High Correlation between Net Promoter Score and the Development of Consumers' Willingness to Pay (Empirical Evidence from European Mobile Markets)


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1 Hgh Correlaton between et Promoter Score and the Development of Consumers' Wllngness to Pay (Emprcal Evdence from European Moble Marets Ths paper shows that the correlaton between the et Promoter Score and consumers' Wllngness To Pay n fve European moble marets s very strong. The et Promoter Score s provded by a survey and the Wllngness To Pay s calculated usng the Spoes Model whch s an economc model based on horzontal dfferentaton among frms. The model nput data (frms revenues, number of subscrbers and profts are provded by Merll Lynch, Ban of Amerca. The wellnown correlaton between et Promoter Score and Revenues s weaer and arses from the prevous correlaton. The same s true of the correlaton between et Promoter Score and Profts. D, D43, L3, L96, M3 et Promoter Score, recommend ntenton, customer satsfacton, consumer's Wllngness to Pay Ths paper represents the analyss of the author and not necessarly a poston of France Telecom
2 Introducton Measurng customer satsfacton and Wllngness To Pay, or WTP, s a maor strategc obectve for managers and mareters, and the best method for dong so has been hotly debated for years. In recent years, the arrval of the "et Promoter Score" (PS ndcator: created a small revoluton. Whle t not always the most accurate ndcator, t s probably the easest to use, snce t requres only one queston: How lely s that you would recommend us to a frend or a colleague? The people who answer most postvely are called promoters, whle; those that respond less favourably are called detractors. The PS calculates the dfference between promoters and detractors. Ths ease of mplementaton has prompted managers wdely to adopt ths new metrc. In hs paper (Rechheld, 2003, The One umber You eed to Grow, Fred Rechheld hghlghted the strong correlaton between a company's growth rate and ts et Promoter Score n most compettve ndustres. A second paper, (Rechheld, 2006, The Mcroeconomcs of Customers Relatonshp, sought to offer a ratonal explanaton of the success of PS. He suggests that promoters have a good customer experence meanng that they are more loyal and more lely to repurchase. Promoters spend more than detractors; ther lfetme wth a company s longer because of ther loyalty. Consequently, acquston costs are amortzed over a longer perod and thus become cheaper. Promoters are less prcesenstve than detractors because they beleve they are gettng a good value overall from the company. Moreover, promoters help to recrut newcomers by recommendng ther provder to frends (Word of Mouth. A good PS tends to ncrease both maret share and sale prce and therefore revenues. PS has, however, been crtczed by other authors. (Morgan & Rego, 2006, as well as (Kenngham, Cool, Andreassen, & Asoy, 2007 (Kenngham, Asoy, Cool, Andreassen, & Wllams, have ponted out that PS was not always the best ndcator for predctng corporate revenue growth, and results vared by ndustry.. Emprcal evdence has emphaszed the PS' relevance n the telecommuncatons ndustry. Ths paper shows that n the European moble marets, the ln between the PS and Wllngness To Pay s very strong and s even stronger than the correlaton between PS and corporate revenue growth. PS appears to be proportonal to the rate of development of WTP and could represent a good proxy for t. When choosng ther provder, all customers had a preference for t wthout beng ether promoters or detractors. Promoters are those who have mantaned or ncreased ths preference over tme, whle detractors are those who have been dsapponted and have changed t. PS s a clear sgn of consumers' changng opnons over a gven perod of tme as compared to ther ntal choce. Some tme later, detractors of the prevous perod wll have probably changed ther provder, provded that the maret s suffcently compettve, (swtchng costs are not too hgh and commtments are not too longterm and promoters wll have 2
3 helped recrut new customers. Promoters n the new perod are those who have mantaned or ncreased ther preference from one perod to the next, and detractors n the new perod are those were dsapponted durng the prevous perod, snce former detractors have already cancelled ther servce. The PS for the new perod thus represents customers' changng opnons from one perod to another. More generally, PS ndcates consumers' changng opnons over tme. A postve PS means that promoters outnumber detractors and thus that customers' opnons are changng postvely. Smlarly, a negatve PS means that customers' opnons are changng negatvely. When the maret s not compettve enough, customers tend to be captve and cannot change provders as they wsh. In ths case, there s a sgnfcant gap between customers' actual behavour and ther wshes; PS therefore does not accurately reflect the fnancal results. A strong correlaton between PS and fnancal performances s thus the sgn of effectve competton, whle an uncorrelated PS mples an mpedment to customers' desres. Rechheld (Rechheld, 2006 has shown that PS dd not apply for monopoles. Ths paper conssts of 6 sectons. Secton 2 presents a theoretcal model of competton n order to determne the relatonshp between WTP and fnancal performance (prces, revenues and profts. Secton 3 descrbes the data used for the emprcal evdence, ncludng both fnancal data and survey data (PS. Secton 4 compares the two sets of data and hghlghts the strong correlaton between them. Secton 5 compares ths correlaton to the correlaton between PS and corporate revenue growth or between PS and corporate proft growth and shows that t s much stronger. The dfference stems from the fact that WTP depends essentally on customer choces whle revenues and profts also depend on other parameters and partcularly on margnal costs. Improvng customer satsfacton has a cost; we found that frms whch ncrease PS the most are often also those whch ncrease ther margnal costs the most. Secton 6 s the concluson. 2 The Spoes Model The spoes model, as descrbed by (Chen & Rordan, 2007 s a verson of the Hotellng model for more than two frms. The maret s represented by a spoe wheel where consumers are unformly dstrbuted. Each frm s located at the end of a spoe. The wheel dameter s normalzed to ; the length of each spoe s thus /2. The sze of the maret s also normalzed to. Each consumer located wthn a spoe compares the utlty to purchase the offer by the frm located at the end of the spoe and the offer he prefers from among the other frms. Le all the spoes converge at the centre of the wheel, the comparson can be made n pars between all frms. If there are frms, there wll ( be comparsons. Each frm s nvolved n ( comparsons. 2 We assume and p are respectvely the consumer s wllngness to pay and the prce of frm s offer. We wll focus on the comparson between frms and. The length of the two oned spoes s. A consumer located at a dstance x from frm s located at a dstance (x from the frm. Hs utltes of purchasng frm s and frm s offer are respectvely: 3
4 4 ( x t p U tx p U Wth t, the coeffcent of dfferentaton. The ndfferent consumer between and s located at t t p p x Frm s maret share s wrtten: x ( 2 σ We assume that frm ncurs a margnal cost c. The proft of frm s: ( c p n σ π n represents the total number of customers. The frst order condton allows us to determne p : (2 ( c c t p ( and hence: + t c c (2 (( ( σ (2 Let us denote: c c c, the relatve margnal cost, whch s the devaton of frm s margnal cost from the average margnal cost. In the sale way, represents the relatve consumer wllngness to pay. Frm s maret share can be rewrtten: t c (2 + σ Let us note that σ σ the dfference between frm s maret share and the average maret share. Therefore, frm s relatve Wllngness To Pay s: 2 ( c t + σ (3
5 3 Data and methodology 3. Avalablty of data: Fve countres were studed from to 200: Belgum (3 frms, France (3 frms, Span (4 frms, Swtzerland (3 frms and the Unted Kngdom (5 frms. (Data for Swtzerland and Unted Kngdom s gven usng ther natonal currency and requred quarterly exchange rates to be converted nto, the exchange rates used are gven n appendx 7., for a total of 9 varatons quarter by quarter for 8 frms, or 62 observatons. However, some observatons are not relevant and must be excluded. In Span, the PS for the fourth operator Yogo s only avalable from 4, so we must reect all the prevous quarters. In Unted Kngdom, the merger between Orange and T Moble maes data rrelevant from 200. A total of 30 observatons must be reected, leavng 32 relevant observatons. 3.2 Hypothess We are seeng to verfy the hypothess formulated n the ntroducton: PS s proportonal to the speed of the development of WTP. The speed of the development of WTP s the dervatve of WTP wth respect to tme τ. d β PS dτ If we tae nto account the relatve WTP of frm, the hypothess can be wrtten: d βps τ (4 d In order to test our hypothess, we wll compare the relatve PS, PS, to the changes n the relatve WTP,, calculated usng the spoes model, for each frm n all of the countres studed. 3.3 Calculatng WTP from the database usng the Spoes Model The Ban of Amerca, Merll Lynch database provdes us wth the followng quarterly data for each frm n each country:  umber of subscrbers, q.  Revenues, R  Ebtda, π 5
6 The GfK Customer Experence Tracer provdes us the quarterly PS for each frm n each country. The total number of subscrbers n a country s n q q Frm 's maret share s: σ n R (Average prce of frm : p q (Average margnal cost of frm : Equaton ( can be rewrtten: c R π q p t + c + ( c 2 whch allows us to calculate the sum of the frms prces: p t + c and thus to determne the coeffcent of dfferentaton t: ( p c t Ths data provdes everythng we need for to calculate the relatve wllngness to pay for each frm,, usng equaton (3. 4 Emprcal evdence 4. Frst model: Sgnfcant correlaton but low accuracy As we dd n secton 2, we wll denote PS PS PS, the relatve PS of frm. We wll denote (PS, the relatve PS of frm for quarter and Δ ( ( ( and. From equaton (4, Δ (, the varatons of relatve wllngness to pay between β PS ( τ dτ. Because PS s measured quarterly, we assume that the PS s steady durng a quarter and ( PS represents the PS for all of quarter from the end of quarter to the end of quarter. Thus 6
7 durng ths tme PS ( τ ( PS, so PS ( τ dτ ( PS equaton to be tested s: Δ ( ( PS + ( and the β ε (5 β s the proportonalty rato and ( ε the error term. The coeffcent of correlaton between Δ ( and ( PS s 0.90 for 32 observatons. It s sgnfcant n the table of crtcal values for the Pearson correlaton, and the hypothess of correlaton can be accepted wth an error rs lower than 5%. However, the results are not very accurate. The mean of both seres s equal to zero because each value s the devaton from the mean. The standard devaton for Δ ( s.6 whle the standard error s.59. The useful sgnal s bured n the nose, whch s why the correlaton coeffcent s not hgher. The graph below (fg. represents the scatter plot: 6 uarterly change n WTP Δ( PS (fg. Ths rases the queston of whether the error results from a lac of correlaton between sets or f t s smply a resdual error whch s ndependent of the correlaton. In the latter case, the correlaton coeffcent s low because the WTP has not had enough tme to suffcently exceed the error level. 4.2 Second model: Hgher and ncreasng accuracy The only way to answer ths, lettng WTP evolve over a longer perod, usng several quarters nstead of a sngle quarter. The standard devaton of should ncrease over 7
8 tme, and f the standard error does not ncrease n the same proportons, the correlaton should mprove and the coeffcent of correlaton should ncrease. We wll compare the evoluton of relatve PS to that of relatve WTP, perod of tme of quarters. In ths case, because PS s steady durng a quarter: Δ ( β β PS ( τ dτ β PS ( τ dτ As a result, we wll test the followng expresson: Δ ( PS over a ( β ( PS + ( ε (6 Data for Span was avalable for only 4 quarters (from 4 to 200 and data for the UK for only 7 quarters (from to 200. There are thus 8 avalable observatons for each value of when 4, for a total of 72 observatons. For 4 < 7, the Spansh data s not avalable and there are 4 avalable observatons for each value of, for a total of 42 observatons. For 7 < 9, the Brtsh data s not avalable and there are 9 avalable observatons for each value of, for a total of 8 observatons. We thus have a total of 32 avalable observatons. For all countres wth the excepton of Span, the value of n equaton (6 s the second quarter of :. For Span, s the thrd quarter of : (See appendx 7.2. The coeffcent of correlaton s now 0.745, whch s hghly sgnfcant. The standard devaton for the set of 32 observed has reached 2.35, as opposed to.6 n the prevous model, whle the standard error has remaned almost steady at.58. The graph below (fg.2 represents the scatter plot for the second model: R 2 0,555 Change n relatve WTP ( ΣPS (fg.2 8
9 The ncrease n the duraton of the evoluton of WTP has dramatcally mproved the correlaton, whch suggests that the standard error does not stem from a poor correlaton but from a resdual error whch s ndependent of the correlaton. 4.3 Test of ncreasng correlaton In order to confrm ths, we wll wegh each PS value wth the number of quarters,. We wll then perform the followng lnear regresson: Δ ( ( β + β ( PS + ( ε 2 The regresson provdes a postve and sgnfcant value for β 2 (see appendx 7.3 whch means that the correlaton s ncreasng. 4.4 A useful sgnal emerges from the nose The mean of the seres Δ ( and ( PS s equal to zero because and PS are the devaton of each frm from the natonal average. However, when the number of quarters ncreases, the standard devaton of the seres also ncreases, whle the standard error between the two seres remans roughly steady, despte fluctuatons quarter by quarter. It s worth notng that standard devaton of both seres seems evolve almost le a standard normal dstrbuton whose standard devaton s σ.. Indeed, each addtonal quarter amounts to add such standard normal dstrbuton to the prevous one. After quarters, the standard devaton of the sum of such standard normal dstrbutons s σ. The fgure below (fg.3 represents the evoluton of the standard devaton of Δ (, σ ( (blac curve, the evoluton of the standard devaton of a standard normal dstrbuton, σ ( (gray curve, and the standard error ε (, (whte curve accordng to. Ths suggests that the dstrbuton of the values of Δ ( around the mean are almost randomly dstrbuted. 9
10 Standard Devaton and Standard Error ( σ ε ( ( σ umber of quarters (fg.3 The ncrease n standard devaton means that the absolute values of the seres ncrease and as a result, the correlaton ncreases too. The rato Standard devaton on Standard error can be nterpreted as a sgnal to nose rato. The fgure below (fg.4 σ ( represents the Sgnal to ose Rato (n decbel, SR ( 0log, (blac ε ( ε ( σ ( curve and σ ( SR ( 0log, (gray curve, wth μ, the μ 9 mean of ε ( on the 9 quarters. One can notce the strong ncrease n standard error for 2 and 6. Ths corresponds to the 4 and 4 for Belgum, France, Swtzerland and UK. (ot for Span where 2 corresponds to 200 and where 4. 4 th quarters seem to generate more errors than other. Ths s probably the effect of Chrstmas season when many promotons are offered to customers. 9 Sgnal to ose Rato db.00 SR SR umber of quarters (fg.4 0
11 An ncrease n SR mproves the correlaton. The fgure below (fg.5 llustrates the relatonshp between SR ( and the coeffcent of correlaton between the two seres Δ ( and ( PS. Relatonshp between SR and coeffcent of correlaton Coeffcent of correlaton (fg.5 SR (db One can notce that for SR ( 0, the coeffcent of correlaton s close to zero, n such a case, the level of nose s equal to the level of sgnal. When ncreases, SR ( tends to ncrease and the coeffcent of correlaton ncreases as well (excepted for 6. For 2, despte the slght mprovement of the SR, the coeffcent of correlaton ncreases anyway because of the very strong slope of the curve here. When s great, the coeffcent of correlaton tends toward. In ths study, for 9, the coeffcent of correlaton attans The useful sgnal whch s bured n the nose for the low values of, emerges from the nose when ncreases and consequently, the correlaton becomes stronger and stronger. Lewse the coeffcent of correlaton, the coeffcent of determnaton R 2 ncreases wth the SR and hence tends to ncrease wth. For 9, adusted R , PS explans more than 72% of the Wllngness to Pay. The followng graph (fg.6 2 represents the evoluton of the adusted R accordng to SR (
12 Relatonshp between SR and coeffcent of determnaton Adusted R² SR (db (fg.6 Ths ncreasng correlaton confrms the hypothess estmate parameter β. d β PS and allows us to dτ 4.5 Estmaton of parameter β The accuracy of the estmaton ncreases le the correlaton wth the number of quarters,. Therefore the most accurate estmaton s gven for 9. In such a case, the estmaton leads to β 5 cent / month wth a 5% standard error. That means that β has a probablty of 50% to be n the range: 4.3 to 5.8 cent or a probablty of 95% to be n the range: 3.3 to 6.8 cent. β 5 cent / month means that a 0pont PS per quarter corresponds to a 0.5 ncrease n consumer Wllngness To Pay. The PS s measured each quarter and the results are cumulated over tme. In other words, a 5pont PS per quarter durng a year corresponds to ncrease n Wllngness To Pay. However, f all frms have the same PS, ther relatve PS wll reman unchanged and therefore also ther relatve Wllngness To Pay. Ths does not mean ther ndvdual Wllngness To Pay does not ncrease; only that t ncreases dentcally for all frms. In such a case, all thngs beng equal, revenues and profts remans steady. Frms can beneft from the ncrease of Wllngness To Pay of ther customers, only when t s hgher than that of ther compettors. There are no sgnfcant dfferences between countres, addng a dummy country does not provde addtonal nformaton. A comparson of the relatve evoluton of WTP, and β PS usng the coeffcent β we have estmated and a smulaton of the evoluton of the relatve WTP by country are avalable n the appendces (Appendx
13 Frms that have the greatest changes are often also those that gve the most accurate results because they devate more from the margn of error for example: Swsscom (Swtzerland; Hutchnson 3 (UK; Bouygues (France or Yogo (Span. 5 Correlaton between PS, revenues and profts The correlaton between PS, revenues and profts has already been clearly ndcated by (Rechheld, We am to show that ths correlaton s much weaer than for WTP. WTP depends essentally on customers' choces and thus on ther satsfacton whch can be measured by PS, whle revenues and profts, whle they heavly depend on PS, are also subect to other factors whch are ndependent of customers, ncludng margnal cost, coeffcent of dfferentaton t and total maret sze n. Equaton can be rewrtten: ( c p c + t + (2 Revenues and proft of frm can be wrtten: 2 n t c c t c R (7 t (2 (2 2 n t ( 2 c π + (8 t Equatons 7 and 8 show that revenues and proft wll evolve quadratcally wth the development of relatve Wllngness To Pay, and thus wth the relatve PS. Ths fulfls the second generalzaton of (Gupta & Zethaml, 2006 The ln between satsfacton and proftablty s asymmetrc and nonlnear However, Revenues and Proft are also very senstve to varatons n effcency, c, dfferentaton t, or the total maret sze n. Equaton (6 llustrates the relatonshp between WTP and PS. We can wrte the smlar relatonshp between the evoluton of Revenues and PS: Δ ( R β ( PS + ( ε (9 The coeffcent of correlaton s for 32 observatons. It s stll sgnfcant but weaer than the correlaton between WTP and PS (coeffcent of correlaton The graph below represents the correspondng scatter plot (fg.7 3
14 200 Change n relatve revenues (mllons / quarter (fg.7 In the same way, the relatonshp between the development of profts and PS can be wrtten: Δ ( π β ( PS + ( ε (0200 ΣPS The coeffcent of correlaton s 0.085, whch s too low to be sgnfcant. The graph below (fg.8 represents the correspondng scatter plot. 00 Change n relatve Ebtda (mllons /quarter (fg.8 Σ PS 4
15 Equaton (8 ndcates that profts are very senstve to margnal costs. Let us add margnal costs to the regresson. Δ ( PS + β 2 ( π β ( c + ( ε ( β and β 2 are both qute sgnfcant: sgns of β and 2 β and The opposte β suggest that frms wth a hgh PS whch ncrease ther consumers WTP the most qucly are also generally those whch ncrease ther margnal costs the most. In other words, ths suggests that the ncrease n WTP and margnal costs are correlated. The correlaton coeffcent s for 32 observatons, whch ndcates a strong correlaton. Ths explans why the correlaton between proft development and PS s so wea. The ncrease n PS often requres an ncrease n qualty for consumers. Ths tends to ncrease margnal costs and reduces the benefts provded by consumers satsfacton. In equaton 7, the ncrease β of margnal costs reduces the term but t s compensated by the term c. In c equaton 8 the term c dsappears and can no longer compensate for the reduced effcency. Moreover, the coeffcent of correlaton between evoluton of profts and ( s c for 32 observatons, whch s qute sgnfcant. The graph below (fg.9 represents the scatter plot between WTP and margnal costs. 2 8 Change n relatve WTP (/subscrber (fg.92 Development of relatve margnal costs (/subscrber 5
16 6 Concluson The correlaton between PS and WTP s very strong n the European moble marets whch we studed. It explans most of the varatons n WTP. It s clearly the sgn of compettve marets where customers can swtch provders at wll wthout much hndrance. The standard error does not vary sgnfcantly wth the duraton of observaton, whle PS tends to ncrease; therefore, the relatve error decreases and causes the ncrease n the correlaton between PS and WTP. We can consder that the PS fathfully reflects changes n WTP. A 5pont PS per quarter over a year corresponds to about ncrease n Wllngness To Pay. The correlaton between PS and Revenues exsts but s less pronounced due to the fact that WTP depends entrely on consumers whle Revenues also depend on strategc nteractons among frms. The correlaton between PS and profts s even lower because profts are very senstve to varatons n margnal costs and frms whch ncrease ther customers WTP the most are also often those whch ncrease margnal costs the most. As part of further research, t mght be relevant to fnd out how PS could be used as an ndcator of the compettveness of a maret, loong at the correlaton coeffcent between WTP and PS. Ths would dstngush what comes from the merts of the frms that manage to dfferentate themselves from ther compettors and what comes from an abusve customer retenton. The author thans Bruno Julen and Wlfred SandZantman for ther helpful tps and hs colleagues at France telecom Orange for ther remars and comments, especally Mhasonrna Andranavo and Dane Flpn as well as Mare Clare Lampaert who provded the data. 6
17 7 Appendces 7. Exchange rate CHF> 0,620 0,62 0,656 0,667 0,66 0,658 0,662 0,684 0,708 0,75 GBP>,26,259,9,099,36,49,05,27,70, Calculated values of Δ ( and Δ ( ( PS : Country Belgum frm ,0 0,062,05 ,842,32,533,592,382,95 frm 2 0,78 0,550,980,990,8 0,44 0,25,04 0,75 frm 3 0,230,60 3,03 2,83 2,94 2,09 3,33,34 2,20 France frm,42,420,72 ,690,284,05 ,68 ,55 ,45 frm 20,2 ,780,450,520,77 ,740,95 ,30,33 frm 30,92 4,20,7 2,22,06 5,79 2,64 2,68,78 Span frm,02,630,37 ,03 frm 2 0,720,230,420,3 frm 30,300,880, 0,42 frm 4 ,52 3,75 0,90,77 Swtzerland frm 0,94 0,5 3,97 3,38 3,39 3,52 4,8 5,30 7,37 frm 20,32,093,842,692,42 ,882,233,332,27 frm 30,62 ,600,30,700,97 ,64 ,95 ,965,0 UK frm 0,230,50 0,45 0,72 0,20,86,93 frm 2 0,460,43 0,80,0 2,80,52 2,83 frm 3 0,330,00,28 0,99,68 2,92 3,42 frm 40,850,59,86,690,32 0,52 0,6 frm 5 0,29,532,834,504,366,828,35 ( PS Country Belgum frm 6,00 ,676,007,000,003,332,335,009,33 frm 25,007,67,00 3,00 5,00 3,67 3,67 5,00 4,67 frm 3,00 9,33 5,00 4,00 5,00 9,67 8,67 0,00 4,67 France frm ,67 2,00 3,33 5,00 4,33 8,00 3,33 5,00 2,67 frm 27,670,003,6724,0023,6732,0034,6740,004,33 frm 3 9,33 8,00 0,33 9,00 9,33 24,00 3,33 35,00 38,67 Span frm 8,508,0024,504,00 frm 2 3,50 6,00 4,50,00 frm 35,5030,0044,5056,00 frm 4 20,50 42,00 64,50 96,00 Swtzerland frm 7,00 8,00 37,33 53,00 72,33 87,33 06,00 23,67 44,67 frm 26,008,007,676,0024,6737,6748,0060,3372,33 frm 3 ,000,0029,6737,0047,6749,6758,0063,3372,33 UK frm 3,00,20,60 4,60,40 0,80 7,80 frm 2 23,00 44,20 67,60 80,60 02,40 25,80 46,80 frm 34,002,80 5,60 6,60 22,40 26,80 3,80 frm 4 ,002,8024,409,409,6026,2029,20 frm 5 ,0029,8050,4082,4006,6037,2067,20 7
18 7.3 Test of ncreasng correlaton: Regresson Statstcs Multple R 0, R Square 0, Adusted R Sq 0, Standard Erro, Observatons 32 AOA df SS MS F Sgnfcance F Regresson 2 45, , ,822438,2967E24 Resdual 30 30,9934 2, Total ,3963 Coeffcents Standard Error t Stat Pvalue Lower 95% Upper 95% β 0, ,002488, , , , β 2 0, , , , , , Frm by frm comparson of PS and development of WTP Let us compare the varatons of relatve WTP and β PS usng the coeffcent β we estmated Belgum Operator (Belgum   β PS
19 Operator 2 (Belgum β PS Operator 3 (Belgum   β PS France Operator (France β PS
20 Operator 2 (France   β PS Operator 3 (France β PS Span Operator (Span β PS
21 Operator 2 (Span β PS Operator 3 (Span β PS Operator 4 (Span β PS
22 7.4.4 Swtzerland Operator (Swtzerland β PS Operator 2 (Swtzerland β PS Operator 3 (Swtzerland β PS
23 7.4.5 Unted Kngdom Operator (UK β PS Operator 2 (UK β PS Operator 3 (UK β PS
24 Operator 4 (UK β PS Operator 5 (UK β PS Chen, Y., & Rordan, M. H. (2007. Prce and arety n the Spoes Model. The Economc Journal, 7(522, Gupta, S., & Zethaml,. (2006. Customer metrcs and ther mpact on fnancal performance. Maretng Scence, 25(6, 78. Kenngham, T. L., Asoy, L., Cool, B., Andreassen, T. W., & Wllams, L. (. A holstc examnaton of et Promoter. Journal of Database Maretng & Customer Strategy Management, 5(2,
25 Kenngham, T. L., Cool, B., Andreassen, T. W., & Asoy, L. (2007. A longtudnal examnaton of net promoter and frm revenue growth. Journal of Maretng, 7(3, Morgan,. A., & Rego, L. L. (2006. The value of dfferent customer satsfacton and loyalty metrcs n predctng busness performance. Maretng Scence, 25(5, 426. Rechheld, F. (2003. The one number you need to grow. Harvard Busness Revew, 8(2, Rechheld, F. (2006. The mcroeconomcs of customer relatonshps. MIT Sloan Management Revew, 47(2,
SIMPLE LINEAR CORRELATION
SIMPLE LINEAR CORRELATION Smple lnear correlaton s a measure of the degree to whch two varables vary together, or a measure of the ntensty of the assocaton between two varables. Correlaton often s abused.
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