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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Taking measurements at parameter settings further from control parameter settings leads to a lower variance estimate of the slope of the line relating the metric to the parameter.

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Measuring Incrementality: Controlled Experiments to the Rescue!

Occam's Razor

We have to do Search Engine Optimization. This: You understand all the environmental variables currently in play, you carefully choose more than one group of "like type" subjects, you expose them to a different mix of media, measure differences in outcomes, prove / disprove your hypothesis (DO FACEBOOK NOW!!!),

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Experimentation and Testing: A Primer

Occam's Razor

This post is a primer on the delightful world of testing and experimentation (A/B, Multivariate, and a new term from me: Experience Testing). Experimentation and testing help us figure out we are wrong, quickly and repeatedly and if you think about it that is a great thing for our customers, and for our employers.

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Why models fail to deliver value and what you can do about it.

Domino Data Lab

This means many projects get stuck in endless research and experimentation. A concise goal (“I want to save costs by optimizing my store staffing roster”) is much more likely to lead to a successful initiative than a broad statement (“I want to increase revenue in my retail stores”). Identify metrics that measure this variability.

Modeling 101
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Interview with Dominic Sartorio, Senior Vice President for Products & Development, Protegrity

Corinium

Yes, definitely! For example, P&C insurance strives to understand its customers and households better through data, to provide better customer service and anticipate insurance needs, as well as accurately measure risks. It definitely depends on the type of data, no one method is always better than the other. Governance 101.

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The Lean Analytics Cycle: Metrics > Hypothesis > Experiment > Act

Occam's Razor

Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. First, you figure out what you want to improve; then you create an experiment; then you run the experiment; then you measure the results and decide what to do. Measure and decide what to do.

Metrics 156
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Knowledge

Occam's Razor

Key To Your Digital Success: Web Analytics Measurement Model. " Measuring Incrementality: Controlled Experiments to the Rescue! Barriers To An Effective Web Measurement Strategy [+ Solutions!]. Measuring Online Engagement: What Role Does Web Analytics Play? "Engagement" How Do I Measure Success?

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