The Death of Mass Marketing: Big Data and Sensors bring ‘Minority Report’ to Life
For the last half of the 20th century, individuals mainly consumed media through television, print, and radio. In a world dominated by broadcast television and newspapers like The New York Times, millions of people would tune in every night to airings of 60 minutes and Seinfeld. During important national events like Watergate, every channel broadcasted the coverage. To reach consumers, advertisers bought shows for an entire year and would develop content to run during that entire period. According to Duke University professor, John M. McCann, “These annual plans were difficult to develop because of the need to get approval from all levels of the product hierarchies…Once they were approved, it was difficult to make changes.” Lacking much of the market feedback that exists today, advertisers were unable fine-tune their advertisements despite culture shifts and world events. In many cases, the data they received about viewers was confined to location, age, race, and gender; and “these reports would not arrive on their desks until at least a month after the period was over” (McCann). Pairing this lack of feedback and its timeliness with the fact that media consumption was exclusive to a handful of markets (television channels, radio stations, and publications), advertisers had to create homogeneous content that would run for an entire year and resonate with millions of people at a time. To hit an older demographic, for example, they’d run a spot during 60 Minutes. To reach younger people, they’d publish an ad in People magazine. These advertisements were cost effective since only 2–3 spots ran in a given cycle and provided a huge return on investment due to the sheer magnitude of viewers at any given time. As McCann states, the combination of a homogeneous market, broadcast technology, central hierarchy, and a mass production economy during this time created the world of mass marketing we know today.
According to Soviet economist Nikolai Kondratiev and his K-wave theory, technological advancements lead to the creation of completely new markets every 45–60 years. With the mass marketing era beginning shortly after WWII, the industry was primed for disruption towards the end of the century. Right on cue, the advent of the internet singled in a new era in marketing and forever changed the way advertisers interact with consumers and understand their habits.
With the arrival of the digital age and the internet, people now had the opportunity to consume media in more ways than ever before. Rather than three broadcast channels, a handful of terrestrial radio stations, and various magazines/newspapers, people now had access to hundreds of television channels, internet radio, satellite radio, terrestrial radio, online publications, print publications, and all that exists online. With the introduction of all of these outlets; however, many problems arose for advertisers. Rather than being able to count on millions of viewers to tune into their TV sets every night, audiences became fragmented across dozens of platforms and devices. In order to adapt, firms were forced to streamline their processes and target consumers more directly. To do so, they had to learn about the habits of their audiences and change the way the operated, using Larry Kramer’s four C’s as a basis.
According to Larry Kramer, the author of “C-Scape: Conquer the Forces Changing Business Today,” there are four C’s changing the way we do business: consumers, content, curation, and convergence. Because today’s media landscape is so fragmented, consumers in particular have become the driving force in advertising. Advertisers now search for where certain demographics are most tuned-in and then try to create meaningful content. This content is fine-tuned based on data gained from observing a given demographic. For example, Apple Music keeps track of all of their users listening habits and monitors them closely. When creating content, they first pinpoint a target audience and then use their tastes and preferences to create campaigns that have a higher chance of being successful. By using Taylor Swift in a commercial, for example, they’ll be able to send a message that they know will resonate with millennials. This form of curated advertising has recently become a way for advertisers to be more careful with their money and to assure the impact of their content. However, learning the habits of consumers takes time and careful filtering through swarms of data both online and off. In particular, meta data has become a huge tool for advertisers.
As defined by the National Information Standards Organization, “Metadata is structured information that describes, explains locates, or otherwise makes it easier to retrieve, use, or manage an information resource.” In layman’s terms, “metadata is data that describes other data” (Allen). In business, metadata is used by marketers to pinpoint their target audience. Since everything online is tracked and categorized, an advertiser simply has to search for identifiers associated with their product and target market to then find consumers who align. Already, companies like Google, Facebook, and Twitter use metadata to fine-tune advertising as you use their platform. You might have seen that after you search for new shoes on amazon and return to Facebook, advertisements for deals on those exact shoes and ones that you may like associated with those exact shoes pop up all over your news feed. As you surf the web, data accumulates as to your search habits, time on webpages, and website activity. This data, when piled into a profile of a single user, paints a pretty accurate picture of the type of consumer he/she is and then advertisers are able to send content appropriately. One glaring problem with metadata; however, is that this kind of information only exists in cyberspace. When a consumer goes to the mall and shops for clothing, none of their habits are tracked and no deals/coupons are sent their way (rewards programs don’t count). Imagine a world where you walk into your favorite store and immediately, sensors recognize you’re there via facial recognition and a personalized message pops up above the rack of new additions, pinpointing an outfit that’s catered to your preferences and past purchasing habits.
Surely, this scenario seems like the stuff of a Hollywood sci-fi movie where you’re played by a leather-clad Tom Cruise, but in reality this curated consumer experience is very likely in our not-too-distant future. With advancements in facial recognition, data, and sensors, the consumerist world of Tom Cruise’s ‘Minority Report’ will simply become our new reality. To realized just how possible this world is, it’s important to first take a step back and examine this new world in alignment with DEGEST and Edward Cornish’s Six SuperTrends.
Demography, or demographics as most people are familiar with, is defined as the study of populations over time and has been widely used in the advertising world as a driving force of targeting and curation for decades (Avinash). As we transition into the immersive, consumerist world of the future, demographics will be at the heart of ad targeting. Using data accumulated through internet usage and in-person consumer habits like dining out and shopping, firms will be able to seek out their target demographics like never before. Instead of rough groupings like: Male 18–24, data tracking will advance to the point where advertisers are able to, for example, pinpoint: high-school juniors who have recently applied to college in the northeast and like to shop at boutiques.
Some of the biggest barriers in today’s world are privacy laws. While people are generally accepting of new advancements in technology, they’re increasing weary of how their privacy is being compromised. According to a 2016 Pew Research Center study, “Americans express a consistent lack of confidence about the security of everyday communication channels and the organizations that control them” (Rainie). As a sign of this increased apprehension, “86% of internet users have taken steps online to remove or mask their digital footprints” (Rainie). In order for curation to hit as personal a level as we see in ‘Minority Report,’ people are going to have to feel confident that their data is secure. In order for this to happen, the legal infrastructure must be in place for the protection of consumer data while allowing for that data to be facilitated through explicit channels. One solution to this problem could see retailers asking for your permission to white-list the use of your personal data in a similar way to how Facebook asks for your permission from 3rd party sites.
As society transitions from today into the future, it’s important to look at culture from the eyes of those driving change. While Millennials are currently at the forefront, a new generation, described by PWC as “Generation C” will begin taking the reins. In their 2010 journal article, the firm provides a snapshot of an average “Generation C” adolescent:
Consider the typical Gen C ‘digital native’ in 2020… He has a primary digital device (PDD) that keeps him connected 24 hours a day…his PDD automatically downloads relevant content, notices, even bills for fees, and he can authorize their payment later… His PDD does most of the work for him when he’s shopping too. Though he prefers to shop online, when he does visit a store, the PDD automatically connects to the store’s network, guiding him through product choices, offering peer reviews, and automatically checking out and paying for items he purchases.
The biggest takeaway from this snapshot is the lack of apprehension towards the sharing of data. To consumers of the future, ease of access and connectivity will far outweigh previous generational gripes about security and privacy. As PWC further elaborates, “Ubiquitous connectivity will continue to transform the retail industry, seamlessly integrating the online and offline worlds, and ultimately leading to a form of augmented reality that allows a more elaborate presentation of retail goods. Peer reviews will become a real-time decision-making tool in physical stores as well as online, and social networks will become critical for brand awareness and customer preference” (Freidrich).
Just announced this past week, Amazon Go, a “just walk out” driven shopping experience, is slated to open in Seattle, WA. Using “computer vision, sensor fusion, and deep learning…Just Walk Out technology automatically detects when products are taken from or returned to the shelves and keeps track of them in a virtual cart” (Amazon). The shop will be the first of its kind and is hopefully a sign of what’s to come in the retail shopping experience. While the current state of the store will simply facilitate a “no lines, no checkout” shopping experience, one can only assume how Amazon plans to use the data acquired through this experience. Rather than act as a passive facilitator of shopping, it’s easy to assume Amazon will become an active curator of the shopping experience. From suggesting products to buy on later trips to recognizing who you are when you enter the store through facial recognition and connecting you to your Amazon account, the possibilities are endless. Now, let’s imagine this hands-free world.
It’s 8am on a December morning near Christmas time and you wake up from the vibrations of your Tempurpedic smart-bed. You’re perfectly rested, as your bed learn your sleep habits and adjusts temperatures while you rest to ensure a perfect night’s sleep. Once awake, you reach over to your phone and open the Samsung Home app. After a brief glance, the app, which is connected to your Samsung fridge, tells you that the milk in your refrigerator is growing stale and the bread you bought last week in beginning to grow mold.
Realizing that your daughter, Becca’s, soccer game is in two hours, you call out to your Amazon Alexa asking if you have enough time to go grocery shopping. She charts the route via google maps, automatically checking for any delays via traffic or road work and calculating that to the route time. Once responding that the route prepared, she asks whether or not you’re ready to go. You respond that you’re going to shower and then change and get on the road. Immediately, the shower turns on, warming to the perfect temperature by the time you pull off the covers, walk over and get inside. After your shower, Alexa hears the water shut off and then sends signal for your Tesla in the driveway to start so it’s warm by the time you get in.
You shower and get ready then hop in the car and it automatically takes you to the nearest grocery store. Upon entering, scanners recognize you via facial recognition and greet you. Alexa has already alerted the store that you’re on the way and in need of milk and bread. Immediately, a path lights up beneath you directing you to your favorite brands and routes you through the store to ensure efficient shopping. On the way out, scanners recognize that you’re leaving and a charge is sent to your bank account; no store clerks, no lines, no time wasted. This is efficiency to a T.
You’ve left the store 20 minutes before your daughter’s game and you hop in the car and head on over. The game is sensational Becca scores the winning goal. In celebration, you head to the mall to do some shopping. Once arriving to the mall, holograms greet you at the entrance and, realizing who you are with facial recognition. The night before, you had been online checking out prices on the dress that Becca’s been talking about for months. You were going to get it for her for Christmas but why not celebrate the game with a bit of an early present. You had favorited the dress on Macy’s.com and using that data, the holograms direct you to Macy’s to pickup the dress in person. However, once you start walking, the illuminated path changes a new direction towards the Betsy Johnson store a little ways off. The dress is in-stock there and even on sale for a lover price than that at Macy’s.
You arrive at the store and the path directs you to the rack holding the dress. Becca is thrilled and you congratulate her on a great game before grabbing the dress, in her perfect size, and heading out of the store. Your bank account is automatically charged as you walk out and the two of you head back to the car. The car picks you up in the roundabout and you tell it to head home. It’s only 1:30, but Today’s already been a great, stress free day. Time for lunch!
Allen, Robert. “What Is Metadata and How Is It Used?” Smart Insights. N.p., 07 Dec. 2015. Web. 05 Dec. 2016.
Amazon. “Amazon.com: Amazon Go.” Robot Check. Amazon Inc., n.d. Web. 07 Dec. 2016.
Avinash. “Demographic Targeting.” Know Online Advertising. N.p., n.d. Web. 05 Dec. 2016.
Freidrich, Roman, Michael Paterson, Alex Koster, and Sebastian Blum. “The Rise of Generation
C: Implications for the World of 2020.” Strategy & (2010): n. pag. Strategyand.pwc.com. PWC, 2010. Web. 5 Dec. 2016.
Frederick, Donna E. “Understanding EBooks, Metadata, and Managing Metadata.” Managing
Ebook Metadata in Academic Libraries (2016): 1–10. Niso.org. National Information Standards Organization, 2004. Web. 5 Dec. 2016.
“The Kondratieff Theory.” Kondratieff Long Wave Cycles Past and Present. N.p., n.d. Web. 05 Dec. 2016.
Kramer, Larry. C-scape: Conquer the Forces Changing Business Today. New York: Harper Business, 2010. Print.
McCann, John M. “Changing Marketing.” Fuqua School of Business. Duke University, 10 Mar. 1995. Web. 04 Dec. 2016.
Rainie, Lee. “The State of Privacy in Post-Snowden America.” Pew Research Center. Pew, 21 Sept. 2016. Web. 05 Dec. 2016.
States, Congress of The United, and Congressional Budget Office. “The Budget and Economic
Outlook: 2016 to 2026.” (n.d.): n. pag. CBO.gov. CONGRESS OF THE UNITED STATES CONGRESSIONAL BUDGET OFFICE. Web. 5 Dec. 2016.