits taken from her PPO card, or find out if she’s a non-smoker and the base amount fluctuates up and down depending on the outcome of each search.
Raw data can become extremely personal as well. A vendor such as Johnson & Johnson can query a store’s database and access one or all customers. They can find the average sales per customer, per age group, per visit, per year, per product, or products. They can determine the results of their most recent ad campaign per anyone of the demographic statistics thus helping them to analyze their marketing scope and in turn formulate new strategies to increase sales. Anyone can see the benefits to this information—these bits of 1’s and 0’s sitting in the middle of Kansas somewhere. Information is the key to success and marketing gurus will take advantage of this fact. Say J & J might want to convert a user of a competing product to their brand under a new marketing campaign, how about Kotex to their Stayfree.
Querying that same Winn Dixie database we find Marcy Peterson once again. She last bought Kotex on July 18
th
. She uses the competition but does she always? A simple query would suffice. Turns out, the answer is yes and with the enclosed dollar coupon. In the database is Marcy’s demographics file containing address, age, phone number, etc… Presto—send a coupon; send all users of Kotex a coupon in the mail, maybe even a free sample. Better yet, a timely coupon so it is fresh in the memory and not lost or thrown away. Querying the database again yields that Marcy Peterson buys Kotex on average once every 4 to 5 weeks and using a simple calculation it can be surmised with a 96% accuracy that her next period will fall somewhere on the week of August 15
th
, therefore to assure the maximum benefits of a marketing campaign, the coupon should be sent no earlier than a week before her period. She gets her coupon on the 12
th
and uses it on the 16
th
, all without ever knowing that the big conglomerates are watching and learning from her every move, they even know when she’s menstruating. The timely campaign is a success with a 62.76% switch rate just by sending a coupon. Follow it up with one or two more and hopefully buying Stayfree becomes a habit and gains a customer for life or at least until menopause.
. . .