Data science could revive targeted marketing after iOS 14 privacy crackdown
By Alex Song, contributing writer for Venturebeat.com
Even before the iOS 14.5 update, protecting consumer privacy has been a high priority for big tech and for brands and marketing. Over the next few years, major platforms are likely to implement more consumer controls and these controls can further complicate digital marketing strategies. Data scientists are addressing this challenge by figuring out how to preserve consumer privacy while also optimizing ad performance.
How to improve demand forecasting with machine learning and real-time data
By Vasudevan Sundarababu, contributing writer for Forbes.com
The Covid-19 pandemic has proved that history no longer repeats itself when it comes to understanding consumer behavior. Demand forecasting systems have been ill-equipped to address disruptions to our daily lives on a global scale. Consumer packaged goods (CPG) companies and retailers were caught flat-footed as unforeseen panic-buying took hold throughout 2020, resulting in product shortages. As it turned out, the onset of the Covid-19 pandemic in 2020 was just a warmup for what was to follow. An ongoing global supply chain disruption, inflation and the emergence of Covid-19 variants have continued to wreak havoc with demand forecasting.
Machine learning applications in the manufacturing industry
By Mobidev, contributing writers for Iotforall.com
Manufacturers, to keep up with the latest changes in technology, need to explore one of the most critical elements driving factories forward into the future: machine learning. Let’s talk about the most important applications and innovations that ML technology is providing in 2022.
Automated analysis of animal behavior
By Fabio Bergamin, contributing writer for Sciencedaily.com
Researchers engaged in animal behavior studies often rely on hours upon hours of video footage which they manually analyze. Usually, this requires researchers to work their way through recordings spanning several weeks or months, laboriously noting down observations on the animals’ behavior. Now researchers at ETH Zurich and University of Zurich have come up with an automated way to analyze these kinds of recordings. The image-analysis algorithm they have developed makes use of computer vision and machine learning. It can distinguish individual animals and identify specific behaviors, such as those that signal curiosity, fear, or harmonious social interactions with other members of their species.
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