Remove 2009 Remove Measurement Remove Metrics Remove Statistics
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Brand Measurement: Analytics & Metrics for Branding Campaigns

Occam's Razor

One of the ultimate excuses for not measuring impact of Marketing campaigns is: "Oh, that's just a branding campaign." It is criminal not to measure your direct response campaigns online. I also believe that a massively under appreciated opportunity exists to truly measure impact of branding campaigns online.

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Excellent Analytics Tips #20: Measuring Digital "Brand Strength"

Occam's Razor

Bonus One: Read: Brand Measurement: Analytics & Metrics for Branding Campaigns ]. There are many different tools, both online and offline, that measure the elusive metric called brand strength. I love using this tool to measure " unaided brand recall." Amazon is an interesting example.

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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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Understanding Simpson’s Paradox to Avoid Faulty Conclusions

Sisense

One of the simplest ways to start exploring your data is to aggregate the metrics you are interested in by their relevant dimensions. This is an example of Simpon’s paradox , a statistical phenomenon in which a trend that is present when data is put into groups reverses or disappears when the data is combined.

Testing 104
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Fact-based Decision-making

Peter James Thomas

This piece was prompted by both Olaf’s question and a recent article by my friend Neil Raden on his Silicon Angle blog, Performance management: Can you really manage what you measure? It is hard to account for such tweaking in measurement systems. Pertinence and fidelity of metrics developed from Data.

Metrics 49
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Themes and Conferences per Pacoid, Episode 9

Domino Data Lab

Visualizations are vital in data science work, with the caveat that the information that they convey may be 4-5 layers of abstraction away from the actual business process being measured. measure the subjects’ ability to trust the models’ results. Use of influence functions goes back to the 1970s in robust statistics.

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A Picture Paints a Thousand Numbers

Peter James Thomas

Some authorities describe them as any diagram using a map to display statistical data; I cover this type of general chart in Map Charts below. The single red line shows the actual value of some metric up to the middle section of the chart. See also: Data Visualisation according to a Four-year-old. Cartograms.

Sales 61