It’s often been said that data will be the oil of the 21st century. Now, when managing an advertising campaign for the mobile realm, it’s important to know the differences between types of data and how you can best leverage them for your needs. Here are just a few insight on the various kinds of data, and why each is potentially valuable to your marketing efforts.
Programmatic ad delivery in the U.S. is poised to become a $20 billion market next year, with mobile ranking as the number one opportunity. Driving this massive growth in the mobile ad market is the proliferation of consumer data and advertisers’ ability to glean actionable insights. In fact, the data we have at our disposal today powers not only more knowledgeable and efficient media buys but also near-complete personas for targeting.
We’ve evolved from manual integrations to machine-learning algorithms and far more efficient mobile media buys. Advertisers are realizing the possibilities in cross-channel and multi-screen, but also how to use their own first-party data to improve performance and increase campaign ROI.
앱리프트는 최근에 뛰어난 기술력을 가진 모바일 광고주 플랫폼 DSP 기업인 비드스톡의 인수를 발표함으로써, 광고주 스택에 중요한 구성요소를 하나 더 갖게 되었습니다. 이러한 결정은 하루 아침에 이루어진것이 아니라 시장의 비전과 애드테크와 마테크 (마케팅 테크놀로지, 기존 고객 기반의 관리를 가능하게 하는 솔루션) 미래에 대한 이해에서 비롯된 것입니다.
저는 수 년간 공급과 수요 양쪽과 일해온 앱리프트가 실시간 입찰 (RTB)기반의 프로그래매틱 DSP에 투자하는 것이 왜 중요했는지의 배경에 대해 좀 더 설명하고자 합니다.
移动应用营销平台AppLift 最近宣布收购 Bidstalk公司，这是一个需求方平台（DSP），其技术力量十分雄厚，因此收购该公司从而能够为本公司的推广需求输入一批重要的技术人才。这种发展演变并非一日一时之事，而是根据我们对市场前景以及广告技术（adtech）与营销技术（这是使你能够管理现有客户群的优秀解决方案）的发展前景进行深思熟虑的分析和解读所得出的解决方案。
AppLift recently announced the acquisition of Bidstalk, an enterprise DSP provider with some amazing technological capabilities, thereby adding another important ingredient to our demand stack. This evolution didn’t come in a day, but was rather the result of our market vision and our understanding of the future of adtech (advertising technology) and martech (marketing technology, solutions enabling you to manage your existing customer base).
Here I would like to give a bit more background on one of the main reasons why it was important for us to invest in a programmatic DSP with RTB capabilities and, having spent years on both the demand as well as the supply side, explain how I see the industry evolving forward.
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 바잉이 무엇일까요?
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.
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.
What is programmatic media buying?
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.
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.