By February 2016, after many nights and weekends of emailing Asian manufacturers and reading up on Food and Drug Administration compliance, the vision of a viable business was coming into focus. The pair had found an F.D.A.-approved manufacturer in Asia and figured out how to meet the necessary regulations. Still, Cogan was reluctant. He had been accepted to Wharton and had even put down a deposit. He believed that was the smarter option. At best, the contact-lens business would become a side project.
Before shelving their venture, they decided to try one more tack. They recruited two friends: Paul Rodgers, a buddy of Horwitz’s from Columbia who knew how to write computer code, and Dan Rosen, a friend of Cogan’s from Bronx Science who was handy with Adobe Photoshop and Illustrator. Together the four built what is known in the world of online retailing as a demand experiment. The technique, credited to Harry’s founders (who give away its basic code), amounts to a two-page website. The first page explained the concept of a monthly subscription for contacts and asked those who were interested to submit their email addresses. Visitors who did so were taken to a second page and were made an offer: Share this referral code with friends, and if enough of them sign up, you’ll get free contacts.
They posted a link to their site on the walls of about 40 Facebook friends. Within a few days, not only had their own friends signed up, but friends of friends of friends had, too — some 2,000 people in all. Some of those distant connections were even evangelizing the company on their own Facebook walls. “It went mini-viral,” Cogan says.
He and Horwitz applied to tech incubators — organizations that invest in and coach young companies in exchange for minority stakes — using the demand experiment as one slide in their 16-page PowerPoint presentation. They pitched a few venture capitalists based in New York as well. They decided that if they were admitted to an incubator, they would work on the project full time. If not, Cogan would go to Wharton. By April, they had not only been called back for interviews with five incubators; venture funds were also offering to invest a total of $3.5 million in their idea.
Cogan dropped his Wharton plans. He and Horwitz ordered 50,000 contact lenses and, with Rosen as creative director and Rodgers as chief technology officer, began working out of their investors’ offices, stacking boxes and boxes of lenses along the walls by their desks. They eventually named their enterprise Hubble, after the orbiting telescope that can see into deep space.
Facebook helped them succeed with their demand test; now it would generate their first sales. During the summer of 2016, a friend of one of Hubble’s prospective investors, a start-up veteran named Joshua Liberson, recommended that the founders try a new type of Facebook advertising called Lead Ads. No outside website was needed: Would-be customers simply clicked a button on the ad to submit their email addresses, directly from Facebook. Hubble directed its ads to ZIP codes in New York and Chicago, where they had already signed up optometrists willing to write prescriptions. After people clicked the ads, Horwitz emailed them to coordinate appointments and take their orders.
When Hubble’s online store opened officially on Nov. 1, 2016, Cogan and Horwitz knew how to run a Facebook advertising campaign, and they were confident it would continue to generate sales. They planned to spend the additional $3.7 million they raised almost entirely on Facebook ads.
In 2017, everyone seems to be wondering: Is Facebook taking over the world? Most of us now realize that the social network has become far more than a repository for selfies and political rants of its more than two billion users. To ad sellers, Facebook is now a gluttonous monster, which, along with Google, is gobbling up the digital advertising business in the United States; according to Pivotal Research Group, the two companies controlled 70 percent of the market and most of the growth in 2016. From the perspective of American intelligence agencies, Facebook is practically a weapon, used by a company linked to the Kremlin to foment extremism and influence the 2016 presidential election with at least $100,000 worth of targeted ads. For those with privacy concerns, Facebook plays the role of Big Brother, compiling ever more data on what we like, what we post and what we buy and even tracking where we are both online and in the physical world by tapping into the GPS locator on our phones.
In considering Facebook’s far-reaching influence, it’s worth keeping in mind the perspective of the more than five million advertisers whose money is financing the social network’s rampant growth. For them, Facebook and Instagram, which the company also owns, are the stuff of fantasy — grand bazaars on a scale never seen before. By advertising directly in users’ news feeds, companies can, at any time of day, target potential customers at moments when they are often bored and open to novelty. What better time to hear a product pitch?
“Facebook created the world’s greatest infomercial,” says Roger McNamee, a founder of Elevation Partners, who invested early in Facebook but has since become critical of the company’s influence. “It’s really inexpensive to produce ads and unbelievably inexpensive to reach exactly the market that you’re looking for.” As a result, Facebook has become especially lucrative for companies trying to sell new products online. The leaders of more than half a dozen new online retailers all told me they spent the greatest portion of their ad money on Facebook and Instagram.
“In the start-up-industrial complex, it’s like a systematic transfer of money” from venture-capital firms to start-ups to Facebook, says Charlie Mulligan, the founder of BrewPublik, which uses a “Beergorithm” to deliver personalized selections of craft beers to customers every month. At 500 Startups, the tech incubator based in Silicon Valley that funded BrewPublik, Facebook advertising is a topic covered in classes. In fact, social-network advertising is an assumed prerequisite for anyone studying marketing at a tech incubator these days — or at any business school across the country. “There is a formula for this stuff,” Mulligan says. “And the reason why there is a formula is because it works.”
The process is easy, cheap and effective. With a few hundred dollars and a morning’s effort, an entrepreneur can place his or her ads before social-media users that same afternoon. Companies unsure which ads are best can upload a handful of them and let Facebook’s artificial-intelligence software test their efficacy. If they don’t know who should see their ads, they can embed code on their websites that enables Facebook to monitor the traffic and then show ads to recent visitors. Or companies can send the email addresses of their existing customers to Facebook, and it will locate their Facebook accounts and put ads in front of so-called Lookalikes, users who like and click on the same things that your proven fan base does. It’s all about as straightforward as setting up an online dating profile. Steph Korey, a founder of Away, a luggage company based in New York that opened in 2015, says that when the company was starting, it made $5 for every $1 it spent on Facebook Lookalike ads.
The ease of opening a business on Facebook has in turn spawned a wild proliferation of specialty digital sellers that depend on the social network’s algorithm to find their early customers. Many of them follow the same playbook and even share a similar aesthetic. They spend money on traditional public relations, on sponsored links that appear next to Google search results and on “influencer” marketing, or giving away their product to people with large social-media followings, in hopes of creating buzz. Then they buy ads on Facebook and Instagram. Inevitably you will encounter them there: They feature a sleek photograph or a video loop of a product — a wood-handled water filter, woolen shoes, an electric toothbrush. At the top, in bold, the company’s name appears, often ringing with the same friendly, typically two-syllable whimsy. Soma. Allbirds. Goby.
“Sometimes we’ll look at each other and say, ‘God, there are just so many of them,’ ” says Ellie Wheeler, a partner at the venture fund Greycroft Partners, which invested in Hubble last year. Her firm has also taken ownership stakes in Thrive Market, which sells health foods; Plated, a meal-kit delivery service; Trunk Club, which mails a box of clothes to its customers; and Eloquii, a fast-fashion retailer specializing in plus sizes.
While not all of these companies and others like them will survive, plenty are encroaching on established brands, which are taking the threat seriously. In July 2016, Unilever, the European consumer-products conglomerate, acquired Dollar Shave Club for a reported $1 billion. In June, Walmart agreed to buy Bonobos, an internet-based apparel brand, for $310 million. Companies that sell products exclusively online continue to grow faster than any other type of retailer in the United States — some 17 percent annually since 2011, more than six times the rate of retail over all, according to Euromonitor International.
And Facebook has even been taking steps to influence offline sales, in order to bring traditional retailers into its orbit. In September, the social network introduced a tool that lets businesses with physical stores show ads to shoppers and their Lookalikes even if they visit the store but don’t buy anything. Day by day, Facebook is extending its reach further and further into the American marketplace.
One afternoon in March, I watched as Rosen selected three new ads from an extensive photo shoot the week before, his third in four months. Rosen resembled a sleep-deprived new parent — mussed hair, dull gaze. He spoke in a monotone. He attributed his fatigue, I would learn later, to Facebook’s artificial-intelligence software that placed Hubble’s ads. Rosen and his colleagues simply referred to it as “the algorithm.”
The basic building block of Facebook advertising is an ad set. It consists of the ads themselves and choices in three other categories: audience, goal and budget. That day, Rosen was designing a set to reach an audience of people on Instagram who had visited hubblecontacts.com in the past 30 days. His goal was “conversions,” or persuading users who had seen the company’s ad to make a purchase. Finally, he set a budget of $1,000 per day. He uploaded the three images. Now they were ready to be tested, to see if any of them were winners in the eyes of users and the algorithm.
What happened at 8 a.m. the next morning, when the ad set became active, was complex — and far removed from human sight. Just before Facebook places an advertisement in a user’s feed, it holds a sort of instantaneous auction to determine which advertiser gets the space. The amount of each advertiser’s bid is influenced by its budget size, of course, but the algorithm also weighs what it knows about the company, the ad and the individual Facebook user. Seeking to act like an intuitive matchmaker, the algorithm draws inferences from personal interests, current online behavior, the user’s potential value to each advertiser and the ad’s general appeal. Sometimes the winner is the advertiser that offered Facebook the most money. Sometimes the algorithm decides you are more likely to click a different ad and awards the space to that advertiser for less money.
This detailed handicapping process involves thousands of advertisers per auction. Millions of auctions take place every minute as users across Facebook load their feeds. The process is never the same twice. The algorithm is constantly learning, using past results to inform how it weighs bids in the next auction. The intent, Facebook says, is to maximize value for everybody: to pair the advertiser with its likeliest customers, and to show ads that users want to see. And, of course, to make money for Facebook.
But from Rosen’s perspective, nothing much had happened before he ambled into the office a little after 10 a.m. Facebook had spent a grand total of $1.86 on his ads. It had shown the first ad to 51 people, the second to 45 and the third to only two. The first ad had been clicked once. Rosen, unperturbed, poured himself a cup of coffee from the single-serve machine. The algorithm takes a little while to get warmed up, he said. “In an hour, it’ll get exciting.”
Twenty minutes later, Rosen refreshed his browser. The Ads Manager window displayed the latest numbers: Rosen could see only the results, not the process that produced them, but it seemed as if the click had inspired the algorithm to favor the first ad. During those 20 minutes, the first ad appeared before 76 more people — that is, it won 76 more auctions than the other two ads. Over the next hour, the algorithm showed the first ad, which featured a photo of colorful Hubble boxes against a blue background, to more and more users; the algorithm had begun to favor it, apparently. As Rosen refreshed his browser, the sensation was like watching a seed sprout. The ad got more views. Some led to clicks. And eventually, sometime between 11:28 a.m. and 11:53 a.m., one of those clicks led to the test’s first sale. Commerce was in bloom.
The moment felt odd. Obviously there was science behind the scenes; the algorithm was a set of rules written by Facebook engineers. But from where Rosen sat, the operation might as well have been run by the Holy Spirit. Facebook’s artificial-intelligence algorithm had wound its way through the server farms, reached out among two billion users, found an individual and showed her a Hubble ad on Instagram — and she used her credit card to buy a subscription for contact lenses.
In quick succession, the first ad generated two more sales. The algorithm started increasing how much it bid on Hubble’s behalf, thus winning even more auctions for ad space and spending more of Hubble’s money on it — first $1 a minute, then $2 a minute, then more than $3. By 2 p.m., Facebook’s A.I. had charged Hubble $306.50 to put that ad in front of 9,684 users. The second ad, after an outlay of $8.03, had been all but abandoned. And the third ad was hardly given a chance: Since 8 a.m., it had appeared before only 30 people.
“No idea why,” Rosen said, shaking his head. Rosen could see all sorts of data arranged in neat rows on Facebook’s Ads Manager program: the number of views, clicks, sales and the average cost, in advertising, of acquiring each new customer. But none of the metrics at Rosen’s fingertips could resolve a fundamental mystery: why the algorithm behaved as it did, why it preferred some ads over others and why the third ad got little attention whatsoever.
The morning’s ads were incredibly similar: “hubblecontacts,” the company’s Instagram handle, appeared at the top, above pictures of boxes in peach, blue, yellow and green. The only differences were that the first ad showed the boxes of contact lenses lined up against a blue background; in the second and third ads, they were set against a split pink-and-blue background and were arranged diagonally in the second and scattershot in the third. But they were all just boxes! Did Instagram users really prefer contact-lens ads with strict rows of boxes or blue backgrounds? Had rules been written into the algorithm favoring orderly arrangements? (The Hubble team knew Facebook favored certain aesthetics.) To what extent was the day’s outcome, apparently set in motion when the first ad happened to get that first click in the morning, actually random? Rosen could only guess.
Advertising has always been an uncertain business. No one has ever known why, exactly, some people respond to an ad in a newspaper or a spot on TV, much less why specific individuals decide to buy products when they do. (The oldest cliché in the ad world, usually attributed to the department-store magnate John Wanamaker: “Half my advertising is wasted. The trouble is, I don’t know which half.”) But to make money in advertising, you don’t have to be all-knowing; your ads simply need to work better than those of a competitor. To this end, advertisers inevitably pursue some combination of two major approaches. They test and refine their messages, trying to craft one as efficient and targeted as possible (junk-mailers of preapproved credit-card offers, for example). Or they showboat, putting on a huge spectacle that’s sure to attract someone (Super Bowl advertisers).
In the early 2010s, direct-to-consumer companies showboated. But lacking the money for big TV ad campaigns, they relied instead on old-fashioned public relations, panache and luck. Warby Parker hired a public-relations firm to pitch its concept to Vogue and GQ and debuted its website on the same day issues reached subscribers. It also held an event featuring bespectacled models at the New York Public Library during Fashion Week. Dollar Shave Club first succeeded on account of the exquisite timing, both commercial and comedic, of its founder, Michael Dubin. He made a funny, low-budget video introducing his company, then uploaded it to YouTube on the same day TechCrunch reported Dollar Shave Club’s first round of venture funding. Within days, after some immediate attention at the South by Southwest festival in Austin, Tex., Dubin had three million views online.
Facebook’s sales pitch — putting the right ad in front of the right person, thanks to the wonders of data technology — isn’t exactly new. As far back as 1964, William Allan, a business editor for The Pittsburgh Press, reported that in the near future, “computers will tell businessmen which half of their advertising budgets are being wasted.” Thirty years later, The Economist described an effort to take advantage of American Express’s transactional records: “Powerful data-crunching computers known as massive parallel processors, equipped with neural-network software (which searches, like the human brain, for patterns in a mass of data), hold out a vision of marketing nirvana.” Companies like Acxiom, Experian and Datalogix have been offering similar data-mining services to direct marketers for years. What sets Facebook (and Google) apart are scale and sophistication.
A recent study by a Princeton professor, Arvind Narayanan, and a doctoral candidate, Steven Englehardt, provides a sense of how thoroughly the two online giants monitor user behavior. In early 2016, they examined the top one million websites in the world, using special bots they developed to scour them for tracking mechanisms. Google had trackers on 76 percent of these sites, Facebook on 23 percent of them. (Twitter, in third place, had trackers on just over 12 percent of the sites.) The tech giants can examine all this data looking for patterns and then match them back to prospective customers.
What also sets Facebook and Google apart from their direct-marketing forebears is that they give access to everyday advertisers. Anyone with a credit card can go online and test ads on Facebook’s platform, one of the most sophisticated direct-marketing operations ever. But while average people can use the machine, there’s still a lot of mystery about how it works. The methods and calculations of the algorithm — why it ends up pushing some ads and not others — are all hidden.
Almost as soon as they began, Rosen, Horwitz and the others at Hubble became determined to fathom the algorithm’s secrets — to figure out why some ads succeeded and others didn’t. Soon they were trading hypotheses with other entrepreneurs, cribbing ideas from other companies’ ads and taking a formal approach to testing, rooted in the scientific method. They uploaded ads with identical images but different wordings, for example. The Hubble team wound up concluding all sorts of things. Ads with third-party endorsements — from GQ, say, or BuzzFeed — beat those with their own slogans. Ads featuring close-ups of the Hubble boxes outperformed those with human models. Ads that included a button that said “Shop Now” or “Learn More” fared worse than an ad with no button at all; viewers simply preferred to click anywhere on the picture to go to the website.
But even as the Hubble team gleaned more about what yielded successful Facebook ads, the algorithm could be unpredictable, almost moody. If you kept loading the same ads into the same ad set every day, they stopped performing as well. The founders figured at first that users were tiring of the same ads. But actual viewer numbers revealed that practically no individual user had seen any ad more than once. The algorithm itself seemed to grow bored. At night, meanwhile, the algorithm spent lots of money and rarely found customers. The Hubble executives started shrinking the budgets at 11 p.m., which they called “putting the algorithm to bed.” The algorithm could also be impulsive and streaky — some days it might go on a sudden jag, blowing a thousand dollars in a few hours with nothing to show for it. At any time, any one of the 15 different ad sets might go haywire. Rosen found himself checking the Ads Manager compulsively on his laptop and his iPhone. (Facebook offers an iOS app for advertisers.) “It occupies my brain constantly,” he says. “It’s that feeling of ‘Did you leave the oven on?’ ”
One night we went to a standup-comedy night Rosen hosted at a bar called Muchmore’s in Brooklyn. (For the past four years, he has moonlighted as a comedian.) But while the other comics were onstage, Rosen was on the Ads Manager the whole time. “Who cares about jokes?” he quipped afterward.
Eager for help, Rosen sought guidance from a former Facebook employee named Faheem Siddiqi, who now runs his own marketing agency. Hubble’s sales representative at Facebook told him that Siddiqi had figured out the best ways to optimize Facebook advertising campaigns. But it turned out that Siddiqi and his employees checked the Ads Manager even more compulsively than Rosen — every half-hour, for up to 16 hours a day. When I asked Siddiqi to share his tips for managing Facebook ads, he replied, “Step 1 is meditation.”
“It’s like a baby,” Jesse Horwitz told me. “If you go more than half an hour without checking in on it, it’s probably dead.” (Horwitz, who is married, does not yet have children.)
Middlemen — creative agencies, media planners, publishers — have long ruled the advertising business. Yet until recently they have not been as omnipresent, opaque and inhuman as Facebook. The social giant now dictates, more fully and precisely than ever before, which ads we see and who sees which ads. Some of the implications of this are amusing, others troubling.
In my house, the strange new world of advertising announced itself in the form of a water pitcher. The Soma 6-Cup Pitcher is a paragon of Brooklynite beauty: folksy oak handle, sleek minimalist reservoir, filter cones made out of coconut shells (or something). I had never heard of it before my wife ordered one online. Plenty of my friends hadn’t, either. When our visitors opened the fridge, half of them were like me: Soma ignorant. The other half knew the brand immediately: Hey! You got a Soma? They had seen the pitcher on Facebook, on Instagram, all over the place. What was a familiar brand to some was totally unknown to me and others. We had been divvied up. It’s something I’ve noticed again and again: I see an ad for Aaptiv, a running app; my wife sees ads for a furniture website called Article that I’ve still never visited. Just as Facebook steers conservative and liberal talking points to users who already share those perspectives, we’re being sorted into commercial bubbles as well.
Recently ProPublica, the investigative-journalism nonprofit, showed how bad actors can abuse this process: Facebook’s software gave advertisers the option to target “Jew Haters,” for instance. In a separate investigation, ProPublica found that Facebook made it possible to exclude specific “ethnic affinities” from seeing ads, noting that ads excluding people based on race are prohibited by federal housing and employment laws.
This stereotyping isn’t a glitch of Facebook’s machine-learning process — it’s how the software works. To formulate audiences, the algorithm scours profiles and analyzes them for shared traits and correlations and self-identified interests and, it assumes, our preferences, grouping us into tribes that can be targeted. It’s up to Facebook and advertisers to constrain this amoral process in ethical and lawful ways. Yet the ethics of targeting are not clear-cut. In May, The Australian reported that Facebook employees had prepared a document showing how they could gather details on teenagers during vulnerable moments — when Facebook users feel “stressed,” “insecure,” “defeated” or “worthless.” Is that immoral, or simply crass?
Such challenges are opening a new front for companies and corporate-responsibility watchdogs. Bad human actors don’t pose the only problem; a machine-learning algorithm, left unchecked, can misbehave and compound inequality on its own, no help from humans needed. The same mechanism that decides that 30-something women who like yoga disproportionately buy Lululemon tights — and shows them ads for more yoga wear — would also show more junk-food ads to impoverished populations rife with diabetes and obesity.
“Sometimes data behaves unethically,” Antonio Garcia-Martinez, a former Facebook employee who worked on the advertising team, wrote in an essay in The Guardian. He provided an example from his time at the company: “Someone on the data-science team had cooked up a new tool that recommended Facebook pages users should like. And what did this tool start spitting out? Every ethnic stereotype you can imagine.”
As algorithms sort users in increasingly complex ways — already the multivaried criteria for determining a Lookalike group defies human comprehension — regulators and companies will have to confront how to determine who is being nudged, and why, and whether that’s benefiting the public or exacerbating societal ills. An algorithm that draws its lessons from the present reality can’t be counted on to improve the course of the future on its own.
Facebook’s A.I. isn’t operating unattended, certainly: Garcia-Martinez wrote that Facebook decided not to release the recommendation tool. Facebook points out that it makes efforts to prevent harmful advertising. It does not, for instance, allow ads for payday loans, which prey on the poor. It says it has removed advertisers’ ability to target users by ethnicity when promoting housing, employment or credit; it removed “Jew Haters” and other objectionable categories and said it would increase human review of its ad-targeting options. In response to the report in The Australian, Facebook said the analysis “was intended to help marketers understand how people express themselves on Facebook. It was never used to target ads.”
Yet managing a platform this way — seeing what mischief the algorithm and its users gets up to, then responding with countermeasures — can be difficult to sustain. “This is a whack-a-mole problem, one among many Facebook has,” Garcia-Martinez told me. It makes Facebook, a company still largely controlled by a single man, Mark Zuckerberg, the ultimate arbiter of morality and taste for all two billion of its users. It also means the company has unilateral power to make or break companies when it tweaks its system.
This is not a hypothetical possibility. In 2013, media sites like those measured by the BuzzFeed Partner network, which includes BuzzFeed, Thought Catalog and The New York Times, noticed a huge surge in referrals from Facebook — a jump of more than 50 million page views from August to October. A year later, Facebook announced that it had adjusted its news-feed algorithm to eliminate so-called click bait. Upworthy, a peddler of stories with headlines like “9 Out of 10 Americans Are Completely Wrong About This Mind-Blowing Fact,” had its total page views decline by half in the span of three months, from 90 million to 48 million visitors. (At the time of these huge shifts, 30 percent of Americans got news from Facebook. In 2017, 45 percent of Americans do, according to Pew Research Center.)
“We always knew that Facebook is sort of like the weather,” says Eli Pariser, Upworthy’s co-founder and president. “There’s going to be sunny days and stormy days.” In response to the algorithm adjustment, Pariser instructed his staff to stop posting as many videos to YouTube, which is owned by Google, and start publishing more videos directly to Facebook instead.
“That certainly served Facebook well,” Pariser admits. “But you know, I also wouldn’t be able to reach 200 million people on any other medium,” he says, citing the reach of Upworthy’s videos on Facebook. The platform may be mercurial, but Upworthy still relies on it.
Imagine, now, that Facebook tweaks its algorithm in a way that — rather than cause wild swings in web traffic to a purveyor of viral videos — leads to a steep decline in advertising and sales for a consumer-products company, one that happens to be the largest employer in a small town. Or imagine multiple companies shaken up by such an adjustment, or an entire industry overhauling its practices to suit Facebook. Even the threat and uncertainty of those possibilities could hurt businesses, which depend on predictable returns to invest in future projects.
As we delegate more control to artificial intelligence, both businesses as well as users are venturing into uncertain territory. In the meantime, more and more companies — start-ups, mom-and-pop stores, major corporations — are handing their dollars and their data to the social-networking giant. Facebook’s Ads Manager is user-friendly. Sales are plentiful. And if you don’t take advantage of it, your competitors will. How could you not go there?
By mid-March, a few weeks after I first followed Rosen, the Hubble team no longer had 15 Facebook and Instagram ad sets. It had 40 — all pointed at discrete audiences, each with its own handful of ads. But Rosen looked more rested, less frazzled. He explained that he and Paul Rodgers had developed something they called “Robo-Dan,” a few lines of code that checked the Ads Manager every hour, then adjusted the budget as Rosen would. He could wake up and let the ads run (although he had to fight the compulsion to check on Robo-Dan). Soon, he told me, they would upgrade to Robo-Dan 2, which would switch in new ads, taking over the nightly bedtime routine. (With 40 audiences, he told me, going through the process lasted almost as long as an entire episode of “The Late Show With Stephen Colbert.”) Finally, he said, he was getting some distance from Facebook’s everyday machinations. Someday soon, he would be able to go to bed early, he told me. Or have an evening to himself.
But by the end of June, Rosen’s stress-free life was still a dream. A new problem arose: No matter what new ads they put in an ad set, the growth rate of sales declined and the cost per acquisition went up. They began to think it was an audience problem: Had they found all the customers in those groups? With their ad sets going fallow, the Hubble team scrambled to find fresh and fertile ground. Their ideas for new audiences got quirkier, more outlandish. One week they zeroed in on people who like Sweetgreen, the salad chain. Next they went after people who had indicated that they were fans of bottled water, whoever they are. Each group fizzled after a few days — the cost per each new customer climbed higher and higher; sales dwindled. As they searched for more and more audience descriptors, they landed upon a novel idea: They began trading their Lookalike groups with other online retailers, figuring that the kind of people who buy one product from social media will probably buy others. This sort of audience sharing is becoming more common on Facebook: There is even a company, TapFwd, that pools together Lookalike groups for various brands, helping them show ads to other groups.
Cogan and Horwitz have decided that they need to reduce their dependence on Facebook advertising, for the sake of their business and their own sanity. In May, they tested their first 15-second cable-television commercials. With TV, the data is vaguer, and it takes longer to get results back. Yet even though the old medium provides them with less information than Facebook, in some ways the ignorance is bliss. “There’s fewer levers; there’s less to stress out about.” Rodgers says. “You can push the button and get on with your life.”
In August, the Hubble team finally handed over their domestic Facebook advertising work to an outside agency, Ampush, that charges them based on how many new customers sign up. Ten people at Ampush now do the job of Rosen and Robo-Dan. Still, the handoff was bittersweet. “We ran their numbers — it’s something we could beat,” Rosen says, meaning Hubble could get more customers for less money if it did the ad buying in-house. “But it would destroy our lives.”
Thanks largely to Facebook, Hubble is on track to finish its first full year in business having made $20 million in revenue. In August, Hubble raised $10 million, valuing the company at $210 million. In January, Hubble will use those funds to expand its business to Continental Europe. Its advertising strategy? Robo-Dan, with some help from Rosen. As Hubble advances into new territories, Facebook and the algorithm will be tagging along with them.