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Customer Lifetime Value

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Customer Lifetime Value

Katherine Munro in Towards Data Science

A graph with Customer’s recency on the y axis (values from 35 to 0 as you travel up the axis), and Customer’s Frequency on the x axis (values from 0 to 25). The entire graph is filled with a colour gradient reflecting the probability that a customer is alive. Close to both axes, the colours indicate high P-Alive. But most of the graph, spreading from the upper right corner, indicates low P-Alive.

From Probabilistic to Predictive: Methods for Mastering Customer Lifetime Value

The final chapter in a…

Mikkel Dengsøe in Towards Data Science

The Hidden Cost of Data Quality Issues on the Return of Ad Spend

How leading companies drive…

Maja Pavlovic in Towards Data Science

The Paper: “A Deep Probabilistic Model for Customer Lifetime Value Prediction”

A run through the…

Aparna Dhinakaran in Towards Data Science

Best Practices In ML Observability for Customer Lifetime Value (LTV) Models

Tips for improving LTV…

h3ik0 in Towards Data Science

Notes from Industry

Buy ‘Til You Die: Predict Customer Lifetime Value in Python

Anastasia Reusova in Towards Data Science

Customer Lifetime Value via Retention — in Python, using Pandas and Numpy

On a Subscription Based…

Sophia Yang, Ph.D. in Towards Data Science

Customer lifetime value in a discrete-time contractual setting (math and Python implementation)

Adam Brownell in Towards Data Science

Customer Behavior Modeling: The Problem with Aggregate Statistics

Using Aggregate Statistics to Judge…

Adam Brownell in Towards Data Science

RNNs vs Traditional ML for Predictive Customer Lifetime Value

The company I work for, RETINA.ai…

Leif Arne Bakker in Towards Data Science

Know your customers with RFM

In this blog post we’ll show you how to do customer segmentation using…