Mobirise



Direct Mail Planning System

A Project Example

Using Machine Learning methods to increase direct mail efficiency.

Industry: Marketing

Project description: 

Our customer sends out direct mail asking for donations. This project is aimed at optimizing mailing efforts, taking into account that all letters are sent via a post service and each letter has a non-zero cost.

Tasks implemented: 

  • Use of Cost-sensitive reinforcement learning method.
  • This approach concentrates on building an optimal sequence of decisions with the goal of maximizing total benefits accrued over a period of time instead of immediate benefits.
  • In the scope of this project the popular Markov Decision Process model with value function approximation was adopted.
  • Development of a scalable Reinforcement Learning framework capable of running on Hadoop / Apache Spark clusters using Apache Spark’s MLlib machine learning library
  • Creating an app to demonstrate the functionality of the new environment. The demonstration is based on data related to direct mail statistics from public sources.

Algorithms and mathematical methods:

  • Reinforcement learning, Sarsa, Q-learning
  • Logistic and Linear Regression
  • Decision Trees
  • Cost-Proportionate subsampling

Development environment:

  • Cluster framework: Hadoop, Apache Spark
  • Programming language: Scala
  • Math libraries: MLlib, Breeze
  • Database: HBase
  • Operating Systems: Linux, Windows
Project size:
  • 6 thousand lines of Scala code
  • 2 months, 3 developers

© Copyright 2019 ProWide Labs Ltd. All rights reserved.