a research agenda for making scanner data more useful to

Marketing Letters 5:4, (1994): 395-4121994 Kluwer Academic Publishers, Manufactured in the Netherlands.

A Research Agenda for Making Scanner Data MoreUseful to Managers'

SCOTT NESLIN. Amos Tuck School of Business Administration, Dartmouth CollegeGREG ALLENBY. College of Business, Ohio State UniversityANDREW EHRENBERG. South Bank Business School, South Bank University: Stern Schoolof Business. New York UniversitySTEVE HOCH. Graduate School of Business, University of ChicagoGILLES LAURENT, Graduate School of Management, Groupe HECROBERT LEONE, College of Business, Ohio State UniversityJOHN LITTLE. Sloan School of Management, Massachusetts Institute of TechnologyLEONARD LODISH, The Wharton School, University of PennsylvaniaROBERT SHOEMAKER, Stern School of Business, New York UniversityDICK WITTINK, Johnson School of Management, Cornell University

Key words: scanner data, advertising, promotion

Abstract

This paper presents a research agenda for making scanner data more useful to managers. Recom-mendations are generated in three areas. First, we draw on three case examples to distill a list ofcharacteristics of research that encourage managerial impact. Second, we generate a master list ofmanagerial research issues, and then, compile a list of nine priority topics. Third, we define a setof research topics on translating research into action. These topics involve the manner in whichmanagers react to research and how they communicate research findings with other managers. Thetheme throughout is that a combination of rigor and relevance is a potent force for improvingmanagerial practice.

1. Introduction

The environment is changing rapidly in the packaged goods industry, both formanufacturers and retailers. Shifting consumer lifestyles and manufacturers'brand extension policies have fostered an ever-increasing tide of new productsvying for sales and shelf space. The traditional supermarket distribution systemhas been shaken by mass merchandisers, price clubs, and convenience stores.Regionalization and its logical extension, micromarketing, have led many manu-facturers to decentralize their marketing plans, loosening the control previouslyenjoyed by headquarters. The result has been an increased emphasis on trade andconsumer promotion at the expense of manufacturer advertising. Manufacturersand retailers alike are looking for ways to cope with these changes.

Scanner data have emerged as a potential source of support. Approximately 77percent of U.S. supermarkets now are collecting scanner data {Progressive Gro-cer, 1992, p. 36). Information Resources, Inc. (IRI) and Nielsen Market Researchcurrently assemble this information. The data come in two forms: weekly sales

396 SCOTT NESLIN ET AL.

data from samples of 2,000 to 3,000 retail outlets, and household-level panel databased on a sample of upwards of 60,000 households (Little, 1993). Store data havemade weekly sales tracking for fifty markets and a limited number of individualkey accounts the norm for manufacturers. Retailers now have the ability, on astore basis, to determine how brands and product categories respond to merchan-dising activities. Panel data allow manufacturers increasingly to quantify brandswitching, loyalty, and market segmentation.

The purpose of this paper is to define a research agenda for making scannerdata more useful to managers. Our discussion derives from the following simpleframework:

Research < = > Manager < = > Other Managers, CompetitionThe manager is central to this schema because he or she is the direct user of theresearch findings. Implementation of the results of a scanner data analysis oftenrequires discussion with other managers within the company and consideration ofcompetitive reactions (see, e.g., Leeflang and Wittink, 1992). For example, con-sider the case where a brand manager is concerned about allocating promotionaldollars. Analysis may suggest that pay-for-performance trade promotions areprofitable and should be implemented more often. The manager first must under-stand and have faith in these results. Then, he or she must convince senior man-agement, the sales force, and eventually retailers, that this is the way to go. Allalong, the manager must consider how the competition will respond.

This scenario has three crucial elements: First, research must cover manageri-ally relevant topics - in this case, measuring promotion effectiveness. Second, theresearch must focus on important metrics - in this case, profit. Third, the managermust be able to effectively absorb the findings of the research and interact wellwith other managers in implementing the results. These elements suggest threeareas to discuss: (1) What are the characteristics of academic research that ensuremanagerial impact, (2) what are the research topics that need to be investigatedby academic research, and (3) what is needed for academic research to be effec-tively translated into managerial action!

2. Characteristics of research that encourage managerial impact

We first summarize three cases where research-based scanner data analyses havehad impact. We then distill the characteristics of the research that helped bringabout this success.

2.1. Case 1: Applying the Dirichiet model to developing a marketing plan for anew product

The new product development group at a major U.S. packaged goods companywas working on the marketing plan for a new brand of instant coffee (Ehrenberg

RESEARCH AGENDA FOR MAKING SCANNER DATA USEFUL TO MANAGERS 397

and Uncles, 1993). The plan determined a target sales rate of twenty-four jars per100 households. One plan for achieving this was a niche strategy whereby only 1percent of the population would buy the brand, but do so 24 times per year. Thequestion was whether this was a feasible strategy.

Panel data on the market together with the NBD-Dirichlet model (Chatfield andGoodhardt, 1975; Bass, Jeuland, and Wright, 1976; Ehrenberg, 1988) were usedto provide insight on this issue. The NBD-Dirichlet model is a stochastic modelof purchase incidence and brand choice based on the assumptions that householdcategory purchase is described by a Poisson distribution, and household brandchoice is described by a multinomial distribution. Each distribution is indepen-dent and stationary over purchase occasions. While each household is describedby this model, the parameters for the distributions vary across households, ac-cording to a gamma distribution for purchase incidence, and the Dirichlet distri-bution for brand choice.

The analysis in Table 1 shows penetration and purchase frequency - the keyelements of the marketing plan under scrutiny - for the currently existing brandsin the market. Both the observed statistics and those predicted by the NBD-Dir-ichlet model are displayed. These predictions are based on four observed inputs:the market shares shown in Table 1, the aggregate category-level penetration, theaggregate category-buying frequency, and the observed penetration of just onebrand (see also Ehrenberg, 1988). The model captures the observed statistics well.The data (and the model) reflect a commonly observed phenomenon - namely,that brands hardly differ in their repeat-buying frequencies, other than for a dou-ble jeopardy effect, whereby high market share brands have high penetration andhigh purchase frequency, while low market share brands have low penetration andlow purchase frequency.

The observed statistics in Table 1 show that the brand's marketing plan, callingfor 1 percent penetration and purchase frequency of twenty-four purchases per

Table I. NBD-Dirichlet predictions of penetration and purchase frequency

Brand

Maxwell HouseSankaTasters ChoiceHigh PointFolgersNescafeBrimMaxim

share (%)

1916141311843

Penetration(% of households buying

at least once)

Observed Predicted

24212222181896

28232120171476

Purchase frequency(average purchases per

year pei

Observed

3,63,32,82,62,72,92,02,6

r brand buyer)

Predicted

3,33,02,92,92.92.82.62,6

Source: Ehrenberg and Uncles (1993),

398 SCOTT NESLIN ET AL,

buyer, is out of step with the characteristics of existing brands. The resultsshowed management that they needed to search for a different plan in order tomeet their sales goals.

2.2. Case 2: Applying the logit model to evaluating coupon promotions

Current statistics suggest that more than 300 billion coupons were distributed bypackaged goods marketers in 1992 (NCH Promotional Services, 1993). There areseveral reasons for the popularity of this marketing instrument (see Blattberg andNeslin, 1990, Chapter 10), but foremost in the minds of brand managers are therelated questions of whether these promotions generate enough incremental salesto be profitable, and how couponing programs can be designed to enhance theireffectiveness.

The logit model has been used to evaluate these issues. The logit model wasfirst applied to scanner data by Guadagni and Little (1983). It provides a house-hold-specific description of brand choice including the effects of the marketingenvironment. This enables the model to derive a sales baseline - in this case, thelevel of sales the brand would have achieved had the coupon not been distributed.Actual sales, which refiect the effect of the coupon program, can be compared tothis baseline. The difference between actual and baseline sales is an estimate ofincremental sales (see Abraham and Lodish, 1993, for discussion of baseline anal-ysis).

Figure I illustrates the use of logit baseline analysis to evaluate a particularcoupon program. The coupon drop date is January 25, 1988. Prior to that date,the baseline fits actual sales fairly well, as it should, because in this period, noimportant predictor of sales, e.g. couponing, is left out of the model. Note thereis no apparent bias in this baseline. While there are period-to-period differencesbetween baseline and actual, these overages and underages approximately aver-age out to zero over extended periods. After January 25, sales lie consistentlyabove the baseline, and it takes roughly five months for baseline and actual salesto begin to coincide again. The rather long time for the coupon program to run itscourse exists because consumers can wait several months before redeeming theircoupons, so the incremental sales impact is distributed over several months.

The above analysis has been conducted for approximately 1,000 coupon pro-grams. A major generalization is that coupons with infinite expiration dates (thedate at which the consumer can no longer redeem the coupon) generate fewerincremental sales per redemption than coupons with finite expiration dates. Stateddifferently, redemptions that take place in the weeks immediately following cou-pon distribution are more likely to generate incremental sales than redemptionsin later weeks (see Klein, 1985; Neslin, 1990). It is noteworthy that many com-panies now are moving toward finite expiration dates (NCH Promotional Ser-vices, 1993).


INCREMENTAL SALES I S THE DIFFERENCE OF ACTUAL AND BASE SALES

Actual Sales Base Sales

198B

Figure I.

Source: Little and Wang (1991).

Case 3: Applying the nested logit model to simulating alternative marketingplans

The marketing plan for Cicada Nuggets (a disguised name and category) was beingdebated by management. Of particular interest was whether the base plan, includ-ing three coupon drops, should be abandoned in favor of a reallocation plan, withlower advertising but six coupon drops and additional consumer promotions. Akey question was what would be the one-year sales impact of the alternativeplans.

Since the managerial issue here was brand sales, a model was needed that ex-amined purchase incidence as well as brand choice. The nested logit model pro-vides one methodology for addressing these issues (Guadagni and Little, 1987).Most important for the application on hand, the nested logit model seeks to de-scribe how marketing mix elements influence both of these decisions. The modelcould therefore be used to simulate the level of sales that would occur under eachof the marketing plans being debated by Cicada Nuggets management.


(.36 FSI

$.40/1.30FSIt $.40 FSI

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O' ' I I I I I I I I I I I I I I I I I I I I I I I 1 I I I I

06.26.89 06.07.69 09.16.89 10.30.69 12.11.89 01.22.90 03.06.90 04.16.90 06.28.90

Week

Base Plan Reallocatlon Plan

Figure 2. Cicada nuggets unit sales per 100 households, base and reallocation plansSource: Honnold (1991).

Figure 2 shows predicted sales using the base plan and reallocation plan. Onecan see the three additional peaks in the data predicted by the three new coupondistributions under the reallocation plan. There is little apparent loss due to thereduced level of advertising. The net result was that the reallocation plan gener-ated 17 percent additional sales compared to the base plan. An estimated 5.1 per-cent decrease in sales due to decreased advertising was more than offset by a22.5% increase in sales due to couponing, additional consumer promotions, andsynergies among these activities. The final decision of which plan to adopt couldnow rest on additional strategic issues and cost considerations, but the key issueof sales impact was addressed by the model.

2.3. What the cases show: Five research characteristics that encourage impact

The above examples suggest five desirable characteristics of high impact re-search:

• Focus on important managerial issues: In each of these cases, research-basedmethodologies could be adapted to focus on decisions that managers make: thepenetration/purchase frequency plan for a new product, the design of a couponprogram, the emphasis on advertising versus promotion in a marketing plan.


• Identify important generalizations: In Case 1, the observance of double jeop-ardy in prior testing of the NBD/Dirichlet model was crucial to convincing man-agement that a low penetration/high purchase frequency plan was probably notfeasible. In Case 2, the generalization that later coupon redemptions yield lowerincremental sales per redemption strongly suggested that the industry shouldmove toward shorter expiration dates.

• Require simple inputs from the manager. In Case 1, the key inputs were thepenetration/purchase frequency plan and four easily obtained descriptive mea-sures of the market structure. In Case 3, the key inputs were scanner panel dataplus two alternative marketing plans. Note that the research methodology itselfcan be quite complicated. Dirichlet heterogeneity in a stochastic model may notbe simple for the manager, but the basic assumptions of these models are easyto understand.

• Generate simple, relevant, interpretable output: In all three cases, the majorresults could be presented in simple tabular or graphical form. For example, inCase 3, sales was the relevant metric and sales results from alternative planscould be compared. The reasons for differences between the plans could bereadily and intuitively diagnosed.

• Include a clear, concrete validation: In Case 1, the correspondence betweenactual and model-produced penetration and purchase frequency numbers wasconvincing. In Case 2, the match between baseline and actual in the periodsprior to the coupon drop was convincing. Use of holdout data periods can beeffective for establishing the validity of a model (see Guadagni and Little, 1983).Experience with other product categories can also establish validity (see Ehren-berg, Goodheart, and Barwise, 1990). Note the validation does not have to beperfect, just accurate enough to allow a decision to be made. In Case 1, clearlya 1 percent per 24 purchases-per-year scenario was not in step with the market.In Case 2, there is no apparent bias in the baseline so the estimate of incrementalsales due to the coupon should also be unbiased.

We also note that the cases illustrate two general approaches to handling scan-ner data. One is to search for generalizabie patterns in the data, starting for ex-ample as in Case 1 with market shares and predicting various brand performancemeasures from them. Another approach, illustrated in Cases 2 and 3, is to searchfor causal effects (e.g., advertising, promotion) using an econometric model,which then can be used to predict sales or market share. The cases show that bothapproaches can be useful depending on the managerial problem.

3. Managerially important research topics

We first present a rather extensive master list based on our experiences plus in-depth managerial interviews. The list is not meant to be exhaustive but simplysuggests one set of managerial problems. We then cull this list to generate priority


topics. This required combining or rephrasing the items on the master list. Twocriteria used in developing these topics were managerial importance and the ca-pability of scanner data to provide answers.

3.1. The master list

Table 2 displays our master list in a framework that differentiates between themanufacturer and the retailer and shows where the topic fits in terms of the strat-egy formulation/implementation/evaluation process. Some issues relate to manu-facturer/retailer relationships, and are placed between the manufacturer and re-tailer headings.

One interesting observation regarding Table 2 is that many topics appear onboth sides of the manufacturer/retailer ledger. Both manufacturers and retailersare concerned about EDLP ("value pricing"), consumer loyalty, private labelstrategy, category management, micromarketing, and promotion evaluation. Thisundoubtedly reflects a growing sophistication on the part of retailers to managetheir stores as products rather than as distribution outlets for manufacturers (seeMulhern and Leone, 1991).

3.2. Priority topics

Based on the criteria of relevance and feasibility described above, we generatedthe following list of priority topics.

3.2.1. Developing private label strategy. Private labels ("store brands") have at-tained a significant presence in the packaged goods industry (Hoch and Banerji,1993). Manufacturers therefore face the problem of how to compete with the pri-vate label brand - for example, what is the role of advertising and promotion?Retailers need to decide whether to market a private label brand, and if so, howto price and position it versus the manufacturers' brands and the private labels ofother retailers. What quality level should the private label brand have? What ef-forts should the retailer make in "branding" their private label?

There is evidence for an asymmetry in promotional cross-elasticities that favorsnational brands over private labels (Blattberg and Wisniewski, 1989). This findingneeds to be generalized and understood (see Allenby and Rossi, 1991). For ex-ample, despite a possible advantage due to asymmetry, manufacturers of nationalbrands have recently felt pricing pressure from private labels (see Hoch and Ba-nerji, 1993). More generally, we need to understand the extent to which privatelabels threaten manufacturers, or whether they expand the market or help definethe positioning ofthe national brands. More research is needed on identifying andunderstanding the purchase behavior of the "private label prone" consumer.


Table 2. "Master list" of managerial issues

Manufacturer Retailer:-> Develop "win-win" programsManage cooperative programsManage pay-for-performance promotionsStandardize informationAchieve "fair" division of profitsManage EDI/ECR systems

Strategy formulation Strategy formulationManage organization decentralization Complete with other forms ofDefine product categories retailersCompete with private label Formulate category strategiesStimulate primary demand Decide between EDLP versusAllocate between advertising and promotion high/lowDetermine appropriate promotion frequency Set margin planDecide between EDLP and high/low Define price zones strategyFormulate chain-specific promotion policies Develop private label strategyIncrease brand loyalty Increase store loyalty

ImplementationMonitor new productsEnhance salesperson effectivenessDesign salesperson incentivesIdentify brand performance indicesDevelop micromarketing programsDevelop consumer profiles

ImplementationSelect items to carrySelect items to promoteManage warehouse/store interfaceSet promotion priceSet regular priceDevelop micromarketing programs

EvaluationMeasure competitive reactionMeasure cross elasticitiesEnsure data integrityDevelop response "yardsticks"Measure effectiveness of continuity

programsMeasure effectiveness of cross-couponingMeasure positioning and image effectsMeasure effects of multiple promotionsDevelop long-term baselinesMeasure effectiveness of new forms of

couponingMeasure advertising response

EvaluationMeasure category elasticitiesMeasure category cross-elasticitiesMeasure promotion effect on profitMeasure effects of continuity

programsMeasure effects of marketing mix

on store choiceMeasure store image effects

3.2.2. Formulating category strategy. A significant trend for both manufacturersand retailers is an emphasis on category rather than brand management. For themanufacturer, this is partly the result of proliferating brands, sizes, and varieties.For the retailer, there is the recognition that category performance, not brandperformance, determines store profitability and should govern the decision of


which brands to promote. Refiecting this emphasis, both manufacturers and re-tailers have defined increasing roles for category managers.

The key questions for the manufacturer are which brands, sizes, and varietiesto offer and how to coordinate the marketing mix among these items? In answer-ing these questions, researchers will be faced with the formidable problem ofestimating marketing mix elasticities and cross-elasticities at the level of thebrand-size-variety (see Allenby and Rossi, 1991; Russell, 1992). The retailer facesa similar problem in deciding which brands, sizes, and varieties to carry, and howpricing, promotion and shelf-space should be coordinated among these items (see,e.g., Gupta, 1993). Retailers are particularly concerned with cross-category ef-fects, since their objective is to maximize store sales.

3.2.3. Micromarketing. Both manufacturers and retailers are faced with major de-cisions regarding how much emphasis to put on marketing programs that are tar-geted directly at individual customers (see Blattberg and Deighton, 1991).

The manufacturer needs to decide how finely to design its marketing programs,how to tailor marketing programs to particular retailers (key accounts), and howmuch to invest in various loyalty programs and targeted promotions. For example,Catalina Marketing can distribute coupons at the check-out counter to individualconsumers based on the brand(s) the consumer has just purchased. Should thesecoupons be targeted at current users or potential brand switchers? What redemp-tion rates should be expected, and how many incremental sales per redemptionare generated? In light of the fact that the consumer expends virtually no effortto obtain these coupons, how do they influence long-term brand loyalty? Theretailer faces a similar set of issues. The retailer needs to define store clusters thathave the same marketing and pricing strategy (see Hoch, Dreze, and Purk, 1994).The retailer also needs to gauge the effectiveness of various "frequent shopper"programs. Do these programs enhance loyalty among members? Do they decreaseloyalty among non members? In general, how can the retailer harness the tremen-dous amount of household-specific information that can be collected at the check-out counter?

Positioning and image. Positioning and image have traditionally been addressedby consumer surveys. Positioning maps and segmentation studies derived fromscanner data offer the advantages of unobtrusive data collection, direct linkage tobehavioral data, direct linkage to marketing mix activities, and consistency ofmethod across time and categories. Recent advances in scanner data-based mapshave been made by Elrod (1988, 1991), Shugan (1986), Allenby (1989), and Chin-tagunta(1993).

While there are still significant methodological issues, for example, assump-tions such as homogeneous perceptions, we now seem poised to examine manysubstantive issues that are critical to today's manager. For example, what hasbeen the long-term evolution of brand differentiation? Are positioning maps more


"crowded" today then they were ten years ago? How do advertising, promotion,and new product activities relate to these changes? Does promotion have anyeffect on the positioning of a brand? Does it degrade perceived quality at theexpense of a low price position? Has newly implemented EDLP or value pricingstrategies affected the positioning of brands? Are private labels positioned differ-ently than national brands? While the above topics refer to product positioning,we also need to answer the question of how various types of retailers are posi-tioned in the marketplace.

3.2.5. Advertising versus promotion. While several issues remain in analyzing ad-vertising and promotion separately, many marketing decisions require simultane-ous consideration of both advertising and promotion. Accordingly, responsemodels should include both advertising and promotion. There are challengingmethodological issues here. For example, recent sales promotion research sug-gests that the store-level is preferred over the market-level as the unit of analysis(Wittink, Porter, and Gupta, 1993). However, are store-level data best for detect-ing advertising effects? Managers often talk about advertising as "brand-build-ing." Can such effects be picked up in store-level data, or in even more disaggre-gate panel data?

An additional question is how marketing funds should be allocated betweenadvertising and promotion? The common view is that promotion handles short-term objectives while advertising addresses the long term. Is this accurate? Doespromotion have any advertising effect, e.g., in creating brand awareness (seeBawa and Shoemaker, 1989)? Does advertising have any promotion effect, e.g.,in altering purchase timing? How does the advertising content of promotions re-late to their effectiveness? Are there interactions between advertising and pro-motion? Does one increase the effectiveness of the other (see Boulding, Lee, andStaelin, 1992)? Is there a common set of pretest procedures that can predict theeffectiveness of both advertising and promotion?

Management yardsticks. There is a need to go beyond the reporting of coefficients,toward the generation of interpretable measures that can be computed periodi-cally. Managers pay significant attention to simple measures that monitor abrand's performance, e.g., the extent to which a brand's demand derives frombrand switchers, loyals, and light users (see McQueen, Foley, and Deighton,1993). Incremental promotion sales need to be decomposed into sales due to brandswitching, sales accelerated from future periods, and sales due to expansion ofthe category. Managers want to know what percentage of their brand's sales in agiven period was taken from Brand B, and what percentage of Brand B's saleswas drawn from their brand. They want to know if their brand is fundamentally"healthy" (see Bhattacharya and Lodish, 1993), While the desire to separateswitching, acceleration, and category expansion effects is sensible and easy tounderstand, it can be difficult to do in practice.


3.2.7. Coping with channel diversity. A significant trend in packaged goods mar-keting has been the breakdown of the traditional supermarket distribution channelinto different forms of retailing. These include mass merchandisers, price clubs,convenience stores, and in-home shopping. For the manufacturers, the issue ishow to juggle the competing needs of various retailer types. Should they producea particular package size for a mass merchandiser? Will this cannibalize sales ofthe product in supermarkets? Will this change consumer shopping habits and dis-tort consumer reference prices? How in general do different pricing, product, andsales force strategies affect the performance of the various channels of distribu-tion? What types of strategies are best for appealing to both EDLP and high/lowretailers?

For retailers, especially supermarkets, channel diversity is seen in a competi-tive light. Supermarkets want to know how to compete with price clubs. Shouldthey adopt EDLP strategies to reassure the consumer of low prices, or shouldthey price high/low so that they always can advertise some prices that are lowerthan the price clubs? Which categories should supermarkets emphasize? Is thefuture of supermarkets to view all packaged goods as loss leaders and meat, pro-duce, and dairy as their profit centers?

3.2.8. Store choice. The process by which consumers choose stores is of criticalimportance to retailers (see Kumar and Leone, 1988; Walters and MacKenzie,1988; Uncles and Ehrenberg, 1990). The manufacturer needs to understand thisprocess in order to "sell-through" its trade promotions to retailers. The argumentthat this promotion will sell more of Brand A no longer has potency with theretailer. Even a more sophisticated argument involving category growth may notwork. The key question is how many additional customers will the store attractby promoting Brand A? There is undoubtedly a signal to noise problem here: howmuch impact could promotion of even a number one brand have on store sales,and is that impact detectable given the level of "unexplained" week-to-week vari-ation in total store sales?

3.2.9. Long-term impacts of promotion. Much scanner data research has focusedon the impact of a promotion during the week in which the promotion takes place.Intermediate-term effects that influence sales in the weeks following promotionhave received less attention, and long term effects that infiuence sales over pe-riods of years have received little attention (Blattberg and Neslin, 1989). Whenscanner data first became available, the immediate spikes generated by promotioncommanded attention. However, many managers have become concerned withwhat happens after the promotion. Some are looking at the sales spikes of com-petitors and the apparent sales baselines and wondering whether it is possible foreveryone to be benefiting from promotion. Are promotions growing the category?Are there acceleration effects that are masked by weekly sales data? Can re-


searchers disentangle acceleration effects, which merely alter the timing of salesthat would have eventually occurred anyway, and category expansion, which rep-resent truly incremental sales?

Research is needed that considers the question of three promotions versus six,rather than zero versus one. We need to understand how promotion affects brandloyalty and price sensitivity. The issue is made difficult by the probability that thecausality works both ways (see Boulding, Lee, and Staelin, 1992). A fundamentalquestion is whether promotions merely rearrange the pattern and timing of sales,but result in no aggregate brand or category volume differences. We need empir-ical research that identifies whether there are long-term winners and losers in apromotion environment.

4. Tt'anslating research into action

While research that addresses the right topics and that possesses the right char-acteristics should go a long way toward making scanner data more useful for man-agers, there is a need to understand the broader issues of decision-maker behaviorand organizational behavior that determine whether this research will be trans-lated into action. This is an exciting area in that it will surely require interdisci-plinary work among organization theorists, psychologists, and management sci-entists. We have distilled six areas of research as follows:

• How do we unfreeze managers from current practice? This is not to imply thatcurrent practice is necessarily wrong, but clearly there is inertia that slowsadoption of new ideas. Is the issue education, organizational barriers, the needfor continuity? Scanner data can be particularly threatening because it quanti-fies what was once not quantifiable, at a frightening level of detail. Researchneeds to understand these barriers to adopting new methods.

• How do we enhance managers' abilities to "sell" the results of scanner dataanalyses? Managers need to interact with each other when implementing scan-ner data results. The manager may want to convince the sales force that couponsrather than trade deals are effective. The salesperson armed with scanner dataperformance data needs to know how to use the data to convince the retailer topromote his or her brand. Often these situations require the ability of the man-ager to understand the viewpoints of others.

• How can we help managers to distill the forest from the trees? The primaryadvantage of scanner data - greater detail - is at the same time its primarydeficiency. The problem is to design marketing information systems that deliverthe right level of detail to the right manager, and to discover methods of presen-tation and manager training that will enable managers to absorb that appropriatelevel.


• How do managers develop stories? Managers appear to develop "stories" abouthow the world behaves (see Goldstein, 1993). We need to understand how par-ticular presentation formats, measures, methods, and results influence the typesof stories managers develop. The key issue is how can we make this inferentialprocess more accurate.

• How do we resolve the tension between rightness and complexity? The potentialproblem here is that accurate analyses may be inherently more complex,whereas managers prefer simplicity. A given data set can be subjected to a verycomplex analysis, but a simple analysis, even if possibly less accurate, might bemore readily believable by management. Research is needed that unravels therole of complexity, perceived as well as actual, in the adoption of scanner dataresearch.

• Are managers better off with or without models? This is a traditional question(see for example Chakravarti, Mitchell, and Staelin, 1979; Fudge and Lodish,1977) but takes on new importance with scanner data. Do models by themselvesenhance decision-making, or is it models plus managerial judgment (Blattbergand Hoch, 1990)? What is the role of manager involvement with the model? Forexample, many scanner data analyses are being conducted as staff studies, butwith increased computer power, many of these analyses could be conducted bythe managers themselves. Is this desirable?

5. Summary

Our purpose has been to develop a research agenda for making scanner data moreuseful to managers. We have presented recommendations in three main areas:Desired characteristics of useful research, managerially important topics, andtranslating research into action. The overall theme is to blend rigor and relevanceto develop research that will have a positive impact on the practice of manage-ment.

We should note that scanner data contain inherent limitations that present ad-ditional challenges to researchers. Their ability to prescribe courses of actionhinges on the degree of variability present in the data. One way to address this isthrough experimentation (e.g., Hoch, Dreze, and Purk, 1994). Another concernis that category definitions available may be too narrow or broad for the purposesof a particular analysis. Researchers need to carefully consider the appropriatecategory definition. Finally, the strength of scanner data is the precision it pro-vides regarding inputs (e.g., promotions) and outputs (sales). However, the datado not record the intermediate behaviors that link the two, for example whethera consumer not purchasing the category in a particular week considered purchas-ing it and examined available brands, or simply decided not to purchase the cat-egory before entering the store.

Despite these limitations, scanner data have become a mainstay of the manag-er's information environment, and are influencing decisions that these managers


make. Our hope is that this paper will help researchers to provide the insights andtools necessary to ensure that these decisions are made more effectively.

Notes

1. This work is a group effort conducted as part of the Duke Invitational Symposium on Choice,held July 28 to August 1 at the Fuqua School of Business, Duke University. Scott Neslin servedas the Chair. We extend our appreciation to Joel Huber and the Fuqua School for organizingand hosting this forum, and to groups chaired by Don Lehmann, Itamar Simonson, and RussWiner, for insights gained by joint meetings. We also thank the Editor, Don Lehmann, and twoanonymous reviewers for their valuable suggestions regarding this manuscript.

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a research agenda for making scanner data more useful to

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