Quantitatively evaluating the effects of price promotions using data mining Gan, Min and Dai, Honghua 2013, Quantitatively evaluating the effects of price promotions using data mining. In Pooley, Rob, Coady, Jennifer, Linger, Henry, Barry, Chris, Lang, Michael and Schneider, Christoph (ed), Information systems development : reflections, challenges and new directions, Springer, New York, N.Y., pp.485-497, doi: 10.1007/978-1-4614-4951-5_39. Attached Files Name Description MIMEType Size Downloads Title Quantitatively evaluating the effects of price promotions using data mining Author(s) Gan, Min Dai, Honghua Title of book Information systems development : reflections, challenges and new directions Editor(s) Pooley, Rob Coady, Jennifer Linger, Henry Barry, Chris Lang, Michael Schneider, Christoph Publication date 2013 Chapter number 39 Total chapters 55 Start page 485 End page 497 Total pages 13 Publisher Springer Place of Publication New York, N.Y. Summary Price promotions (also called discount promotions), i.e. short-term temporary price reductions for selected items (Hermann 1989), are frequently used in sales promotions. The main objective of price promotions is to boost sales and increase profits. Quantitative evaluation of the effects of price promotions (QEEPP) is essential and important for sales managers to analyse historical price promotions and informative for devising more effective promotional strategies in the future. However, most previous studies only provide insights into the effects of discount promotions from some specific prospectives, and no approaches have been proposed for comprehensive evaluation of the effects of discount promotions. For example, Hinkle [1965] discovered that price promotions in the off-season are more favourable, and the effects of price promotions are stronger for new products. Peckham [1973] found that price promotions have no impact on long-term trend. Blattberg et al. [1978] identified that different segments respond to price promotions in different ways. Rockney [1991] discovered three basic types of effects: effects on discounted items, effects on substitutes and effects on complementary items. Notes This paper was presented at the International Conference on Information Systems Development (20th : 2011 : Edinburgh, Scotland) ISBN 1461449510 9781461449515 Language eng DOI 10.1007/978-1-4614-4951-5_39 Field of Research 080109 Pattern Recognition and Data Mining Socio Economic Objective 890202 Application Tools and System Utilities HERDC Research category B1 Book chapter Copyright notice ©2013, Springer Persistent URL http://hdl.handle.net/10536/DRO/DU:30060722 Document type: Book Chapter Collection: School of Information Technology Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Thu, 20 Feb 2014, 11:08:31 EST Thu, 20 Feb 2014, 11:33:25 EST Tue, 18 Mar 2014, 10:28:08 EST Wed, 09 Apr 2014, 16:32:59 EST Thu, 10 Apr 2014, 08:37:34 EST Fri, 11 Apr 2014, 12:27:06 EST Fri, 11 Apr 2014, 13:10:41 EST Fri, 11 Apr 2014, 13:14:43 EST Fri, 11 Jul 2014, 09:23:24 EST Tue, 15 Jul 2014, 11:52:31 EST Wed, 23 Jul 2014, 13:45:26 EST Fri, 22 Aug 2014, 16:20:48 EST Thu, 20 Nov 2014, 08:51:25 EST Wed, 18 Nov 2015, 07:20:45 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 207 Abstract Views, 10 File Downloads  -  Detailed Statistics Created: Thu, 20 Feb 2014, 11:08:31 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.