Do Changes in Customer Satisfaction Lead to Changes in Sales Performance in Food Retailing?

Transcription

1 Do Changes n Customer Satsfacton Lead to Changes n Sales Performance n Food Retalng? Mguel I. Gómez Research Assocate Food Industry Management Program Department of Appled Economcs and Management Cornell Unversty 149 Warren Hall Ithaca, NY Phone: (607) E-mal: mg7@cornell.edu Edward W. McLaughln Robert G. Tobn Professor of Marketng Department of Appled Economcs and Management Cornell Unversty 111 Warren Hall Ithaca, NY Phone: (607) E-mal: ewm3@cornell.edu Dck R. Wttnk General George Rogers Clark Professor of Management and Marketng Yale School of Management Yale Unversty 135 Prospect Street New Haven, CT Phone: (203) E-mal: dck.wttnk@yale.edu Paper prepared for presentaton at the Amercan Agrcultural Economcs Assocaton Annual Meetng, Montreal, Canada, July 27-30, 2003 Copyrght 2003 by Mguel I. Gómez, Edward W. McLaughln and Dck R. Wttnk. All rghts reserved. Readers may make verbatm copes of ths document for non-commercal purposes by any means, provded that ths copyrght notce appears on all such copes.

2 Do Changes n Customer Satsfacton Lead to Changes n Sales Performance n Food Retalng? ABSTRACT We measure the lnks between store attrbute perceptons and customer satsfacton, and between customer satsfacton and sales performance, n the food retal sector. Our data set conssts of sx waves of customer satsfacton and sales nformaton for about 250 stores over the perod for a publcly held supermarket company. We construct a statstcal model to address nonlneartes and asymmetres n the satsfacton-sales performance lnks, and we llustrate how food retalers can affect store revenues by managng customer satsfacton. Contrbutons of our study nclude the analyss of behavoral consequences of customer satsfacton n the food retal sector, the measurement of complextes of the satsfacton-sales performance lnks based on an emprcal model of frst dfferences, and a dscusson of how managers can use such results for customer satsfacton polces. 1

3 INTRODUCTION Food retalers recognze that customer satsfacton (CS) plays a key role n a successful busness strategy. What s unclear s the exact nature of that role, how precsely satsfacton should be managed, and whether manageral efforts amed at ncreasng satsfacton lead to hgher store sales. Today, managers n the food retal sector undertake substantal efforts to conduct CS surveys. Yet t appears that n most cases the data are used to smply montor specfc store attrbutes, and especally overall satsfacton, over tme. Unless the mpact of customer satsfacton on store revenues s assessed, managers have lttle bass for allocaton of resources. In general, the lnkages between drvers of customer satsfacton and sales performance have not been frmly establshed n the food ndustry. For the estmaton of these lnkages, recent research ndcates that several ssues must be addressed. These nclude possble multcollnearty among attrbute ratngs, asymmetres and nonlneartes n the lnks, and tme lags. Models and manageral tools that gnore these ssues mght lead to napproprate manageral decsons. We measure the lnks between attrbute perceptons and customer satsfacton, and between customer satsfacton and sales performance, n the food retal sector. The study reles upon an extensve data set comprsed of sx waves of customer satsfacton and sales nformaton for approxmately 250 stores over the perod for a publcly held supermarket company operatng n the Eastern US. We construct a statstcal model n frst dfferences that addresses the nherent nonlneartes and asymmetres n these lnks. We also provde an example of how frms can use the estmated lnkages to develop satsfacton polces that are predcted to ncrease store revenues. 2

4 Our study makes three contrbutons to the lterature, one methodologcal and two substantve. Frst, we examne nonlneartes and asymmetres n the satsfacton-sales performance lnks based on an emprcal model expressed n frst dfferences. Second, the study advances the measurement of behavoral lnks between customer satsfacton and performance n the food retal sector wth frm-specfc data. Thrd, our study shows how frms can employ such results to develop approprate customer satsfacton polces. Ths paper s organzed as follows: n the next secton, we dscuss customer satsfacton n food retalng. We then provde a revew of the relevant lterature, we descrbe the data, and we elaborate the statstcal model. We conclude wth the presentaton of results and a dscusson of possble extensons for future research. IMPORTANCE OF CUSTOMER SATISFACTION TO FOOD RETAILERS The food and beverage market s often the largest ndustral sector n developed economes. In the US, expendtures on food n both retal stores and food servce establshments account for nearly 30 percent of all retal spendng (US Department of Commerce 2002). Food retalng alone s among the largest of all retalng sectors n most countres. In 1997, the most recent ndustral census year n the US, grocery stores accounted for about seventeen percent of total retal trade revenues, and the ndustry employed about eghteen percent of all workers n retal establshments (US Department of Commerce 2002). Ths represents the second largest share among all retal census categores n the US, surpassed only by automotve. Current sector trends of ncreased competton, enhanced retaler ablty to analyze markets and greater shopper expectatons make satsfyng customers especally crtcal to food retalers. Furthermore, food retalng has unque characterstcs that make t dfferent from other 3

5 retal sectors wth regard to customer satsfacton. Food retalers offer a varety of goods and servces smultaneously, as opposed to other sectors that frequently specalze n offerng ether goods or servces. Indeed, for the customer there s more to vstng a supermarket than the mere acquston of consumpton goods. Dfferences n the shoppng experence between food retal outlets (e.g. store ambence, dsposton of assocates, store servces) are often as mportant to the customer as dfferences n the physcal characterstcs of the goods they buy (prce, qualty, etc). Another factor that dfferentates the food retal sector from other retal ndustres s hgh and frequent customer traffc. Accordng to the Food Marketng Insttute, customer traffc n supermarkets s roughly two tmes per person per week, the second hghest among all establshments n the retal channel after only convenence stores (Progressve Grocer 2001). However, customer counts n the convenent store ndustry are only a fracton of those n the supermarket ndustry. Thus t s not surprsng that Anderson and Sullvan (1993) report that the elastcty of repurchase ntentons wth respect to customer satsfacton n the supermarket ndustry s one of the hghest among all retal sectors. Because of hgh customer frequency and presumed low swtchng costs due to the prolferaton of supermarkets and competng retalers wth smlar product offerngs, unsatsfed customers are unlkely to reman loyal. After an unsatsfactory experence n a gven supermarket, the customer decson to shft stores mght follow almost mmedately, thus affectng store sales performance n a short perod. Conversely, food retalers who understand the lnkages between customer satsfacton drvers and sales performance may be able to avod creatng the unsatsfactory experence n the frst place. Thus, by makng the rght decsons to satsfy ther customers, nformed retalers may enjoy greater sales payoffs relatve to ther compettors. 4

6 The food retalng ndustry s aware of the ncreasng mportance of havng satsfed customers. For example, a natonal study conducted by Progressve Grocer and the NPD Group found small but sgnfcant decreases n the general level of customer satsfacton n the supermarket ndustry from 1995 to 1996 (Mathews 1997). Such customer dssatsfacton, the study suggests, could drve customers out of the supermarket channel--to competng channels--f managers are unable to redress these levels of dssatsfacton. Decson makers n food retalng frms now appear to agree on the vtal mportance of customer satsfacton (cf., Bannster 2001). For example, two of the most consulted trade sources n the food ndustry, Progressve Grocer and Supermarket Busness, together have produced more than a hundred artcles addressng customer satsfacton n the last decade. Whle food retalers recognze that customer satsfacton s vtal to the creaton of a successful busness strategy, what s unclear s the exact nature of that role, how to manage satsfacton, and whether nvestng n customer satsfacton leads to hgher sales. The lnkages between drvers of customer satsfacton and sales performance n the food ndustry have not been frmly establshed. Earler research helped retalers understand that nvestments n customer satsfacton must be fnancally justfed. A key component of ths thnkng s that management requres nformaton that accurately descrbes the lnkages between satsfacton and sales performance to gude customer satsfacton polces. LITERATURE REVIEW Our study focuses on the relatons between attrbute perceptons, overall customer satsfacton and store sales performance. Such lnks are part of a broader conceptual framework proposed by Heskett et al. (1994), namely the Servce-Proft Chan. Anderson and Mttal (2000) 5

7 strengthened ths framework by accommodatng nonlneartes and asymmetres n the lnks, and they renamed t the Satsfacton-Proft Chan. Hereafter we use the acronym CSSP, Customer Satsfacton-Sales Performance, to refer to the lnks of nterest. We show the elements of the CSSP chan n Fgure 1. Frst, we dentfy specfc and measurable attrbutes that are expected to nfluence overall satsfacton. The attrbutes are summarzed nto factors to accommodate commonalty and to mnmze multcollnearty. These satsfacton factors, n turn, capture product and servce varables that lead to overall satsfacton. It follows that mprovements n these satsfacton factors, n turn, should ncrease overall customer satsfacton. And ncreased customer satsfacton should lead to greater store sales, va ncreased lkelhood of repurchase and favorable word of mouth. We dscuss extant fndngs on these lnkages next. [Fgure 1 About Here] Attrbute Perceptons and Customer Satsfacton To capture the relatonshp between attrbute perceptons and overall customer satsfacton, we must dentfy how customers nterpret and respond to the products and servces they buy and experence. Here t s essental to dstngush between specfc attrbutes of a product or a servce and the satsfacton factor they represent. In food retalng, for nstance, consumers may put hgh value on a factor that mght be called customer servce provded by the supermarket. Ths s an example of an abstract or subjectve beneft. Ths abstract beneft depends on a set of related measurable attrbutes such as the dsposton of the cashers and sales assocates, speed and accuracy of checkout, and avalablty of everyday grocery tems and store cleanlness, among others. In addton to customer servce, other relevant factors affectng overall customer satsfacton n grocery stores nclude the store ambance, the perceved product qualty of (growng) pershables departments--now 50 percent or more of store sales n some 6

8 stores--such as fresh produce, del/bakery, seafood, fresh meat and floral, as well as the perceved value of products relatve to ther prce. These lnks have been the subject of ntense scrutny by marketng researchers. Snce the semnal behavor-orented research by Olver (1981), several artcles have focused on the antecedents of customer satsfacton n a wde varety of contexts, rangng from frm-specfc studes to naton-wde assessments. Although satsfacton factors vary accordng to the type of products, servces and busness sectors consdered, emprcal studes provde vast evdence of ther mpact on overall satsfacton (e.g. Szymansky and Henard 2001). Most studes on antecedents of customer satsfacton utlze models revewed by Johnson (1998) and show sgnfcant correlaton between varous satsfacton factors and overall satsfacton (Szymansky and Henard 2001; Bernhardt, Donthu and Kennett 2000; Mttal, Ross and Baldasare 1998; Wttnk and Bayer 1994). In general, these studes tend to collect nformaton on consumer ratngs of specfc attrbutes. Often, multvarate statstcal models are constructed to dentfy latent varables representng satsfacton factors (e.g. Johnson and Gustafsson 2000; Johnson 1998; Fornell et al. 1996; Bolton and Drew 1991). In the majorty of past research, overall customer satsfacton s then modeled as a lnear functon of these latent varables. Much recent research, however, s crtcal of the ncomplete treatment of the CSSP lnks, and researchers call for more elaborate analyss (e.g., Anderson and Mttal 2000). Addressng the Consequences of Customer Satsfacton Unlke the antecedents of satsfed customers, the consequences of satsfed (or dssatsfed) customers have receved lttle attenton from researchers (Szymansk and Henard 2001). Perhaps the frst study was the poneerng research conducted by Zahork and Rust (1992) on the consequences of customer satsfacton. Ther work ncluded a mathematcal framework to 7

9 evaluate the fnancal value of satsfacton (Rust and Zahork 1993) based on the effect of satsfacton on customer retenton, and the subsequent mpact on market share. Anderson and Sullvan (1993) addressed the smultaneous estmaton of the antecedents to and consequences of customer satsfacton, wth data from more than twenty thousand Swedsh consumers patronzng a hundred or so Swedsh companes. Ther model dentfes factors that determne customer satsfacton, whch n turn have a postve assocaton wth fnancal performance. Perhaps the most mportant contrbuton of ths work s the dentfcaton of asymmetres n the lnkages between dsconfrmaton of expectatons and customer satsfacton. After Anderson and Sullvan (1993), several studes have examned the relatonshps n the Satsfacton-Proft (or Servce- Proft) Chan wth data from a varety of channels (c.f., Kamakura et al., 2002; Schartzer and Kollarts 2000; Soterou and Zenos, 1999; Johnson 1998; Loveman 1998; Anderson, Fornell and Lehmann 1994). Mttal, Ross and Baldasare (1998) and Anderson and Mttal (2000) pont out that, for the most part, earler research gnored nonlneartes and asymmetres n the lnks of the CSSP chan. They mantan that the relatonshps n the CSSP chan are far more complex than orgnally postulated and, specfcally, that lnear models are nsuffcent. To llustrate the asymmetry concept, consder the qualty of the produce department and the frendlness of cashers n a supermarket. Stronger consumer evaluatons of the qualty of the produce department mght not mply strongly postve effects on customer satsfacton, whle weaker qualty mght be qute damagng. Or, mprovements n customer-orented dspostons of cashers and assocates could have a large postve mpact on customer satsfacton whle reductons n casher performance may be only mldly negatve. 8

10 Now consder the potental role of nonlnearty n the lnk between customer satsfacton and sales performance. A retal store wth low current levels of customer satsfacton may requre only small nvestments n satsfacton drvers to mprove sales performance. In contrast, a store wth hgh current levels of satsfacton s lkely to need a much larger nvestment n drvers to produce mpacts on performance of a smlar magntude. Ignorng relevant nonlneartes and asymmetres nevtably leads to ncorrect estmates of the lnkages n the CSSP chan. Furthermore, f the results of CSSP chan research are to be adopted by retal managers, ncorrect measures are certan to lead to ncorrect strategy formulaton thus doomng further strategc use of satsfacton data. Bernhardt, Donthu and Kennett (2000) suggest that another ptfall of many satsfacton studes s the tendency to rely on cross sectonal analyss for statstcal nference (Anderson, Fornell and Lehmann 1994, provdes an excepton). Bernhardt, Donthu and Kennett argue that a proper analyss of the lnks between satsfacton and performance requres a dynamc approach. Ths argument echoes Rust and Zahork s (1993) contenton that efforts to mprove customer satsfacton must be fnancally accountable over tme. Bernhardt, Donthu and Kennett (2000) study customer satsfacton n a fast-food chan based on monthly data. Although based on smple correlatons, the study shows that a dynamc model outperforms a cross-sectonal model n the examnaton of the CSSP lnks. Extant research has focused prmarly on the CSSP lnks at the aggregate level and for selected sectors such as telecommuncatons, bankng, healthcare, automoble and pharmaceutcals, among others (cf., Anderson and Fornell 2000; Schartzer and Kollarts 2000; Mttal, Ross and Baldasare 1998; Bryant and Cha 1996; Anderson, Fornell and Lehmann 1994). Conversely, only a few frm-specfc CSSP assessments have been conducted. Examples nclude 9

11 fast-food restaurants (Bernhardt, Donthu and Kennett 2000) and department stores (Rucc, Krn and Qunn 1998). Anderson and Mttal (2000) dscuss several examples where the ncorporaton of non-lneartes and asymmetres added sgnfcant value to a frm's understandng of the CSSP lnks. It s especally desrable to use frm-specfc data so the lnkages between satsfacton and performance are examned n the context of a frm s strategy. We note that academc research on the CSSP lnkages n the food retal sector s scarce. Practcally all emprcal nvestgatons on food retalng, n the U.S. as well as nternatonally, address the drvers of customer satsfacton but do not address ther ultmate mpact on store revenues. Among the drvers often dentfed are: perceved value of products relatve to ther prces, staff frendlness and wllngness to help, qualty and freshness of products, store appearance, and the degree of customer servce (cf., Jn and Ja-Ok 2001; Hackl, Schartzer and Zuba 2000; Gal and Scott 1995). However, whle the drvers of satsfacton are known qualtatvely, and managers beleve that satsfacton affects performance, t s necessary to measure explctly the mpact of satsfacton on store sales n order to prortze strateges to manage the drvers of satsfacton. Ths study advances the measurement of the behavoral lnks n the CSSP Chan n the food retal sector. We lnk attrbute perceptons, overall satsfacton, and store sales, and we allow for nonlnear and asymmetrc effects. We specfy the model n frst dfferences and we allow for tme lags between changes n satsfacton and changes n store sales performance. We also provde an example to show how managers can use the results to develop approprate customer satsfacton polces. MODEL AND DATA 10

12 Model The model s a system of equatons lnkng attrbute performance, customer satsfacton, and store sales over tme, where stores are the unt of analyss. Consder a store wth satsfactonrelated attrbutes perceved by consumers at tme t. Consumers rate the store on each of these attrbutes at each tme t (CA 1,t, CA 2,t,, CA K,t ). To reduce the overlap n nterrelated varables, t s convenent to reduce the number of varables based on ther observed correlatons. Consequently, a vector of M latent varables s used to represent satsfacton factors, (SF 1,t, SF 2,t,..., SF M,t ), wth M<K. To capture the dynamc propertes of the CSSP chan over tme, we defne changes n latent varable m ( SF m,t ) as a functon of the changes n specfc store attrbutes ( CA 1,t,, CA, t ). As a result, there are M equatons for the changes n satsfacton factors: k m SFm, t = Fm ( CA1, t, CA2, t,..., CAk t ) for m 1,..., M 1 m, =. (1) We propose that changes n the satsfacton factors, ( SF 1,t, SF 2,t,..., SF M,t ), affect the evoluton of overall satsfacton at tme t, or CS t. Next, changes n overall customer satsfacton CS t may produce changes n the store sales performance at tme t, SP t. Therefore, n addton to the M equatons n (1), the system also ncludes: CS = G SF, SF,..., SF ), (2) t ( 1, t 2, t M, t SP = H ( CS, STR, DEM, GEO), (3) t t where STR, DEM and GEO represent multple store, demographc and geographc characterstcs respectvely. We argue that t s more approprate to model the CSSP lnks n changes than n levels. For example, the parameter estmates n levels may be confounded by varous cross-sectonal 11

13 dfferences that are dffcult or mpossble to consder. By focusng on changes, we remove unobserved cross-sectonal dfferences from the data. Further, we defne percentage change n sales performance (% SP t ) to accommodate the dependence of sales performance on store sze. Customer satsfacton scores, however, are not rato scaled so that t s napproprate to use percentage change for satsfacton. However, we do nclude the level of lagged satsfacton, CS t- 1, to account for hypotheszed nonlneartes n the lnks between customer satsfacton and sales performance, as s explaned below. We estmate the relatonshps n equatons (2) and (3) to estmate how changes n satsfacton factors affect changes n sales performance va changes n overall satsfacton, and to determne the relevance of nonlnear and asymmetrc effects n the relatons between satsfacton factors and overall satsfacton, and between overall satsfacton and store sales performance. Data We use an extensve data set consstng of customer satsfacton nformaton, store sales, market demographcs and store characterstcs for more than 250 stores over the perod for a publcly held supermarket company operatng n the Eastern US. At each store, customer satsfacton data are collected semannually by mal (February and August) from approxmately one hundred of the top 40 percent customers, based on a random sample from the company s loyalty card data base (responses are anonymous). Thus, the data do not represent the entre customer base. We note, however, that the top 40 percent of customers represent approxmately 82 percent of total store sales. By workng wth a sample of the hghest revenue customers, we actually have data that pertan closely to storewde actvty. In the survey nstrument, customers rate the store, from 1 (poor) to 6 (excellent), on 21 questons pertanng to attrbute perceptons and customer satsfacton. The last queston pertans 12

14 to overall satsfacton (CS t ), whle questons 1-20 measure attrbute perceptons (CA t ). Addtonally, the survey collects demographc nformaton, such as respondent s age and household sze. These varables serve as controls n the statstcal model that follows. Random samples are drawn ndependently each semannual perod, thus the relevant unt of observaton s the store, not the ndvdual customer. By averagng the customer responses for each store, we create store-level panel data wth tme seres on dfferences of length sx on more than 250 unts. 1 We employ sales per square foot as the measure of store sales performance (SP). Ths s preferred over alternatve performance measures such as profts for the followng reasons. Frst, any proft measure s necessarly subject to accountng conventons, and these conventons may change over tme. Second, gven the behavoral focus of our study we expect sales per square foot to ncrease wth customer satsfacton, but such a lnk s less obvous for profts. Fnally, and related to the prevous pont, profts depend on other store-specfc varables that affect economc effcency such as labor and operatonal costs that are related to customer satsfacton. To capture the dynamcs, we consder the tme lag between change n overall customer satsfacton ( CS t ) and percent change n store sales performance (% SP t ). We propose that changes n overall satsfacton result n changes n sales per square foot n the three months after the satsfacton survey s conducted. Snce sales per square foot s avalable on a monthly bass, the measure of sales performance, SP t, s the monthly average of sales per square foot correspondng to the three months after customer satsfacton wave t s conducted. Thus the measure of change n store sales performance expressed as a percent s: 2 SP t SP t 1 % SP = t * 100. (4) SP t 1 13

15 To llustrate, consder the customer satsfacton surveys correspondng to February, 2000 (t-1) and August, 2000 (t). Here CS t s the change n overall satsfacton of the store s customers between these two survey waves. Accordngly, change n store sales performance s measured as the percent change n sales per square foot between the monthly average of March, Aprl, May 2000 (the three months followng the February survey) and the monthly average of September, October, November 2000 (the three months followng the August survey). All dollar fgures represent real values deflated by the US consumer prce ndex. Fnally, addtonal data were gathered from the cooperatng company regardng store age, store sze, and whether major or mnor remodelng had been done durng the perod of analyss. We show descrptve statstcs of the percent change n store sales performance (% SP t ) and change n customer satsfacton ( CS t ) between waves for the seven perods February 1998 February 2001 n Table 1. The data show that sales per square foot on average declned durng the study perod whle customer satsfacton changed very lttle. The dsperson of both measures, CS t and % SP t, evdenced by the standard devaton, s relatvely stable over the perod of analyss. Although customers rate satsfacton from 1 to 6, the range of varaton at the store level s far smaller because we average ndvdual responses per store (the mnmum and maxmum average values are 3.5 and 5.3, respectvely). We also show the means and standard devatons of the levels. The average monthly sales per square foot across waves vares between $13.04 and $14.70 (n 1996 dollars). The average customer satsfacton (across the stores) shows a low of 4.69 and a hgh of Fnally, the number of stores ncreased from 236 to 262 durng the perod , ndcatng a substantal expanson of operatons of approxmately 11 percent n number. [Table 1 About Here] 14

16 Factor Analyss Respondents to the customer satsfacton survey rated twenty store attrbutes relevant to ther shoppng experence. We show eghteen survey elements n Table 2 (two are absent because those attrbutes apply to a subset of the stores). However, ncluson of all eghteen attrbutes separately n the model weakens statstcal analyss and makes t dffcult to dentfy manageral mplcatons of the CSSP chan n food retalng. Consequently, we conducted a prncpal components factor analyss, employng a Varmax factor rotaton, to reduce the store attrbute measures to a smaller set of factors, each of whch s a lnear combnaton of a subset of the attrbutes. We consdered all factors wth egenvalues exceedng one. To be consstent wth the dynamc model specfed n terms of changes, the factor analyss was also conducted on changes n specfc attrbutes ( CA m,t ) so as to group varables that move together over tme. [Table 2 About Here] We show the factor loadngs for the three-factor soluton n Table 2. These three factors account for 76 percent of the varaton n the eghteen attrbutes. To facltate nterpretaton and use of subsequent results, we do not use the factor scores but nstead use smple averages of the attrbutes loadng hghly on a factor (0.6 or more). Ths mples that we allow for a modest amount of correlaton between the three factors. We defne the three satsfacton factors as follows: customer servce (CU), referrng largely to the overall atttude of the employees toward customers, ncludng servce levels; qualty (QU), relatng to qualty and varety of meats and produce, avalablty of everyday grocery tems as well as cleanlness nsde the store; and value (VA), referrng to the prce-performance rato of the tems purchased and the benefts of beng loyal to the store. Emprcal model 15

17 The model conssts of two parts. The frst presents an unrestrcted specfcaton of equatons (2)-(3) that allows for all possble nonlneartes and asymmetres. The second part dscusses nonlnearty and asymmetry n CSSP. Our emprcal specfcaton of equaton (2) expresses changes n customer satsfacton as a functon of a vector of changes n the three factors (customer servce, qualty, value). Separatng negatve from postve changes n the three factors allows us to control for asymmetry, whle we capture nonlnearty by squarng the changes n satsfacton factors. Next, we allow changes n store sales to be explaned by changes n customer satsfacton, store characterstcs and customer characterstcs. Followng Mttal, Kumar and Tsros (1999), the level of customer satsfacton at t-1 may affect the relaton between changes n store sales and changes n customer satsfacton. Consequently, the model ncludes the store satsfacton score at tme t-1 (CS t-1 ), and ts product wth CS t. We also allow for separate postve and negatve CS t effects to account for asymmetres, and we use the nteracton between CS t-1 and subsequent changes on overall satsfacton to measure nonlneartes. Demographc and store-specfc varables such as average customer age, average household sze, store locaton (urban or rural) and real estate nformaton are ncluded as control varables. Snce changes n store sales vary over tme, we also use tme dummes to accommodate wave-specfc effects. Wth all possble asymmetres and nonlneartes, the emprcal specfcaton yelds: CS t, = α + α CU 0 + α VA 7 1 t, t, + α CU + α VA 8 2 t, t, + α VA 9 + α CU 3 2 t, + e 2 t, 1t, + α QU 4 t, + α QU 5 t, + α QU 6 2 t, (5) % SP t, = β + β CS 0 t, + β HSZ 7 1 t, + β CS 2 t, + β CS t 1, + β ME + β NW + β β CS 10 4 MR t, CS + β 11 t 1, + β CS 5 t, CS t 1, EXP + δwd+ γ REG+ e + β AGE 6 2t, t, (6) 16

18 where, CU t, s change n customer servce factor score n store at wave t, CU t, equals t CU, f ts value s negatve; zero otherwse, 2 CU t, s change squared n customer servce factor score n store at wave t, QU t, s change n qualty factor score n store at wave t, QU t, s QU t, f ts value s negatve; zero otherwse, 2 QU t, s change squared n qualty factor score n store at wave t, VA t, s change n value factor score n store at wave t, VA t, s VA t, f ts value s negatve; zero otherwse, 2 VA t, s change squared n value factor score n store at wave t, CS t, s change n overall customer satsfacton score n store at wave t, CS t, equals CS t, f CS t, s negatve; zero otherwse, % SP t, s monthly percentage change n sales per square foot n the three months after the satsfacton survey s conducted, CS t 1, s average customer satsfacton score n store at wave t-1, AGE t, s change n average age of survey respondents n store at wave t, HSZ t, s change n average household sze of survey respondents n store at wave t, ME s one f store s located n a metropoltan area; zero otherwse, EXP s one f store was expanded, for all perods snce expanson; zero otherwse, NW s one f store s new for all perods snce openng; zero otherwse, MR REG WD s one f store was remodeled, for all perods snce remodelng; zero otherwse, s a vector of dmenson four of (0,1) dummy varables representng regonal locatons of the stores, and s a vector of (0,1) dummy varables correspondng to customer satsfacton waves. We summarze the test results of nonlnearty and asymmetry between CS t and the vector of satsfacton factors ( CU t, QU t, VA t ) n Table 3. We compare three models to the unrestrcted verson (asymmetrc-nonlnear), namely (1) lnear-symmetrc, (2) lnear-asymmetrc and (3) nonlnear-symmetrc, constranng the same effects to apply to all three factors. We therefore use F tests to determne whch specfcaton fts the data best. The lnear-symmetrc model s rejected, ndcatng the presence of asymmetres and/or nonlneartes (p<0.01). Test 17

19 statstcs also suggest that a lnear-asymmetrc model s preferred over ts nonlnear-symmetrc counterpart (p-values are 0.12 and 0.80 respectvely). Gven that there s very lttle evdence for nonlnearty, we next mpose lnearty on the otherwse unrestrcted verson, and test for asymmetry once more. Ths test s hghly sgnfcant, suggestng that asymmetres matter especally for a lnear model (p<0.01). These test results favor a lnear-asymmetrc specfcaton n the three factors. Thus, t appears that postve changes n the factors have dfferent effect magntudes than negatve changes do on overall satsfacton. We employ a dfferent approach to address nonlnearty and asymmetry n the lnk between overall satsfacton and sales. Contrary to equaton (5), n whch we explore the smultaneous nfluences of the three satsfacton factors on overall satsfacton, n equaton (6) we examne nonlnear-asymmetrc effects of only a sngle varable, CS, on store sales. We use the results for the parameters β 1 -β 5 to dscuss the nature and magntudes of these asymmetres and nonlneartes. In equaton (6), these lnks are nonlnear f the parameter correspondng to the nteracton between the level of satsfacton at t-1and the subsequent change n overall satsfacton, β 4, dffers from zero. Asymmetry exsts f β 2, the parameter capturng the dfference between postve and negatve changes n CS, dffers from zero. The parameter β 5 captures the combned effect of nonlnearty and asymmetry. [Table 3 About Here] The test results show that all fve parameter estmates pertanng to the effects of overall satsfacton on sales are statstcally sgnfcant (Table 4). Ths s a strong result, gven that some of the predctor varables are correlated to a consderable degree (the fve predctor varables capture man- and nteracton effects). Indeed, three of the fve effects are only sgnfcant at the 10 percent level, two-taled. 18

20 We note that Ordnary Least Squares estmates (OLS) are based and nconsstent f current-perod endogenous varables appear as regressors n other equatons n the system. However, OLS s approprate n the case of smultaneous equatons when the models are recursve wth lagged endogenous varables as n equatons (5)-(6), as long as the dsturbances e 1t and e 2t are uncorrelated. If these dsturbances are correlated, seemngly unrelated regresson (SUR) yelds consstent and unbased estmates. In our applcaton, statstcal tests fal to reject the null hypothess of zero contemporaneous correlaton between these dsturbances. We therefore use OLS to obtan unbased and effcent estmates. FINDINGS We show OLS estmates of the lnear, asymmetrc specfcaton of equaton (5) and the nonlnear, asymmetrc specfcaton of equaton (6) n Table 4. Changes n the three customer satsfacton factors explan nearly three quarters of the varaton n the changes n customer satsfacton. Ths s a hgh degree of explanatory power gven that the factors and overall satsfacton represent changes over tme as opposed to levels. Mttal and Kamakura (2000) suggest that a hgh R-square may be the consequence of a common method bas, resultng from measurng satsfacton attrbutes and overall satsfacton n the same survey nstrument. A common method bas also apples to changes but the focus on changes nevertheless reduces R- square. We note that ths common method bas s also a compellng reason for managers to go beyond the lnks between attrbute perceptons and overall satsfacton, and address the ultmate mpact of satsfacton on store revenues. The factor analyss of the perceptual attrbutes allows us to dentfy the dstnct components n the nstrument, and t s of nterest to determne dfferences n the effects of 19

21 changes n the three factors on overall satsfacton. These effects vary dramatcally across factors. We use the results n Table 4a to classfy the factors nto what have elsewhere been descrbed as satsfacton-enhancng and satsfacton-mantanng drvers (Anderson and Mttal 2000). Our results suggest that a one pont negatve change n qualty decreases overall satsfacton by 0.35 (α 4 +α 5 ), whch s seven tmes larger than the mpact of a one pont postve change n qualty (α 4 =0.05). 3 Ths asymmetry suggests that qualty s a satsfacton-mantanng factor n food retalng. That s, mprovements n qualty ratngs produce far smaller benefcal mpacts on customer satsfacton than the damagng effects created by negatve changes n qualty percepton of the same magntude. However, a one pont ncrease n the value factor has a somewhat larger mpact on overall satsfacton (α 7 = 0.36) than does a negatve change of the same magntude (α 7 +α 8 = 0.25). Thus, value appears to be prmarly a satsfacton-enhancng factor. The estmates ndcate that customer servce s the most mportant determnant of overall satsfacton, and that t s also prmarly a satsfacton-enhancng factor. That s, both effects are qute large, and the postve effect (α 1 =0.69) s larger than ts negatve counterpart (α 1 +α 2 = 0.55). In Table 4b we show that the second equaton explans thrteen percent of the varablty n % SP t. The parameter estmates and the assocated standard errors ndcate that changes n customer satsfacton and n some demographc and store characterstcs are relevant predctors of changes n store sales. In partcular, the coeffcents of the fve customer satsfacton varables are all sgnfcant at least at the 10 percent level, confrmng that nonlneartes and asymmetres matter. The parameter estmates of CSt and CS ndcate that sales performance s more t senstve to negatve ( ) than to postve changes (+52.30) n overall satsfacton. The nonlnear effects, represented by the coeffcents of CS t-1 and ts nteracton wth CS, are 20

22 stronger for lower values of CS t-1. To llustrate, the effect of a one-pont ncrease n CS n a store wth CS t-1 = 4 s larger than the effect of the same ncrease n a store wth CS t-1 = 5 (27.8 and 22.8, respectvely). 4 These dfferences suggest that the effect of postve CS on sales s decreasng wth the level of customer satsfacton. [Table 4 About Here] To llustrate the nonlneartes and asymmetres n the lnks between customer satsfacton and store sales, we show % SP t as a functon of CSt n Fgure 2 under alternatve levels of customer satsfacton at tme t-1 (CS t-1 ): the bottom 10% of stores (CS 4.26); the top 10% of stores (CS 5.05), and an average store (CS = 4.74), keepng everythng else constant. Snce the emprcal model s specfed n changes over tme, the functons n Fgure 2 represent the estmated partal dervatve SP/ CS condtoned on CS t-1. Note that the magntudes of the estmated dervatves depend on CS t-1 and on whether the change s postve or negatve. For the top 10% of the stores, for nstance, postve CS t has much smaller mpacts on sales performance relatve to negatve changes of the same magntude. Ths asymmetrc result suggests that once a hgh level of customer satsfacton s acheved mantanng t s crtcal. [Fgure 2 About Here] Fgure 2 also shows the nonlneartes between customer satsfacton and store sales performance. Gven a postve CS t of a specfc magntude, the slope correspondng to stores n the top 10% s smaller than the slope assocated wth average stores, whch s n turn smaller than the slope of stores n the bottom 10%. Thus the slope E/ CS tends to decrease as the level of customer satsfacton ncreases,.e. the functon lnkng overall satsfacton to sales performance s postve at a decreasng rate. Smlarly, the lnk between satsfacton and sales performance s nonlnear wth respect to negatve CS t. As the level of overall satsfacton at 21

23 tme t-1 decreases, the mpact of negatve CS t on sales performance tends to decrease. Hence the satsfacton-performance functon decreases at a decreasng rate for negatve changes n CS. The other varables n the model serve as controls to the CSSP chan. The demographc varables AGE t and HSZ t help control for dfferences n the samples of customers respondng the CS survey, both between stores and over tme. For example, on average stores that started operatons (NW ) or were remodeled (MR ) durng the perod show hgher changes n sales performance. Also, stores n metropoltan areas (ME ) have hgher sales performance changes than ther rural counterparts, as do stores located n Regon 4 (REG4), holdng other varables constant. We conduct tests of the parameter estmates on a holdout sample, employng customer satsfacton data correspondng to an addtonal wave (August 2001). Consderng the lnks between satsfacton factors and overall satsfacton, the correlaton between predcted and actual CS s 0.85, whch s n the lne wth expected decreases from the estmaton sample for a vald model. To examne the lnks between satsfacton and store revenues, we employ the followng three alternatve specfcatons: (1) wthout CS varables, (2) the nonlnear-asymmetrc specfcaton of CS, and (3) the parsmonous specfcaton of CS (.e., wth a lnear effect of CS). The Mean Squared Errors between predcted and actual values ndcate that both the nonlnear-asymmetrc specfcaton and the parsmonous specfcaton of CS margnally outperform the model wthout CS. 5 However, the parsmonous (lnear-symmetrc) specfcaton performs better than the nonlnear-asymmetrc one. Ths suggests that the sgnfcant nonlnear and asymmetrc complextes may reduce the forecastng accuracy of CS. These complextes are nevertheless relevant as ndcated by the statstcal sgnfcance of each of the fve terms. 22

24 We also nvestgate the presence of seasonalty n our sales data, conduct heteroskedastcty tests, and verfy that multcollnearty does not affect the model estmates. The only sgn of seasonalty observable n the data s that sales exhbt a peak n December; however, our analyss does not nclude sales n ths month. Nevertheless, to ensure that our model does not suffer from omttng seasonalty, we employed an alternatve specfcaton n whch changes were calculated wth respect to the prevous year (nstead of wth respect to the prevous wave). Parameter estmates of ths alternatve specfcaton are smlar to the estmates presented n Table 4. We also calculate Whte s test statstcs for both equatons n Table 4, and conclude that the null hypothess of homoscedastcty cannot be rejected. Addtonally, Varance Inflaton Factors and correlaton coeffcents ndcate that satsfacton factors n equaton (5) are only modestly correlated, as s the case for the customer satsfacton constructs relatve to the control varables n equaton (6). 6 The estmates n Table 4 also allow us to estmate the ultmate mpact of changes n satsfacton factors on changes n store sales performance. For nstance, a one pont decrease n qualty (QU) n a store n the top 10% group, ceters parbus, s lkely to produce a negatve change n CS of about 0.34 ponts. Ths decrease n CS, n turn, results n a reducton of monthly sales per square foot of about 2.2 percent. In contrast, a postve change n qualty QU of the same magntude has a very small effect on CS t (only 0.05 ponts), producng a much smaller ncrease n store sales performance (0.4 percent). Such asymmetres have to be consdered n manageral actons. Our results suggest that mprovements n satsfacton-mantanng factors such as qualty are not expected to ncrease sales performance dramatcally; however, dsregardng qualty as a satsfacton factor mght reduce store sales performance consderably. Changes n customer servce (CU), both postve and negatve, have the greatest mpacts on store 23

25 sales performance among the satsfacton factors. Specfcally, a one pont ncrease (decrease) n customer servce (CU) n a store n the Average group, produces a 0.63 ncrease (0.55 decrease) n CS, whch n turn results n a 1.95 percent ncrease (2.5 percent decrease) n monthly sales per square foot. Fnally, postve changes n value (VA) also can substantally mprove sales per square foot. However, the mpact of changes n ths factor on profts s less clear, n partcular f the tactcs nvolve, say, aggressve prce reductons and promotons. In summary, a frst-dfferences approach provdes valuable nsghts nto the behavor of the CSSP chan. The results ndcate the relevance of nonlneartes and asymmetres n the lnkages between satsfacton factors and store revenues. Snce customer satsfacton s ndeed crtcal to store sales performance, retal frms must understand these complex relatonshps n order to make approprate satsfacton-related decsons. IMPLICATIONS FOR MANAGERS: THE IMPACT OF CUSTOMER SATISFACTION ON STORE SALES We smulate how alternatve customer satsfacton polces lead to changes n store sales performance. 7 Informaton on alternatve satsfacton polces and store characterstcs can be combned wth the parameter estmates n Table 4 to determne the relatve mpact of changes n CS on changes n store revenues. We llustrate ths by means of smulatons to show how managers can make practcal use of the fndngs of ths study. A system to montor the lnks between customer satsfacton and sales performance Suppose management of the retal company contemplates how to allocate manageral efforts among the three satsfacton factors, customer servce (CU), qualty (QU) and value (VA) for a partcular store n year t. Assume ths store s 45,000 square feet n sze and ts average 24

26 sales per square foot s $25 per month. Both sze and sales are our sample averages. Assume further that, based on hstorcal data, management determnes that a gven level of effort produces a net ncrease across satsfacton factors of 0.3 ponts (for example, one combnaton that produces ths result s CU t =0.5, QU t =-0.1, VA t =-0.1). Ths amount of manageral effort mght be consdered the margnal cost of customer satsfacton (MCCS) n terms of manageral effort. One expects that MCCS s not constant; nstead, t s lkely to ncrease at an ncreasng rate as the scores of each factor ncrease. However, for llustratve purposes and to facltate the dscusson, we assume that MCCS s constant and equal across satsfacton factors. We show smulatons under alternatve satsfacton polces for a store n the top 10%, average, and bottom 10% ter CS scores n Table 5. Alternatve A conssts of management focusng exclusvely on the customer servce factor (CU). The result of ths partcular satsfacton polcy s CU t =0.5 but QU t =-0.1 and VA t =-0.1. Alternatve B conssts of allocatng equal amounts of effort among all the three factors. In ths case, each factor score ncreases by a tenth of a pont ( CU t = QU t = VA t =0.1). Fnally, alternatve C conssts of zero manageral effort on customer satsfacton. Based on hstorcal data, we dentfed that no effort n a satsfacton factor decreases that factor s score by 0.1 ponts each year (see explanaton n footnote of Table 5). Usng these nputs and applyng the parameter estmates of equatons (5) and (6), we show n Table 5 that alternatve manageral decsons can produce substantally dfferent store revenue outcomes, dependng on the level of CS. For nstance, as expected, CS manageral efforts exhbt decreasng margnal returns on store sales as the level of customer satsfacton ncreases under varous resource allocaton alternatves. Addtonally, the negatve mpacts of not makng any effort are much larger for stores n the top 10% ter than for stores n the bottom 10% group, suggestng that the opportunty cost of customer satsfacton vares across 25

27 stores. We emphasze that the proper comparson of a partcular level of effort that results n the smulated CS changes and revenue mpact s not no change n revenues but the negatve mpact on revenues assocated wth a zero level of effort (alternatve C). [Table 5 About Here] A comparson of the outcomes of alternatves A and B llustrates the benefts of measurng the CSSP lnks. Alternatve A produces larger postve mpacts on annual revenues than alternatve B. However, both of these resource allocaton alternatves produce the same decreasng margnal returns dscussed above, especally when the level of satsfacton s already hgh (stores n the top 10% ter). Although the smulatons n Table 5 consder a relatvely smple stuaton -- all satsfacton factors havng the same score and the same unknown constant margnal costs n terms of manageral effort these results llustrate how a quanttatve model of the CSSP chan can gude polces regardng customer satsfacton. In the applcaton consdered here, the model results ndcate that a polcy focusng on customer servce (CU) s superor to a polcy dstrbutng manageral efforts equally across factors. In alternatve C, wth no manageral effort at all, we show that the negatve effect of gnorng customer satsfacton ncreases wth the level of CS. Manageral mplcatons Ths study demonstrates that the degree of customer satsfacton nfluences store sales performance n the supermarket sector. Managers must regard ther satsfacton surveys not smply as a mechansm to learn to what extent ther stores are satsfyng customer needs and expectatons. Instead, customer satsfacton montorng should be vewed as a tmely manageral tool that can help ncrease store sales. Even f real-world managers n food retalng understand from ther experence that customer satsfacton nfluences sales, the lnkages are not ntutve 26

28 and cannot be determned from observaton, smple logc and descrptve statstcs alone. Thus a quanttatve model that converts raw customer satsfacton data nto useable nformaton to support management decsons provdes value for the supermarket busness and can justfy efforts to comple and analyze satsfacton data contnuously. Ths s especally crtcal n today s era of major structural and compettve changes n food retalng n whch companes are seekng more aggressve strateges smply to survve. In the case of the cooperatng retal company n ths study, our results suggest that managers must focus on customer servce, qualty and value to affect overall customer satsfacton and ts ultmate mpact on sales. Our results also allow us to dscuss more subtle manageral mplcatons of the CSSP chan. Our parameter estmates, on the one hand, ndcate that changes n overall customer satsfacton are partcularly senstve to changes n customer servce. On the other hand, customers may consder qualty as a pre-condton to satsfacton: postve changes n qualty have modest effects on sales performance but negatve changes n qualty result n substantally reduced sales per square foot. The cooperatng company s takng the ntal steps towards mplementng a system to montor the CSSP lnks. In the past, t employed CS data n the same way that characterzes many other supermarket companes. That s, analyss was lmted to descrptve statstcs of the CS data set and, subsequently, to a comparson of stores that dffer n satsfacton scores accordng to varous demographc and geographc varables. Addtonally, the analyss ncluded a comparson of ndvdual stores to the companywde average for the varous attrbutes. In certan nstances, the satsfacton results were also used as a crude metrc to determne store management bonuses. Although management was aware that customer satsfacton should affect performance, responses from CS data were not lnked to store revenues pror to ths study. 27

29 The company s now facng unprecedented competton from other channels, n partcular from large mass merchandsers. In the past, the company emphaszed low prces as the prmary means to ncrease customer satsfacton -- equvalent to an emphass on the value factor (VA) n our model. However, because t s extremely dffcult to compete wth mass merchandsers strctly on prce, the management team recognzes the urgent need to adjust ts strateges amed at ncreasng customer satsfacton and at more effectve montorng of the CSSP lnks. Therefore, our results contrbute to ther planned strategy focusng on customer servce rather than one of emphaszng low prces. Drectons for future research Ths nvestgaton can be extended to several areas of the CSSP chan n the food retal sector. Frst, future emprcal studes may ncorporate customer retenton and loyalty snce these are essental components of the lnks between satsfacton and performance. Smlarly, they should address medatng and moderatng factors that mght affect the CSSP lnks. Second, future research should nclude data on market structure (e.g. number of compettors n relevant market) to accommodate the effects of competton on customer satsfacton. Addng nformaton on competton wll mprove the valdty of model results and would further enhance the potental utlzaton of the CSSP chan as a manageral tool. Thrd, future research could address the senstvty of satsfacton factors to nvestment levels n specfc underlyng physcal components. Fourth, longer tme seres of satsfacton data would facltate the utlzaton of more sophstcated statstcal technques appled to panel data, thus producng superor parameter estmates and therefore makng the CSSP system a more relable manageral tool. Ffth, longerterm assessments of the mpact of satsfacton on sales performance are desrable to dentfy how changes n ndustry structure and changes n customer preferences affect the parameters n the 28