What happens when you analyze college football like the CIA? | Cultivate Labs Blog

What happens when you apply CIA-style analysis normally used to understand China or Russia to your favorite sports team?

Price elasticity: a missing factor in retailers’ forecasts - Supply Chain Movement

We all know that a product’s price influences demand. Yet there are still retailers who regard pricing and forecasting as two separate activi

The price elasticity of basic necessities such as milk, eggs and bread is low. Consumers will always continue to buy these.

How a team learns to trust a quote it did not build - The Loadstar

Four in five air freight quotes on cargo.one now go out without human review. Here are the six patterns that build forwarder trust in AI quoting.

We have run enough of these deployments to see the same six patterns emerge.

Label Leakage: The Bug That Makes Churn Models Look Brilliant - Dataconomy

The model scores 0.94 AUC on held-out data. The notebook is clean, the cross-validation is stratified, nobody touched the test

Food Prices Today: US Produce Price Charts from USDA Data

Food prices today for onions, potatoes, tomatoes, lettuce, avocados and strawberries: USDA wholesale and shipping point quotes with price charts, change against a year earlier, grocery ad prices by region and daily updates. Latest report 18 Sep 2026.

5 Levers Drive CAC, Says Ad Pro - Practical Ecommerce

Hal Smith of H Street Digital says lowering customer acquisition cost on Meta and Google starts with the offer.

https://searchengineland.com/google-top-ranking-factors-survey-131-seo-professionals-487723

Google AI Mode shows the same products 21.6% more expensive than traditional search [Data Study] | Productrise

A US and UK data study: when the same product ranks in both Google AI Mode and traditional search, the AI Mode price is about 21.6% higher.

How AI search has impacted the web traffic of over 50 advertisers

Data spanning one agency’s entire client roster shows the true impact of AI search on web traffic and conversions.

How to use MCP to get more data from the tools you already use

Your SEO tools already contain the answers. MCP makes it easier to ask questions your dashboards weren't built to handle.

Understanding Cross-Entropy Loss: The Training Signal Behind Perplexity

Learn how cross-entropy loss turns next-word prediction into the training signal behind perplexity.

The Great Blogging Collapse: What Happened to 100 Successful Blogs? [Study] | DanielStanica.com —

A 4-year study of 100 successful blogs reveals how Google's updates and AI transformed blogging. See which sites survived and which disappeared.

The loyalty metric no one wants to explain

Repeat purchases aren't proof of loyalty. Incrementality is.

5 Scipy.stats Tricks for Simulating 'What If' Scenarios - KDnuggets

In this article, we will take a look under the hood of scipy.stats, exploring five essential tricks to design high-performance, rigorous simulations using only NumPy and SciPy.

Your North Star Metric is Lying to You

The measurement philosophy that blinds the room it’s supposed to illuminate

How metrics learn to lie

Five diagnostic shapes that reveal whether a KPI is fit for service — before the dashboard turns dangerous.

What is Tokenization Drift and How to Fix It? - MarkTechPost

Analyzing Tokenization Drift: Using Token Overlap Metrics to Identify Out-of-Distribution Risks and Optimizing Prompts

Calculating The Process Capabilities Of Cleaning Processes: A Primer

The industry has begun the movement to science-, risk-, and statistics-based approaches to cleaning process development and validation. Process capability has become an important measure for demonstrating acceptable cleaning processe performance. Explore process…

What Google thinks you're worth | Proton

Proton analyzed 54,000 profiles using real ad auction data to determine your worth to Google. How valuable is your data?

The Concise Guide to the Law of Large Numbers

Understand the law of large numbers and why larger datasets produce results closer to theoretical truth.

he more times an experiment is repeated, the closer the average of its results will be to the expected theoretical result.

Coding agents for data analysis

Here's the handout I prepared for my NICAR 2026 workshop "Coding agents for data analysis" - a three hour session aimed at data journalists demonstrating ways that tools like Claude …

Browser Ad Bidding Process

ChatGPT helps you get answers, find inspiration, and be more productive.

LCD defect metrics

Implementing Softmax From Scratch: Avoiding the Numerical Stability Trap - MarkTechPost

Learn about implementing Softmax from scratch and discover how to avoid the numerical stability trap in deep learning projects.

This function implements the Softmax activation in its most straightforward form.

numerically unstable—large positive logits can cause overflow, and large negative logits can underflow to zero. As a result, this version should be avoided in real training pipelines

The Psychology of AI SERPs and Shopping

The rise of "zero-click" searches may signal the coming effect of AI shopping on product discovery and decision-making.

5 Statistical Methods That Dominate Their Industries (and Why)

Discover which statistical methods dominate healthcare, finance, tech, retail, and sports analytics, and why these industry-method pairings became indispensable standards.

Shapley Values Explained: Seeing Which Features Drive Your Predictions

Learn what Shapley values are and how SHAP tools help explain machine learning predictions.

The Concise Guide to Perplexity

5 Effect Size Measures You’ll Actually Use

Learn to calculate and interpret five essential effect size measures with Python examples and clear guidance.

Cohen’s d — the standardized difference between two group means r (correlation coefficient) — the strength of a relationship between variables Odds ratio — how much more likely an outcome is in one group than another η² (eta squared) — how much of the total variance your variable explains Cliff’s…

5 Common LLM Parameters Explained with Examples

Learn the 5 common LLM parameters explained with examples to optimize your model's performance and generate desired results.

max_completion_tokens, temperature, top_p, presence_penalty, and frequency_penalty

Max Tokens is the maximum number of tokens the model can generate during a run

Understanding the 4 Main Approaches to LLM Evaluation (From Scratch)

Multiple-Choice Benchmarks, Verifiers, Leaderboards, and LLM Judges with Code Examples

four common ways of evaluating trained LLMs in practice: multiple choice, verifiers, leaderboards, and LLM judges

training loss, perplexity, and rewards, but they are usually used internally during model development.)

How to Calculate Support, Confidence, and Lift in Python

Learn to calculate support, confidence, and lift metrics for market basket analysis using Python's mlxtend library.

Overall equipment effectiveness - Wikipedia

Understanding Market Basket Analysis: A Beginner’s Guide

Discover hidden customer purchasing patterns with market basket analysis to optimize product placement and increase sales.

A Guide to RFM Analysis using Python

In this article, I'll take you through a practical guide to RFM Analysis using Python. A Guide to RFM Analysis using Python.

🎁 Guide to HW Product Quality

Ensuring users receive world-class products.

FMEAs

SPC

A look at the ARC-AGI exam designed by French computer scientist François Chollet to show the gulf between AI models' memorized answers and…

By Matteo Wong / The Atlantic. View the full context on Techmeme.

What is METEOR score? - Dataconomy

METEOR Score is a metric used to evaluate the quality of machine translation based on precision, recall, word alignment, and linguistic flexibility.

What is triplet loss? - Dataconomy

Triplet loss is a machine learning function that minimizes distances between similar data points while maximizing distances between dissimilar ones.

How to Fine-tune Model Thresholds with Yellowbrick’s ROC-AUC Visualization

The key challenge lies in finding the right balance: How cautious should our model be when making classifications?

Umami is a simple, fast, privacy-focused alternative to Google Analytics.

Umami is a simple, fast, privacy-focused alternative to Google Analytics. - umami-software/umami

WTF is open-source marketing mix modeling?

Marketers should be wary of open-source programs promoted by purveyors with a walled garden history.

SWOT Analysis

A SWOT analysis helps teams understand how well a product, service, or organization is positioned in the market to serve its customers.

Stop Using Zip Codes for Geospatial Analysis

Uncover deeper insights beyond ZIP codes with geospatial analysis. Explore the limitations of ZIP codes and discover alternatives for spatial understanding.

Time Series Decomposition: Extracting Seasonal, Trend, and Residual Components

Still, the exact application, challenges and shortcuts related to this technique are relatively unknown, and that’s what this article seeks to change.

Testing Visual Design: A Comprehensive Guide

Use methods like 5-second testing, first-click testing, and preference testing to gain insights into how users perceive your visual design.

Four Cutting-Edge Methods for Evaluating AI Agents and Enhancing LLM Performance

The advent of LLMs has propelled advancements in AI for decades. One such advanced application of LLMs is Agents, which replicate human reasoning remarkably. An agent is a system that can perform complicated tasks by following a reasoning process similar to humans: think…

DataExpert-io/data-engineer-handbook: This is a repo with links to everything you'd ever want to learn about data engineering

This is a repo with links to everything you'd ever want to learn about data engineering - DataExpert-io/data-engineer-handbook

Beyond Pageviews: Measure Content Performance And User Engagement In GA4

GA4 provides valuable insights into user interactions on your website. Learn how to analyze key metrics like sessions, session duration, and events to refine your digital strategy.

Goodhart's Law Isn't as Useful as You Might Think

Goodhart's Law is useless. It tells you about a phenomenon, but it doesn't tell you how to solve it. We look at how organisations actually prevent Goodhart's Law, and illustrate this with Amazon's Weekly Business Review as an example.