Measuring Customer Satisfaction for Various Services Using Multicriteria Analysis

Transcription

1 Measurng Customer Satsfacton for Varous Servces Usng Multcrtera Analyss Yanns Sskos and Evangelos Grgorouds Techncal Unversty of Crete Decson Support Systems Laboratory Unversty Campus, Chana, GREECE Key words: Abstract: Customer satsfacton, Preference dsaggregaton, Ordnal regresson, and Multcrtera analyss Qualty evaluaton and customer satsfacton measurement s a necessary condton for applyng contnuous mprovement and total qualty management phlosophes. Ths justfes the need for developng modern operatonal research and management tools, whch wll be suffcent enough to analyse n detal customer satsfacton. The orgnal applcatons presented through ths paper mplement the MUSA method, a preference dsaggregaton model followng the prncples of ordnal regresson analyss. These applcatons concern customer satsfacton surveys from the publc and the prvate sector as well, and they are selected n such a way so that can ndcate the contrbuton of multcrtera analyss to the qualty evaluaton problem. Furthermore, the presented analyses demonstrate n practce the mplementaton process of satsfacton measurement projects n dfferent types of busness organsatons. 1. INTRODUCTION Customer satsfacton s one of the most mportant ssues concernng busness organsatons of all types, whch s justfed by the customerorentaton phlosophy and the man prncples of contnuous mprovement of modern enterprses. For ths reason, customer satsfacton should be measured and translated nto a number of measurable parameters. Customer satsfacton measurement may be consdered as the most relable feedback, 1

2 2 Yanns Sskos and Evangelos Grgorouds consderng that t provdes n an effectve, drect, meanngful and objectve way the clents preferences and expectatons. In ths way, customer satsfacton s a baselne standard of performance and a possble standard of excellence for any busness organsaton (Gerson, 1993). The am of ths paper s to present orgnal customer satsfacton evaluaton projects n dfferent busness organsatons from the publc and the prvate sector. The objectves of the customer satsfacton surveys are focused on the assessment of the crtcal satsfacton dmensons, by means of qualtatve questons, and the determnaton of customer groups wth dstnctve preferences and expectatons. The methodologcal approach s based on the prncples of multcrtera modellng, whle the preference dsaggregaton MUSA (MUltcrtera Satsfacton Analyss) method s used for data analyss and nterpretaton. The paper conssts of sx sectons. Secton 2 s devoted to the contrbuton of multcrtera analyss to the customer satsfacton evaluaton problem, whle an analytcal presentaton of the MUSA method s dscussed n secton 3. The next two sectons present fve orgnal customer satsfacton surveys n publc servces (post offce, unversty department) and the prvate sector (moble phone servce provder, arlne company, and fast food company). Fnally, secton 6 presents some concludng remarks, as well as future research n the context of the proposed methodologcal approach. 2. CUSTOMER SATISFACTION AND MULTICRITERIA ANALYSIS Although, extensve research has defned several alternatve approaches for the customer satsfacton evaluaton problem, all these proposed models and technques, so far, adopt the followng man prncples (Grgorouds, 1999): a) The data of the problem are based on the customers judgements and should be drectly collected from them. b) Customer satsfacton measurement s a multvarate evaluaton problem gven that customer s global satsfacton depends on a set of varables representng servce characterstc dmensons. c) Usually, an addtve formula s used n order to aggregate partal evaluatons n a global satsfacton measure. Based on these assumptons the customer satsfacton evaluaton problem can be formulated n the context of multcrtera analyss, assumng that clent s global satsfacton depends on a set of crtera or varables representng servce characterstc dmensons (Fgure 1).

3 Measurng Customer Satsfacton for Varous Servces Usng Multcrtera Analyss 3 Customer's global satsfacton Satsfacton accordng to the 1st crteron Satsfacton accordng to the 2nd crteron... Satsfacton accordng to the n-th crteron Fgure 1. Aggregaton of customer's judgements The preference dsaggregaton MUSA method s an ordnal regresson based approach (Jacquet-Lagrèze and Sskos, 1982; Sskos, 1985; Sskos and Yannacopoulos, 1985) n the feld of multcrtera analyss. The method s used for the assessment of a set of margnal satsfacton functons n such a way that the global satsfacton crteron becomes as consstent as possble wth customer s judgements. Thus, the man objectve of the MUSA method s the aggregaton of ndvdual judgements nto a collectve value functon. * The MUSA method assesses global and partal satsfacton functons Y * and X respectvely, gven customers judgements Y and X (for the -th crteron). The ordnal regresson analyss equaton has the followng form: Y n * = 1 = b n = 1 = 1 b X * (1) * * where the value functons Y and X are normalsed n the nterval [0,100], n s the number of crtera, and b s a postve weght of the -th crteron. In several cases, as presented n Sectons 4-5, t s useful to assume a value or treelke structure of crtera, also mentoned as value tree or value herarchy (Keeney and Raffa, 1976; Keeney, 1996; Krkwood, 1997). 3. THE MUSA METHOD 3.1 Basc nference procedure The MUSA method proposed by Grgorouds and Sskos (2001) nfers an addtve collectve value functon * Y and a set of partal satsfacton

4 4 Yanns Sskos and Evangelos Grgorouds * functons X. The man objectve of the method s to acheve the maxmum * consstency between the value functon Υ and the customers judgements Υ. Based on the modellng approach presented n the prevous secton and ntroducng a double-error varable (see Fgure 2), the ordnal regresson equaton becomes as follows: ~ * Y n * + = b Χ σ + σ = 1 (2) * where Y ~ * + s the estmaton of the global value functon Y, and σ and are the overestmaton and the underestmaton errors, respectvely. σ Y * y *m... σ j + σ j - y *2 0 y 1 y 2... y m... y α Y Fgure 2. Error varables for the j-th customer In order to reduce the sze of the mathematcal program, removng the monotoncty constrants for Y * and X *, the followng transformaton equatons are used: * m+ 1 * m zm = y y * k + 1 * wk = b x b x k for m= 1, 2,...,α 1 for k= 1, 2,...,α 1 and = 1, 2,...,n (3) Accordng to the aforementoned defntons and assumptons, the basc estmaton model can be wrtten n a lnear program formulaton, as follows:

5 Measurng Customer Satsfacton for Varous Servces Usng Multcrtera Analyss 5 M + [ mn] F = σ j + σ j j = 1 subject to n t j 1 t j 1 + wk zm σ j + σ j = 0 for = 1 k = 1 m= 1 α 1 zm = 100 m= 1 n α 1 wk = 100 = 1 k = 1 zm 0, wk 0 m,,k + σ j 0, σ j 0 for j= 1, 2,, M j= 1, 2,,M (4) where M s the sze of the customers sample, n s the number of crtera, and j x, y are the j-th level on whch varables X and Y are estmated. j The preference dsaggregaton methodology ncludes also a post optmalty analyss stage n order to overcome the problem of model stablty. The fnal soluton s obtaned by explorng the polyhedron of multple or near optmal solutons, whch s generated by the constrants of the prevous lnear program. Ths soluton s calculated by n lnear programs (equal to the number of crtera) of the followng form: α [ ] = 1 max F wk for = 1, 2,..., n k = 1 under the constrants * F F + ε all the constrants of LP (4) (5) where ε s a small percentage of F *. The average of the solutons gven by the n LPs (5) may be taken as the fnal soluton. In case of non-stablty, ths average soluton s less representatve, due to the large varaton among the solutons of LPs (5). A more detaled dscusson about post optmalty analyss n ordnal regresson modellng s gven n Jacquet-Lagrèze and Sskos (1982).

6 6 Yanns Sskos and Evangelos Grgorouds 3.2 Satsfacton ndces The assessment of a performance norm may be very useful n customer satsfacton analyss. The average global and partal satsfacton ndces are used for ths purpose and are assessed through the followng equatons: S = S = α m= 1 α k = 1 p p m y x * m k * k for = 1,2,, n (6) where S and S are the average global and partal satsfacton ndces, and p m k and p are the frequences of customers belongng to the y m k and x satsfacton levels, respectvely. It can be easly observed n equaton (6) that the average satsfacton ndces are bascally the mean value of the global and partal satsfacton functons. So, these ndces can gve the average level of satsfacton value globally and per crteron. 3.3 Demandng ndces The shape of global and partal satsfacton functons can ndcate customers demandng level. The average global and partal demandng ndces, D and D respectvely, are assessed through the followng equatons (see Fgure 3): D = D = α 1 m= 1 α 1 k = 1 ( m 1) 100 y α 1 α 1 m α 1 m= 1 ( k 1) 100 * k x 1 α α 1 k α 1 k = 1 * m for for α > 2 α > 2 and = 1,2,, n (7) where α and α are the number of satsfacton levels n global and partal satsfacton functons, respectvely. It should be mentoned that these ndces are normalsed n the nterval [-1, 1] whle the followng possble cases hold:

7 Measurng Customer Satsfacton for Varous Servces Usng Multcrtera Analyss 7 a) D = 1 or D = 1: customers have the hghest demandng ndex. b) D = 0 or D = 0 : ths case refers to neutral customers. c) D = 1 or D = 1: customers have the lowest demandng ndex. α y 100( m 1) α 1 Y * ( m 1) * µ 100 α 1 y y *m 1 y m y α ψ Y Fgure 3. Calculatng average demandng ndces These ndces represent the average devaton of the estmated value functons from a normal (lnear) functon. The average demandng ndces can be used for customer behavour analyss, and they can also ndcate the extent of company s mprovement efforts: the hgher the value of the demandng ndex, the more the satsfacton level should be mproved n order to fulfl customers expectatons. 3.4 Acton dagrams Combnng weghts and average satsfacton ndces, a seres of acton dagrams can be developed (Fgure 4). These dagrams ndcate the strong and the weak ponts of customer satsfacton, and defne the requred mprovement efforts. Each of these maps s dvded nto quadrants, accordng to performance (hgh/low) and mportance (hgh/low) that may be used to classfy actons: a) Status quo (low performance and low mportance): Generally, no acton s requred. b) Leverage opportunty (hgh performance/hgh mportance): These areas can be used as advantage aganst competton. c) Transfer resources (hgh performance/low mportance): Company s resources may be better used elsewhere. d) Acton opportunty (low performance/hgh mportance): These are the crtera that need attenton.

8 8 Yanns Sskos and Evangelos Grgorouds PERFORMANCE Low Hgh Transfer resources (hgh performance/low mportance) Status quo (low performance/low mportance) Leverage opportunty (hgh performance/hgh mportance) Acton opportunty (low performance/hgh mportance) Low Hgh IMPORTANCE Fgure 4. Acton dagram (Customers Satsfacton Councl, 1995) In several cases, t s useful to assess the relatve acton dagrams, whch use the relatve varables b and S n order to overcome the assessment problem of the cut-off level for the mportance and the performance axs. The normalsed varables b and S are assessed as follows: b b S S b = and S = for = 1, 2,,n (8) 2 2 ( b b) ( S S) where b and S are the mean values of the crtera weghts and the average satsfacton ndces, respectvely. Ths way, the cut-off level for axes s recalculated as the centrod of all ponts n the dagram. Ths type of dagram s very useful, f ponts are concentrated n a small area because of the low-varaton that appears for the average satsfacton ndces (e.g. case of a hgh compettve market) These dagrams are also mentoned as decson, strategc, perceptual, and performance-mportance maps (Dutka, 1995; Customers Satsfacton Councl, 1995; Naumann and Gel, 1995), or gap analyss (Hll, 1996; Woodruff and Gardal, 1996; Vavra, 1997), and they are smlar to SWOT analyss. Detaled presentaton of the mathematcal development of the MUSA method may be found n Grgorouds and Sskos (2001), and Sskos et al. (1998).

9 Measurng Customer Satsfacton for Varous Servces Usng Multcrtera Analyss 9 4. EVALUATING CUSTOMER SATISFACTION IN PUBLIC SERVICES 4.1 Applcaton to a post offce Satsfacton crtera The assessment of a consstent famly of crtera representng customers satsfacton dmensons s one of most mportant stages of the mplemented methodology. Ths assessment can be acheved through an extensve nteractve procedure between the analyst and the decson-maker (company). In any case, the relablty of the set of crtera/subcrtera has to be tested n a small ndcatve set of customers. The herarchcal structure of customers satsfacton dmensons s presented n Fgure 5 and ndcates the set of crtera and subcrtera used n ths survey. The man satsfacton crtera nclude: GLOBAL SATISFACTION PERSONNEL PRODUCT /SERVICE ACCESS CREDIBILITY Sklls and Knowledge Varety Locaton of stores Responsblty Responsveness Prcng Internal dsposton On tme delvery Behavour Workng hours Frequency of delvery Understandng Troubles n the servce system Watng tme Fgure 5. Herarchcal structure of satsfacton dmensons personnel (sklls and knowledge, responsveness, behavour, etc), product/servce (prcng, varety of provded servces),

10 10 Yanns Sskos and Evangelos Grgorouds access (locaton and nternal dsposton of stores, workng hours, watng tme, etc), and credblty (on tme delvery, confdence and responsblty, etc) Global satsfacton analyss Company s customers seem to be qute satsfed wth the provded servce, gven that the average global satsfacton ndex s almost 90%. Moreover, crtera satsfacton analyss shows that customers are qute satsfed accordng to the total set of crtera (average satsfacton ndces 80-91%). Accordng to the results presented n Table 1 and Fgure 2, the followng remarks can be made: Table 1. Global satsfacton results Crtera Weght Average satsfacton ndex Average demandng ndex Personnel 5.48% 91.07% % Product/Servce 5.73% 88.90% % Access 80.96% 89.82% % Credblty 7.83% 80.91% % Global satsfacton 89.20% % RELATIVE PERFORMANCE Low Hgh Personnel Product/Servce Credblty Access Low RELATIVE IMPORTANCE Hgh Fgure 6. Relatve Acton dagram (global satsfacton level) The Access crteron s the most mportant one, wth a sgnfcant weght of almost 81%. Ths can be justfed by the fact that the satsfacton survey was not conducted to the total clentele of the company, but t was orented only to the customers vstng the stores.

11 Measurng Customer Satsfacton for Varous Servces Usng Multcrtera Analyss 11 Although the average satsfacton ndces for all crtera are relatvely hgh, t seems that there s a sgnfcant potental for further mprovement, gven the hgh compettve condtons n the market (new prvate companes offerng express mal servces). Customers do not seem to be demandng accordng to the total set of crtera. The acton dagram shows that there are no crtcal satsfacton dmensons requrng mmedate mprovement efforts. However, f company wshes to create addtonal advantages aganst competton, the credblty crteron should be mproved Crtera satsfacton analyss The crtera satsfacton analyss confrms the conclusons of the prevous secton. In general, the company s performance s qute hgh n almost all satsfacton dmensons, whch are consdered mportant by the customers. Ths fact justfes the satsfactory level of the dstnctve satsfacton ndces. On the other hand, however, there are several areas where the company has sgnfcant margns for mprovement. The detaled results of Table 2 ndcate the followng ponts: Company s compettve advantages seem to be personnel s responsveness and behavour, varety and prces of provded products and servces, watng tme and company s responsblty. Table 2. Crtera satsfacton analyss Subcrtera Weght Average satsfacton ndex Average demandng ndex Sklls/Knowledge 8.5% 74.6% -53.2% Responsveness 26.0% 92.5% -64.2% Behavour 39.5% 93.2% -21.3% Understandng 26.0% 91.8% -69.2% Varety 50.0% 86.2% -92.0% Prcng 50.0% 91.6% -92.0% Locaton of stores 10.0% 61.8% -20.0% Internal dsposton 4.6% 38.9% -13.3% Workng hours 2.5% 71.5% * Servce system troubles 3.2% 88.5% * Watng tme 79.7% 96.9% -89.9% Responsblty 77.1% 80.5% -94.8% On tme delvery 14.1% 89.4% -71.5% Delvery frequency 8.8% 70.9% -54.4% * crtera wth 2-level ordnal satsfacton scale

12 12 Yanns Sskos and Evangelos Grgorouds Although Access crteron s the most mportant strength of the post offce, there are several aspects of ths partcular satsfacton dmenson wth large mprovement margns (lke workng hours, locaton and nternal dsposton of stores). Customers seem less demandng n these subcrtera, and thus, mprovement efforts may have an mmedate mpact. 4.2 Applcaton to a unversty department Crtera assessment The case of measurng satsfacton n a unversty department can also be consdered as an nternal servce qualty evaluaton process (Sskos et al., 2001). Ths applcaton refers to a publc and busness admnstraton department. Although t s focused on students satsfacton, department s global evaluaton should be orented to all academc personnel (professors, admnstratve personnel, etc), as well as to external evaluators (busness organsatons, communty, etc). The man set of students satsfacton crtera used n ths partcular survey conssts of: 1. Academc personnel: ths crteron refers to the educatonal sklls and the knowledge of the academc personnel, ther communcaton and collaboraton wth students, as well as the number of professors n the department. 2. Educatonal process: ths crteron ncludes all aspects of the educatonal process lke provded textbooks and notes, students evaluaton process, educatonal approach chosen for each course, etc. 3. Syllabus: ths crteron refers manly to the number of the provded courses, the ablty to adapt course of study to students needs, etc. 4. Labour market: ths crteron refers to the vocatonal rehabltaton of the graduated students (carrer servces after graduaton, adaptaton of courses to labour market needs). 5. Admnstraton: secretarat, academc advsor, admnstraton servce process, etc. 6. Addtonal servces: ths crteron ncludes the addtonal servces that are provded to the students, lke lbrary, tutoral courses, subscrpton to journals and Internet servces, audo-vsual equpment, computer labs, etc. Addtonal analyss has also been conducted, based on a detaled herarchcal structure of evaluaton dmensons proposed by Sskos et al. (2001) for the case of a unversty department.

13 Measurng Customer Satsfacton for Varous Servces Usng Multcrtera Analyss Global satsfacton analyss The average global satsfacton ndex s relatvely low (61%) manly because students are not satsfed from the opportuntes offered to the labour market (26%), the syllabus (26%), and the provded admnstratve servce (39%) as shown n Table 3. Table 3. Crtera satsfacton results Crtera Weght Average satsfacton ndex Academc personnel 15% 72% Educatonal process 29% 83% Syllabus 15% 26% Labour market 13% 26% Admnstraton 11% 39% Addtonal servces 17% 77% Global satsfacton 61% Addtonally, the form of the global satsfacton functon ndcates that students are not partcularly demandng (Fgure 7). On the other hand, students seem to be qute satsfed accordng to the crteron of educatonal process (83%), whch s also the most mportant satsfacton dmenson (weght 29%). Fnally, t should be noted that although the rest of the crtera have hgher satsfacton ndces (72-77%) compared to the global satsfacton level, they appear to have sgnfcant mprovement margns , ,3 68, , ,0 Unsatsfed Moderately satsfed Average global demandng ndex: -22.7% Satsfed Very satsfed Completely satsfed Fgure 7. Global satsfacton functon (added value curve)

14 14 Yanns Sskos and Evangelos Grgorouds Segmentaton satsfacton analyss The man am of ths partcular analyss s to determne students clusters wth dstnctve preferences and expectatons n relaton to the total set. The dscrmnatng varables that have been used for dentfyng specal groups of students are the year of studes, the sector of studes, and the average grades. The most mportant dstnctve results relate to the segmentaton accordng to the year of studes. The results of ths analyss are presented n Tables 4-5, and reveal the followng: Globally, 3 rd and 4 th year students are very dssatsfed from the unversty department. These students are the man reason for the low global satsfacton level. 1 st and 2 nd year students are less demandng, and thus, they have relatvely hgher average satsfacton ndex. The satsfacton level of the 1 st year students s hgher compared to the other groups accordng to almost all of the crtera. The academc personnel, the syllabus, and the provded admnstratve and addtonal servces have the lowest satsfacton level for 3 rd and 4 th year students. As students are closer to graduate, they seem to be more demandng at these partcular satsfacton dmensons. Table 4. Global satsfacton analyss per year of studes Year of studes Average satsfacton ndex Average demandng ndex 1 st 78.6% -55.3% 2 nd 72.1% -44.8% 3 rd 24.5% 50.3% 4 th 35.2% 27.7% Table 5. Average partal satsfacton ndces per year of studes Crtera 1 st year 2 nd year 3 rd year 4 th year Academc personnel 61% 65% 59% 33% Educatonal process 67% 64% 29% 72% Syllabus 62% 91% 31% 18% Labour market 91% 26% 25% 21% Admnstraton 70% 59% 31% 22% Addtonal servces 73% 66% 13% 40% All these results can be explaned by the way the course of studes s mplemented: a) Durng the 1 st year, students are bascally taught elementary subjects (mathematcs, socology, etc). b) At the begnnng of the 3 rd year, students have to choose the sector of studes they wll follow. Ths wll affect n great extent all of ther next choces.

15 Measurng Customer Satsfacton for Varous Servces Usng Multcrtera Analyss 15 Segmentaton satsfacton analyss s performed through the mplementaton of the MUSA method n each student s cluster separately. For ths reason, the fttng and the stablty level of the results may vary causng a problem of nconsstency when tryng to compare global wth segmentaton analyss results. In ths partcular applcaton, the problem manly concerns the average satsfacton ndces due to the hgh error level n the global satsfacton analyss (the global set s less homogenous than the segments of students). 5. EVALUATING CUSTOMER SATISFACTION IN THE PRIVATE SECTOR 5.1 Applcaton to a moble phone servce provder Satsfacton crtera and survey conduct The mplementaton of the MUSA method ncludes a prelmnary customer behavoural analyss n whch, the assessment of the set of satsfacton crtera s made as presented n the prevous applcatons. In ths partcular case, customers were asked to evaluate/express ther satsfacton accordng to the followng crtera: 1. Stores (network expanson, locaton and appearance of stores, etc). 2. Servce n stores (personnel, servce processes, workng hours, watng tme, etc). 3. Servce by the call centre (personnel, servce processes, watng tme, etc). 4. Products/Servces (varety, mal phone, customer servce, roamng, and addtonal nfo servces) 5. Prcng (moble phone devce, fxed rate, prces per type of servces, etc). 6. Image (technologcal excellence, credblty, ablty to satsfy future needs, etc). 7. GSM network (expanson, sgnal, communcaton qualty, and dsturbances). 8. Customer loyalty servces (phone devce replacement, lower fxed rates, etc). The presented customer satsfacton survey took place n two retal stores of the busness organsaton located n dfferent areas. The survey was conducted wthn summer 2000 n a randomly selected customer sample.

16 16 Yanns Sskos and Evangelos Grgorouds Global satsfacton analyss The average global satsfacton ndex s not relatvely hgh (79.1%), gven the hgh compettve condtons of the moble phone servce sector. Moreover, Table 6 shows that customers are qute satsfed accordng to the servce provded n stores and to offered loyalty servces, whle lower satsfacton ndces appear for the rest of the crtera (60%-79%). The most mportant crtera seem to be Loyalty servces (24.9%), Servce n store (19.1%), and Products/Servces (11.4%). Ths justfes the relatve far value of the global satsfacton ndex. Customers are more satsfed accordng to the most mportant crteron and less satsfed on the dmensons that seem to play a less mportant role to ther preferences. Table 6. Global satsfacton results Crtera Weght Average satsfacton ndex Average demandng ndex Stores 9.1% 74.1% -12.3% Servce n store 19.1% 88.4% -58.0% Servce by the call centre 8.2% 60.6% -2.7% Products/Servces 11.4% 79.2% -29.6% Prcng 9.1% 67.5% -12.3% Image 9.1% 74.5% -12.3% GSM network 9.1% 70.2% -12.3% Customer loyalty servces 24.9% 85.9% -67.8% Global satsfacton 79.1% -45.7% RELATIVE PERFORMANCE Hgh Low Product/Servce Image Stores GSM network Prcng Servce by the call centre Servce n store Customer loyalty servces Low RELATIVE IMPORTANCE Hgh Fgure 8. Relatve acton dagram

17 Measurng Customer Satsfacton for Varous Servces Usng Multcrtera Analyss 17 The acton dagram shows that there are no crtcal satsfacton dmensons requrng mmedate mprovement efforts, as presented n Fgure 8. However, f company wshes to create addtonal advantages aganst competton, the crtera wth the lowest satsfacton ndex should be mproved. These mprovement efforts should focus on servce by the call centre, prcng, GSM network, stores, and company s mage Segmentaton satsfacton analyss The most mportant results from customer satsfacton segmentaton analyss are manly focused on the dscrmnaton of the total clentele to customers havng or not havng prevous experence wth other compettors. As shown n Table 7, experenced users are more satsfed accordng to almost all of the crtera set. The hgher demandng level that appears n ths partcular customer group may confrm ths result (Table 8). Table 7. Average satsfacton ndces per segment of customers Crtera Experenced users Inexperenced users Stores 75.5% 70.6% Servce n store 84.7% 73.8% Servce by the call centre 63.0% 54.6% Products/Servces 75.0% 72.8% Prcng 64.1% 68.9% Image 89.5% 73.2% GSM network 68.2% 91.0% Customer loyalty servces 70.4% 75.8% Global satsfacton 79.3% 79.3% Table 8. Average demandng ndces per segment of customers Crtera Experenced users Inexperenced users Stores -14.2% -4.4% Servce n store -36.0% -4.42% Servce by the call centre -1.1% 3.0% Products/Servces -14.2% -8.5% Prcng -7.6% -8.5% Image -25.5% -8.5% GSM network -5.2% -77.9% Customer loyalty servces -14.2% -34.5% Global satsfacton -46.9% -42.5% The detaled results of the prevous segmentaton analyss reveal the followng:

18 18 Yanns Sskos and Evangelos Grgorouds When company s efforts are orented to customers wth prevous experence from other moble phone servce provders, the advantages appearng n Image and Servce n stores crtera should be used. On the other hand, the company should take advantage of the GSM network crteron, when ts efforts are orented to new customers wth no experence n moble phone servces. In any case, company s mprovement efforts should nclude Prcng and Servce by the call centre. These crtera appear relatvely low satsfacton ndces n both customer segments. The varant level of homogenety between the global set and the customer segments causes also n ths case an nconsstency problem when tryng to compare global wth segmentaton analyss results (see also 4.2.3). A detaled dscusson on how to deal wth possble mplementaton problems of the MUSA method (e.g. modfcatons of the LP formulaton) s presented by Grgorouds and Sskos (2001). 5.2 The case of an arlne company Prelmnary analyss The applcaton presented n ths secton refers to a plot customer satsfacton survey for an arlne company. Passengers on board and customers vstng arlne s agences have both partcpated n ths survey. The set of man satsfacton crtera conssts of: 1. Tdness (delays, bookng system, tmetable, etc). 2. Servce (personnel, servce tme, watng tme, etc). 3. Prcng (tcket prce and specal dscounts) 4. Credblty (safety of trp, baggage clam, damages, etc). 5. Comfort/Servce qualty (seats on board, qualty of food, addtonal servces, etc) Global satsfacton analyss The man results of the MUSA method are presented n Table 9, from where the followng ponts rase: Globally, customers are qute satsfed from the provded servce (global average satsfacton ndex 88.6%). Nevertheless, based on the hgh compettve condtons of the market, there are sgnfcant mprovement margns for several satsfacton dmensons.

19 Measurng Customer Satsfacton for Varous Servces Usng Multcrtera Analyss 19 Table 9. Global satsfacton results Crtera Weght Average satsfacton ndex Average demandng ndex Tdness 36.3% 94.2% -89.0% Servce 5.3% 70.6% -22.8% Prcng 4.4% 58.0% -9.0% Credblty 6.6% 76.0% -34.3% Comfort/Servce qualty 47.3% 90.1% -91.5% Global satsfacton 88.6% -51.1% The hghest satsfacton ndces appear for the crtera of Tdness and Comfort/Servce qualty, 94.2% and 90.1% respectvely. Also, customers seem to gve hgher mportance to these crtera. The rest of the crtera have a low level of mportance for the customers ( %), whle the performance of the company s rather modest (average satsfacton ndces 58-76%). Regardng the mprovement efforts for the arlne company, an nspecton of the acton dagram (Fgure 9) reveals that there s no partcularly crtcal satsfacton dmenson callng for an mmedate mprovement. Nevertheless, the mprovement prortes should be focused on the crtera wth the lowest satsfacton ndces. Assumng that the company has small mprovement margns for the Prce crteron due to the competton, ts efforts should be focused on the credblty and the provded servce. RELATIVE PERFORMANCE Hgh Low Credblty Servce Prcng Tdness Comfort/Servce qualty Low RELATIVE IMPORTANCE Hgh Fgure 9. Relatve acton dagram

20 20 Yanns Sskos and Evangelos Grgorouds Segmentaton satsfacton analyss The satsfacton analyss n dfferent customer groups has been focused on the purpose of the trp. It should be noted that ths partcular customer satsfacton survey ncludes only nternatonal flghts. Accordng to Tables 10-11, the comparatve analyss of the customer clusters reveals the followng: Passengers travellng for busness gve sgnfcant mportance to the qualty of the provded servce, whle they are not satsfed accordng to the company s prcng, servce, and credblty. These customers can be also charactersed as frequent users. On the other hand, passengers travellng for toursm gve hgher mportance to company s credblty, whle they are not satsfed accordng to crtera of Prcng and Servce. Usually these customers are not frequent users. In any case, the lowest satsfacton ndces appear for the company s prces, fact that justfes the man results presented n the prevous secton. Table 10. Crtera weghts per purpose of trp Crtera Busness Toursm Personal Other Tdness 20.0% 19.2% 51.5% 35.8% Servce 5.2% 4.5% 4.3% 14.7% Prcng 4.4% 4.2% 4.2% 9.5% Credblty 5.2% 49.2% 20.0% 20.0% Comfort/Servce qualty 65.2% 22.9% 20.0% 20.0% Table 11. Average satsfacton ndces per purpose of trp Crtera Busness Toursm Personal Other Tdness 90.00% 92.40% 97.10% 96.00% Servce 71.90% 65.70% 60.90% 93.00% Prcng 48.60% 48.30% 60.50% 45.40% Credblty 67.40% 97.20% 83.10% 82.00% Comfort/Servce qualty 94.30% 90.50% 89.40% 91.00% Global satsfacton 89.50% 91.60% 92.60% 92.60% 5.3 Applcaton to a fast food company Satsfacton crtera The presented customer satsfacton survey refers to a fast food company, whch takes advantage of franchsng optons n order to hold more than 150 restaurants n four countres. The survey was conducted n a randomly

21 Measurng Customer Satsfacton for Varous Servces Usng Multcrtera Analyss 21 selected customer sample and t took place n three restaurants of the fast food company located n dfferent towns. The value herarchy of customers satsfacton dmensons presented n Fgure 10 ndcates the set of crtera and subcrtera used n the analyss. The man satsfacton crtera nclude: 1. Personnel: ths crteron ncludes all the characterstcs concernng personnel (sklls and knowledge, responsveness, frendlness, communcaton and collaboraton wth customers, etc). 2. Product: ths crteron refers manly to the offered products (qualty and quantty of food, varety of dshes, and prces). 3. Servce: ths crteron refers to the servce offered to the customers; t ncludes the appearance and the cleanlness of the stores, the watng tme durng busy and non-busy hours, and the servce tme. 4. Access: the locaton and number of stores, as well as parkng avalablty are ncluded n ths crteron. GLOBAL SATISFACTION PERSONNEL PRODUCT SERVICE ACCESS Sklls and Knowledge Qualty of food Appearance of stores Locaton of stores Responsveness Quantty of food Watng tme (busy hours) Number of stores Frendlness Varety (menu) Watng tme (non- busy hours) Parkng Prces Servce tme Cleanlness Fgure 10. Herarchcal structure of satsfacton dmensons Global satsfacton analyss The average global satsfacton ndex s approxmately 90%, whle company s performance accordng to the whole set of crtera vares between

22 22 Yanns Sskos and Evangelos Grgorouds 86% and 92%. Gven the hgh compettve condtons of the market, ths performance s not consdered relatvely hgh. The detaled results of Table 12 reveal the followng: The most mportant crteron, wth a sgnfcant mportance level of 45.2%, s Product. Customers do not consder mportant the rest of the crtera. The low weght for the Access crteron can be explaned by the fact that the man compettors have no better performance n ths partcular crteron. Table 12. Global satsfacton results Crtera Weght Average satsfacton ndex Average demandng ndex Personnel 22.00% 92.44% % Product 45.20% 86.56% % Servce 25.00% 88.08% % Access 10.70% 87.19% % Global satsfacton 90.81% % RELATIVE PERFORMANCE Hgh Access Personnel Servce Product Low Low RELATIVE IMPORTANCE Hgh Fgure 11. Relatve acton dagram (global satsfacton level) Combnng weghts and satsfacton ndces, the acton dagram can be formulated, as shown n Fgure 11. In ths dagram, the Product crteron appears as a crtcal satsfacton dmenson requrng mmedate mprovement efforts: t has the lowest average satsfacton ndex comparng to the rest of the crtera, whle t s consdered as the most mportant crteron by customers.

23 Measurng Customer Satsfacton for Varous Servces Usng Multcrtera Analyss Crtera satsfacton analyss The analyss of the partal satsfacton dmensons allows for the dentfcaton of the crtera characterstcs that consttute the strong and the weak ponts of the company. The detaled results of Table 13, reveal the followng: Personnel s frendlness consttutes a sgnfcant compettve advantage for the fast food company. The qualty of food appears as one of the strongest ponts of the company, although customers do not seem to be satsfed accordng to the quantty of food. Ths result s related to the low satsfacton ndex appearng for the Prce crteron. Partcular attenton should be pad to the watng tme durng busy hours and the servce tme as well. On the other hand, the appearance of the restaurants seems to be one of the compettve advantages for the fast food company. The satsfacton level wth respect to the Access crteron could have been hgher, f customers were more satsfed accordng to the provded parkng facltes. Table 13. Crtera satsfacton results Subcrtera Weght Average satsfacton ndex Average demandng ndex Sklls/Knowledge 34.0% 93.10% -71.3% Responsveness 15.9% 83.60% -62.1% Frendlness 50.1% 94.80% -82.0% Qualty of food 49.8% 90.40% -84.4% Quantty of food 13.3% 77.30% -40.9% Varety (menu) 25.0% 90.90% -68.5% Prces 11.9% 71.70% -33.7% Appearance of stores 42.8% 90.80% -81.0% Watng tme (busy hours) 8.5% 68.60% -29.8% Watng tme (non-busy hours) 19.2% 90.90% -69.4% Servce tme 8.3% 74.10% -28.8% Cleanlness 21.2% 93.30% -62.7% Locaton of stores 87.1% 90.70% -93.4% Number of stores 6.8% 81.10% -42.3% Parkng 6.1% 43.90% -12.9% 6. CONCLUSIONS The orgnal applcatons presented n ths paper llustrate the mplementaton of the preference dsaggregaton MUSA method n several

24 24 Yanns Sskos and Evangelos Grgorouds busness organsatons from the publc and the prvate sector. The most mportant results nclude: the determnaton of the weak and the strong ponts of the busness organsaton, the performance evaluaton of the company (globally and per crtera/subcrtera), and the dentfcaton of the dstnctve crtcal groups of customers. The applcatons show that the MUSA method can measure and analyse customer satsfacton n a very concrete way, and thus t may be ntegrated n any busness organsaton s total qualty approach. Several applcatons of the method n orgnal customer satsfacton surveys can be found n Grgorouds et al. (1999a, 1999b), Mhels et al. (2001), and Sskos et al. (2001). Also, the MUSA method may be used n a smlar way to measure and analyse employee satsfacton (Grgorouds, 1999). Furthermore, analysng clents' preferences and expectatons s the basc step to evaluate customer loyalty. The nstallaton of a permanent customer satsfacton barometer s consdered necessary, gven that t allows the establshment of a benchmarkng system (Edosomwan, 1993). Thus, the mplementaton of the MUSA method through a perod of tme can serve the concept of contnuous mprovement. Grgorouds and Sskos (2001) propose several extensons and future research regardng the MUSA method. Among others, the comparatve analyss between the results of the MUSA method and the fnancal ndces (market share, proft, etc) of a busness organsaton can help the development of busness strateges and the evaluaton of the cost of qualty. It should be mentoned that, although customer satsfacton s a necessary but not a suffcent condton for the fnancal vablty, several researches have shown that there s a sgnfcant correlaton among satsfacton level, customer loyalty, and proftablty (Dutka, 1995; Naumann and Gel, 1995). 7. ACKNOWLEDGEMENTS The authors are thankful to all the students of the Natonal Techncal Unversty of Athens and the Unversty of Cyprus who helped customer satsfacton survey conduct and analyss. Especally, the authors would lke to thank: P. Apseros, N. Eteokleous, T. Georgou, L. Gannoudou, A. Grgorou, E. Karekla, M. Konstantndou, K. Kotsons, L. Morftou, A. Mousa, D. Nkolaou, L. Panagdou, E. Papadopoulou, Y. Papathanasou, M. Pmps, E. Pssard, M. Sprou, G. Stathopoulos, A. Taman, and D. Venezs.

25 Measurng Customer Satsfacton for Varous Servces Usng Multcrtera Analyss REFERENCES Customers Satsfacton Councl (1995). Customer Satsfacton Assessment Gude, Motorola Unversty Press. Dutka A. (1995). AMA Handbook of customer satsfacton: A complete gude to research, plannng and mplementaton, NTC Busness Books, Illnos Edosomwan J. A. (1993). Customer and market-drven qualty management, ASQC Qualty Press, Mlwaukee. Gerson R. F. (1993). Measurng customer satsfacton: A gude to managng qualty servce, Crsp Publcatons, Menlo Park. Grgorouds E. (1999). Measurng and analysng satsfacton methodology: A multcrtera aggregaton-dsaggregaton approach, Ph.D. Thess, Techncal Unversty of Crete, Department of Producton Engneerng and Management, Chana (n greek). Grgorouds E. and Y. Sskos (2001). Preference dsaggregaton for measurng and analysng customer satsfacton: The MUSA method, European Journal of Operatonal Research (to appear). Grgorouds E., A. Samaras, N. F. Matsatsns and Y. Sskos (1999a). Preference and customer satsfacton analyss: An ntegrated multcrtera decson ad approach, Proceedngs of the 5th Decson Scences Insttute s Internatonal Conference on Integratng Technology & Human Decsons: Global Brdges nto the 21st Century, Athens, Greece, (2), Grgorouds E., J. Malandraks, J. Polts and Y. Sskos (1999b). Customer satsfacton measurement: An applcaton to the Greek shppng sector, Proceedngs of the 5th Decson Scences Insttute s Internatonal Conference on Integratng Technology & Human Decsons: Global Brdges nto the 21st Century, Athens, Greece, (2), Hll, N. (1996). Handbook of customer satsfacton measurement, Gower Publshng, Hampshre. Jacquet-Lagrèze E. and J. Sskos (1982). Assessng a set of addtve utlty functons for multcrtera decson-makng: The UTA method, European Journal of Operatonal Research, (10), 2, Keeney R. L. (1996). Value-focused thnkng: A path to creatve decsonmakng, Harvard Unversty Press. Keeney R. L. and H. Raffa (1976). Decsons wth multple objectves: Preferences and value tradeoffs, John Wley and Sons, New York. Krkwood G. W. (1997). Strategc decson makng, Duxbury Press, Belmont. Mhels G., E. Grgorouds, Y. Sskos, Y. Polts and Y. Malandraks (2001). Customer satsfacton measurement n the prvate bank sector, European Journal of Operatonal Research, (130), 2, Naumann E. and K. Gel (1995). Customer satsfacton measurement and management: Usng the voce of the customer, Thomson Executve Press, Cncnnat. Sskos J. (1985). Analyse de regresson et programmaton lnéare, Revue de Statstque Applquée, 23 (2), Sskos J. and D. Yannacopoulos (1985). UTASTAR: An ordnal regresson method for buldng addtve value functons, Investgaçao Operaconal, 5 (1), Sskos Y., E. Grgorouds, C. Zopounds and O. Sauras (1998). Measurng customer satsfacton usng a collectve preference dsaggregaton model, Journal of Global Optmzaton, 12,

26 26 Yanns Sskos and Evangelos Grgorouds Sskos Y., Y. Polts and G. Kazantz (2001). Multcrtera methodology for the evaluaton of hgher educaton systems: The case of an engneerng department, HELORS Journal (to appear). Vavra T. G. (1997). Improvng your measurement of customer satsfacton: A gude to creatng, conductng, analyzng, and reportng customer satsfacton measurement programs, ASQC Qualty Press, Mlwaukee. Woodruff R. B. and S. F. Gardal (1996). Know your customer: New approaches to understandng customer value and satsfacton, Blackwell Publshers, Oxford.