Jun

20

“Change is the process by which the future invades our lives.”
It is 5:45 p.m. on a Thursday and Todd Martin heads out the door after a long day at work. His wife is recovering from a cold and he is eager to get home to care for her. As he enters his car, his mobile phone beeps. It is a message from BigMart, a national chain of stores that sells consumer products—from produce to plasma TVs. The BigMart email informs Todd that he likely needs to purchase milk and cereal and offers a 10 percent discount on these products, if purchased before 9:30 p.m. Todd calls his wife who confirms that, in fact, they ran out of milk that morning, the cereal box is almost empty and, by the way, she needs cold medicine. Todd usually shops at a BigMart in the suburbs, but tonight he decides to look for a store location closer to his office. He uses a voice command to request the address of the nearest BigMart from his car’s navigation computer. Within seconds, a map is displayed with the fastest route to the nearest store. At the store—like at all BigMarts—getting a parking space is easy. Digital displays at the end of each parking row indicate which spaces are open.
Todd is certain that there are other items he needs to purchase besides milk and cereal. However, this is not a planned grocery trip and he does not have a shopping list. So, as usual, Todd’s first stop at BigMart is the Digital Customer Kiosk by the entrance. He swipes his mobile phone over the scanning receiver and the kiosk prints out a shopping list with items he is likely to need—in addition to special offers. As Todd reaches the end of the first aisle, a digital touch screen mounted on the shopping cart displays a list of items that might complement the products he just placed in the cart. For example, it suggests three different kinds of cheeses that would go well with the bottle of Pinot Noir he selected. On the next aisle, the screen proposes chicken soup and tea to go with the cold medicine he selected. Todd moves along at a brisk pace, but in the last aisle he cannot decide which brand of spaghetti sauce to purchase. He engages the touch screen to retrieve both product information and recommendations from other BigMart customers. From the recommended items, he chooses a highly rated sauce.
At the checkout counter, yet another screen automatically lists the items being purchased—without the need to scan the items one by one. All Todd needs to do is press the accept key and swipe his mobile phone. His bank account is automatically debited. Todd always shops at BigMart. The shopping experience is effortless and, yes, fun. They always seem to know what he needs and when he needs it. Why would he shop anywhere else?
The day before Todd’s present shopping excursion, BigMart’s powerful computer network—encoded with statistical formulas and predictive mathematical models—sorted through 1,000 terabytes of data. The database has been compiled—and is continuously updated—from a variety of sources including store and warehouse inventories, customer information, loyalty programs, purchase transactions, public records, credit organizations, weather forecasters, business and social networking websites, research companies and consumer data brokers. The data is used to develop multidimensional, predictive behavioral models for existing and prospective customers. Each day BigMart’s computers draft a list of likely buyers to target with offers. A score—based on monetary value and likely offer-response rates—is assigned to those on the list. The offer content and timing are then customized for each individual prospect.
One of the prospects with the highest score is Todd Martin. He has been a customer of BigMart for approximately three years and is a member of its loyalty program. Thanks to its robust database, BigMart has been able to accurately estimate Todd’s after-tax income, home size and value, place of employment, hobbies, travel preferences, automobile type and many other characteristics and behaviors that define him as a unique consumer. In addition, BigMart knows every item that he has ever purchased at its stores. It knows that on average, Todd makes a major purchase of groceries every four weeks and stops for perishable items—such as milk and bread —once a week. According to his record, it has been four and one-half weeks since Todd did a major purchase of groceries and precisely eight days since he last bought milk. Based on his behavioral profile and purchase patterns, BigMart mathematical models estimate that there is a 45% probability that Todd will respond to a discount offer on milk the day it is received. Furthermore, the models calculate that if a 10% discount on cereals is also offered, there is a 65% likelihood that he will turn the visit into a $217 shopping trip. An electronic offer for milk and cereal is drafted and scheduled to be sent at 5:45 p.m. on Thursday—the time and day he is most likely to respond.
At 6:03 p.m. on Thursday, BigMart computers receive a customer login request from one of its stores downtown. The login identification number matches Todd Martin’s record. Within seconds a suggested shopping list based on his profile is sent to the Digital Customer Kiosk. The shopping cart is fitted with digital touch screen and a Radio Frequency Identification (RFID) receiver that has locked to Todd’s RFID-enabled mobile phone. BigMart is able to track the cart’s location at any given time. If too much time is spent at any given location, a customer service associate is dispatched to offer assistance. As each item is placed in the cart, the computer sends product suggestions to the cart-mounted digital display. The suggestions are neither random nor generic. Only items that match Todd’s behavioral profile are displayed. Furthermore, discount offers are made only if they increase the likelihood of purchase and meet profitability criteria. At the cash register, the total purchase comes to $223.
Over the next few days BigMart will analyze the data collected during Todd’s visit to its downtown store. His predictive behavioral model will be refined. New offers that will increase Todd Martin’s response rate and monetary value to BigMart will be customized and scheduled.
This story is fictional, but the technology and know-how necessary to make it real exist today. In the twenty-first century, emerging technologies, media, mathematics and an unconventional generation of consumers have rendered the 30-second commercial obsolete. Successful companies must blend digital technologies, data mining, and a fragmented media landscape to reach the new millennial consumer: a consumer who is always on the move, multitasks, has little brand loyalty and is mistrustful of traditional advertising. Companies must shift from business models that center on launching a never-ending number of products and services to customer-centric micromarketing that focuses on meeting the specific needs and problems of select customers. Most companies; however, are failing to understand—much less adapt to—this new reality.
A new kind of marketer will be at the forefront of tomorrow’s leading companies. From consumer electronics to business information systems to digital media, she will be well versed in technology. She will be part researcher and part social scientist. She will have strong analytical and mathematical skills and focus on making marketing more science and less art. More importantly, she will be accountable for delivering results—financial results.
Nevertheless, these new, essential marketing skill sets will not entirely replace old ones. Rather, they will supplement a century of marketing thought and practice. Like a living organism, marketing will continue to evolve – sometimes by slow steps, sometimes at light speed.