  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
