Deal RADAR (Real-time Abandonment Detection and Recourse)
Nishant Vibhav Saxena, Suresh Arumugam, Chiranjiv Roy · Journal of Information and Optimization Sciences · 2016
Almost every organization faces the challenge of static or declining revenue at some point in time. Smartuse of analytics can not only turn the tides in their favour but can also catalyze some astonishing discoveries.We elucidatethe same through ‘Deal RADAR’. This paper first explains how we did data discovery by combining Naïve Bayes & Hunt’s Classification Algorithmsto unearth a set of neglected deals from the big unstructured sales pipeline data.We named those deals as abandoned deals and showed how they were silently leading to loss of opportunities worth millions of dollars year-over-year. This paper then explains how Deal RADAR can be deployedtodetect potential leakages in-time for effective recourse, eventually culminating in additional revenue.Deal RADAR is builton SAS® using best in class statistical techniques like KNN &Mean of n-Mode Imputation, Proximity Based Outlier Detection and Multivariate Stepwise Logistic Regression.Deal RADAR can help you optimize your sales pipeline and can not only increase your revenue but also improve your return on investment.