# Customer Lifetime Value Prediction Using Embeddings

arXiv: https://arxiv.org/abs/1703.02596 · HTML: https://mlsanity.com/paper/1703.02596/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2017.

## Built on by (most cited first, 16 of 16)

- [Out of the Box Thinking: Improving Customer Lifetime Value Modelling via Expert Routing and Game Whale Detection](https://mlsanity.com/paper/2308.12729/) (2023)
- [OptDist: Learning Optimal Distribution for Customer Lifetime Value Prediction](https://mlsanity.com/paper/2408.08585/) (2024)
- [Collaborative-Enhanced Prediction of Spending on Newly Downloaded Mobile Games under Consumption Uncertainty](https://mlsanity.com/paper/2404.08301/) (2024)
- [Personalized Promotion Decision Making Based on Direct and Enduring Effect Predictions](https://mlsanity.com/paper/2207.14798/) (2022)
- [ADSNet: Cross-Domain LTV Prediction with an Adaptive Siamese Network in Advertising](https://mlsanity.com/paper/2406.10517/) (2024)
- [Dynamic Customer Embeddings for Financial Service Applications](https://mlsanity.com/paper/2106.11880/) (2021)
- [Predicting Customer Lifetime Values -- ecommerce use case](https://mlsanity.com/paper/2102.05771/) (2021)
- [Data Science Methodologies: Current Challenges and Future Approaches](https://mlsanity.com/paper/2106.07287/) (2021)
- [Modelling customer lifetime-value in the retail banking industry](https://mlsanity.com/paper/2304.03038/) (2023)
- [Contrastive Multi-view Framework for Customer Lifetime Value Prediction](https://mlsanity.com/paper/2306.14400/) (2023)
- [Autoencoder-based General Purpose Representation Learning for Customer Embedding](https://mlsanity.com/paper/2402.18164/) (2024)
- [RQ-Reg: A Residual-Quantization-Based Framework for Continuous Value Prediction in Recommender Systems](https://mlsanity.com/paper/2602.23012/) (2026)
- [Understanding & Predicting User Lifetime with Machine Learning in an Anonymous Location-Based Social Network](https://mlsanity.com/paper/2103.01300/) (2021)
- [Clustering-based Aggregations for Prediction in Event Streams](https://mlsanity.com/paper/2210.09738/) (2022)
- [CASPR: Customer Activity Sequence-based Prediction and Representation](https://mlsanity.com/paper/2211.09174/) (2022)
- [A Meta-learning based Stacked Regression Approach for Customer Lifetime Value Prediction](https://mlsanity.com/paper/2308.08502/) (2023)

## Abstract (arXiv)

We describe the Customer LifeTime Value (CLTV) prediction system deployed at ASOS.com, a global online fashion retailer. CLTV prediction is an important problem in e-commerce where an accurate estimate of future value allows retailers to effectively allocate marketing spend, identify and nurture high value customers and mitigate exposure to losses. The system at ASOS provides daily estimates of the future value of every customer and is one of the cornerstones of the personalised shopping experience. The state of the art in this domain uses large numbers of handcrafted features and ensemble regressors to forecast value, predict churn and evaluate customer loyalty. Recently, domains including language, vision and speech have shown dramatic advances by replacing handcrafted features with features that are learned automatically from data. We detail the system deployed at ASOS and show that learning feature representations is a promising extension to the state of the art in CLTV modelling. We propose a novel way to generate embeddings of customers, which addresses the issue of the ever changing product catalogue and obtain a significant improvement over an exhaustive set of handcrafted features.