Artificial Intelligence

Merchant Attrition Prediction

merchant attition prediction

A Fortune 1000 company providing financial technology services required a solution to predict the churn probability for each of its merchants in order to reduce merchant attrition through targeted campaigns.

Client Challenges and Requirements

  • The Client had a large reserve of data with insufficient information of factors influential towards merchant attrition in order to build a prediction model
  • No concrete definition of label variable (Merchant Attrition)

Bitwise Solution

Bitwise created a prediction model that enabled the Client to make reliable predictions for a merchant’s propensity to churn. Implementation included:

test (4)

Identification of influential characteristics of merchants on the verge of attrition

checklist (7)

Formulation of a definition of attrition using data exploration techniques

support (7)

Implementation of supervised learning with multiple algorithms

cloud (15)

On-demand predictions using end-to-end pipeline on Google Cloud Composer (Airflow)

data (20)

Integration with Google Data Studio for interactive reports

Tools & Technologies We Used

TensorFlow

Python

Google Cloud Airflow

Google Cloud BigQuery

Google Cloud ML Engine

Google Studio (BI Tool)

Key Results
Group 3

Identified influential factors leading to merchant attrition

feature (1) Created with Sketch.

Predicted attrition with 83% accuracy using supervised learning

target (8) Created with Sketch.

3% to 9% reduced attrition rate with retention schemes offered to merchants likely to churn

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