Data Analytics

AI, Data Analytics

Why Your 2023 AI-Based Analytics Are Already Obsolete

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For the past few decades, SAS, Python and R have dominated exploratory analytics and modeling, supplemented by a progression of interactive tools, starting with OLAP in the 1990’s and progressing to visual tools like Tableau in recent years. The most productive versions of these solutions still required weeks of researching data sources, downloading them (often for a fee), multiple trial-and-error cycles on quality, and wrestling with mismatched keys across datasets. Once the data was ready, producing analysis required laborious hand-coding to produce and present insights.

Business, Data Analytics

Behind the Metrics: Finding Business Success in the Unmeasurable

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On a recent morning at Denver’s airport, I witnessed the hidden switch that drives customer loyalty. I spotted one of my favorite servers on the train looking worn out and quiet – a stark contrast to her usual upbeat self. I gave her space and headed to my go-to restaurant (one that we locals actually arrive early to visit). As I took my first sips of coffee, I saw that same server now laughing with teammates as they prepped for the day. In minutes, she’d completely transformed. Whether through smart training or personal grit, she’d tapped into that hard-to-measure skill that turns first-time visitors into regulars.

Customer Concepts, Data Analytics

Institutional Customer Keys: Navigating the Maze of B2B Relationships

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Retail customer relationships can range from simple to complex. By comparison, institutional relationships can seem like a giant maze. Proving my point, new employees and external vendors often find themselves asking lots of questions about organizational structures and how decisions are made. Sometimes they even use language like “navigating the organization” or “mapping the hierarchy.” Large companies can have hundreds or even thousands of employees. Some make the buying decisions, while others influence them. In most cases, there are many others who represent the actual customer need as users of the provider company’s products.

Customer Concepts, Data Analytics

Why Customer Keys are Key to Success

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Customer Catalytics emphasizes the importance of designing robust customer identification systems early in a business’s lifecycle. These identifiers are crucial for gaining customer insights, personalizing experiences, and scaling effectively. Neglecting this can lead to costly issues. The article explores the multi-dimensional benefits and complexities of proper customer ID management.

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