Gilberto Vilar De Carvalho Santos
Feature importance analysis for User Lifetime Value prediction in games using Machine Learning: an exploratory approach.
Rel. Barbara Caputo, Giuseppe Rizzo. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2020
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Abstract
The main characteristic of a freemium business model is that only a small share of users drives the largest part of revenue for the company, financing the product for the rest of the users. In the growing gaming industry this scenario becomes even more critical, since anyone that has a cellphone and internet connection can access thousands of Free-to-Play games. Therefore, firms need to perform two difficult tasks in order to start or keep revenue growth: first, find and attract potentially high value users; second, retain and up-sell such valuable users. Customer Lifetime Value (LTV) is the most used metric to identify high value users and drive marketing budget in a business decision-making scenario.
Its prediction became a crucial part of game companies daily work, since players generate an exceptionally rich dataset that can be used to understand and predict their purchasing behavior over time
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