Data & Analytics

40 Retail Analytics Use Cases Every Data Leader Should Know

Retail analytics is one of the most dynamic and mature domains in data. It sits at the crossroads of customer behavior, real-time operations, and constant decision-making. But here’s the truth: while the retail context is unique, the analytics patterns it relies on are highly transferable. Whether you’re in fintech, education, manufacturing, or SaaS, the tactics used in retail analytics—segmentation, forecasting, personalization, optimization—are universally applicable. Retail’s dynamic environment—marked by real-time customer behavior, high transaction volumes, and …

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What Every Industry Needs in Their Marketing Stack (Even If They Call It Something Else)

Tools Change, Needs Don’t This is Part 3 in my Marketing Patterns That Work Everywhere series. In the earlier posts (Part 1 | Part 2), I shared how core marketing principles—like conversion, personalization, and attribution—show up across fintech, edtech, supply chain, manufacturing, and automotive. I know what you really want to know – what tech stack should be powering this. Simply put… you don’t need 20 tools. You need tools to support one clear loop: Track behavior → Store it …

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What Car Shopping Taught Me About Marketing Funnels

This is Part 2 of my series on universal marketing patterns that work across industries. In Part 1, I shared five core patterns—lead generation, customer journeys, next best action, conversion, and attribution—and why they matter no matter what space you’re in. Lessons Travel Well Some of the best marketing strategies don’t start in a boardroom—they start in the field, solving real problems. What works in one industry often works in another, once you strip away the …

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Beyond the Buzzwords: Marketing Patterns That Transcend Industries

Different Industries, Same Problems Every business thinks its challenges are special. And sure, industries can feel different—healthcare isn’t retail, and selling to a college isn’t the same as selling to a homeowner. But at the core, most teams are asking the same big questions: After working across industries, I’ve seen just how often these questions come up, regardless of the product or customer. The language might change, but the patterns don’t. This is the first …

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Why Structured Data and Data Quality Matter More Than AI Hype

The Temptation of the AI Silver Bullet AI is having its moment. Everywhere you look, companies are rushing to implement large language models (LLMs), AI agents, and other cutting-edge techniques in hopes of solving their data problems. It’s easy to get swept up in the hype—after all, who wouldn’t want an intelligent system that magically fixes business data challenges? But there’s a fundamental issue that gets overlooked in this excitement: garbage in, garbage out (GIGO). If your …

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Why You Shouldn’t Query Raw Data—And When It Makes Sense

I was recently asked why it’s not advisable to query raw data directly. The question caught me off guard at first, but it made me think deeply. Raw data is often viewed as the foundation of analytics, yet querying it without proper processing can lead to inefficiencies, inconsistencies, and potential misinterpretations. In this post, I’ll attempt to explain why querying raw data should generally be avoided, when it’s necessary to do so, and best practices …

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