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dc.contributor.advisorMichael Cusumano.en_US
dc.contributor.authorAdam, Matias Ben_US
dc.contributor.otherTechnology and Policy Program.en_US
dc.date.accessioned2018-09-17T15:53:41Z
dc.date.available2018-09-17T15:53:41Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/118010
dc.descriptionThesis: S.M. in Management of Technology, Massachusetts Institute of Technology, Sloan School of Management, 2018.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 92-99).en_US
dc.description.abstractToday'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.en_US
dc.description.statementofresponsibilityby Matias B. Adam.en_US
dc.format.extent107 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectSloan School of Management.en_US
dc.subjectTechnology and Policy Program.en_US
dc.titleImproving complex sale cycles and performance by using machine learning and predictive analytics to understand the customer journeyen_US
dc.typeThesisen_US
dc.description.degreeS.M. in Management of Technologyen_US
dc.contributor.departmentSloan School of Management.en_US
dc.contributor.departmentTechnology and Policy Program.en_US
dc.identifier.oclc1051454073en_US


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