Predicting Customer Lifetime Values – ecommerce use case

02/10/2021
by   Ziv Pollak, et al.
0

Predicting customer future purchases and lifetime value is a key metrics for managing marketing campaigns and optimizing marketing spend. This task is specifically challenging when the relationships between the customer and the firm are of a noncontractual nature and therefore the future purchases need to be predicted based mostly on historical purchases. This work compares two approaches to predict customer future purchases, first using a 'buy-till-you-die' statistical model to predict customer behavior and later using a neural network on the same dataset and comparing the results. This comparison will lead to both quantitative and qualitative analysis of those two methods as well as recommendation on how to proceed in different cases and opportunities for future research.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/16/2013

A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services

Marketing literature states that it is more costly to engage a new custo...
research
04/20/2023

Causal Analysis of Customer Churn Using Deep Learning

Customer churn describes terminating a relationship with a business or r...
research
10/18/2018

Convolutional Collaborative Filter Network for Video Based Recommendation Systems

This analysis explores the temporal sequencing of objects in a movie tra...
research
10/06/2020

Friendship is All we Need: A Multi-graph Embedding Approach for Modeling Customer Behavior

Understanding customer behavior is fundamental for many use-cases in ind...
research
11/04/2019

"Predicting" after peeking into the future: Correcting a fundamental flaw in the SAOM – TERGM comparison of Leifeld and Cranmer (2019)

We review the empirical comparison of SAOMs and TERGMs by Leifeld and Cr...
research
09/04/2021

Customer 360-degree Insights in Predicting Chronic Diabetes

Chronic diseases such as diabetes are quite prevalent in the world and a...
research
07/31/2023

Metric@CustomerN: Evaluating Metrics at a Customer Level in E-Commerce

Accuracy measures such as Recall, Precision, and Hit Rate have been a st...

Please sign up or login with your details

Forgot password? Click here to reset