r it I!!Ib p $ 11 F ii it 4 i1} it I: ' r '. r, II It i i p I; i f i v p t-u i C~r~,. rtiIw. P t~~,: $ t.tr.tPb r:t.tit } R t'I tck~v it r I, I.. oI q I It ~ lU.??'t 1 4 h~ V Oasis or Mirage: Does Customer Delight Really Yield Disproportionate Gains in Customer Retention and Loyalty? Douglas C. Friedman University of Michigan

2 ABSTRACT The idea that businesses need to delight their customers to win their loyalty rather than merely satisfying them has become a popular topic recently in the business press. However, with the exception of the motion picture and possibly fast-food restaurant industries, customer delight does not produce disproportionately large gains in customer retention. And except for the automobile, motion picture and tobacco industries, customer delight does not lead to disproportionately large gains in customer loyalty. Instead, in 30 of 35 industries examined, customer satisfaction yielded diminishing marginal returns insofar as customer retention and loyalty were concerned. I r_ Zt^ vhA^ iSk^t&A.. MfMHf&nkJ3 dtlf as^^ I^w^ ua 'h]RIPk' tnIt* Mmf

3 INTRODUCTION Practitioner journals and the business press have been exhorting companies in recent years to delight their customers, not merely satisfy them, warning that failing to delight customers will result in having customers defect to other firms (e.g., Chandler, 1988; Schlossberg, 1990; Schumann, 1994). Jones and Sasser (1995) argue that in intensely competitive markets such as hard and soft durables, business equipment, financial services, and retailing. "In markets like these, there is a tremendous difference between the loyalty of merely satisfied and completely satisfied customers" (p. 89). They argue, in effect, that the Law of Diminishing Marginal Returns does not apply to customer satisfaction insofar as customer retention (which they call loyalty) is concerned. Following Hirschman's exit-voice theory, we would expect the immediate consequences of increased customer satisfaction to be a decrease in customer complaints and an increase in customer loyalty (Fornell and Wernerfelt, 1987 and 1988). Satisfied customers are more likely to repurchase a brand or company than dissatisfied customers and to have a more favorable attitude towards the brand that goes beyond simply repurchase intentions (Fornell, Ryan, and Westbrook, 1990; Yi, 1990 at 104-105). Satisfied customers are also more willing to tolerate price increases (Anderson, 1996). Loyal customers help improve a company's financial performance since the cost to retain a customer is generally lower than the cost to acquire a new one and in part by contributing to brand equity (Aaker, 1992; Bhote, 1995). Loyalty thus serves as a proxy for profitability (Reichheld and Sasser, 1990). The influence of customer satisfaction on loyalty varies from industry to industry (Fornell, 1992). Loyalty has generally been

4 declining regardless of whether purchasers are heavy, medium or light users, but this does not diminish its importance (East and Hammond, 1996). If delighting customers really is the key to customer loyalty or customer retention, then we should expect to see a disproportionate increase in customer loyalty or repurchase likelihood once satisfaction crosses some threshold. Jones and Sasser (1995) define delighted or "completely satisfied" (and thus loyal) customers as those answering a 5 on a question of satisfaction. Allowing for rounding, that should work out to approximately a 90 or higher on a 100 point scale. The delight phenomenon should display itself by having a positive interaction component in a piecewise linear regression. This would indicate that customer loyalty was increasing at a much faster rate above the delight point than below it. Alternatively, in the case of an industry whose satisfactionloyalty relationship were described by a cubic function, the cubic term would be positive so that if customer satisfaction were above the inflection point, loyalty would increase at an increasing rate. Delight could also have a disproportionate impact on loyalty if the industry's equation were described by a quadratic function with a positive quadratic term, which would yield a constantly increasing slope or by an exponential function with a positive coefficient on the exponential term, which would likewise yield a constantly increasing slope. METHODOLOGY To test the argument that delighted customers are disproportionately loyal, we used data from the American Customer Satisfaction Index, which surveys annually over 44,000 consumers about their experiences with more than 200 firms with sales exceeding $2.7 trillion (Fornell et al., 1996). Those firms compete in 40 industries in seven sectors. 1......., L -I di dt. JUZ ia.ta ik. F~iE"T- - -- - ""tal " M-IMM3,11UNIMR19 I. rwztll Ilk, ENZIMUR a M, a; MMMMIM~ =M,"UMUIL- Ujwaz-Adt EL?

E4i.01 1~4 ~~PaWft8Pa~P~pr~rrt~~~l~s 2~~ ~uB~:~~i~4~~Cs1 ~~Yr tW~B TlreF~JI~(O~!lj87?~I~B~91 ClllqPS~'IWU S~iSP~t~BHPZ~tF~~e~l~P IIR1W ~ ~R!~ZY~V"~~~~WP~~ll ~iBS~P Ilsl~FT~['I'E.~f m~l~)~ ~ PY:sF1 F~~Jgl~g~~pYl'Sp ~ ~ tlff I -qmt 7Ip.10M-1:r,:r1, aP ~~ q Ir.";t~~~,,M W~~~~~~~~~~~~~~~~~~~~~~~., M, I _T ", I M-. ~~~~~~~~-rtII~~ _ Li -LII_- _ — ~-~- ~I- ~C~- ^I~-~~lL -~- ~ICs~~L~ILIL~~~~ 5 For this study, we exclude the government sector and its five entrants because there has been no suggestion that customer delight is relevant to government services. The six studied sectors are manufacturing non-durables; manufacturing durables; transportation, communications, and utilities; retail; finance and insurance; and services. The 35 specific industries in this study are listed in Tables 1 and 2. Sample sizes ranged from 230 for the U.S. Postal Service to 4,556 for the automobile industry. To measure customer satisfaction and loyalty, we use latent variables. To measure customer retention, we use the response to a single question that asks about likelihood of repurchase. The customer satisfaction (LVSAT) latent variable is obtained by adding weighted averages of answers to questions on overall satisfaction, expectancy disconfirmation (performance that exceeds or falls short of expectations), and performance versus the customer's ideal product or service in the category. The customer loyalty latent variable is similarly obtained by adding weighted averages of answers to questions on likelihood of repurchase, price tolerance (increase) given repurchase, and price tolerance (decrease) to induce repurchase. Using the latent variables produces an average 22 percent improvement in precision compared to use of responses from a single question (National Quality Research Center, 1995; Ryan, Buzas, and Ramaswamy, 1995). Since the business press articles on customer delight have focused on customer retention (while often calling the phenomenon being studied customer loyalty), we examine the relationship between customer delight and the single-item measure of repurchase likelihood (customer retention) as well as the relationship between customer delight and brand loyalty. With a few notable exceptions, which are addressed in the Discussion section below, the results were fairly comparable.

6 Repurchase likelihood and loyalty were regressed separately on customer satisfaction using the equation that best fit the data for an individual industry. Cubic, quadratic, linear and piecewise linear equations were considered. Logarithmic, arctangent, and exponential transformations were considered as alternatives, but these consistently yielded much lower R2 values than the cubic, quadratic and linear equations. FINDINGS Table 1 lists the type of equation that describes the connection between customer satisfaction and repurchase likelihood for each of the industries examined and the amount of variance explained by the equation (R2). Table 2 contains similar information for the connection between customer satisfaction and loyalty. The cubic equations have the form: REPURCH = Po + PILVSAT + P2LVSAT2 + J3LVSAT3 or LOYALTY = Po + PILVSAT + 02LVSAT2 + P3LVSAT3 or where REPURCH equals the response on a 10-point scale to a question asking how likely the respondent is to repurchase the product from the company in question, LOYALTY equals the value of the latent construct Customer Loyalty on a 100-point scale and LVSAT equals the value of the latent construct Customer Satisfaction, which is reported as the company's ACSI score in the published ACSI. For negative cubic equations, 3 has a negative coefficient, which means that the function will be concave past the inflection point. A concave function signifies diminishing returns to scale, so a one point gain in ACSI here will provide less of an increase in loyalty than will a gain of one point in the area to the left of the inflection point. Some of the equations have a negative Pi term, which gives them a local minimum in the data range, usually around: U'' IIE1,91 il; M P

I - ~fqI,21 _"T 4W 3 I "M"r f P f IS2=2J 7 j 7 LVSAT = 4. Since companies are unlikely to be concerned about moving people from 0 satisfaction to only a 4 on a 100-point scale, this dip has little practical significance. In all cases except electric utilities, the function's maximum was outside the range of data; for utilities, where loyalty was the dependent variable, the maximum came at LVSAT = 99.6. The positive cubic function for the tobacco and motion picture industries indicates that there is an increasing return to scale past the inflection point. The quadratic equations have the form: REPURCH = po + 3ILVSAT + P2LVSAT2 or LOYALTY = po + PILVSAT + P2LVSAT2 where the variables have the same meaning as above. In the.negative quadratic equations, P2 is negative, leading to concave functions with constantly diminishing returns throughout the data range. All of the functions had maxima outside the data range. For the automobile industry, where loyalty is concerned, the positive quadratic term means the equation is convex throughout the data range - loyalty increases at an increasing rate throughout the data range. (The minimum occurs where LVSAT is less than zero, which is not a meaningful number given the way the variable was constructed.) For the linear piecewise regressions, the equations take the form: REPURCH = po + PILVSAT + 32DLT + P3DLT*LVSAT or LOYALTY = po + 3PLVSAT + J32DLT + p3DLT*LVSAT where DLT is a dummy variable that takes a value of 1 if LVSAT is above a point at which the slope changes sharply and 0 if it is below that point and DLT*LVSAT represents the interaction between DLT and LVSAT. Below the critical point, DLT will equal zero, so the p2 and P3 terms will drop out and the equation will have the form:

8 REPURCH = Po + PiLVSAT or LOYALTY = Po + J3ILVSAT Above the critical point, the equation will be: REPURCH = (po + p2) + (pI + p3)LVSAT or LOYALTY = (Po + 02) + (PI + P3)LVSAT If P3 is negative, there will be diminishing returns to scale for satisfaction above the transition point. If 03 is positive, then satisfaction above a certain level will result in disproportionately large increases in repurchase likelihood or loyalty - the delight phenomenon at work. Only the fast-food restaurant/pizza industry displayed this form, though the coefficient for p3 narrowly missed statistically significance for both repurchase likelihood and loyalty. If p3 equals zero, then the equation will be linear and an increase in customer satisfaction will result in a constant increase in customer retention or customer loyalty. A logarithmic function, which fit the broadcast TV industry nearly as well as a negative cubic function where repurchase likelihood was concerned, would have the form: REPURCH = po + PlIn(LVSAT) or LOYALTY= Po + [lln(LVSAT) and would exhibit constantly declining returns to scale. An arctangent function would substitute arctan(LVSAT) for the natural log in the equations above and would likewise exhibit constantly declining returns to scale. The arctangent functions were generally ~F~;,,~ —,r,-,,i;l;r~,HI-r i- — `-.i- IMINE;l~;- l~~

I ~~~~~-~~~~~~~~~ II N W14-1~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~..~ s' -, —,.-L ~ ~ ~ ~.p 2 9 TABLE 1 REPURCHASE LIKELIHOOD i i L Industry IR2 Function Tvne -11 I Inflection Point1 -- ---- -- - -- --- V — gr 4 -~, 1 -- 1 W-,,, Food processing Beverages-beer Beverages-soft drinks Tobacco-cigarettes Apparel Athletic shoes Publishing-newspapers Personal care products Gas-service stations Personal computers/ printers Household appliances Consumer electronics - TV & VCR Automobiles Parcel delivery/ express mail US Postal service Airlines-scheduled Telecommunications-long distance Telecommunications-local Broadcasting-TV Utilities-electric service Department stores Discount stores Supermarkets Restaurants-fastfood-pizza-carry out Banks Life Insurance Personal Property Insurance Hotels Hospitals Motion Pictures.306 Negative Quadratic.462 Negative Cubic.320 Piecewise Negative.135 Negative Quadratic3.435 Negative Cubic.622 Piecewise Negative4.157 Negative Cubic.328 Negative Cubic.236 Negative Cubic.358 Linear.477 Linear.501 Negative Cubic j.391 Negative Cubic 3.341 Negative Cubic.211 Linear5.338 Negative Quadratic 1.623 Negative Cubic i.516 i Negative Cubic.289 j Negative Cubic6.519 Negative Cubic j.462 Negative Cubic |.436 I Negative Quadratic 1.331 Negative Quadratic.328 Linear7 j.557 I Negative Cubic I.440 Piecewise Negative8 |.474 Negative Cubic.438 1 Negative Cubic.370 Negative Quadratic.101 Positive Cubic _ _ _. _ —:-:::-........:~::.._: ~ ~.......... II I I I I I I 49 872 58 84 57 51 49 67 86 54 44 45 64 52 58 51 83 45 55 46 -..~ ~ ~ ~ ~ ~ ~ ~~~~~~~,,,. h -,,. 9 f _ _ Notes; 1. for cubic functions, rounded to the nearest whole number; for the piecewise function it represents the point where the slope changes. 2. Setting the cutoff point at 86 or 88 also yielded statistically significant values. 3. The coefficient on the cubic term was positive, but not statistically significant (p =.119), so there may be some customer delight effect here. 4. The coefficient for the interaction - the change in slope - narrowly missed statistical significance (p =.057). If the standard is enforced strictly, then the function would be linear with an R2 of.619. 5. The coefficient for the quadratic term narrow missed statistical significance (p =.071). Relaxing the standard yields a negative quadratic with an R2 of.223. 6. A logarithmic function explained the variation almost as well, reaching an R2 of.280. 7. The coefficient for the quadratic term narrow missed statistical significance (p =.096). Relaxing the standard yields a positive quadratic with an R2 of.329. In a piecewise regression using LVSAT = 85 as the cutoff, yielded an almost-significant positive interaction coefficient (p =.08) with an R2 of.326. Fitting an exponential function yielded an R2 of.295. 8. A negative cubic function with an inflection point at 43 fit the data equally well, with the coefficient on the cubic term narrowly missing significance (p =.066). T22.........

10 poor in their explanatory value and were tested only to rule out alternative possibilities. An exponential function would take the form REPURCH = poe P *LVSAT or LOYALTY = -poe P*LVSAT and would exhibit monotonically increasing returns to scale if pi is greater than zero. An exponential function fit the fast-food restaurant industry data almost as well as a linear function. The amount of variance in customer retention explained by customer satisfaction varies from. 101 for the motion-picture industry to.623 for the long-distance telecommunications industry. One likely reason for the low explanatory value in the motion picture industry is that people in most cases choose movies by the genre or the stars in the movie, not generally by the studio that produces them. In recognition of this, the ACSI in the future will record loyalty only at the industry level, not at the company level. Other industries where the R2 were particularly low have their own idiosyncrasies leading to a limited link between satisfaction and loyalty. For example, with local newspapers and the U.S. Postal Service enjoying virtual monopolies, available alternatives are limited; "loyalty" may simply reflect lack of choice. Drivers likely choose service stations on the basis of factors other than satisfaction, such as convenience, location, price, even habit. Habit, in the literal sense, may account for cigarette smokers brand purchase decisions and thus for the low R2 and generally high levels of loyalty observed for the tobacco industry. -"U-' '' ' ' ": —I I "' i ' ~............. I,I I9 m, aII,t iI

I Iw —w p ' f I Q # Lf l-W-J I ~ T- - 11 TABLE 2 CUSTOMER LOYALTY i i i I i I I Industry II i 4 Function Type I Inflection Point'......... Food processing Beverages-beer Beverages-soft drinks Tobacco-cigarettes Apparel Athletic shoes Publishing-newspapers Personal care products Gas-service stations Personal computers/ printers Household appliances Consumer electronics - TV & VCR Automobiles Parcel delivery/ express mail US Postal service Airlines-scheduled Telecommunications-long distance Telecommunications-local Broadcasting-TV Utilities-electric service Department stores Discount stores Supermarkets Restaurants-fastfood-pizza-carry out Banks Life Insurance Personal Property Insurance Hotels Hospitals Motion Pictures 4 i I I L I.296.429.298.166.423.582.168.300.228.316.431.457.358.278.229.327.597.463.289.481.439.406.331.322.499.451.403.354.100 I -1 L Negative Quadratic Negative Cubic2 Piecewise Negative Positive Cubic Negative Cubic Negative Quadratic Negative Cubic Negative Cubic i Negative Quadratic Negative Cubic Linear Negative Cubic Positive Quadratic Negative Cubic Negative Quadratic Negative Quadratic Negative Quadratic2 Negative Cubic Negative Cubic Negative Cubic Negative Cubic Negative Quadratic Negative Quadratic Linear Negative Cubic Negative Cubic Negative Cubic Negative Cubic Negative Quadratic2 Positive Cubic I i 1 I tI t i i I I! i i I I I t i I I I J I 49 87 77 57 57 52 61 63 55 46 64 51 54 51 51 41 53 46 - -,. - Notes: 1. for cubic functions, rounded to the nearest whole number; for the piecewise function it represents the point where the slope changes 2. The cubic term has a p value of.05 1, just above the.05 level. If this standard is adhered to strictly, the function becomes negative quadratic and the R2 falls to.425. The long-distance telecommunications (p =.063) and hospitals (p =.077) cubic coefficients also narrowly missed significance. If the broader p <.10 level were used, the functions on both would be negative cubic with inflection points of 38 for long-distance and 43 for hospitals.

12 Of the four industries whose functions are recorded as linear above, the household appliance and personal computer industries both had non-significant (p >.2) negative piecewise components with the lowest p values for transition points at 82 for household appliances and 84 for personal computers. The Postal Service might be described by a negative quadratic, since that term narrowly missed statistical significance (p =.071). The fast-food/pizza industry had a non-significant (p=.096) positive piecewise component when satisfaction was greater than 85 and was also fit moderately well by an exponential function, so that industry may have increasing returns to scale at least at some point. The R2 levels ranged from.100 for the motion picture industry to.597 for the long-distance telecommunications industry. The explanatory value was somewhat less for loyalty than for customer retention, suggesting that customer satisfaction is a better indicator of repurchase likelihood than of willingness to be influenced by potential price changes, the other items in the latent loyalty measure. Most of the equations were of the same form (e.g. negative cubic) for both repurchase likelihood and customer loyalty, not surprising since repurchase likelihood is an important component of loyalty. The tobacco industry appears to undergo a dramatic change, but there was a non-significant (p =.117) positive cubic term for the retention function, so the equations may be the same or it may be that the delighted customers are willing to absorb large price increases even though customer retention is subject to diminishing marginal returns. The reverse may be the case with athletic shoes, where customers become constantly more likely to repurchase until they are quite satisfied (85),. but are more influenced by price changes. The auto industry shows increasing returns to scale for repurchase until satisfaction reaches 86; the combination of the functions for this?- ~ ~ ~ ~ ~ ~ -,ei

i i I 4w.~,r~j~e~: IE~~ptrrlrws ~ c~~T;Ft,g9nV1~ Th Rf6At~*WU~l~WWWrl N!sf~IBB~iflel~J 18~fn~ FPr~~~sl~~~- 1~f~ ~? i~~!fl~y Ii~~?~ A~~~f;~: 13 industry may show some tolerance for price rises among satisfied customers, which may partly explain why satisfaction with automobiles has been rising by many measures over the past several years even as sticker prices have continued to climb. It is not entirely clear that the functions for the life insurance industry and the Postal Service were different, since mild relaxation of the p =.05 standard for statistical significance would result in those having the same functions for retention and loyalty. If life insurance does have different forms of equations, the picture is similar to that for athletic shoes, with satisfaction leading to constant gains in willingness to repurchase (up to LVSAT = 83), but not to tolerate price increases. DISCUSSION The phenomenon of customer delight can be said to exist with certainty in only one industry: the motion-picture industry. That is the only industry showing increasing returns to scale for both customer retention and customer loyalty. (The second derivative is positive and increasing above the inflection point, demonstrating a slope that is increasing at an increasing rate.) However, since customer satisfaction is a poor indicator of either repurchase or loyalty, delight doesn't seem to mean that much. Three other industries show at least some signs of having customer delight operating: fast-food/pizza, tobacco, and automobiles. This presence of increasing returns to scale likely accounts for the great emphasis both those industries lay on customer satisfaction. The automobile industry's positive quadratic function means that no matter how much it increases satisfaction, loyalty will increase at a faster rate; retention increases at an increasing rate until customers become very satisfied and then retention gains slow. The tobacco industry's positive cubic function indicates that the greatest

14 increases in loyalty per unit increase in satisfaction come when LVSAT is greater than 77. Companies in that industry will thus experience their greatest gains in loyalty by increasing the satisfaction of those who are already highly satisfied. The effect of satisfaction on retention is somewhat more equivocal; there may be a delight phenomenon at work or tobacco may face diminishing marginal gains in retention. The tobacco and automobile industries' satisfaction-loyalty functions are shown in Figure 1. Figure 1 90 j 50....... -- Tobacco J?40 4..............Auto Industry 30 20 10 C - CvJ @to c1 0 V) Custom er Satisfaction The fast-food restaurant-pizza industry shows signs of providing customer delight, though not at a level that is considered statistically significant. For both customer retention and loyalty, it is marked by a linear satisfaction function. However, a piecewise regression shows a positive coefficient P3, though the term is not statistically significant (p =.096 for retention and.118 for loyalty). A positive P3 term indicates that retention and loyalty are rising more quickly above the point where LVSAT equals 85 than below it. However, since the term is not statistically significant, we cannot conclude that this - 7. iia k Mktfkim aM I

I i -i lKwil lip,me r 1mm i t W Y m F w s4 n 15 industry experiences the benefits of delighting its customers. Still, the linear function suggests at least constant returns to scale in increasing satisfaction. Since an exponential function fits the data reasonably well, this provides some more evidence for delight leading to disproportionate gains in retention. A graph of the predicted retention levels using the linear, piecewise linear, and exponential functions appears in Figure 2. Figure 2 Fast-Food Restaurants and Pizza '.33[ ' ' I. WL~ ri5 j1 E ~.:-.: -....... - Piecew ise!;5?: [~ i 3,-::~,l E x p o n eta 0. ~~~~~~~~. *~~~~~~~~~~~~~~~~~~~~~~~~'-'-': ~:;~ z '] {f -$ -- ~ t~~~~~~~~~~~~~~~~t4 2 - n.; F o 3 "-,qtlvvl* 3B Erl:trr 66~ x U~~~~~~~~~~~^.Bri"tnear...... IV:S:. t ~ ~ ~ ~ ~ ~ ~ ~ ~ Ip~~~~~~~~~-iecewise ar, -~-~ - ~~~~~~~~~~~~~~~~~~Exponlential Li 4 — z SEf 3 - - C C CO t u) CD oe S a 0 sf0 0) 0 Customer Satisfaction The household appliance industry also has a linear function with a non-significant piecewise term, though in this case the sign of the term is negative, though the term is significant only at a point slightly above the.15 level (p =.153). Thus, this industry experiences either a linear link between satisfaction and loyalty, or possibly a link that is linear until satisfaction reaches 82 out of 100 and then the gains from additional satisfaction decline slightly.

16 That is the picture faced by the soft drink industry, which has constant rates of increase in retention and loyalty until satisfaction hits 87, after which the rates taper off. The personal computer and printer industry shows constant increases in retention from climbing satisfaction. If there is any change in slope, it is negative after satisfaction reaches 84, but the term is not significant (p =.244). This improvement in retention does not translate into a similar picture for loyalty, so price sensitivity in this industry is presumably relatively strong. For all the other industries studied, either customer satisfaction shows constantly diminishing returns (negative quadratic) or it shows increasing returns up until a point and then diminishing returns (negative cubic). All of the points at which diminishing returns kick in on the cubic equations occur at relatively moderate levels of satisfaction, so while companies may experience notable benefits from turning their moderately dissatisfied customers into moderately satisfied customers, they will reap fewer benefits from turning their moderately satisfied customers into very satisfied or even delighted customers. The broadcasting industry provides an extreme case of this. While it shows an unusual dip with an- increase in satisfaction at low levels, up to about 28, the more interesting part of the graph is over at the right, as shown in Figure 2. After satisfaction climbs above 64, the industry starts to exhibit declining marginal gains in loyalty from additional satisfaction until the gain where customer satisfaction reaches 100 is practically zero - because loyalty is also virtually 100 percent. The logical conclusion to be drawn from these results is that in most industries, what businesses need to do is not delight customers, but rather avoid dissatisfying them. In most industries, companies will do better at improving customer retention and loyalty i -. ~~~~~~~~~~~~~~~~~~~~~~~a~l.-~~i~tdltl~~

I I ,- q - IRP WPM - - -.. W-ml, 711 -"- M. =a PIMINFRI!TT!T!! -- - i" 17 by turning moderately dissatisfied customers (LVSAT in the 40s or 50s) into moderately satisfied customers (LVSAT in the 60s or 70s) than by turning moderately satisfied customers into delighted customers (LVSAT in the 80s or 90s). Figure 3 Broadcast TV 100 90 80 70 - 60 50 0 40 30 20 10 0 IH'ill inhi-iin;: it Hr 5ir;:r 1 t11111 ti iiiiir itit r:2n iu iii ii;t u iiutiiintt N - '.CO C to,- COO) 0 L r- N CO) 0) LO - C O C') C) a n It) CD c O a N s ) * a0 Customer Satisfaction More typical are the graphs displayed by the beer industry, using either quadratic or cubic, equations. (The cubic term was significant at the p=.051 level, so both the cubic and quadratic functions are displayed.) Figure 4 Beer Industry (Quadratic) 100 80 ~60 3 40 20 - 0 C us; LO to e - oai fto r sXCustomer Satisfaction Ns ) Customer Satisfaction

18 Figure 5 Beer Industry (Cubic) 60 50 > 40 30 a 30 20 10 0 - r.C~O'J 0 1, r-.- CO) O T 0.r — N cO O) U' t" to co co X A _ W '* - -' C CO C ' U' ) (O D N N t C 0C m) Customer Satisfaction If customer delight so rarely produces the promised benefits, why did Jones and Sasser (1995) argue that it is of major importance for firmns to delight their customers or risk defection if they fail to do so? One reason Jones and Sasser got the results they did is the fortuitous choice of the automobile industry. In their article, they provide a graph of Satisfaction vs. Loyalty for the five industries they studied. The airline and hospital industries show graphs that appear to be negative quadratics, the same as we found. Only the personal computer and automobile industries actually appear to show a notable effect of customer delight on loyalty. Jones and Sasser looked at the business computer market whereas the ACSI looks at computers for both business and personal use. It may be that customer delight provides benefits in the business user segment but not in the home or home office segments. Delight may function in business-to-business markets but not consumer markets (Saunders, Scherer and Brown, 1995). Finally, Jones and Sasser found customer delight operating in the automobile industry, with the same sort of positive quadratic I'i~R~s~s~"s~YU~~~3X~t~ ~.- _i, ~ ~i'7, — - - * * I, I...a MM -11 R I- - l -

I 4 I I 1I A M, "ikqq.. 1?14 1'"-Fm'wN-wffl^" I 19 graph that we did for loyalty. Our results were different from theirs for customer retention. The difference may be due to the recency of new car purchase. The ACSI includes people who purchased new cars at least six months but not more than three years previously, whereas the data Jones and Sasser used included only those who had purchased cars within the previous year. Jones and Sasser note that 90 days after purchasing a car, 60 to 80 percent of customers say they intend to repurchase the same brand whereas three to four years later only 35 to 40 percent actually do so. Perhaps customer delight's effect on retention is ephemeral. The auto industry is at most an exception to the rule of diminishing returns; it does not prove a rule of customer delight as Jones and Sasser suggest. The case of the local telephone industry is also curious. Using data from an unspecified Bell operating company, Jones and Sasser found that, as they had expected, customer delight did not influence repurchase likelihood since even the most dissatisfied customers have had no choice but to deal with the local phone company. However, the ACSI findings demonstrate that the local telecommunications industry follows the same general pattern as other industries, and willingness to repurchase is indeed influenced by satisfaction. The days of, in the words of comedienne Lily Tomlin's Emestine the Operator, "We're the telephone company. We don't care. We don't have to" are fast disappearing thanks to cellular phones and pending deregulation. Without more specific information about the nature of the data they used, it is not possible to explain the difference in the findings. In much of the business press, the term customer loyalty is used when customer retention is intended. However, as Ryan et al. (1995) point out, a composite measure

20 provides a better indicator of loyalty than a single-item measure. The single-item intent to repurchase measure is useful only so long as prices of competing goods remain constant. Including price tolerance measures show how secure customers' loyalty really is. CONCLUSION The much-ballyhooed idea that delighting customers is the only way to ensure loyalty turns out to be unfounded for all but a very limited group of industries. However, this analysis did not consider whether individual companies might experience loyalty benefits from customer delight even if their industries do not. Owing to the confidential nature of the data, it would not be possible to publish a list of what companies might experience such delight. The data also were collected for major companies. It may be that for small companies operating in niche markets, customer delight is indeed a factor in determining loyalty. The same may be true for industries not covered by the ACSI, such as wholesale industries or full-service restaurants. In addition, this study does not look at what is causing the satisfaction; perhaps continually introducing that surprise and delight attributes (i.e., unexpected favorable features) as included in the Kano Model would lead to delight that might produce greater loyalty, but the cost of such continual innovation is likely to be high (Kano et al., 1984). Further, just as the surprise and delight attributes eventually become expected, the same may happen with innovation - continual innovation becomes expected, as in the computer industry, and may fail to provide the same delight benefits as before. An area that this study suggests should be investigated is that of optimal satisfaction point. Given that satisfying consumers costs firms money and that those. c j eh^.tl+kikJl4 t 4k 414fi n p7;u4;;7w.L; ili, J11Ji 11St, 4Bi~ ~,~~ri0~fi!h~iA ~ ~~

.1 I 11 q i - I I 3 -1a, 2 w -Ia- iMr^prWInr. MM w 21 costs tend to increase more rapidly at higher levels of satisfaction, while at the same time the gains in loyalty from increased satisfaction are diminishing, it should be possible to calculate an optimal point that companies should strive for. That is, delighting the customer may not be the most profitable strategy for companies to pursue.

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