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Bringing an AI Product to Market

O'Reilly on Data

Without clarity in metrics, it’s impossible to do meaningful experimentation. AI PMs must ensure that experimentation occurs during three phases of the product lifecycle: Phase 1: Concept During the concept phase, it’s important to determine if it’s even possible for an AI product “ intervention ” to move an upstream business metric.

Marketing 362
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Robust Experimentation and Testing | Reasons for Failure!

Occam's Razor

Since you're reading a blog on advanced analytics, I'm going to assume that you have been exposed to the magical and amazing awesomeness of experimentation and testing. And yet, chances are you really don’t know anyone directly who uses experimentation as a part of their regular business practice. Wah wah wah waaah.

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The ethics of data flow

O'Reilly on Data

In this case, the surprise was that Target used this customer's purchase history to identify her as pregnant, and send circulars advertising products for pregnant women and new mothers to her house. The data that’s flowing isn’t just the feed to the marketing contractor. What might that responsibility mean?

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Digital listening reveals 3 leading innovation drivers

CIO Business Intelligence

It surpasses blockchain and metaverse projects, which are viewed as experimental or in the pilot stage, especially by established enterprises. Metaverse Opportunities Advertising: Advertisers see the metaverse as a powerful way to connect with and reach consumers.

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Worthy of Your Attention

Perceptual Edge

Stigchel is a professor in the Department of Experimental Psychology at Utrecht University in the Netherlands. Everyone whose job involves guiding people’s attention, like website designers, teachers, traffic engineers, and, of course, advertising agents, could be given the title of “attention architect.”

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What is a data scientist? A key data analytics role and a lucrative career

CIO Business Intelligence

Social networking: Social networking data can inform targeted advertising, improve customer satisfaction, establish trends in location data, and enhance features and services. Quantitative analysis: Quantitative analysis improves your ability to run experimental analysis, scale your data strategy, and help you implement machine learning.

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Experiment design and modeling for long-term studies in ads

The Unofficial Google Data Science Blog

accounting for effects "orthogonal" to the randomization used in experimentation. For example in ads, experiments using cookies (users) as experimental units are not suited to capture the impact of a treatment on advertisers or publishers nor their reaction to it.