In recent years, most companies now interact with their customers for businesses purposes through contact or call centers. Substantially, customers now perceive a company through their interaction with Customer Services Representatives (CSR's) in their centers. The CSR has become the key role and channel in maintaining brand reputation and ensuring customer retention. Explicitly, in a contact center environment Customer and CSR are the two main transactional entities, and business development depends on their interaction. Contact center management routinely adopts numerous processes to enhance centre services by training their CSRs, call recording for quality monitoring, acquiring customer feedback after the call, and assessing similar factors. However, these factors are often inadequate in advancing the customer experience, due to operational scale and being exclusively focused on the telephone as the medium for interaction. Every contact center strives to maximize its value through, improved customer satisfaction, retention and first call resolution; and minimized communication expenditures, for example, call handling time or talk time. Smart call routing can manage these improvements to enhance overall customer experience, leading to sales and maintained quality of service. The CSR to Customer call-outcome is the critical success factor (CSF) to improvement and optimization. This paper considers a new operational model for achieving significantly improved call-outcomes. A call outcome in contact center environment is most typically random, like flipping a coin, to tell whether a call achieves a sale or not. This random outcome can be made more certain if predicted and optimized by exploiting personal chemistry as a critical factor. Fortunately contact centers are more controlled environments within which to gain psychographic and demographic insights to gauge customer/CSR chemistry. This paper proposes that descriptive, predictive and prescriptive analytical techniques can be applied to psychographic and demographic insights; to find the ideal mapping between them. By using those techniques, the model shows a ten to fifteen percent improvement in call-outcomes. © 2011 ACM.