RFM Analysis

Basic RFM Analysis Implementation with Elasticsearch and FeatherJS

About

This project uses:

• RFM Analysis for Customer Segmentation.
• Feathers. An open source web framework for building modern real-time applications.
• Elasticsearch for storing and indexing Data.

Prerequisites

1. NodeJS Installed
2. Elasticsearch Installed. It'll be easier with Docker
3. Logstash or any ETL tool that can ship customer and invoice data into Elasticsearch

Getting Started

Getting up and running is as easy as 1, 2, 3, 4, 5.

1. Make sure you have NodeJS and npm installed.

2. Make sure you have Elasticsearch installation and configure it in ./config/default.json

3. Use Logstash to ship data from Database into Elasticsearch. In my case I shipped data from Oracle ==> Elasticsearch. Simple logstash config is in ./logtash/shipper.conf

4. Install your dependencies

   cd path/to/rfm-analysis; npm install
   

5. Start your app

   npm start
   

Indexing RFM Data

This call will index rfm data of periods (3 months, 6 months & 12 months) from customer and invoice that we shipped from database.

Commandline

`curl -XPOST localhost:3030/rfm`

License

Copyright (c) 2018

Licensed under the MIT license.