모바일 광고 업계에서 “프로그래매틱 바잉”은 꾸준히 회자되는 주제였음에도 아직 많은 이들이 정확한 뜻을 알지 못하는 실정입니다. 그러나 광고주와 마케터들은 프로그래매틱 바잉에 대해 반드시 알아야 하는 시점이 되었습니다.
프로그래매틱 미디어 바잉은 빅데이터에서 파생된 용어라 볼 수 있습니다. 빅데이터는 소비자들의 소비 습관에 관해 통찰력을 제공합니다. 그러나 이러한 정보들은 서버에 축척된 엄청난 양의 데이터 일뿐 이것을 가공하고 적용하는 것은 결국 마케터의 역할입니다. 물론 컴퓨팅기술은 이전에도 있었지만 방대한 양의 정보를 활용하여 최적화하기 어려움이 있습니다. 프로그래매틱 바잉은 마케터가 자동적으로 인벤토리를 구입하고 미디어 캠페인을 운영할 수 있도록 변모하고 있고, 모바일 시장은 적극적으로 이를 받아들이기 시작했습니다.
程序化媒体购买的兴起已有一段时间了，但仍然有很多人对此一无所知。这是大家需要转变观念的时候了，因为程序化媒体购买正在逐渐成为当今展示广告的主流形式，所以移动广告主需要进行规划以跟上时代的步伐。
程序化媒体购买是另一个时髦热词- “大数据”的衍生热词。大数据为企业提供对消费者的购买行为进行深度洞察。然而，如果那些信息只停留在一堆服务器上，那就无法发挥其作用，因此营销人员必须得弄清楚如何对其进行分析并灵活地运用他们的新发现。电脑已出现在人们的生活中并发挥其强大的作用，但是购买媒体这个过程仍然存在一些技术障碍。目前这种购买过程还不够成熟，无法充分利用所有的信息。程序化媒体购买通过让营销人员自动购买与精细化运作的媒体宣传活动相结合的方式正在逐渐改变现状。而移动应用市场也开始对其热情拥抱。
Programmatic media buying has been around for a while, but there are still a lot of people who don’t really know what it is. That’s about to change, because programmatic media buying is going mainstream, and mobile advertisers need to start planning now to avoid being left behind.
Programmatic media buying is an offshoot of another buzzword: big data. Big data gave businesses invaluable insights into consumer buying habits. But that information didn’t do any good just sitting around on a bunch of servers, so marketers had to figure out how to analyze it and apply what they found. The computing power to do that has been around for a while now, but there was still one technological roadblock: the process for buying media. The existing process wasn’t sophisticated enough to capitalize on all of that information. Programmatic media buying is changing that by letting marketers automatically buy and run media campaigns with granular segmentation. And the mobile market is beginning to embrace it enthusiastically.
モバイル広告主が「プログラマティック·メディアバイイング」を愛すべき 5 つの理由「プログラマティック·メディアバイイング」という単語がアドテク業界に登場して久しくなりますが、本当の意味をまだ多くの人が知りません。しかしこれから「プログラマティック·メディアバイイング」はアドテク業界の主流になりつつあり、モバイル広告主は取り残されないために今、その意味を把握しておく必要があります。
Machine learning is probably one of the most hyped words of the last few years, and rather justifiably so. The field is currently the subject of widespread theoretical research, practical industrial implementations as well as a few distant fears (most of them being about robots killing all humans).
Machine learning is typically defined as “a type of artificial intelligence (AI) that provides computers with the ability to do certain tasks, such as recognition, diagnosis, planning, robot control, prediction, etc., without being explicitly programed. It focuses on the development of algorithms that can teach themselves to grow and change when exposed to new data.”How is machine learning used in our industry? We sat down with two data scientists from AppLift, Dr. Florian Hoppe and Bruno Wozniak, to understand how machine learning algorithms are currently helping mobile advertisers drive campaigns more efficiently and cost-effectively. We selected three use cases: Real-Time Bidding (RTB), lookalike targeting and user data enhancement.
