Remove Data Collection Remove Experimentation Remove Measurement Remove Optimization
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eCommerce Brands Use Data Analytics for Conversion Rate Optimization

Smart Data Collective

There are many ways that data analytics can help e-commerce companies succeed. One benefit is that they can help with conversion rate optimization. Collecting Relevant Data for Conversion Rate Optimization Here is some vital data that e-commerce businesses need to collect to improve their conversion rates.

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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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How Svevia connects roads, risk, and refuse through the cloud

CIO Business Intelligence

Taking out the trash Division Drift has been key to disruptively digitize Svevia’s remit with the help of the internet of things (IoT), data collection, and data analysis. Since the route optimization came into place, fewer emptyings are required, he notes. But we do our best to achieve the right deliveries together.”

Risk 87
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What you need to know about product management for AI

O'Reilly on Data

Because it’s so different from traditional software development, where the risks are more or less well-known and predictable, AI rewards people and companies that are willing to take intelligent risks, and that have (or can develop) an experimental culture. Measurement, tracking, and logging is less of a priority in enterprise software.

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The Lean Analytics Cycle: Metrics > Hypothesis > Experiment > Act

Occam's Razor

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. Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. Measure and decide what to do.

Metrics 156
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Of Muffins and Machine Learning Models

Cloudera

blueberry spacing) is a measure of the model’s interpretability. We can think of model lineage as the specific combination of data and transformations on that data that create a model. This maps to the data collection, data engineering, model tuning and model training stages of the data science lifecycle.

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Practical Skills for The AI Product Manager

O'Reilly on Data

AI PMs should enter feature development and experimentation phases only after deciding what problem they want to solve as precisely as possible, and placing the problem into one of these categories. Experimentation: It’s just not possible to create a product by building, evaluating, and deploying a single model.