Strategies to Increase Pipeline Status: A Case Study from Eclinical Data

Abstract

In this paper we perform a case study regarding Eclinical data of Intelligent Medical Objects (IMO) which currently operates with eight pipeline statuses. It has been observed that the higher the pipeline status, the fewer consumers there tend to be. In this study, we aim to identify which factors significantly influence consumer presence at these advanced pipeline stages. Logistic regression is useful for predicting binary outcomes based on one or more independent variables. It estimates the probability of a particular outcome, allowing us to understand how different factors impact the likelihood of an event occurring. This method is widely used in fields like medicine, finance, and social sciences for classification problems and determining the significance of predictors, making it valuable for identifying key factors and making informed decisions based on probabilities. We apply logistic regression, using the probability of reaching the eighth status as our primary dependent variable. Of all the independent variables considered, only a select few are significant in explaining this outcome.