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[R] Improving predictive CLV by using non-transactional data

Traditional approaches to predicting CLV (Customer Lifetime Value), like EP/NBD, continue to be effective many years after their initial formulation. In fact, attempts to improve on traditional methods using advanced ML techniques have been minimally effective. We explored combining an ML approach with the use of additional (non-transactional) data and have found early results to be quite promising. We’ve summarized our results in a blog post[1] and a more detailed paper[2].

  1. https://eng.amperity.com/posts/2019/12/predictive-clv
  2. https://amperity.com/assets/downloads/research/predictive-customer-lifetime-value.pdf

submitted by /u/derekslager
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.