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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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Excellent Analytics Tip #8: Measure the Real Conversion Rate & "Opportunity Pie"

Occam's Razor

Mostly because short term goals drive a lot of what we do and if you are selling something on your website then it only seems to make logical sense that we measure conversion rate and get it up as high as we can as fast as we can. So measure Bounce Rate of your website. Even though we should not obsess about conversion rate we do.

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What is ITIL? Your guide to the IT Infrastructure Library

CIO Business Intelligence

ITIL 4 contains seven guiding principles that were adopted from the most recent ITIL Practitioner Exam, which covers organizational change management, communication, and measurement and metrics. The two bodies formed an alliance at the end of 2006 to further IT service management.

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The Complete Digital Analytics Ecosystem: How To Win Big

Occam's Razor

Digital Analytics Ecosystem: Optimal Execution: Three Phases. Digital Analytics Ecosystem: Optimal Execution: Timing Expectations. Helpful post: You Are What You Measure, So Choose Your KPIs (Incentives) Wisely! ]. The landing page optimization team will demand regular reports of all entry points to the site/app.

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SAP Industry Insights Podcast Highlights of 2021 with Host Tom Raftery

Timo Elliott

For example, we’re see all kinds of legislation requiring companies to measure set targets, measure and report their emissions, for example. He also cited the Costco example of using analytics and machine learning to create algorithms to optimize bread production, again using cameras.

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Our quest for robust time series forecasting at scale

The Unofficial Google Data Science Blog

For us, demand for forecasts emerged from a determination to better understand business growth and health, more efficiently conduct day-to-day operations, and optimize longer-term resource planning and allocation decisions. We forecast this time series from the middle of 2006 through the end of the data, for a 30-month forecast horizon.

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A Big Data Imperative: Driving Big Action

Occam's Razor

The current flawed data org structure, its challenges, and the new optimal org structure to truly bring big action to big data. " That is the title of my post from June 2006. I'd structured my keynote into three big pieces: 00:00 – 01:15 Intro. My new favorite data quote by Zack Matere, a Kenyan farmer.

Big Data 127