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Defining Customer Value by Predicting Customer Conversion — Allan Dieguez (Creditas)

The objective of this presentation is to describe the challenges of modeling a customer conversion predictor using real leads data observed on different levels of the conversion funnel. This predictor is useful for segmenting customers by estimated effort of conversion, which allows intelligence-based decision making for many areas of the company, such as marketing, customer success and credit analysis. The discussion will include the solution sketching process, as well as the data extraction, feature engineering and model evaluation. It’ll also be covered some challenges of using such models as a support system for the operations analysts, as well as collecting their feedback to improve the solution.


Allan Dieguez is a Data Scientist at Creditas, responsible for conceptualizing, building and deploying optimization solutions in many areas of the company. He has 11 years of experience in designing and building machine learning solutions in many fields such as image recognition, NLP, dynamic pricing and data mining. He holds an MSc in Computer and Information Sciences from the Rio de Janeiro Federal University (UFRJ), Brazil.