User Perception of the Retail Loyalty Programs in the City of Kolkata, India
User Perception of the Retail Loyalty Programs in the City of Kolkata, India Present affiliation of Authors Dr. Atish Chattopadhyay Professor of Marketing, SPJIMR, India email@example.com And Dr. Kalyan Sengupta Professor of IT and Systems, IISW&BM, Kolkata, India firstname.lastname@example.orgThe authors would like to acknowledge the contributions made by two of the students of the class of 2007of ICFAI Business School, Kolkata namely Shri Sanmitra Sarkar and Shri Debojyoti Banerjee whowere involved throughout the course of a live project for a leading retail group of India based on which thispaper is written.
User Perception of the Retail Loyalty Programs in the City of Kolkata, IndiaAbstractLoyalty programs are being increasingly used as CRM tactics. Recent studies have questioned the fate ofloyalty programs. This study explored the user perception of various retail loyalty programs in Kolkata,through a consumer survey. It was observed that retailers need to have a clear insight of shopperexpectations while designing a loyalty program and the relative importance of various factors which makesthe loyalty program successful.The current research addresses the issue of identifying the factors which are critical to the success of aretail loyalty program in the Indian context.Keywords – Loyalty, loyalty program, shopper perception, CRM1. IntroductionDuring the past decade, loyalty programs have been intensively experimented throughout the globe mostlyto create a new generation of CRM tactics as was evident from ample experiences including Japaneseretailing, US airlines and hotels, French banks, UK groceries and so forth (Brown, 2000; Kalokota andRobinson, 1999; Field, 1997). In India it was observed that Shoppers’ Stop, a leading retail chain,managed to achieve 60 percent of its sales from repeat customers (as against the Indian average of 30percent) by virtue of its highly pushed loyalty programs (Dasgupta 2005).However, a group of researchers (Uncles et. al, 2003; Miranda et. al, 2004; Stauss et. al, 2005) observedfrom empirical researches that loyalty in repeat purchases is a result of passive acceptance of brands ratherthan from positive efforts to improve customer attitudes. A recent study (Noordhoff et. al, 2004) questionedthe fate of loyalty programs in the long run. Store customers of Netherlands and Singapore were comparedin terms of behavioral and attitudinal loyalty with respect to loyalty cards. It was concluded from the studythat efficacy of store loyalty programs appeared to diminish with an increasing number of alternative cardprograms in the market. It also diminished with the habituation of customer with these cards. While thesustainability of loyalty scheme is in question, the marketers need to be clear about relative importance ofdata collection and rewarding loyal customers for achieving sustainable loyalty (Lisa O’ Malley, 1998).Understanding of appropriate factors which could build a cordon around the customers is extremelyessential. Organizational and regular feedback from the marketplace may extract customers’ latent needs insome ongoing manner. A well designed loyalty scheme could be considered as a useful instrument forcontinuous tracking of customers, which may enable a successful CRM and hence a sustainable loyaltyimprovement system. The present study aims to identify factors which influence customer likings anddisliking with respect to retail loyalty programs in the city of Kolkata, India.2. MethodologyThe experiment performed for the current research was a spot survey where respondents were any existingloyalty card holders. The idea was to assess the perception of existing loyalty program members who couldshare their experiences of such membership in terms of attitude towards the benefits offered by the firmsthrough such program. The consumer survey was based on simple random sampling. Target population wasthose who visited established shopping centers in the city of Kolkata. The sample frame consisted ofexisting large shopping malls where customers were interviewed as they left the centers. Randomizedselection procedure was used whereby interviewers walked from one exit door to the other consecutively,approaching the shopper as he or she exited the mall (Sudman, 1980). The valid respondents were those,who already possessed any retail loyalty program card in the city of Kolkata. A set up six senior students ofICFAI Business School, Kolkata were trained for conducting the survey. They intercepted a total of 528respondents in six shopping malls and asked about willingness of the respondents to participate in thesurvey. Out of these shoppers, only 278 respondents agreed to fill in the forms. Ultimately a set of 250 fullyfilled in forms could be collected and this formed our sample for the study.
The survey form contained a list of potential parameters for which the respondents indicated their likings ordisliking on a five point scale. Initially a list of 34 such parameters were considered and discussed with anexpert group, consisting of two senior managers of two retail stores, two regular retail consumers and aProfessor of Marketing of a reputed Business School. After a long brain storming of two hours a group of18 parameters came out to be realistic and relevant for the purpose of our study. A questionnaire wasdesigned containing these 18 items given as under:i. Allotment of Reward Points based on x. Exchange Facilities Spending xi. Separate Queuing / Billing Countersii. Periodic Discounts xii. Ability of the Service Personnel toiii. Home Delivery Recognize without the Cardiv. Invitation for Special Events xiii. Membership Chargesv. Redemption Vouchers xiv. Renewal Feesvi. Special Offer / Gifts on Birthdays, xv. Carrying the Plastic in the Wallet Anniversaries xvi. Process of Redemption of Reward Pointsvii. Periodic Information Catalogues xvii. Regular Mailsviii. Gift Hampers xviii.Feedback through phone callsix. Personalized Services / Special TreatmentThe respondents were also asked whether they would like to become member of a new loyalty program, bycarrying one more plastic in their wallet. The question was dichotomic in nature, requesting for either ‘yes’or ‘no’ as reply.3. FindingsThe distribution of the demographic parameters of the respondents, including gender, age group, incomegroup, profession and ownership of prime assets, etc. are illustrated in the Table – 1.Table 1: Demographic Distribution of the Respondents Serial Particulars Sub Factors Proportion of Sample (in %) Number 1 Gender Male 62.4 Female 37.6 Academician 7.6 Service/Consultancy 48.8 2 Profession Professional 10.0 Businessman 12.4 Government Service 1.2 Retired/ Housewife/ Student 20.0 Up to 16000 18.0 3 Income per 16000-32000 38.4 month 32000 and above 42.0 Not revealed 1.6 <20 2.0 20-30 44.8 4 Age 30-40 29.2 40-60 20.8 60 and above 3.2
Owns laptop 28.4 Owns personal computer 75.6 5 Ownership Owns car 64.8 Owns 2 wheeler 30.8 Owns air conditioner 74.4 Owns house 72.4The sample represents a typical middle and upper middle class of an urban society of Kolkata who aremostly patrons of large retail stores and chains in the city. The distribution of retail loyalty cards held bythe respondents is illustrated in table 2.Table 2: Retail Loyalty Cards held by the Respondents Retail Loyalty card Number of holders Proportion of Sample Type among the sample (in %) First Citizen 138 27.1 Green Card 118 23.2 Club West 120 23.6 C3 4 0.8 Planet M 19 3.7 Oxford 39 7.7 Crossword 30 5.9 Club Wills 37 7.3 Big Bazar 4 0.8The responses from the survey of 250 respondents revealed that around 70 percent of the members of theloyalty programs shared the benefits of the same with their family and friends, which is given in table 3.Table 3: Facilities of the loyalty program were availed by Proportion of Sample Availed by (in %) Member only 30.8 Member along with family and 69.2 friendsThe responses collected from the data revealed that only six parameters out of 18 had an average score of3.5 and more, which means that only 5 parameters were relatively liked by the respondents. These arePeriodic discounts, Special Offer/Gifts on Birthdays/Anniversaries, Personalized Services/SpecialTreatments, Exchange Facility, Separate Queuing/Billing Counters. The parameters which were dislikedwith a score of less than 2 were Process of Reward Point Redemption, Feedback through Phone Calls andRegular Mails.An exploratory factor analysis was performed on the items and 7 factors could be extracted whichexplained 58 percent of the total variance contributed by all the 18 items. A Bartlett’s Test of Sphericityconfirmed the factor model to be significant at 0.000 percent level of significance. Also KMO (Kaiser-Meyer-Olkin) measure of sampling adequacy was estimated to be quite satisfactory at 0.628. The 7 factorsthus extracted, were interpreted as (1) Gift Offer (GO), (2) Discount, Exchange and Special Queue(DESQ), (3) Charges and Fees (CF), (4) Feedback and Mails (FM), (5) Home Delivery and Special Events(HDSE), (6) Personalized Service (PS), (7) Failure of Recognition / Redemption (FRR). The factorloadings are shown in table 4.
Table 4: Rotated Component Matrixa Component GO DES CF FM HDS PS FRR Q E Point Redemption 0.673 Periodic Discounts 0.704 Home Delivery 0.754 Invitation to Special Events 0.467 Redemption Vouchers 0.535 Special Offer / Gifts on Birthdays, 0.707 Anniversaries Periodic Information Catalogues 0.480 Gift Hampers 0.764 Personalized Services / Special Treatments 0.756 Exchange Facility 0.467 Separate Queuing / Billing Counters 0.498 Failure of Recognition without the Card 0.712 Membership Charges 0.784 Renewal Fees 0.827 Carrying the Plastic in the Wallet 0.449 Reward Points that are Difficult to Redeem 0.379 0.457 Regular Mails 0.842 Feedback through Phone-calls / Feedback 0.699 FormsExtraction Method: Principal Component AnalysisRotation Method: Varimax with Kaiser Normalizationa. Rotation Converged in 8 Iterations.ANOVA study on the factor scores, thus extracted, revealed that none of these factors were dependent onany of the demographic characters viz. age, gender, income, profession, etc. The findings revealed, that therespondents, who frequently visited modern shopping malls and also possessed retail loyalty programmemberships could not be differentiated in terms of demographic characteristics. At the same time, it isinteresting to note from the table 5 that there are significant differences between means of two groups ofrespondents (who had desired to become a loyalty member and who did not) with respect to only 3 factorsout of 7 extracted factors. These were HDSE, CF and PS. Difference of mean were significant for thesecases even at 5 percent level or less. Thus these three factors may be considered to be determinant factorsfor willingness or otherwise to have a new loyalty membership.Table 5: Mean difference of factors between two groups Factor Mean Score Mean Score Significance Desired Not DesiredGift Offer -.0434609 .0685518 .389Discount, Exchange and Special -.0190555 .0300567 .706QueueCharges & Fees -.10.14718 -.2321641 .003Feedback & Mails -.0391900 .0618152 .438
Home Delivery & Special Events .1471890 1.0041909 .042Personalized Service -.1155583 .1822724 .021Failure of Recognition / Redemption .0439590 -.0693375 .384Analysis of the dataset further revealed that a vast majority of 61.2 percent of the respondents did not wishto become a member of a retail loyalty program by carrying another plastic in the wallet.In the next part of the analysis, factor scores of each of these 7 factors were evaluated for individualrespondent and a binary logistic regression was performed. The dependent variable was ‘desire to become amember by carrying one more plastic’ (yes / no) and the independent variables were the perceived factorscores of the offerings. In stepwise regression procedures, it was interesting to note that only 3 factors outof 7, could explain the desire for a new membership. These were Home Delivery & Special Events(HDSE), Charges & Fees (CF) and Personalized Services (PS). Table 6 demonstrates the validity of themodel. Estimate of 2 log-likelihood of the step 3 model was 314.99. The model could predict 64.4observations correctly.Table 6: Omnibus Tests of Model Coefficient Chi-square df Sig. Step 8.670 1 0.003 Step 1 Block 8.670 1 0.003 Model 8.670 1 0.003 Step 5.826 1 0.016 Step 2 Block 14.496 2 0.001 Model 14.496 2 0.001 Step 4.436 1 0.035 Step 3 Block 18.932 3 0.000 Model 18.932 3 0.000Table 7 illustrates the B values (Regression Coefficient) and their reliability in the above mentionedregression models. The Wald values and their significance are quite satisfactory in terms of acceptance ofthe models. The sensitivity of the variables in the models to the odds of the output variable may be viewedfrom Exp (B) column of table 5. A value more than 1 indicates a positive impact to the odds while a valueless than 1 indicates a negative impact. According to this, personalized services had a high positivemarginal impact on desire to have a new membership. Home delivery had also a similar but less intensiveimpact. On the other hand Membership Charges had a negative impact on the odds.Table 7: Variables in the Regression Equation B S.E. Wald df Sig. Exp (B) Step 1a PS -0.391 0.136 8.287 1 0.004 0.677 Constant -0.471 0.132 12.653 1 0.000 0.624 HDSE -0.402 0.138 8.564 1 0.003 0.669 Step 2b PS 0.341 0.147 5.369 1 0.020 1.407 Constant -0.487 0.135 13.074 1 0.000 0.614 DESQ 0.284 0.136 4.365 1 0.037 1.329 Step 3c HDSE -0.411 0.139 8.795 1 0.003 0.663 PS 0.345 0.147 5.465 1 0.019 1.412 Constant -0.497 0.136 13.296 1 0.000 0.609a. Variable(s) entered on step 1: PSb. Variable(s) entered on step 2: HDSEc. Variable(s) entered on step 3: DESQFurther to the above classification analysis using logistic regression, the dataset was further tested usingdiscriminant analysis. Desire to have a new membership (yes / no) was the dependent variable, while the 7
factors scores were the independent variables. The model estimated a chi–square significance of 0.004.Function values at the group centroids were: NO = -0.236, YES = 0.372. The model could predict 66percent of the observations correctly. The standardized canonical discriminant function coefficients areillustrated in the table 8.Table 8: Standardized Canonical Discriminant Function Coefficient Function 1Gift Offer - 0.200Discount, Exchange and Special - 0.088QueueCharges & Fees 0.468Feedback & Mails 0.180Home Delivery & Special Events 0.667Personalized Service - 0.527Failure of Recognition / Redemption - 0.202It is interesting to note that Charges & Fees acted as negative incentive while two other factors, viz.Personalized Service and Home Delivery and invitation to Special Events had a positive influence on themembers. Similar to the previous logistic model, Personalized Service had the maximum positive impacton ‘desire to have a new loyalty membership by carrying a plastic card’.4. Managerial Implications and Direction of Future ResearchThis study may be taken as an exploratory initiative to identify customer groups who would like to bemember of a retail loyalty program. The study clearly indicates that the benefits of a retail loyalty programare availed of by the family and not just by the individual who is the member of the program. Further themembers could not be differentiated by means of demographic characteristics, implying they belong to thesame demographic segment. The study clearly demonstrates the importance of personalized service andspecial treatment as the predominant factor followed by home delivery, invitation to special events whichinfluence customer’s intention to opt for a loyalty program. It also revealed that customers are moreinterested in intangible personalised services than tangible benefits like gifts, discounts etc. It wasinteresting to note that customers were averse to charges like renewal and membership fees. Failure ofrecognition by the retail sales person and difficulty in point redemption were factors which created negativeperception.The most revealing aspect of the study was that a vast majority of around 62 percent of the present loyaltyprogram members were unwilling or reluctant to become a member of a new retail loyalty program bycarrying one more plastic in their wallet. This implies that retailers need to find out an alternative to thepresent system of a plastic loyalty card. Further research needs to be undertaken in this direction if loyaltyprograms are to be made successful.While planning for a new loyalty program, retailers need to have a clear insight of shopper likings anddisliking with respect to their loyalty program. This is particularly relevant as some of the techniquesadopted by the retailers may create a negative perception amongst the members of the loyalty program. Itmay be finally recommended that retailers need to be clear about the relative importance of various factorswhich makes a retail loyalty program successful.This research is limited to the holders of retail loyalty programs in the city of Kolkata, where membershipof a loyalty program meant holder of a plastic card. The research is only act as an indicator towards certainfactors about which customers may have particular liking and disliking. Further research may be
undertaken to ascertain these factors. Research may also be undertaken to investigate customerexpectations from a retail loyalty program in the Indian context.References:Brown, S.A. (2000): Customer Relationship Management, John Wiley & Sons, Toronto.Dasgupta, S. (2005): “Who’s afraid of Wal-Mart?” Business Standard, 03 December.Field, C. (1997): Data goes to Market, Computer Weekly, Jan 16, 1997, pp.44-5Kalokota, R. and Robinson, M. (1999): “e-Business”, Addison-Wesley, Reading, MA.Malley, L.O’ (1998): “Can loyalty schemes really build loyalty?”, Marketing Intelligence and Planning,Vol.16, No.1, pp.47-55Miranda M.J.; Konya, L. and Havrila, I. (2004): “Shoppers’ satisfaction levels are not the only key to storeloyalty”, Marketing intelligence and Planning”, Vol.23, No.2, pp.220-232Noordhoff, C.; Pauwels, P. and Schroder, O.G. (2004): “The effect of customer card programs – Acomparative study in Singapore and The Netherlands”, International Journal of Service IndustryManagement, Vol.15, No.4, pp.351-364Stauss, B.; Schmidt, M. and Schoeler, A. (2005): “Customer frustration in loyalty programs”, InternationalJournal of Service Industry Management, Vol.16, No.3, pp.229-252Sudman, S. (1980): “Improving the quality of shopping center sampling”, Journal of Marketing Research,Vol.17, No.2, pp.423-431, November.Uncles, M. D.; Grahame, R. D. and Kathy, H. (2003): “Customer loyalty and customer loyalty programs”,Journal of Consumer Marketing, Vol.20, No.4, pp.294-316