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Excellent Analytics Tip #13: Measure Macro AND Micro Conversions.

Occam's Razor

Part of me is glad because my book and the Trinity strategy and the Web Analytics 2.0 mindset all stress the importance of measuring Outcomes. Hence my recommendation: Focus on measuring your macro (overall) conversions, but for optimal awesomeness identify and measure your micro conversions as well.

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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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How to Set AI Goals

O'Reilly on Data

My book, AI for People and Business , introduces a framework that highlights the fact that both people and businesses can benefit from AI in unique and different ways. In my book, I introduce the Technical Maturity Model: I define technical maturity as a combination of three factors at a given point of time.

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What is data architecture? A framework to manage data

CIO Business Intelligence

Invest in core functions that perform data curation such as modeling important relationships, cleansing raw data, and curating key dimensions and measures. Optimize data flows for agility. Limit the times data must be moved to reduce cost, increase data freshness, and optimize enterprise agility. Seamless data integration.

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Where CIOs should place their 2025 AI bets

CIO Business Intelligence

Deloittes State of Generative AI in the Enterprise reports nearly 70% have moved 30% or fewer of their gen AI experiments into production, and 41% of organizations have struggled to define and measure the impacts of their gen AI efforts. Why should CIOs bet on unifying their data and AI practices?

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What Is Hyperautomation?

O'Reilly on Data

We can see what books and courses our customers are using, and for how long. We know if customers only read the first chapter of some book, and can think about what how to improve it. Books can sit on shelves or in warehouses for a long time before they come back as returns. That’s the bad news. Decide where data fits in.

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Meta-Learning For Better Machine Learning

Rocket-Powered Data Science

So, you start by assuming a value for k and making random assumptions about the cluster means, and then iterate until you find the optimal set of clusters, based upon some evaluation metric. See the related post for more details about the cold start challenge. This is the meta-learning phase. How many hidden layers should there be?