There is no such thing as an average customer. While it’s easy to say the customer is the most important asset, few have spent time quantifying what that truly means. Anthony Choe is one of those few. As Founder at Provenance, a progressive consumer private equity firm, Anthony uses Customer Lifetime Value as the primary lens for evaluating businesses. He explains how predictive CLV provides precision and allows both the investor and company to have a singular focus on the customer among all the data noise. With the amount of data and marketing tools available ever increasing, he believes CLV principles are timeless. Optimizing around CLV will always be the best answer, regardless of channel, regardless of the marketing message, regardless of shifting landscapes.
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.”
