Tag: analytics

The key to using customer lifetime value (LTV) effectively is the understanding that it is a prediction, not a value. In my previous eight posts on LTV, I stressed the importance of LTV to the success of your game and company and the key components in determining LTV. After reading Nate Silver’s The Signal and the Noise, I realized that it is crucial to understand that LTV is a prediction and suffers the same risk as other predictions (e.g., elections, weather, sports scores).

The Uncertainty Principle

Many people mistakenly believe (and I may have inadvertently implied this in a previous post), that LTV is an exact function of virality, monetization and retention. It implies you put those variables into a formula and get out a number that shows precisely how much a player is worth. That would be the case if you did it with historical information after five years and then calculated how much that player had been worth to you. However, you are calculating how much the player will be worth, which is inherently different because you are predicting their future value.

The uncertainty principle, a key tenet of quantum mechanics (as popularized by Stephen Hawking), postulates that perfect predictions are impossible if the universe itself is random. Since you cannot have a perfect prediction, your LTV cannot be a distinctly quantified value. You are predicting future events (how much the player will monetize, how viral they will be and how long they will stay in your game) based on the available data. Your LTV model is a simplification of the world the player is in; you are looking at several variables but you cannot look at everything (e.g., chance of war, plague, everyone switching to Blackberry devices). In effect, your LTV calculation is very similar to a sportscaster’s estimate of how many home runs Albert Pujols will hit or a weatherman’s prediction on the likelihood of a hurricane to hit Cape Hatteras. Continue reading “Lifetime Value Part 9: Uncertainty and LTV”