Personalized Influence Estimation Technique

TL;DR

This paper introduces a Personalized Influence Estimation technique that identifies key factors influencing individual observations across various business applications, aiding targeted decision-making.

Contribution

The paper presents a novel method for estimating feature influence at the individual observation level, enhancing personalized analysis in diverse domains.

Findings

Effectively identifies key influence factors for individual observations.

Justifies reasons for customer churn accurately.

Applicable across multiple business scenarios.

Abstract

Customer Satisfaction is the most important factors in the industry irrespective of domain. Key Driver Analysis is a common practice in data science to help the business to evaluate the same. Understanding key features, which influence the outcome or dependent feature, is highly important in statistical model building. This helps to eliminate not so important factors from the model to minimize noise coming from the features, which does not contribute significantly enough to explain the behavior of the dependent feature, which we want to predict. Personalized Influence Estimation is a technique introduced in this paper, which can estimate key factor influence for individual observations, which contribute most for each observations behavior pattern based on the dependent class or estimate. Observations can come from multiple business problem i.e. customers related to satisfaction study, customer related to Fraud Detection, network devices for Fault detection etc. It is highly important to understand the cause of issue at each observation level to take appropriate Individualized action at customer level or device level etc. This technique is based on joint behavior of the feature dimension for the specific observation, and relative importance of the feature to estimate impact. The technique mentioned in this paper is aimed to help organizations to understand each respondents or observations individual key contributing factor of Influence. Result of the experiment is really encouraging and able to justify key reasons for churn for majority of the sample appropriately