The charm of AI may have been kept alive by both the science community and Hollywood, but Ash Dhupar knows its real-world application can have a huge impact on the bottom line. As Chief Analytics Officer at Publishers Clearing House, he and his team utilize 180 algorithms to predict LTV with a 2-3% accuracy. By adding the laser focus of CLV as an objective, the application of machine learning and AI techniques help to point to the most powerful marketing channels by attribution, better optimizing marketing dollars.
10-year-old grading software leverages deep learning algorithms to “compare notes” with human teachers’ scores, suggestions, and comments. An engineer involved in the project compared its capabilities to those of AlphaGo, the record-breaking AI Go player developed by Google subsidiary DeepMind.
Avi Goldfarb, a professor at the University of Toronto’s Rotman School of Management, explains the economics of machine learning, a branch of artificial intelligence that makes predictions. He says as prediction gets cheaper and better, machines are going to be doing more of it. That means businesses — and individual workers — need to figure out how to take advantage of the technology to stay competitive. Goldfarb is the coauthor of the book “Prediction Machines: The Simple Economics of Artificial Intelligence.”
Too much data and not enough analysts. This article does an excellent job describing the latent power of Analytics, and the potential of citizen data scientists. All-in-all, a good way to kickoff this month’s focus on Analytical Automation.
https://hbr.org/2018/05/why-marketing-analytics-hasnt-lived-up-to-its-promise
Whenever faced with a new job or transition at work, I pull “The First 90 Days” off my shelf. This is one book I wish I had received earlier in my career. And many times a data or analytics issue at work is really a question of successfully navigating one’s organization. Going to give a few copies to new grads…
Too much data and not enough analysts. This article does an excellent job describing the latent power of Analytics, and the potential of citizen data scientists. All-in-all, a good way to kickoff this month’s focus on Analytical Automation.