Artificial IntelliTools

  Predicting Customer Behavior: A Data Science Journey Through Insurance Marketing Imagine you’re a marketing director at an insurance company launching a new product. Your team is excited — this offering consolidates coverage in ways customers have been asking for. You’ve got a database of 14,000 customers. The question keeping you up at night: Who should we target? Common wisdom says target your loyal customers, right? People who already trust you and buy your products. Spend your marketing budget on those established relationships. But what if the data told you the exact opposite? What if your most loyal customers, the ones already using your products, were the least likely to buy your new offering? This is the story of a real predictive analytics project that challenged conventional marketing wisdom — and revealed surprising truths about customer behavior. It’s also a story about mistakes, corrections, and the messy reality of data science work. The Challenge: 14,000 Customers...

  Beauty in the Classroom: What Really Drives Professor Evaluations? ( A data-driven exploration ) A data-driven exploration of how appearance, gender, and other factors influence teaching evaluations Photo by Max Fischer We’ve all been there at the end of a semester: faced with those standardized evaluation forms asking us to rate our professors. Most universities consider these evaluations essential for tenure decisions, promotion, and even annual performance reviews. But what factors are truly being measured in these evaluations? Are they valid indicators of teaching effectiveness, or are they influenced by factors entirely unrelated to a professor’s teaching abilities? In this article, I’ll dive into an analysis of real evaluation data from the University of Texas at Austin to uncover what’s actually driving student ratings. The results may surprise you ,and make you think differently about those evaluation forms. The Dataset: Beauty and the Professor The data comes f...