Remove 2009 Remove Interactive Remove Metrics Remove Optimization
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The Lean Analytics Cycle: Metrics > Hypothesis > Experiment > Act

Occam's Razor

To win in business you need to follow this process: Metrics > Hypothesis > Experiment > Act. We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. That metric is tied to a KPI.

Metrics 156
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Be Real-World Smart: A Beginner's Advanced Google Analytics Guide

Occam's Razor

This is all the way from Aug 2009: Web Analytics Career Advice: Play In The Real World! Along the way I'll share some of my favourite metrics and analytics best practices that should accelerate your path to becoming a true Analysis Ninja. At this point you'll be a little confused about some metric or the other.

Analytics 109
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Emerging Trends: 4 IRM Market Insights to Aid COVID-19 Business Recovery

John Wheeler

Offer capabilities to analyze business impacts at all levels of the organization by linking both strategic and tactical risk metrics. In addition, 73% of the 760 IRM client interactions in 2019 were business leader focused1. It will remain as a new way of conducting business in a cost-optimized, more efficient environment.

Marketing 110
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Adding Common Sense to Machine Learning with TensorFlow Lattice

The Unofficial Google Data Science Blog

TF Lattice offers semantic regularizers that can be applied to models of varying complexity, from simple Generalized Additive Models, to flexible fully interacting models called lattices, to deep models that mix in arbitrary TF and Keras layers. There is a robust set of tools for working with these kinds of constrained optimization problems.

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5 + 4 Actionable Tips To Kick Web Data Analysis Up A Notch, Or Two

Occam's Razor

PALM: People Against Lonely Metrics]. So why not your metrics? This is the problem with lonely metrics. Why not find a BFF for your lonely metric and present something like this. I found a "you complete me" for my Visits metric, Bounce Rate. Or an actual outcome metric. 2: Join the PALM club.

Metrics 96
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Themes and Conferences per Pacoid, Episode 9

Domino Data Lab

If your “performance” metrics are focused on predictive power, then you’ll probably end up with more complex models, and consequently less interpretable ones. Perhaps if machine learning were solely being used to optimize advertising or ecommerce, then Agile-ish notions could serve well enough. PSL models are easy to use and fast.

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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

The need for interaction – complex decision making systems often rely on Human–Autonomy Teaming (HAT), where the outcome is produced by joint efforts of one or more humans and one or more autonomous agents. PDPs for the bicycle count prediction model (Molnar, 2009). This trust must be paramount when human lives are at stake.

Modeling 139