{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T01:57:38Z","timestamp":1791251858398,"version":"4.1.0"},"reference-count":31,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2000,8,1]],"date-time":"2000-08-01T00:00:00Z","timestamp":965088000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Marketing Research"],"published-print":{"date-parts":[[2000,8]]},"abstract":"<jats:p> One of the mysteries of store-level scanner data modeling is the lack of a dip in sales in the weeks following a promotion. Researchers expect to find a postpromotion dip because analyses of household scanner panel data indicate that consumers tend to accelerate their purchases in response to a promotion\u2014that is, they buy earlier and\/or purchase larger quantities than they would in the absence of a promotion. Thus, there should also be a pronounced dip in store-level sales in the weeks following a promotion. However, researchers rarely find such dips at either the category or the brand level. Several arguments have been proposed to account for the lack of a postpromotion dip in store-level sales data and to explain why dips may be hidden. Because dips are difficult to detect by traditional models (and by a visual inspection of the data), the authors propose models that can account for a multitude of factors that together cause complex pre- and postpromotion dips. The authors use three alternative distributed lead and lag structures: an Almon model, an unrestricted dynamic effects model, and an exponential decay model. In each model, the authors include four types of price discounts: without any support, with display-only support, with feature-only support, and with feature and display support. The models are calibrated on store-level scanner data for two product categories: tuna and toilet tissue. The authors estimate the dip to be between 4 and 25% of the current sales effect, which is consistent with household-level studies. <\/jats:p>","DOI":"10.1509\/jmkr.37.3.383.18782","type":"journal-article","created":{"date-parts":[[2003,11,13]],"date-time":"2003-11-13T18:01:53Z","timestamp":1068746513000},"page":"383-395","source":"Crossref","is-referenced-by-count":179,"title":["The Estimation of Pre- and Postpromotion Dips with Store-Level Scanner Data"],"prefix":"10.1177","volume":"37","author":[{"given":"Harald J.","family":"Van Heerde","sequence":"first","affiliation":[{"name":"Department of Marketing, Faculty of Economics, Tilburg University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter S.H.","family":"Leeflang","sequence":"additional","affiliation":[{"name":"Department of Marketing and Marketing Research, Faculty of Economics, University of Groningen"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dick R.","family":"Wittink","sequence":"additional","affiliation":[{"name":"General George Rogers Clark Professor of Management and Marketing, Yale School of Management, and Professor of Marketing and Marketing Research, University of Groningen"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2000,8,1]]},"reference":[{"key":"bibr1-jmkr.37.3.383.18782","doi-asserted-by":"publisher","DOI":"10.1287\/mksc.18.4.504"},{"key":"bibr2-jmkr.37.3.383.18782","doi-asserted-by":"publisher","DOI":"10.1287\/mksc.14.3.G122"},{"key":"bibr3-jmkr.37.3.383.18782","volume-title":"Sales Promotion, Concepts, Methods and Strategies.","author":"Blattberg Robert 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