Remove 2016 Remove Strategy Remove Testing Remove Uncertainty
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Big Data: The Technology Behind Retailers Success

Smart Data Collective

Data-based insights can help make the right decisions, keep up with market trends and navigate the uncertainty. This information can further be used in marketing strategies. Retailers can conduct A/B testing to find out which prices work the best. This global coffee brand has increased its revenue by 26% from 2016 to 2019.

Big Data 125
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Belcorp reimagines R&D with AI

CIO Business Intelligence

These circumstances have induced uncertainty across our entire business value chain,” says Venkat Gopalan, chief digital, data and technology officer, Belcorp. “As That, in turn, led to a slew of manual processes to make descriptive analysis of the test results. This allowed us to derive insights more easily.”

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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

Crucially, it takes into account the uncertainty inherent in our experiments. It is also a sound strategy when experimenting with several parameters at the same time. To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages.

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Global Banking & Finance Review: What Story Does the Office of the CFO Need to Tell Now?

Jet Global

The Office of the CFO sits at the heart of business strategy if it is operating productively and effectively. In the last few months, this level of thinking has been tested more than ever before. And that’s the last thing you want during in periods of uncertainty where things are changing on a daily basis. A New Look.

Finance 64
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The trinity of errors in applying confidence intervals: An exploration using Statsmodels

O'Reilly on Data

Because of this trifecta of errors, we need dynamic models that quantify the uncertainty inherent in our financial estimates and predictions. Practitioners in all social sciences, especially financial economics, use confidence intervals to quantify the uncertainty in their estimates and predictions.

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Using random effects models in prediction problems

The Unofficial Google Data Science Blog

In the context of prediction problems, another benefit is that the models produce an estimate of the uncertainty in their predictions: the predictive posterior distribution. both L1 and L2 penalties; see [8]) which were tuned for test set accuracy (log likelihood). arXiv preprint arXiv:1602.00047, (2016). [8] bandit problems).

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Decision-Making in a Time of Crisis

O'Reilly on Data

We know, statistically, that doubling down on an 11 is a good (and common) strategy in blackjack. But when making a decision under uncertainty about the future, two things dictate the outcome: (1) the quality of the decision and (2) chance. We saw this after the 2016 U.S. To do so, let’s stick with the example of the 2016 U.S.