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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.

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Experiment design and modeling for long-term studies in ads

The Unofficial Google Data Science Blog

Nevertheless, A/B testing has challenges and blind spots, such as: the difficulty of identifying suitable metrics that give "works well" a measurable meaning. accounting for effects "orthogonal" to the randomization used in experimentation. accounting for effects "orthogonal" to the randomization used in experimentation.

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Search: Not Provided: What Remains, Keyword Data Options, the Future

Occam's Razor

In late 2011, Google announced an effort to make search behavior more secure. This is a simple custom report I use to look at the aggregated view: As the report above demonstrates, you can still report on your other metrics, like Unique Visitors, Bounce Rates, Per Visit Value and many others, at an aggregated level.

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Welcome to the Era of the Augmented Marketer

Sisense

Marketing technology tools (also referred to as MarTech tools) have multiplied from about 150 in 2011 to around 8,000 today, a 5,233% increase that sends a clear message: Marketers are embracing digital assistance and data/analytics. We know in marketing that one of the most powerful ideas is experimentation,” Scott told Sisense.

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Estimating causal effects using geo experiments

The Unofficial Google Data Science Blog

A geo experiment is an experiment where the experimental units are defined by geographic regions. This means it is possible to specify exactly in which geos an ad campaign will be served – and to observe the ad spend and the response metric at the geo level. They are non-overlapping geo-targetable regions. by turning campaigns off).

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Deep Learning Illustrated: Building Natural Language Processing Models

Domino Data Lab

We need to take a brief break from natural language-specific content here to introduce a metric that will come in handy in the next section of the chapter, when we will evaluate the performance of deep learning NLP models. Note: Maas, A., Learning word vectors for sentiment analysis. The Area under the ROC Curve. 0.85 = 0.15.

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Unintentional data

The Unofficial Google Data Science Blog

With more features come more potential post hoc hypotheses about what is driving metrics of interest, and more opportunity for exploratory analysis. We data scientists now have access to tools that allow us to run a large numbers of experiments, and then to slice experimental populations by any combination of dimensions collected.