Commercial use of UPC scanner data: Industry and academic perspectives

Citation
Re. Bucklin et S. Gupta, Commercial use of UPC scanner data: Industry and academic perspectives, MARKET SCI, 18(3), 1999, pp. 247-273
Citations number
93
Categorie Soggetti
Economics
Journal title
MARKETING SCIENCE
ISSN journal
07322399 → ACNP
Volume
18
Issue
3
Year of publication
1999
Pages
247 - 273
Database
ISI
SICI code
0732-2399(1999)18:3<247:CUOUSD>2.0.ZU;2-F
Abstract
The authors report the findings from an exploratory investigation of the us e of UPC scanner data in the consumer packaged goods industry in the U.S. T he study examines the practitioner community's view of the use of scanner d ata and compares these views with academic research. Forty-one executives f rom ten data suppliers, packaged goods manufacturers, and consulting firms participated in wide-ranging, in-person, interviews conducted by the author s. The interviews sought to uncover key questions practitioners would like to answer with scanner data, how scanner data is applied to these questions , and the industry's perspective regarding the success that the use of scan ner data has had in each area. The authors then compare and contrast practitioners' views regarding the re solution of each issue with academic research. This produces a 2 x 2 classi fication of each question as "resolved" or "unresolved" from the perspectiv es of industry and academia. Along the diagonal of the 2 x 2, issues viewed as unresolved by both groups are important topics for future research. Iss ues deemed resolved by both groups are, correspondingly, of lower priority. In the off-diagonal cells, industry and academics disagree. These topics s hould be given priority for discussion, information exchange, and possible further research. Practitioners reported that scanner data analysis has had the most success and been most widely adopted for decision making in consumer promotions (i. e., coupons), trade promotions, and pricing. For example, legit and regress ion models applied to scanner data have revealed very low average consumer response to coupons which has directly led to reduced couponing activity. M anagers also reported high levels of comfort with and impact from analyses of trade promotions and price elasticities. While industry views most of th e issues in these areas to be resolved, academic research raises concerns a bout a number of practices in common commercial use. These include price th reshold analysis and trade promotion evaluation using baseline and incremen tal sales. In product strategy, advertising, and distribution management, practitioner s reported that the use of scanner data has had more limited development, s uccess, and impact. In the case of new product decisions, scanner data use has been slow to develop due to the inherent limitations of historical data for these decisions and a heavy reliance on traditional primary research m ethods. In advertising, scanner data is widely analyzed with models, but co nfusion among practitioners is very high due to controversies about methods (e.g., what level of data aggregation is best) and conflicting results. In distribution and retail management, scanner data use has tremendous potent ial but a mixed track record to date. Thus, practitioners view the use of s canner data as unresolved for most issues in product strategy, advertising, and distribution. This view is largely, though not entirely, consistent wi th academic research, which has only begun to address many of the key quest ions raised by practitioners. In Light of the large number of unresolved issues and mixed record of scann er data use to date, the authors offer a series of specific recommendations for immediate and longterm research priorities that are likely to have the greatest impact on commercial utilization of UPC scanner data. Topics of i mmediate priority include price thresholds and gaps, baseline and increment al sales, base price elasticity, competitive reactions, measurement of adve rtising effects, management of brand equity, rationalization of product ass ortments, and category management. Long-term priorities include a greater e mphasis on profitability versus sales or market share, developing prescript ive models versus descriptive models, and the need for industry standards.