Promotion Signal: Proxy for a Price Cut?

Inman, J. Jeffrey; McAlister, Leigh; Hoyer, Wayne D.

doi:10.1086/208538

Cited by 380 publications

(290 citation statements)

References 12 publications

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“…Therefore, the sales response elasticity is likely to be different for a sales promotion, as compared to a change in the regular price. This is also empirically confirmed in, for example, Guadagni and Little (1983) and Inman et al (1990).…”

Section: Distribution Channel Literaturesupporting

confidence: 63%

Sales promotions and channel coordination

Wierenga

Soethoudt²

2009

J. of the Acad. Mark. Sci.

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Consumer sales promotions are usually the result of the decisions of two marketing channel parties, the manufacturer and the retailer. In making these decisions, each party normally follows its own interest: i.e. maximizes its own profit. Unfortunately, this results in a suboptimal outcome for the channel as a whole. Independent profit maximization by channel parties leads to a lack of channel coordination with the implication of leaving money on the table. This may well contribute to the notoriously low profitability of sales promotions. This paper first shows analytically why the suboptimality occurs, and then presents an empirical demonstration, using a unique dataset from an Efficient Consumer Response (ECR) project; ECR is a movement in which parties work together to optimize the distribution channel). In this dataset, actual profit is only a small fraction of potential profit, implying that there is a large degree of suboptimality. It is important that (1) channel parties are aware of this suboptimality; and (2) that they have tools to deal with it. Solutions to the channel coordination problem should ensure that the goals of the individual channel parties are aligned with the goals of the channel as a whole. The paper proposes one particular agreement for this purpose, called proportional discount sharing. Application to the ECR data shows a win-win result for both the manufacturer and the retailer. Recognition of the channel coordination problem by the manufacturer and the retailer is the necessary starting point for agreeing on a way of solving it in a win-win fashion.

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Section: Distribution Channel Literaturesupporting

confidence: 63%

Sales promotions and channel coordination

Wierenga

Soethoudt²

2009

J. of the Acad. Mark. Sci.

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“…Our specific measure of predictive performance is the shift in choice likelihood (cf. Inman et al 1990 One could raise the issue that the SCL measure favors ICERANGE as this approach alone allows PCL (predicted choice likelihood) and SCL values anywhere between 0 and 1 for a given respondent. With other methods, PCL and SCL values for any respondent are dichotomous -0 or 1, representing the miss rate (= 1 -hit rate).…”

Section: Resultsmentioning

confidence: 99%

Reservation Price as a Range: An Incentive-Compatible Measurement Approach

Wang

Venkatesh²,

Chatterjee³

2007

Journal of Marketing Research

128

110

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Drawing on the literature on buyers' uncertainty in preference and product knowledge, the authors make, and empirically test, the proposition that an individual consumer's reservation price for a product is more meaningfully and accurately represented as a range than as a single point. Given this conceptualization, they propose an approach for incentive compatible elicitation of an individual's reservation price range (ICERANGE) that builds on the Becker, DeGroot, and Marschak (BDM, 1964) point-of-purchase method. Two empirical applications of the approach provide an assessment of its practicality and its predictive performance relative to current methodologies for reservation price elicitation. Results demonstrate that the proposed method significantly outperforms the benchmark approaches in terms of predictive validity, while yielding valuable incremental information about uncertainty in product valuation.

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“…For example, Guadagni and Little (1983) report them to be the same while Blattberg and Neslin (1990) show large differences. Part of the confusion stems from the difficulty in completely separating the attention-getting aspect of a temporary price reduction (e.g., the effect of the shelf talker) from the price change itself (e.g., Inman et al 1990). Intuitively, for price reductions where shoppers are cued that the change is temporary, elasticities should exceed base-price elasticities when the discounts induce consumers to stockpile and switch brands.…”

Section: Competitive Reaction and Time Horizonmentioning

confidence: 99%

Commercial Use of UPC Scanner Data: Industry and Academic Perspectives

1999

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The authors report the findings from an exploratory investigation of the use of UPC scanner data in the consumer packaged goods industry in the U.S. The study examines the practitioner community's view of the use of scanner data and compares these views with academic research. Forty-one executives from ten data suppliers, packaged goods manufacturers, and consulting firms participated in wide-ranging, inperson, interviews conducted by the authors. 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 scanner data has had in each area.The authors then compare and contrast practitioners' views regarding the resolution of each issue with academic research. This produces a 2 ‫ן‬ 2 classification of each question as "resolved" or "unresolved" from the perspectives of industry and academia. Along the diagonal of the 2 ‫ן‬ 2, issues viewed as unresolved by both groups are important topics for future research. Issues deemed resolved by both groups are, correspondingly, of lower priority. In the offdiagonal cells, industry and academics disagree. These topics should 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, logit and regression models applied to scanner data have revealed very low average consumer response to coupons which has directly led to reduced couponing activity. Managers also reported high levels of comfort with and impact from analyses of trade promotions and price elasticities. While industry views most of the issues in these areas to be resolved, academic research raises concerns about a number of practices in common commercial use. These include price threshold analysis and trade promotion evaluation using baseline and incremental sales.In product strategy, advertising, and distribution management, practitioners reported that the use of scanner data has had more limited development, success, 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 methods. In advertising, scanner data is widely analyzed with models, but confusion 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 potential but a mixed track record to date. Thus, practitioners view the use of scanner data as unresolved for most issues in product strategy, advertising, and distribution. This view is largely, though not entirely, consistent with academic research, which has only begun to address many of the key quest...

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Promotion Signal: Proxy for a Price Cut?

Cited by 380 publications

References 12 publications

Sales promotions and channel coordination

Sales promotions and channel coordination

Reservation Price as a Range: An Incentive-Compatible Measurement Approach

Commercial Use of UPC Scanner Data: Industry and Academic Perspectives

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