Retailers Miss Mark With ‘Targeted’ Emails

The Wall Street Journal: “Traditional retailers were once pioneers of using data to zero in on what customers want. But as the importance of their catalogs and mailings have been overtaken by email and other online media, they have struggled—sometimes to the frustration of their customers.”

Brendan Witcher of Forrester comments: “Nearly 90% of organizations say they are focused on personalizing customer experiences, yet only 40% of shoppers say that information they get from retailers is relevant to their tastes and interests. The ugly truth is that most retailers haven’t done the (hard) work of understanding how to use the data.”

“At no time is that more evident than during the year-end shopping bonanza, when retailers deluge customers with messages. During last year’s holiday season, retail emails increased 15% compared with the rest of the year, but shoppers opened 15% fewer of them, according to a study of eight billion messages by marketing-services firm Yes Lifecycle Marketing.”

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Google Gaggle: Predicting Restaurant Wait Times

USA Today: “Google Search (and eventually Google Maps) will show diners the estimated wait times for local restaurants to help them skip the crowds and jump the lines. The new feature expands on Google showing consumers looking to change their oil and get their hair cut how busy local businesses typically are. Google gathers this information from aggregated and anonymized data from users who allow Google to track their location using Google apps on their phones or other devices.”

“Now, says Google, diners can click on a time frame and see live or historical data on how busy a restaurant is expected to be and the estimated wait time. The information will be available for nearly 1 million sit-down restaurants around the globe. And up next, says Google: Grocery stores.”

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Clubhouse Culture: How Houston Solved Its Problem

The Wall Street Journal: “Under Jeff Luhnow’s stewardship, the Houston Astros developed into the industry’s most analytically driven organization, relying almost entirely on data to navigate through a full-blown rebuild … But for all of their bold ideas, the Astros too often forgot about one important aspect: their players.” However a willingness to embrace “the value of chemistry and culture paid enormous dividends in 2017: The Astros won the World Series, their first since the franchise’s creation in 196.”2

“It represented a subtle, but crucial shift in the Astros’ thinking. Though numbers remain their focus, the driving force that propelled them from the bottom of the standings to the pinnacle of the baseball universe, Luhnow learned a lesson along the way: To deny the significance of chemistry ignores a critical component of the equation that equals a championship roster.”

“For sure, the Astros didn’t revolutionize the concept of caring about chemistry. The Chicago Cubs, the World Series champions in 2016, also prioritized traits they could not measure in players. Few expected the Astros to do the same. Now, however, they will parade down the streets of downtown Houston as champions.” Luhnow comments: “Culture is a hard thing to really quantify. But when you see it you know it’s there.”

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Slowball: Is ‘Big Data’ Wrecking Baseball?

The Wall Street Journal: “Baseball has never been more beset by inaction. Games this season saw an average gap of 3 minutes, 48 seconds between balls in play, an all-time high … A confluence of hitting, pitching and defensive strategies spawned by the league’s ‘Moneyball’ revolution have all played a role. That makes baseball, whose early use of big-data strategies was embraced by the business world in general, a case study in its unintended consequences.”

For example: “Statistics showing precisely when starting pitchers become less effective have prompted teams to remove them from games earlier than before. That has increased one of the biggest drags on pace of play: pitching changes. Regular-season games this year saw an average of 8.4 pitchers used between both teams, an all-time high. That’s up from 5.8 pitchers a game 30 years ago.”

“Radar and camera measurements of the angle at which balls leave the bat have shown that the optimal swing angle looks more like an uppercut than many hitters preferred. Hitters, in turn, have started swinging for the fences in droves. Home runs this season reached a record level. That all-or-nothing approach means that between each home run there is a lot of standing around and waiting. Some classic displays of athleticism—a daring attempt by a runner to advance more than one base on a teammate’s hit, for instance—have become rarer.”

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Blockchain Grocery: How Walmart Delivers Food Safety

Quartz: “Thanks to technology originally designed to monitor cryptocurrency … something that could put a significant dent in the number of foodborne illnesses that occur every year. It’s part of a new program in which IBM is partnering with Walmart, Nestlé, Dole, Tyson Foods, Kroger, and others, to use blockchain technology to track food throughout the complex global supply chain.”

“Under the new system, if a consumer falls ill from E. coli traced to a batch of lettuce, a food-safety investigator could conceivably scan a barcode on the packaging to quickly learn where it came from and where other lettuce from the same batch went. Retailers will be able to quickly remove contaminated products from shelves, thus stopping the spread of illnesses.”

“Walmart has been using a pilot version of the technology, showing how blockchain can be expanded beyond the financial, health care, and natural resources sectors to be applied to the foods that consumers interact with every single day. Coupled with companies’ efforts to stop food-borne illnesses early on, this could signal a major moment in how humans keep the food system in check.”

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How Netflix Creates ‘Taste Communities’

Wired: “The Defenders provides Netflix with a unique case study. Instead of merely allowing it to find out if someone who likes, say, House of Cards also will like Daredevil (yes, BTW), it tells them which of the people who landed on Daredevil because of House of Cards will make the jump to The Defenders.”

“Wildly different programs lead people to The Defenders’ standalone shows. The top lead-in show for Luke Cage? Narcos. But for Iron Fist, it’s a Dave Chappelle special. Someone who watches Jones probably will watch Cage, but beyond that the groups of people—Netflix calls them ‘taste communities’—gravitating toward those shows enjoy very different programming.”

“Every Netflix user belongs to three or four taste communities. It’s easy to say that this influences what appears in your recommendations, but it’s not quite that simple. Membership in those communities does more than dictate the top 10 comedies appearing in a row of your queue, it determines whether comedies appear there at all … Each time you open Netflix it exposes you to 40 or 50 titles. Netflix considers it a win if you choose one of them.”

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Disney Machine-Learns for Laughs

Quartz: Disney “is using machine learning to assess the audience’s reactions to films based on their facial expressions, it wrote in a new research paper. It uses something called factorized variational auto-encoders, or FVAEs, to predict how a viewer will react to the rest of a film after tracking their facial expressions for a few minutes.”

“The FVAEs learn a set of facial expressions, such as smiles and laughter, from the audience, and then make correlations between audience members to see if a movie is getting laughs or other reactions when it should be—a much more sophisticated version of how Amazon and Netflix make suggestions for new things to buy or watch based on your shopping or viewing history.”

“By placing four infrared cameras and infrared illuminators above a theater screen, the researchers were able to identify 16 million facial landmarks, or expressions, from more than 3,100 theatergoers during 150 screenings of nine Disney movies … the data was then analyzed with a computer. (Before this gets too creepy, Disney isn’t tracking your every move at your local theater. The experiment took place during screenings at one particular 400-seat theater. And audiences likely had to choose to participate.)”

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Technology Cannot Hug a Customer

The New York Times: “Technology, some hotels are finding, has its limits. ‘Technology cannot hug a repeat guest,’ said George Aquino, the vice president and managing director of AHC+Hospitality … That is the reason his company, which manages several hotels, has been running a training program for some of its managers and other staff members to improve their hospitality skills, connect with local business leaders and learn more about local tourist offerings.”

“Similar programs are sprouting in other cities, involving not just hotels but also restaurants and even cities themselves, which see the personal touch as giving them a competitive edge. For business travelers, in particular, talking to someone knowledgeable about a city can lead to a good restaurant. And it can also help expand business leads.”

“A consulting program based in Tucson, Certified Tourism Ambassadors, trains hospitality workers. Mickey Schaefer, the chief executive and founder, said she had developed the idea in 2006 while working for the American Academy of Family Physicians to plan its conventions. Hospitality workers sometimes did not know their own cities, leading to bad experiences, she said … The program, she said, ‘is more than just helping the customer. It is helping them find the richness of whatever they are interested in.’ She added that the program also instills civic pride.”

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Deep Thinking: ‘Artificial’ Trumps ‘Intelligence’

LARB: From a review of Deep Thinking, by Garry Kasparov, the chess champion defeated by Deep Blue, a machine, in 1997: “The history of computer chess is the history of artificial intelligence. After their disappointments in trying to reverse-engineer the brain, computer scientists narrowed their sights. Abandoning their pursuit of human-like intelligence, they began to concentrate on accomplishing sophisticated, but limited, analytical tasks by capitalizing on the inhuman speed of the modern computer’s calculations.”

“This less ambitious but more pragmatic approach has paid off in areas ranging from medical diagnosis to self-driving cars. Computers are replicating the results of human thought without replicating thought itself. If in the 1950s and 1960s the emphasis in the phrase ‘artificial intelligence’ fell heavily on the word ‘intelligence,’ today it falls with even greater weight on the word ‘artificial’ … If a machine can search billions of options in a matter of milliseconds, ranking each according to how well it fulfills some specified goal, then it can outperform experts in a lot of problem-solving tasks without having to match their experience or insight.”

Also: “A bit of all-too-human deviousness was also involved in Deep Blue’s win. IBM’s coders, it was later revealed, programmed the computer to display erratic behavior — delaying certain moves, for instance, and rushing others — in an attempt to unsettle Kasparov. Computers may be innocents, but that doesn’t mean their programmers are.”

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