- What Twitter’s API Anouncement Could Have Said (Anil Dash) — read this and learn. Anil shows how powerful it is to communicate from the perspective of the reader. People don’t care about your business model or platform changes except as it applies to them. Focus on what you’re doing for the user, because that’s why you make every change–right? Your average “we’ve changed things” message focuses on the platform not the user: “*we* changed things for *our* reasons” and the implicit message is because *we* have all the power”. Anil’s is “you just got this Christmas present, because we are always striving to make things better for you!”. If it’s deceitful bullshit smeared over an offensive money grab, the reader will smell it. But if you’re living life right, you’re telling the truth. And they can smell that, too.
- Goodbye, Everyblock — Adrian Holovaty is moving on and ready, once more, to make something awesome.
- Turkopticon — transparency about crappy microemployers for people who work on Mechanical Turk. (via Beta Knowledge)
- Digital Natives, 10 Years After (PDF) — we need to move away from this fetish of insisting in naming this generation the Digital/Net/Google Generation because those terms don’t describe them, and have the potential of keeping this group of students from realizing personal growth by assuming that they’ve already grown in areas that they so clearly have not.
How server logs are making demographics obsolete.
Data isn’t just bigger these days; it is also fundamentally different than it was 10 years ago. The nature of this change is driving several innovations in the way marketing is done, particularly around targeting and measurement.
From a predictive targeting standpoint, ad tech firms are realizing that knowing a user regularly visits an investing blog and regularly searches for stock tickers is more valuable than knowing the age, gender and income of that user when targeting for a financial services brand. Traditionally, demographic and lifestyle data has served as a proxy for a good audience. With modern server logs holding behavioral data that tracks every last click, marketing firms can do away with the proxies and build audience segments with a high likelihood to take some sort of specific action — like converting. Ad tech startups such as Dstillery (full disclosure: the author works for Dstillery) and Rocket Fuel have based their respective approaches around this concept. Big data technology coupled with machine learning best practices has enabled the use of event-stream behavioral data to accelerate in the last five years. The market is starting to notice the value this approach is bringing, with Rocket Fuel being a recent IPO success story.
Better user-level targeting isn’t the only innovation brought by log file data. The digital promise of having better insight is slowly being realized by firms offering third-party ad effectiveness measurement. Companies such as Adometry and Visual IQ are pioneering the use of machine learning to model the causal effectiveness of ad exposures on user conversions. Using these models, brands can better evaluate which digital strategies are the most effective at driving up their ROIs. Read more…
Technology has changed the way we understand targeting and contextual relevance. How will marketing adapt?
Over the past five years, marketing has transformed from a primarily creative process into an increasingly data-driven discipline with strong technological underpinnings.
The central purpose of marketing hasn’t changed: brands still aim to tell a story, to emotionally connect with a prospective customer, with the goal of selling a product or service. But while the need to tell an interesting, authentic story has remained constant, customers and channels have fundamentally changed. Old Marketing took a spray-and-pray approach aimed at a broad, passive audience: agencies created demographic or psychographic profiles for theoretical consumers and broadcast ads on mass-consumption channels, such as television, print, and radio. “Targeting” was primarily about identifying high concentrations of a given consumer type in a geographic area.
The era of demographics is over. Advances in data mining have enabled marketers to develop highly specific profiles of customers at the individual level, using data drawn from actual personal behavior and consumption patterns. Now when a brand tells a story, it has the ability to tailor the narrative in such a way that each potential customer finds it relevant, personally. Users have become accustomed to this kind of sophisticated targeting; broad-spectrum advertising on the Internet is now essentially spam. At the same time, there is still a fine line between “well-targeted” and “creepy.” Read more…