Remove 2011 Remove Experimentation Remove Metrics Remove Reporting
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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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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. The Multi-Channel Funnels folder in Google Analytics contains the Top Conversion Paths report. At the highest level, across visits by focusing on unique people, the report shows the role search plays in driving conversions. See Page Value there?

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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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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.

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How To Suck At Social Media: An Indispensable Guide For Businesses

Occam's Razor

Success Metrics. In my Oct 2011 post, Best Social Media Metrics , I'd created four metrics to quantify this value. I believe the best way to measure success is to measure the above four metrics (actual interaction/action/outcome). It can be a brand metric, say Likelihood to Recommend. Business Leaders.

B2B 167
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When models are everywhere

O'Reilly on Data

It predates recommendation engines, social media, engagement metrics, and the recent explosion of AI, but not by much. The Entertainment” is not the result of algorithms, business incentives and product managers optimizing for engagement metrics. And like a lot of near-future SciFi, it’s remarkably prescient.

Modeling 190