Download Birthday Quotes

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Experiments in attachment. My friend has just had his PC wired for broadband. I meet him in the cafe; he looks terrible - his face puffy and pale, his eyes bloodshot... He tells me he is now detained, night and day, in downloading every album he ever owned, lost, desired, or was casually intrigued by; he has now stopped even listening to them, and spends his time sleeplessly monitoring a progress bar... He says it's like all my birthdays have come at once, by which I can see he means, precisely, that he feels he is going to die.
Don Paterson (The Blind Eye: A Book of Late Advice)
Tinder would work with Justin’s younger brother to throw a birthday party for one of his popular, hyperconnected friends on campus, and use it to promote Tinder. The Tinder team would do all the work to make it an incredible party. The day of the party, students from USC were getting bused to a luxurious house in LA, where everything had been set up to pull you inside. Sean described how it worked: There was one catch with the party: First, you had to download the Tinder app to get in. We put a bouncer in the house to check that you had done it. The party was great—it was a success, and more importantly, the next day, everyone at the party woke up and remembered they had a new app on their phone. There were attractive people they hadn’t gotten to talk to, and this was their second chance. The college party launch tactic worked. For the Tinder team, this one party created the highest ever one-day spike of downloads, however modest it might seem in retrospect. It’s not just the number that matters here, but that it was “500 of the right people”—Sean would explain to me later. It was a group of the most social, most hyperconnected people on the USC campus, all on Tinder at the same time. Tinder started to work. Matches began to happen, as the students who met each other from the previous night started to swipe through and then chat. Amazingly, 95 percent of this initial cohort started to use this app every day for three hours a day.
Andrew Chen (The Cold Start Problem: How to Start and Scale Network Effects)
Tinder would work with Justin’s younger brother to throw a birthday party for one of his popular, hyperconnected friends on campus, and use it to promote Tinder. The Tinder team would do all the work to make it an incredible party. The day of the party, students from USC were getting bused to a luxurious house in LA, where everything had been set up to pull you inside. Sean described how it worked: There was one catch with the party: First, you had to download the Tinder app to get in. We put a bouncer in the house to check that you had done it. The party was great—it was a success, and more importantly, the next day, everyone at the party woke up and remembered they had a new app on their phone. There were attractive people they hadn’t gotten to talk to, and this was their second chance. The college party launch tactic worked. For the Tinder team, this one party created the highest ever one-day spike of downloads, however modest it might seem in retrospect. It’s not just the number that matters here, but that it was “500 of the right people”—Sean would explain to me later. It was a group of the most social, most hyperconnected people on the USC campus, all on Tinder at the same time. Tinder started to work. Matches began to happen, as the students who met each other from the previous night started to swipe through and then chat. Amazingly, 95 percent of this initial cohort started to use this app every day for three hours a day. The Tinder team built one atomic network, but soon figured out how to build the next one—just throw another party. And then another, by going to other schools, and throwing even more parties. Each network was successively easier to start. Tinder quickly reached 4,000 downloads, then 15,000 within a month, and then 500,000 just a month after that—first by replicating the campus launch, but then letting the organic viral growth take over.
Andrew Chen (The Cold Start Problem: How to Start and Scale Network Effects)