Optimizing Customer LTV with AI
When we first started Relativity6, we worked out of the hollowed halls of the Martin Trust Center for MIT Entrepreneurship. There, we would often ask the John Wooden of entrepreneurship, Prof. Bill Aulet, his thoughts about Harvard basketball, Cole Haan footwear, and business unit economics.
One day, when discussing cost of customer acquisition (COCA) and lifetime value (LTV), we came to the idea that artificial intelligence (AI) and machine learning (ML) would eventually help established companies in optimizing recurrent revenue by generating dynamic LTV calculations for individual customers, rather that working off historical averages. We believed some of Nassim Nicholas Taleb’s theory in Black Swan, that averages are too general of a metric; we saw Amazon’s and Uber’s use of dynamic pricing; and, we came to believe that any firm, small or large, in any vertical, could apply AI to their customer databases in order to optimize sales and marketing resource allocation.
By segmenting customers by their propensity to repurchase, firms can enjoy
dynamic sales and marketing strategies that confidently rely on loyal customers, foster the relationships of sporadic customers, and focus on reacquiring lapsed customers with greater accuracy and efficiency. AI can be used to both maximize recurrent revenue and minimize customer acquisition expenses. AI can also predict the value of multiple customer lifetimes, with significantly higher levels of accuracy than metrics generated from historical averages, and these insights can be gleaned even at the individual customer level, not just a cohort level. Dynamic unit economics are core to competitive sales in the digital age. We believe that simple AI applications can provide all business with the sorts of dynamic insights currently gleaned by the likes of Uber and Amazon.
We at Relativity6 love customer data. It’s literally the fuel that feeds our
engine. If you’d like to learn to more about your customers, email us at
hello@relativity6.com.


