Several new advocacy groups have sprung up to push for better housing policies at the state and national level. Their first job: Communicating how significant the problem really is.
The advertising executive Michael Franzini, founder of the nonprofit ad agency Public Interest, has created campaigns to fight AIDS, spur Holocaust awareness, and advocate for STEM education. The cause driving his latest campaign is a tricky one: He wants to bring housing policy—a topic that is now largely the purview of wonks, developers, big city activists, and a select few politicians—into the forefront of our national discourse.
“In the same way that Al Gore put climate change on the map, that’s what we’re hoping to do,” Franzini said. “We want everyone to start demanding change.”
To pull that off, Franzini and Public Interest developed a campaign called Home1, which so far consists of a series of slick explainer-style public service announcements detailing the roots of the current affordable housing crisis. Soon to come: a feature-length documentary that he hopes could be affordable housing’s Inconvenient Truth.
“There is no greater crisis that, at least in my lifetime, has ever faced our country and not been talked about,” Franzini said. “And the reasons for that are all about how it is communicated… As soon as you start talking about the nuts and bolts of it, people glaze over.”
Every pizza display case tells a story. The strategist knew that very well. From the signage to the slicers to the arrangement of the Parmesan and red pepper flake shakers, no visual cue could be left to chance, especially for this client: a 20-unit New York style pizza chain headquartered in San Diego. The CEO was very proud of the organic nature of his restaurants’ interiors, and the lack of “chaininess” to them.
Six different pizzas now rested on burnished metal stands, intermittently punctuated with an assortment of calzones, stromboli, salads, and beverages. It had taken three weeks of recipe testing to bake pizzas this good. For every perfect, client-ready pizza, there were at least six that missed the mark—crusts that weren’t crispy, mozzarella that didn’t stretch, pepperoni that curled when cast in the oven, pockmarking the pie with tiny buckets of grease. (I was a beneficiary of the process. An arsenal of failed recipe prototypes was accumulating in my freezer.)
The strategist carefully removed a stack of miniature chalkboards from her desk. On each one, she inscribed the name of a different pie: The Triboro (meat lover’s). The Whitestone (white pie). The Bronx (everything but the kitchen sink). New York’s exalted status in the pizza universe was essential to this client’s identity, so much that the client had even implemented a reverse osmosis system in the dough-making process to replicate the pH balance of New York water.
When the set up was complete, the strategist called over the head of the agency to evaluate her work.
Rapid advances in technology have left news organizations scrambling to manage how news is created, consumed and delivered. People have shifted towards accessing news first via desktops and laptops, and now through the ubiquitous smartphone.
Since 2011, the rate of adult U.S. smartphone ownership has increased notably from 46 to 82 percent, and is nearing a saturation point among some age groups. In just the past two years, individual mobile news consumption has grown rapidly. In fact, 89 percent of the U.S. mobile population (144 million users) now access news and information via mobile devices. As news organizations seek to better manage this digital transformation across platforms, engage with their audience and stay competitive, what should they understand about their audience’s changing behavior on mobile news? And, how are diverse audiences approaching access to mobile news and information differently?
The findings show that:
There is a substantial audience for mobile news. Nearly the entire population of adult mobile users consume news on their devices, and more users are spending news time on social platforms.
While mobile users only spend 5 percent of mobile time on news, on average, the time they do spend includes “hard” news about current events and global news, as opposed to routine weather reports and other forms of “soft” news.
Mobile users who access news through apps spend more time reading the content, but the overall audience for apps is small, so it’s essential to know who those users are.
Social media sites and apps are important sources of news for social media users, although television remains their top source. However, social media users also depend on friends, contacts and individuals they follow as trusted news sources as much as or more than they depend on media outlets.
Mobile news users active on social networks do not just passively engage with news content but take offline action related to the content.
From skimming and scanning to (the ultimate) reading, a new paper by Nir Grinberg looks at the ways we read online and introduces a novel measure for predicting how long readers will stick with an article.
Grinberg, a research fellow at the Harvard Institute for Quantitative Social Science jointly with the Northeastern’s Lazer Lab, looked at Chartbeat data for seven different publishers’ sites — a dataset of more than 7.7 million pageviews, on both mobile and desktop, of 66,821 news articles from the sites. (To protect the publishers’ privacy, they aren’t named in the paper, but Grinberg looked at a financial news site, a how-to site, a tech news site, a science news site, a site aimed at women, a sports site, and a magazine site.)
Chartbeat, Grinberg said, already offers publishers pretty good tracking. “It’s one of the few companies that track what happens with a user after they click on a news article,” he told me. “Still, the actual measures it provides are kind of raw. It’ll tell you how much time a person has spent on a page, how far down the page they got, even something called ‘engaged time,’ which is the number of page interactions — mouse clicks, cursor movement, etc. But all of these are not particularly tailored to news; they could work on any web page.” Grinberg tailored these raw measures to create new metrics specifically for news articles.
The continuing collapse of public trust in Facebook is welcome news to those of us who have been warning about the perils of “data extractivism” for years.
It’s reassuring to have final, definitive proof that beneath Facebook’s highfalutin rhetoric of “building a global community that works for all of us” lies a cynical, aggressive project – of building a global data vacuum cleaner that sucks from all of us. Like others in this industry, Facebook makes money by drilling deep into our data selves – pokes and likes is simply how our data comes to the surface – much like energy firms drill deep into the oil wells: profits first, social and individual consequences later.
Furthermore, the rosy digital future – where cleverly customised ads subsidise the provision of what even Mark Zuckerberg calls “social infrastructure” – is no longer something that many of us will be taking for granted. While the monetary costs of building and operating this “social infrastructure” might be zero – for taxpayers anyway – its social and political costs are, perhaps, even harder to account for than the costs of cheap petroleum in the 1970s.
Organisations in thrall to metrics end up motivating those members of staff with greater initiative to move out of the mainstream, where the culture of accountable performance prevails. Teachers move out of public schools to private and charter schools. Engineers move out of large corporations to boutique firms. Enterprising government employees become consultants. There is a healthy element to this, of course. But surely the large-scale organisations of our society are the poorer for driving out staff most likely to innovate and initiate. The more that work becomes a matter of filling in the boxes by which performance is to be measured and rewarded, the more it will repel those who think outside the box.
Economists such as Dale Jorgenson of Harvard University, who specialise in measuring economic productivity, report that in recent years the only increase in total-factor productivity in the US economy has been in the information technology-producing industries. The question that ought to be asked next, then, is to what extent the culture of metrics – with its costs in employee time, morale and initiative, and its promotion of short-termism – has itself contributed to economic stagnation?
In late February, an Instagram account called Viral Hippo posted a photo of a black square. There was nothing special about the photo, or the square, and certainly not the account that posted it. And yet within 24 hours, it amassed over 1,500 likes from a group that included a verified model followed by 296,000 people, a verified influencer followed by 228,000, a bunch of fitness coaches, some travel accounts, and various small businesses. “I really love this photo,” one commented.
The commenter wasn’t a bot; nor were any of the accounts that liked the black square. But their interest in it wasn’t genuine. These were real people, but not real likes — none of them clicked on the like button themselves. Instead, they used a paid service that automatically likes and comments on other posts for them. Instagram says this is against its terms of service, but it continues to operate. It’s called Fuelgram and, for a few dollars a month and access to your Instagram log-in credentials, it will use the accounts of everyone who paid that sum to like and comment on your posts — and it will use yours to do the same to theirs.
In other words, Fuelgram creates fake engagement from real Instagram accounts. And it’s quite effective. Fuelgram makes posts appear more popular than they are, tricking Instagram’s algorithm into spreading them further, sometimes right into the service’s high-profile Explore tab. And there’s a reasonable chance there’s one in your feed right now, because Fuelgram is just one of a number of Instagram-juicing services available today, and the photo-sharing platform’s engagement-rewarding algorithm incentivizes people to use it.
First Experience, Zillow and Virtual Properties’:
Tap for a larger version.
Why should we care?
While of obvious appeal to certain urban elites, this picture turns out to be factually wrong. While New York is usually rated as one of the world’s top global cities, prior research using Sassen’s preferred measure indicates that New York’s greatest connectivity is with Washington, DC, ahead of Tokyo, and Chicago and Boston round out its top four con- nections. Other US cities are much less connected inter- nationally: thus, the Los Angeles metro area, the fourth largest in the world in GDP terms after New York, Tokyo, and London,12 counts only one foreign city (Tokyo, at #8) among its top dozen connections.13
Thus, even as long-term trends point to the rising impor- tance of global cities, there is evidence that cities—like countries—conform to the laws of globalization that were articulated in the conclusion of Chapter 1.14 Paralleling the law of semiglobalization, flows often take place more intensively within large cities than between them. For an example pertaining to trade, the value of shipments within a given zip (postal) code in the US (with a median radius of just four miles) is three times larger than the value of shipments across zip code boundaries.15 And in regard to capital flows, investment fund managers are more likely to buy or sell stocks when other managers in the same city are doing so.16
Both laws of globalization are also in evidence when one looks at patterns of who follows whom on Twitter. Over- all, 39% of all Twitter ties turn out to be local as in within the same (roughly metropolitan) regional cluster, 36% fall outside the regional cluster but within the same country, and 25% are international (as we noted in Chapter 1). Nor do these average tendencies necessarily weaken with city size. Thus, in Sao Paulo, one of the biggest hubs of Twitter activity in the world, more than 75% of the ties were local!17 And Figure 3.4 highlights how Twitter ties drop off with physical distance. This analysis of Twitter also backstops the earlier point that even supposedly global cities still tend to be more connected to their domestic hinterlands than to other cities abroad. Figure 3.4 indicates that the overall pattern of extreme distance-dependence is affected notice- ably only by a spike at the New York–Los Angeles distance (a domestic link); New York–London is just a blip, if that, in the overall pattern, and the other city pairs highlighted in the figure have no discernible effect at all.
“I had friends whose rents had doubled, and [I was watching them] scramble to figure out a solution,” Jose said. “People were renting out their basements, taking on roommates, things like that, just to get by.”
And there were another set of numbers on his mind, too: one girl in St. Louis, MO, who he just so happened to be dating long distance. $450 a month for his share of the rent on a huge, newly renovated one-bedroom apartment she wanted to share with him if he’d make the move to be with her. Hundreds of bonus square footage in the basement, plus a garage and a double lot where they could throw lawn parties every weekend. It all sounded pretty perfect.
But it wouldn’t be easy. Jose didn’t have a job lined up in Missouri. They certainly didn’t have the money for the moving costs; those would have to go on credit. Not to mention the fact that he’d also be leaving his home, his friends, and almost everything he knew behind in Colorado.
Like millions of rent burdened Americans, Jose was facing what might seem like a simple choice: to stay or to go. But embedded within that decision were a mountain factors, limitations and uncertainties — and once they’d all been put through their calculus, the choice might, effectively, be made for him.
Let’s compare the Agent App and Keller Williams’ Kelle:
|Fast property search||Yes|
|Share via text, email and Social||Yes|
|Integrated with public app and www||Yes|
|Cross MLS Search||Yes|
|Open House Management||Yes|
- Ease of use
- Lead Generation
- Listing and Buyer Presentations
- Your Brand.
When the European Union’s justice commissioner traveled to California to meet with Google and Facebook last fall, she was expecting to get an earful from executives worried about the Continent’s sweeping new privacy law.
Instead, she realized they already had the situation under control. “They were more relaxed, and I became more nervous,” said the EU official, Věra Jourová. “They have the money, an army of lawyers, an army of technicians and so on.”
Urbanists thought their moment had finally arrived. Those who favor increased urban density and transit options believed the housing bust and the great recession could end decades of development centered on automobiles and suburban sprawl, shifting planners’ focus more to cities, density and transit.
The advocates for this model point to California as the inevitable result of inaction. If you try to grow without increased density and transit you’ll end up with the traffic of Los Angeles and the home prices of San Francisco. Yet the negative effects of political inaction do not make political action inevitable. Another possibility is … Boise.
Even if politics were responsive, policy changes take years or decades to achieve results — and individuals and families planning their lives don’t have that kind of time. “California needs to change its housing policies” might well be true, but that won’t make a home in the Bay Area any cheaper tomorrow.
As justifiable as the focus on Facebook has been, though, it isn’t the full picture. If the concern is that companies may be collecting some personal data without our knowledge or explicit consent, Alphabet Inc.’s GOOGL -1.11% Google is a far bigger threat by many measures: the volume of information it gathers, the reach of its tracking and the time people spend on its sites and apps.
New regulations, particularly in Europe, are driving Google and others to disclose more and seek more permissions from users. And given the choice, many people might even be fine with the trade-off of personal data for services. Still, to date few of us realize the extent to which our data is being collected and used.
“There is a systemic problem and it’s not limited to Facebook,” says Arvind Narayanan, a computer scientist and assistant professor at Princeton University. The larger problem, he argues, is that the very business model of these companies is geared to privacy violation. We need to understand Google’s role in this.
Google also is the biggest enabler of data harvesting, through the world’s two billion active Android mobile devices.
Since Google’s Android OS helps companies gather data on us, then Google is also partly to blame when huge troves of that data are later used improperly, says Woodrow Hartzog, a professor of law and computer science at Northeastern University.
A good example of this is the way Facebook has continuously harvested Android users’ call and text history. Facebook never got this level of access from Apple ’s iPhone, whose operating system is designed to permit less under-the-hood data collection. Android OS often allows apps to request rich data from users without accompanying warnings about how the data might be used.
To be listed in Google’s Android app store, developers must agree to request only the information they need. But that doesn’t stop them from using “needed” data for additional purposes.
Designers call the ways marketers and developers cajole and mislead us into giving up our data “dark patterns,” tactics that exploit flaws and limits in our cognition.
Homeowners often keep track of nearby home value activity. Create useful connections and become their trusted advisor in a few steps.
1. Connect your contacts
2. Create searches that include market statistics
3. Share the searches with your contacts
4. Call them quarterly and discuss the market.
5. Annual listing presentation.
Your digital and personal connections are now top of mind.
Saved searches assist with their next residence.
From Zillow’s 2017 10-K:
Consumers are increasingly turning to mobile devices and the internet to access real estate information. With the widespread adoption of mobile and location-based technologies, consumers increasingly expect home-related information to be available on their mobile devices where, when and how they want it. According to comScore data published in December 2017, Zillow Group brands represent nearly three quarters of market share of all mobile exclusive visitors to the real estate category. More than two-thirds of our flagship brand Zillow’s usage occurs on a mobile device; on weekends it’s more than 75%. We believe that the technological platform shift from desktop computers to mobile devices benefits technology leaders like Zillow Group that are quick to innovate. In 2017, we unveiled a new, first-of-its-kind, mobile app that allows homeowners and real estate professionals to capture 3D tours of their homes from their iPhones ® and post on for-sale and for-rent listings.
We refer to the database as “living” because the information is continually updated by the combination of our proprietary algorithms, synthesis of third-party data from hundreds of sources, and through improvements by us and, importantly, by our community of users. User-generated content from owners, agents and others enriches our database with photos, videos, and additional property information. Individuals and businesses that use Zillow’s mobile applications and websites have updated information on more than 75 million homes in our database, creating exclusive home profiles not available anywhere else. Our inimitable database enables us to create content, products and services not available anywhere else, and attracts an active, vibrant community of users. As of December 31, 2017, we had published more than 3.5 million reviews, including more than 3.0 million reviews of local real estate agents and approximately 495,000 reviews of mortgage professionals submitted by our users on Zillow.
Mobile Leadership and Monetization. Shopping for a home is a far more meaningful consumer experience when it occurs curbside, untethered and on location, so we have developed and operate the most popular suite of mobile real estate applications across all major platforms. For example, on our flagship Zillow brand, during December 2017, nearly 630 million homes, or 234 homes per second, were viewed on a mobile device. More than two-thirds of our flagship brand Zillow’s usage occurs on a mobile device; on weekends it’s more than 75%. We operate one of the most popular suites of mobile real estate applications with more than fifty applications across all major mobile platforms. In 2017, we unveiled a new, first-of-its-kind, mobile app that allows homeowners and real estate professionals to capture 3D tours of their homes from their iPhones ® and post on for-sale and for-rent listings. We monetize our marketplace business on our mobile platform in the same way we do on our web platform.
Enhance Our Living Database of Homes. Enhance the information in our database of more than 110 million homes, and use it as the foundation for new analyses, insights and tools to inform consumers throughout the home ownership lifecycle. Our living database of homes provides a foundation on which we can build new consumer and professional marketplaces in other home-related categories.
We operate one of the most popular suites of mobile real estate applications with more than fifty applications across all major mobile platforms. Our mobile real estate applications provide consumers and real estate, rental and mortgage professionals with location-based access to many of our products and services, including Zestimates, Rent Zestimates, for sale and rental listings and extensive home-related data. Through our mobile applications, for example, a consumer can learn about the home’s for-sale price, Zestimate, number of bedrooms, square footage and past sales, as well as similar information about surrounding homes. The consumer can call a real estate professional through our mobile applications to get more information or schedule a showing. For example, on our flagship Zillow brand, during December 2017, nearly 630 million homes were viewed on a mobile device, which equates to 234 homes per second.
It all ended when the bank’s senior executives learned that they, too, were being watched, and what began as a promising marriage of masters of big data and global finance descended into a spying scandal. The misadventure, which has never been reported, also marked an ominous turn for Palantir, one of the most richly valued startups in Silicon Valley. An intelligence platform designed for the global War on Terror was weaponized against ordinary Americans at home.
Founded in 2004 by Peter Thiel and some fellow PayPal alumni, Palantir cut its teeth working for the Pentagon and the CIA in Afghanistan and Iraq. The company’s engineers and products don’t do any spying themselves; they’re more like a spy’s brain, collecting and analyzing information that’s fed in from the hands, eyes, nose, and ears. The software combs through disparate data sources—financial documents, airline reservations, cellphone records, social media postings—and searches for connections that human analysts might miss. It then presents the linkages in colorful, easy-to-interpret graphics that look like spider webs. U.S. spies and special forces loved it immediately; they deployed Palantir to synthesize and sort the blizzard of battlefield intelligence. It helped planners avoid roadside bombs, track insurgents for assassination, even hunt down Osama bin Laden. The military success led to federal contracts on the civilian side. The U.S. Department of Health and Human Services uses Palantir to detect Medicare fraud. The FBI uses it in criminal probes. The Department of Homeland Security deploys it to screen air travelers and keep tabs on immigrants.
Police and sheriff’s departments in New York, New Orleans, Chicago, and Los Angeles have also used it, frequently ensnaring in the digital dragnet people who aren’t suspected of committing any crime. People and objects pop up on the Palantir screen inside boxes connected to other boxes by radiating lines labeled with the relationship: “Colleague of,” “Lives with,” “Operator of [cell number],” “Owner of [vehicle],” “Sibling of,” even “Lover of.” If the authorities have a picture, the rest is easy. Tapping databases of driver’s license and ID photos, law enforcement agencies can now identify more than half the population of U.S. adults.
JPMorgan was effectively Palantir’s R&D lab and test bed for a foray into the financial sector, via a product called Metropolis. The two companies made an odd couple. Palantir’s software engineers showed up at the bank on skateboards. Neckties and haircuts were too much to ask, but JPMorgan drew the line at T-shirts. The programmers had to agree to wear shirts with collars, tucked in when possible.
The camera is a small, white, curvilinear monolith on a pedestal. Inside its smooth casing are a microphone, a speaker, and an eye-like lens. After I set it up on a shelf, it tells me to look straight at it and to be sure to smile! The light blinks and then the camera flashes. A head-to-toe picture appears on my phone of a view I’m only used to seeing in large mirrors: me, standing awkwardly in my apartment, wearing a very average weekday outfit. The background is blurred like evidence from a crime scene. It is not a flattering image.
Amazon’s Echo Look, currently available by invitation only but also on eBay, allows you to take hands-free selfies and evaluate your fashion choices. “Now Alexa helps you look your best,” the product description promises. Stand in front of the camera, take photos of two different outfits with the Echo Look, and then select the best ones on your phone’s Echo Look app. Within about a minute, Alexa will tell you which set of clothes looks better, processed by style-analyzing algorithms and some assistance from humans. So I try to find my most stylish outfit, swapping out shirts and pants and then posing stiffly for the camera. I shout, “Alexa, judge me!” but apparently that’s unnecessary.
What I discover from the Style Check™ function is as follows: All-black is better than all-gray. Rolled-up sleeves are better than buttoned at the wrist. Blue jeans are best. Popping your collar is actually good. Each outfit in the comparison receives a percentage out of 100: black clothes score 73 percent against gray clothes at 27 percent, for example. But the explanations given for the scores are indecipherable. “The way you styled those pieces looks better,” the app tells me. “Sizing is better.” How did I style them? Should they be bigger or smaller?
Antony Jackson posted a screen shot on Google+ of a new user interface where the search ads and organic search listings kind of blend together. There is this little “Ads X” at the top right of the search results, that looks faded. It is almost impossible to tell the difference between where the ads end and the first organic listing.
The only reason one can tell is that the ads look completely irrelevant. Which makes me wonder if the browser has some malware on it and that this is really not an official Google test?
However, I am told when you click on the “Ads X” at the top right, the ads collapse and go away. Which is an interesting thing to do.
On February 13, Google announced AMP for Email, an attempt to introduce some of the elements of its Accelerated Mobile Pages specification into email, putting the company’s high-performance web publishing system right inside the messages. Gmail will be the first email client to support these new features, which will give senders a way to deliver complex layouts and templates, interactive user actions, and dynamically updated content. That first implementation isn’t even ready yet, and yet already this is looking like a catastrophe. It should not be possible to design dramatic changes to our most widespread communication medium in secret and then deliver them in a surprise announcement! That completely misses the point of communicating.
Last week, Mark Zuckerberg testified in front of the US Congress. He answered more than 500 questions and promised that we would get back on the 40 or so questions he couldn’t answer at the time. We’re following up with Congress on these directly but we also wanted to take the opportunity to explain more about the information we get from other websites and apps, how we use the data they send to us, and the controls you have. I lead a team focused on privacy and data use, including GDPR compliance and the tools people can use to control and download their information.
When does Facebook get data about people from other websites and apps?
Many websites and apps use Facebook services to make their content and ads more engaging and relevant. These services include:
Social plugins, such as our Like and Share buttons, which make other sites more social and help you share content on Facebook;
Facebook Login, which lets you use your Facebook account to log into another website or app;
Facebook Analytics, which helps websites and apps better understand how people use their services; and
Facebook ads and measurement tools, which enable websites and apps to show ads from Facebook advertisers, to run their own ads on Facebook or elsewhere, and to understand the effectiveness of their ads.
When you visit a site or app that uses our services, we receive information even if you’re logged out or don’t have a Facebook account. This is because other apps and sites don’t know who is using Facebook.
Most digital strategies don’t reflect how digital is changing economic fundamentals, industry dynamics, or what it means to compete. Companies should watch out for five pitfalls.
The processing power of today’s smartphones are several thousand times greater than that of the computers that landed a man on the moon in 1969. These devices connect the majority of the human population, and they’re only ten years old.1
In that short period, smartphones have become intertwined with our lives in countless ways. Few of us get around without the help of ridesharing and navigation apps such as Lyft and Waze. On vacation, novel marine-transport apps enable us to hitch a ride from local boat owners to reach an island. While we’re away, we can also read our email, connect with friends back home, check to make sure we turned the heat down, make some changes to our investment portfolio, and buy travel insurance for the return trip. Maybe we’ll browse the Internet for personalized movie recommendations or for help choosing a birthday gift that we forgot to buy before leaving. We also can create and continually update a vacation photo gallery—and even make a few old-fashioned phone calls.
Digital rewards first movers and some superfast followers
In the past, when companies witnessed rising levels of uncertainty and volatility in their industry, a perfectly rational strategic response was to observe for a little while, letting others incur the costs of experimentation and then moving as the dust settled. Such an approach represented a bet on the company’s ability to “outexecute” competitors. In digital scrums, though, it is first movers and very fast followers that gain a huge advantage over their competitors. We found that the three-year revenue growth (of over 12 percent) for the fleetest was nearly twice that of companies playing it safe with average reactions to digital competition. Our research confirms this.
Incumbents moving boldly command a 20 percent share, on average, of digitizing markets. That compares with only 5 percent for digital natives on the prowl. Using another measure, we found that revved-up incumbents create as much risk to the revenues of traditional players as digital attackers do. And it’s often incumbents’ moves that push an industry to the tipping point. That’s when the ranks of slow movers get exposed to life-threatening competition.
More recently, platform businesses like Alibaba and Amazon have made the buying process far more efficient in many categories, leading to major market share gains and the demise (or teetering on the brink) of many brands that could not keep pace. But let’s be clear: Amazon is not “the everything store.” It is, however, quickly becoming the anything you want to ‘buy’ store. Absent a far greater brick & mortar presence, Amazon will continue to struggle in its quest to dominate shopping.
Innovation and growth in ‘buying’ has occurred outside of the purely digital world. Brands such as Aldi, Lidl, Dollar General, Ross, TJX and others have re-worked and expanded their business model by delivering ever greater ‘buying’ value. If there is a retail apocalypse someone needs to tell these brands, as they will collectively add thousands of new stores this year alone.
The same is true in the ‘shopping’ world. Sephora, Ulta, Apple and many others that continue to offer a remarkable shopping experience are growing both online and offline. Moreover, many high profile pure-play e-commerce players have basically started to run out of customers that would approach their brands in ‘buying’ mode and thus they needed to go seek out ‘shoppers’ with brick & mortar locations In fact, several once stated that they would never open stores. This is because they didn’t understand how the buying vs. shopping dynamic would inevitably play out over time. It now turns out that Warby Parker, Peloton and Bonobos are seeing the majority of their incremental growth come from their physical locations.
This is a marked business change for the website, which is mainly a hub of information about real estate properties. Buying up homes will provide added costs and risks, so some investors didn’t like it.
Yet Zillow says it has been testing out this program for about a year and that it is optimistic about its future success.
In an interview with CNBC, CEO Spencer Rascoff said, “we’re ready to be an investor in our own marketplace.” He believes Zillow has “huge advantages because we have access to this huge audience of sellers and huge audience of buyers.”
Rascoff acknowledged that Zillow will be taking on debt to execute on its new mission.
In fact, the opposite is happening. Because these are video format, they often get preferred treatment in Google’s search results, as it helps their search results seem more diverse when including video, images, and other non-link content.
So in a time when countless news publications and blogs are barely scraping by, they now also have this growing obstacle deal with.
And if you think it’s tough now, just wait a few more years before it gets out of hand as AI inevitably becomes smarter, faster, and more efficient.
Thanks for reading.
Unlike traditional home-flippers who bet they can make money on home appreciation, Zillow plans to profit by charging sellers a fee in addition to agent commissions. Wacksman said most owners who use Instant Offers wind up selling their homes the old-fashioned way, but that people like to have choices.
“What drove us here is the seller demand,” he said.
Zillow joins a field of companies using tech platforms to make fast decisions to buy homes. Opendoor and OfferPad have similar business models. To run its new operation, Zillow hired Arik Prawer, a former executive at single-family landlords Colony Starwood Homes and Invitation Homes.
Berlin has emerged as the frothiest property market in the world, with the city engulfed by expensive highrise developments and speculative buying that threatens its traditionally low rents and hip arts scene.
Prices in Berlin jumped by 20.5% in 2017, according to the property consultancy Knight Frank, with other German cities also displacing cities in China in terms of rising prices.
Berlin, Hamburg, Munich and Frankfurt were ranked in the top 10 in the world for price rises, with several Dutch cities not far behind.
London was ranked 101st, with a 2% gain, while Auckland in New Zealand, once gripped by a property frenzy, dropped to 99th with a 2.2% increase.
But attempts by the authorities in Vancouver to quell its soaring prices – including a 15% foreign buyer tax – appear to have stalled, with prices in the Canadian city jumping by 16% in 2017, the fourth fastest in the world.
In new research, Youn and colleagues seek to understand how machines will disrupt the economies of individual cities. By carefully analyzing the workforces of American metropolitan areas, the team calculated what portion of jobs in each area is likely to be automated in coming decades.
They found that, in general, small cities will have higher portions of their workforce replaced by machines than large cities. The reason: While cities of all sizes have many easily automated jobs (like card dealers, fisherman, cashiers, and accountants), large cities like Boston also have larger shares of managerial and knowledge professions (like lawyers, scientists, and software developers). Since these jobs require knowledge and skills that cannot easily be taught to a machine, they will offset the total impact of automation. In smaller cities, fewer of those offsetting jobs exist.
Based on this finding, Youn says small cities could see an exodus of workers, as well as exacerbated income inequality, since robots are likely to hollow out the middle class there. And large cities are not entirely immune. Las Vegas, for example, has two million people in its metropolitan area, but its economy relies heavily on an industry whose jobs are likely to be automated.
Reform movements succeed when the methods, motivation & moment converge. We are reaching such a time.
From a note to clients that landed in my inbox Tuesday:
Apple’s share of smartphone ownership increased for the sixth consecutive Piper Jaffray Taking Stock With Teens survey. Of ~6,000 respondents, 82% have an iPhone, the highest percentage we have seen in our survey (up from 78% in Fall-17). The iPhone may have room to move higher with 84% of teens anticipating their next phone to be an iPhone, also the highest ever recorded in our survey (up from 82% in Fall-17). Android was the runner up with 11%, down from the fall. The Apple Watch was the top smartwatch among teens, garnering 15%, with the Samsung Gear next on the list at 2%. About 20% of teens plan to purchase an Apple Watch in the next six months, up from 17% in the fall. Overall, we view the survey data as a sign that Apple’s place as the dominant device brand among teens remains intact…
Global app downloads and consumer spending in apps had yet another record quarter, according to a new report from App Annie, out on Monday. In the first quarter of 2018, iOS and Google Play downloads grew more than 10 percent year-over-year to reach 27.5 billion – the highest figure to date. In addition, consumer spending on iOS and Google Play grew 22 percent year-over-year to reach $18.4 billion – also a record number.
How did Uber’s ratings become more inflated than grades at Harvard? That’s the topic of a new paper, “Reputation Inflation,” from NYU’s John Horton and Apostolos Filippas, and Collage.com CEO Joseph Golden. The paper argues that online platforms, especially peer-to-peer ones like Uber and Airbnb, are highly susceptible to ratings inflation because, well, it’s uncomfortable for one person to leave another a bad review.
The somewhat more technical way to say this is that there’s a “cost” to leaving negative feedback. That cost can take different forms: It might be that the reviewer fears retaliation, or that he feels guilty doing something that might harm the underperforming worker. If this “cost” increases over time—i.e., the fear or guilt associated with leaving a bad review increases—then the platform is likely to experience ratings inflation.
The paper focuses on an unnamed gig economy platform where people (“employers”) can hire other people (“workers”) to do specific tasks. After a job is completed, employers can leave two different kinds of feedback: “public” feedback that the worker sees, and “private” reviews and ratings that aren’t shown to the worker or other people on the platform. Over the history of the platform, 82% of people have chosen to leave reviews, including a numerical rating on a scale from one to five stars.
We find that cellular traffic represents 16.2% of all global traffic in December 2016. We show that the fraction of traffic traversing cellular links varies widely across countries and continents. For example, while only 16.6% of U.S. traffic is cellular, cellular composes 63% of all traffic in Indonesia and 95.9% of all traffic in Ghana.
rypto-backdoors for law enforcement is a reasonable position, but the side that argues for it adds things that are either outright lies or morally corrupt. Every year, the amount of digital evidence law enforcement has to solve crimes increases, yet they outrageously lie, claiming they are “going dark”, losing access to evidence. A weirder claim is that those who oppose crypto-backdoors are nonetheless ethically required to make them work. This is morally corrupt.
The researcher whose work is at the center of the Facebook-Cambridge Analytica data analysis and political advertising uproar has revealed that his method worked much like the one Netflix uses to recommend movies.
In an email to me, Cambridge University scholar Aleksandr Kogan explained how his statistical model processed Facebook data for Cambridge Analytica. The accuracy he claims suggests it works about as well as established voter-targeting methods based on demographics like race, age and gender.
If confirmed, Kogan’s account would mean the digital modeling Cambridge Analytica used was hardly the virtual crystal ball a few have claimed. Yet the numbers Kogan provides also show what is – and isn’t – actually possible by combining personal data with machine learning for political ends.
Regarding one key public concern, though, Kogan’s numbers suggest that information on users’ personalities or “psychographics” was just a modest part of how the model targeted citizens. It was not a personality model strictly speaking, but rather one that boiled down demographics, social influences, personality and everything else into a big correlated lump. This soak-up-all-the-correlation-and-call-it-personality approach seems to have created a valuable campaign tool, even if the product being sold wasn’t quite as it was billed.
The researcher whose work is at the center of the Facebook–Cambridge Analytica data analysis and political advertising uproar has revealed that his method worked much like the one Netflix uses to recommend movies.
In an email to me, Cambridge University scholar Aleksandr Kogan explained how his statistical model processed Facebook data for Cambridge Analytica. The accuracy he claims suggests it works about as well as established voter-targeting methods based on demographics like race, age, and gender.
If confirmed, Kogan’s account would mean the digital modeling Cambridge Analytica used was hardly the virtual crystal ball a few have claimed. Yet the numbers Kogan provides also show what is — and isn’t — actually possible by combining personal data with machine learning for political ends.
New York City needs lots of additional private housing, but restrictive regulations make building it difficult. The city also requires better subways and buses, but the Metropolitan Transportation Authority (MTA), America’s largest public transit agency, is hampered by funding shortages as well as by poor management.
This paper suggests a housing–public transit “grand bargain”—used successfully, on a smaller scale, for Manhattan’s Hudson Yards development and elsewhere—that would help tackle both problems: it would allow larger residential buildings near public transit hubs across New York City in exchange for more money for the MTA. Specifically, it would relax zoning rules in return for one-time fees (“incentive zoning”) and the continuous higher property-tax revenue generated by larger buildings (“tax-increment financing”).
The only real guarantee that companies won’t go overboard in invading customers’ privacy is that they have large revenue streams which make it an unnecessary risk. Apple is reliable in this regard: It’s a hardware company that also sells content and software on commission and on a subscription basis. Microsoft, with its cloud, software licensing and subscription businesses, is even less likely to go rogue in data collection because it no longer has a mobile platform to speak of.
But for every article about Facebook’s creepy stalker behavior, thousands of other companies are breathing a collective sigh of relief that it’s Facebook and not them in the spotlight. Because while Facebook is one of the biggest players in this space, there are thousands of other companies that spy on and manipulate us for profit.
Harvard Business School professor Shoshana Zuboff calls it “surveillance capitalism.” And as creepy as Facebook is turning out to be, the entire industry is far creepier. It has existed in secret far too long, and it’s up to lawmakers to force these companies into the public spotlight, where we can all decide if this is how we want society to operate and — if not — what to do about it.
There are 2,500 to 4,000 data brokers in the United States whose business is buying and selling our personal data. Last year, Equifax was in the news when hackers stole personal information on 150 million people, including Social Security numbers, birth dates, addresses, and driver’s license numbers.
Food media collapses elaborate, perfect food into the lifestyle it is made to seem to promise. This means it turns our unfulfillable desires into a normative protocol for vigilance over one’s appetites. We can consume as many images of food as we want, but with them we ingest rules for self-management.
These rules are typically yoked especially to the disciplining of female pleasure and appetite, urging the consumer to believe herself at her best when her desires are sublimated rather than indulged in immediate and visceral pleasure. Attitudes toward food are generally divided along a gender binary: privation for women; plenitude for men. As food writer Ruby Tandoh notes in Eat Up, “the boundaries between consumption and self-denial, power and passivity often trace the crude line dividing men and women.” Likewise, in Distinction, Pierre Bourdieu argues that “the accession to manhood” is symbolized by abundance, the need for heaped sustenance to propel a boy through, while “a girl’s accession to womanhood is marked by doing without.” Instagram allows her to do without and like it too — a world of pleasure without calorie content or long-term physical effects, a glossy sanctuary in which food is divorced from anxieties around polluting or adulterating the body or risking social transgression.
Tandoh argues that “a media saturated with images of idealized bodies, and by a pervasive culture of female guilt around food” coerces women into complying with the “superhuman” injunction to transcend physical urges. Online food is an extension of this: women are associated with peripheral, sugary or light foods (salad, cupcakes, iced coffee), acceptable if “naughty” delights. The food must be lean, pretty, attractive, charming — a synecdoche of the invisible woman it champions. This contrasts with food coded male: large, messy portions of meat or the barbecue, a ritual of fire and flesh beyond the feminized kitchen space. These messages normalize the gendering of food, of male desire with primality and women’s with delicacy. Women are expected to conceal their appetites for food and sex unless they can be constructed as providing for the needs of others.
Non-human traffic can create an “inflated number that sets false expectations for marketing efforts,” said Mr. Weinstein.
Marketers often use web traffic as a good measure for how many of their consumers saw their ads, and some even pay their ad vendors when people see their ads and subsequently visit their website. Knowing more about how much of their web traffic was non-human could change the way they pay their ad vendors.
Advertisers have told Adobe that the ability to break down human and non-human traffic helps them understand which audiences matter “when they’re doing ad buying and trying to do re-marketing efforts, or things like lookalike modeling,” he said. Advertisers use lookalike modeling to reach online users or consumers who share similar characteristics to their specific audiences or customers.
Ad buyers can also exclude visitors with non-human characteristics from future targeting segments by removing the cookies or unique web IDs that represented those visitors from their audience segments.
In addition to malicious bots, many web visits also come from website “scrapers,” such as search engines, voice assistants or travel aggregators looking for business descriptions or pricing information. Some are also from rivals “scraping” for information so they can undercut the competition on pricing.
It was a Davos for digital hucksters. One day last June, scammers from around the world gathered for a conference at a renovated 19th century train station in Berlin. All the most popular hustles were there: miracle diet pills, instant muscle builders, brain boosters, male enhancers. The “You Won an iPhone” companies had display booths, and the “Your Computer May Be Infected” folks sent salesmen. Russia was represented by the promoters of a black-mask face peel, and Canada made a showing with bot-infested dating sites.
They’d come to mingle with thousands of affiliate marketers—middlemen who buy online ad space in bulk, run their campaigns, and earn commissions for each sale they generate. Affiliates promote some legitimate businesses, such as Amazon.com Inc. and EBay Inc., but they’re also behind many of the shady and misleading ads that pollute Facebook, Instagram, Twitter, and the rest of the internet.
It was a Davos for digital hucksters. One day last June, scammers from around the world gathered for a conference at a renovated 19th century train station in Berlin. All the most popular hustles were there: miracle diet pills, instant muscle builders, brain boosters, male enhancers. The “You Won an iPhone” companies had display booths, and the “Your Computer May Be Infected” folks sent salesmen. Russia was represented by the promoters of a black-mask face peel, and Canada made a showing with bot-infested dating sites.
After embarking on exactly the kind of cringe-inducing apology tour one would expect following the revelation that Cambridge Analytica plundered the data of millions of Facebook users, Mark Zuckerberg has yet another mess on his hands. Over the weekend, Android owners were displeased to discover that Facebook had been scraping their text-message and phone-call metadata, in some cases for years, an operation hidden in the fine print of a user agreement clause until Ars Technica reported. Facebook was quick to defend the practice as entirely aboveboard—small comfort to those who are beginning to realize that, because Facebook is a free service, they and their data are by necessity the products.
In its current iteration, Facebook’s Messenger application requests that those who download it give it permission to access incoming and outgoing call and text logs. But, as users discovered when prompted to download a copy of their personal data before permanently deleting their Facebook accounts, a certain amount of data was covertly siphoned without explicit permissions. Buried inside those data caches was an unsettling amount of specific, detailed information—in some cases, every phone call or text message ever sent or received on their Android device. Dylan McKay, who apparently owns an Android phone, reported that for the period between November 2016 and July 2017, his archives contained “the metadata of every cellular call I’ve ever made, including time and duration” and “metadata about every text message I’ve ever received or sent.” When people like McKay agreed to share their contacts with Facebook, it appears they didn’t know the extent to which they were giving Facebook access to their personal information.
At the core of the American housing system of today is the fundamental belief that housing should be a vehicle for private wealth creation. Privately owned housing on the market makes up 96.3% of the total housing stock in the U.S. Homeownership, once one of the surest ways for a family to accumulate wealth, has declined across the country; rates dropped to 63.4% in 2016, their lowest since 1967. Big banks and mortgage companies attach stringent criteria and high interest rates to loans that often lock lower-income people out of buying a home.
So instead, they’re forced into the rental market. As wages have stagnated and property costs have continued to rise, an astonishing number of Americans struggle to afford monthly payments. Almost half of all renters spend more than 30% of their income on rent, which is the ratio the federal government deems affordable. One in four renters shell out half their income to hold onto a place to live. Homeowners aren’t any better off: Around 41% are struggling to make mortgage payments, and risking foreclosure as a result. Across market-based housing, people of color, gender nonconforming people, and those with a criminal record routinely face barriers to securing housing.
The recently revealed Facebook data “breach” that allowed Cambridge Analytica to get access to millions of users’ worth of Facebook data has been greeted as a shocking scandal. Reporters and readers have been surprised to learn about the ability to gather personal data on the friends of people who install a Facebook app, the conversion of a personality quiz into a source of political data, the idea that you can target marketing messages based on individual psychographic profiles, and the surreptitious collection of data under the guise of academic research, later used for political purposes. But there is one group of people who are mostly unsurprised by these revelations: the market researchers and digital marketers who have known about (and in many cases, used) these tactics for years. I’m one of them.
Back when the Cambridge Analytica data was getting collected by an enterprising academic, I was the vice president of social media for Vision Critical, a customer intelligence software company that powers customer feedback for more than a third of the Fortune 100 companies. Our enterprise clients wanted to know how social media data could complement the insights they were getting from their customer surveys, and it was my job to come up with a way of integrating social media data with survey data.
Dear All CMOs, everywhere
Hope you don’t mind me writing to you all like this but here’s a brief note, some data-points and a slide deck that could help with an issue more and more of us seem to be having these days.
Have you got a CEO or CFO who just doesn’t seem to ‘get’ brand? Are they happy to allocate budget to performance activity but likely to run a mile before giving you what you need for brand-building? Are you confused about why they devote so little attention and resources to your company’s most valuable commercial asset, its brand? Maybe you even get people saying things like ‘we don’t talk about BRAND here’?
Well you’re not alone. Whilst many of us believe that brands have never been more important, successfully making the case for investing in them has never felt harder.
There are of course loads of reasons for this (quarterly reporting and short-termism chief amongst them) but it’s also partly because, to quote Jeremy Bullmore, ‘brands are fiendishly complicated, elusive, slippery, half-real, half-virtual things. When CEOs try to think about brands, their brains hurt’.
Brands are probably the most powerful and versatile business tool ever invented. And yet there’s a growing breed of business leaders who behave as if creating a famous, preferred, distinctive brand is an unnecessary luxury. Who think it’s enough to pay for PPC, sort the SEO, create content, build a digital eco-system, communicate one-to-one with existing customers ‘for free’, or re-target prospects who’ve signalled some level of intent. Of course there are individual businesses that seem to be ok doing things this way, but if there’s a large body of evidence (not simply a few case studies) that proves the above approach can be used to drive long-term profitable growth across a range of categories, without investment in brand-building, I’d love to see it.
If businesses aren’t investing in their brand, maybe it’s our fault? I suspect we haven’t been selling the idea of brands as a business tool correctly. Marketers have tended to dwell on the mental and emotional side of brands (the hard to value stuff in people’s heads like memories, associations, feelings, values and personality). We’ve forgotten to give enough emphasis to the stuff that really matters to CEOs and CFOs: the rational, commercial stuff about what brands do to drive commercial value. We’ve been selling the magic but forgetting the logic.
Giant Irony Alert: the same is true for the Times, along with every other publication that lives off adtech: surveillance-based advertising. These pubs don’t just open the kimonos of their readers. They treat them as naked beings whose necks are bared to vampires ravenous for the blood of personal data, all ostensibly so those persons can be served with “interest-based” advertising.
With no control by readers (beyond tracking protection which relatively few know how to use), and damn little care or control by the publishers who bare those readers’ necks to the vampires, who knows what the hell actually happens to the data? No one entity, that’s for sure.
For one among many views of what’s going on, here’s a screen shot of what RedMorph, a privacy monitoring and protection extention in Chrome showed going on behind Zeynep’s op-ed in the Times:
visitors to WSJ.com now each receive a propensity score based on more than 60 signals, such as whether the reader is visiting for the first time, the operating system they’re using, the device they’re reading on, what they chose to click on, and their location (plus a whole host of other demographic info it infers from that location). Using machine learning to inform a more flexible paywall takes away guesswork around how many stories, or what kinds of stories, to let readers read for free, and whether readers will respond to hitting paywall by paying for access or simply leaving. (The Journal didn’t share additional details about the score, such as the exact range of numbers it could be. I asked what my personal score was; no luck there, since the scores are anonymized.) “I think back to maybe eight months ago, when we were looking at all these charts with a lot of different data points. Now we’ve got a model that’s learned to a point where, if I get a person’s score, I pretty much know how likely they will be to subscribe,” Karl Wells, the Journal’s general manager for membership, told me when we spoke last week, with a Journal spokesperson on the call. “What we’ve found is that if we open up the paywall — we call it sampling — to those who have a low propensity to subscribe, then their likelihood to subscribe goes up.” (The Journal’s model looks at a window of two to three weeks.)
The Journal has found that these non-subscribed visitors fall into groups that can be roughly defined as hot, warm, or cold, according to Wells. Those with high scores above a certain threshold — indicating a high likelihood of subscribing — will hit a hard paywall. Those who score lower might get to browse stories for free in one session — and then hit the paywall. Or they may be offered guest passes to the site, in various time increments, in exchange for providing an email address (thus giving the Journal more signals to analyze). The passes are also offered based on a visitor’s score, aimed at people whose scores indicate they could be nudged into subscribing if tantalized with just a little bit more Journal content.