jm + adwords   3

Online mattress-in-a-box brands: Why are there so many? - Curbed
“People ask me what it takes to get into this space,” said Bryan Murphy, founder and president of Tomorrow Sleep. “If you have a [Google] AdWords account [to buy digital ads] and you know a subcontractor, you can sell a mattress online.”
mattresses  business  economics  retail  adwords  online 
april 2018 by jm
Explanation of the Jump Consistent Hash algorithm
I blogged about the amazing stateless Jump Consistent Hash algorithm last year, but this is a good walkthrough of how it works.

Apparently one author, Eric Veach, is legendary -- https://news.ycombinator.com/item?id=9209891 : "Eric Veach is huge in the computer graphics world for laying a ton of the foundations of modern physically based rendering in his PhD thesis [1]. He then went on to work for Pixar and did a ton of work on Renderman (for which he recently got an Academy Award), and then in the early 2000ish left Pixar to go work for Google, where he was the lead on developing AdWords [2]. In short, he's had quite a career, and seeing a new paper from him is always interesting."
eric-veach  consistent-hashing  algorithms  google  adwords  renderman  pixar  history  coding  c  c++ 
march 2015 by jm
CausalImpact: A new open-source package for estimating causal effects in time series
How can we measure the number of additional clicks or sales that an AdWords campaign generated? How can we estimate the impact of a new feature on app downloads? How do we compare the effectiveness of publicity across countries?

In principle, all of these questions can be answered through causal inference.

In practice, estimating a causal effect accurately is hard, especially when a randomised experiment is not available. One approach we've been developing at Google is based on Bayesian structural time-series models. We use these models to construct a synthetic control — what would have happened to our outcome metric in the absence of the intervention. This approach makes it possible to estimate the causal effect that can be attributed to the intervention, as well as its evolution over time.

We've been testing and applying structural time-series models for some time at Google. For example, we've used them to better understand the effectiveness of advertising campaigns and work out their return on investment. We've also applied the models to settings where a randomised experiment was available, to check how similar our effect estimates would have been without an experimental control.

Today, we're excited to announce the release of CausalImpact, an open-source R package that makes causal analyses simple and fast. With its release, all of our advertisers and users will be able to use the same powerful methods for estimating causal effects that we've been using ourselves.

Our main motivation behind creating the package has been to find a better way of measuring the impact of ad campaigns on outcomes. However, the CausalImpact package could be used for many other applications involving causal inference. Examples include problems found in economics, epidemiology, or the political and social sciences.
causal-inference  r  google  time-series  models  bayes  adwords  advertising  statistics  estimation  metrics 
september 2014 by jm

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