Improving complex sale cycles and performance by using machine learning and predictive analytics to understand the customer journey

Abstract

Today's business operations and decision management demand that firms respond efficiently in an increasingly dynamic and highly competitive external environment. Business-to-business firms need insight about markets and customers along the entire sales and marketing cycle. This demand is complicated by the inflexibility of legacy systems and growing distributed architectures add even more internal complexity. In addition, gaps and mismatches between strategy and execution constrain the ability to understand the customer experience. This challenging context requires an agile, collaborative, and flexible framework in order to acquire, analyze, model, and evaluate information necessary for improving customer insights and making data-driven decisions to enhance the customer journey. This thesis analyzes how to effectively shorten the customer journey and related sales cycle in business-to-business firms through the use of new technologies. My research examines the benefits and challenges of applied machine learning and predictive analytics to improve critical stages in the sales and marketing process by making assisted decisions that accelerate the sales cycle and increase performance. This thesis focuses on methodologies for promoting and fostering technology adoption, improving business decisions and performance, and accelerating digital transformation.