| Knowing what to sell, when, and to whom. | |
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MedLine Citation:
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PMID: 16515161 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Despite an abundance of data, most companies do a poor job of predicting the behavior of their customers. In fact, the authors' research suggests that even companies that take the greatest trouble over their predictions about whether a particular customer will buy a particular product are correct only around 55% of the time--a result that hardly justifies the costs of having a CRM system in the first place. Businesses usually conclude from studies like this that it's impossible to use the past to predict the future, so they revert to the timeworn marketing practice of inundating their customers with offers. But as the authors explain, the reason for the poor predictions is not any basic limitation of CRM systems or the predictive power of past behavior, but rather of the mathematical methods that companies use to interpret the data. The authors have developed a new way of predicting customer behavior, based on the work of the Nobel Prize-winning economist Daniel McFadden, that delivers vastly improved results. Indeed, the methodology increases the odds of successfully predicting a specific purchase by a specific customer at a specific time to about 85%, a number that will have a major impact on any company's marketing ROI. What's more, using this methodology, companies can increase revenues while reducing their frequency of customer contact-evidence that overcommunication with customers may actually damage a company's sales. |
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Authors:
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V Kumar; Rajkumar Venkatesan; Werner Reinartz |
Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: Harvard business review Volume: 84 ISSN: 0017-8012 ISO Abbreviation: Harv Bus Rev Publication Date: 2006 Mar |
Date Detail:
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Created Date: 2006-03-06 Completed Date: 2006-03-31 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9875796 Medline TA: Harv Bus Rev Country: United States |
Other Details:
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Languages: eng Pagination: 131-7, 150 Citation Subset: H |
Affiliation:
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ING Center for Financial Services, University of Connecticut's School of Business, Stors, USA. vk@business.uconn.edu |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Commerce
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organization & administration* Economic Competition Marketing / methods* United States |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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