Most of my posts about the reasons and methodology for creating accurate customer lifetime value (LTV) predictions have focused on the numbers and metrics, but a key element to predicting accurately LTV is observational (qualitative) data. It all comes down to more data is better, so predictions with qualitative data are going to be more accurate than those that rely solely on quantitative data. A mistake that is commonly made in the analytics world, and particularly in gaming, is to disregard anything that is not a quantitative value.
Some examples of incorporating effectively qualitative data
The example that had the most impact on me is that Billy Beane and the Oakland A’s, the subject of Moneyball (and multiple blog posts by me), has one of the highest scouting budgets in baseball. Scouts provide data on variables, like mental make-up and desire to win, that are not evident in the historical metrics. So although Beane makes personnel decisions based on metrics, he has also invested large sums in getting qualitative data (scouts watch players and prospects and then report on how they perceive the player’s skills). This approach has proven successful, as Beane’s A’s again surprised people by winning their division last year. What Beane has mastered is finding a way to incorporate the scouting reports with the available quantitative metrics. Continue reading “Lifetime Value Part 10: Incorporating qualitative data into your LTV predictions and game development”
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