Work detail

Predicting purchasing intent on ecommerce websites

Author: Mgr. Marek Vařeka
Year: 2020 - summer
Leaders: prof. PhDr. Ladislav Krištoufek Ph.D.
Consultants:
Work type: Finance, Financial Markets and Banking
Masters
Language: English
Pages: 77
Awards and prizes:
Link:
Abstract: This thesis analyzes behavior of customers on an e-commerce website in order
to predict whether the customer is willing to buy something or is just window
shopping. In addition the secondary model predicts, if the customer is going
to leave the e-commerce website in next few clicks. To answer this questions
different frameworks are tested. The base model used is the Logit model. The
base model is compared with more sophisticated methods in machine learning
- with neural networks. The best results were yielded by Recurrent neural
network - the Long Short-Term Memory (LSTM). The results of the analysis
confirm importance of the click stream data and calculated features that track
user behavior on the e-commerce website, type of the page (product, category,
information), product variance and category variance. The thesis emphasizes
practical implications of this models. Two possible practical implementations
are presented. The models are tested in novel ways to see how would they
perform if implemented on the real e-commerce website.

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Deloitte

Sponsors

CRIF
McKinsey
Patria Finance