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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

                                           atishc@spjimr.org


                                                    And


                                          Dr. Kalyan Sengupta
                     Professor of IT and Systems, IISW&BM, Kolkata, India
                                     kalyansen2002@yahoo.co.uk




The authors would like to acknowledge the contributions made by two of the students of the class of 2007
of ICFAI Business School, Kolkata namely Shri Sanmitra Sarkar and Shri Debojyoti Banerjee who
were involved throughout the course of a live project for a leading retail group of India based on which this
paper is written.
User Perception of the Retail Loyalty Programs in the City of Kolkata, India
Abstract

Loyalty programs are being increasingly used as CRM tactics. Recent studies have questioned the fate of
loyalty 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 shopper
expectations while designing a loyalty program and the relative importance of various factors which makes
the loyalty program successful.
The current research addresses the issue of identifying the factors which are critical to the success of a
retail loyalty program in the Indian context.

Keywords – Loyalty, loyalty program, shopper perception, CRM

1. Introduction

During the past decade, loyalty programs have been intensively experimented throughout the globe mostly
to create a new generation of CRM tactics as was evident from ample experiences including Japanese
retailing, US airlines and hotels, French banks, UK groceries and so forth (Brown, 2000; Kalokota and
Robinson, 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 30
percent) 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) observed
from empirical researches that loyalty in repeat purchases is a result of passive acceptance of brands rather
than from positive efforts to improve customer attitudes. A recent study (Noordhoff et. al, 2004) questioned
the fate of loyalty programs in the long run. Store customers of Netherlands and Singapore were compared
in terms of behavioral and attitudinal loyalty with respect to loyalty cards. It was concluded from the study
that efficacy of store loyalty programs appeared to diminish with an increasing number of alternative card
programs in the market. It also diminished with the habituation of customer with these cards. While the
sustainability of loyalty scheme is in question, the marketers need to be clear about relative importance of
data 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 extremely
essential. Organizational and regular feedback from the marketplace may extract customers’ latent needs in
some ongoing manner. A well designed loyalty scheme could be considered as a useful instrument for
continuous tracking of customers, which may enable a successful CRM and hence a sustainable loyalty
improvement system. The present study aims to identify factors which influence customer likings and
disliking with respect to retail loyalty programs in the city of Kolkata, India.

2. Methodology

The experiment performed for the current research was a spot survey where respondents were any existing
loyalty card holders. The idea was to assess the perception of existing loyalty program members who could
share their experiences of such membership in terms of attitude towards the benefits offered by the firms
through such program. The consumer survey was based on simple random sampling. Target population was
those who visited established shopping centers in the city of Kolkata. The sample frame consisted of
existing large shopping malls where customers were interviewed as they left the centers. Randomized
selection 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 of
ICFAI Business School, Kolkata were trained for conducting the survey. They intercepted a total of 528
respondents in six shopping malls and asked about willingness of the respondents to participate in the
survey. Out of these shoppers, only 278 respondents agreed to fill in the forms. Ultimately a set of 250 fully
filled 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 or
disliking on a five point scale. Initially a list of 34 such parameters were considered and discussed with an
expert group, consisting of two senior managers of two retail stores, two regular retail consumers and a
Professor of Marketing of a reputed Business School. After a long brain storming of two hours a group of
18 parameters came out to be realistic and relevant for the purpose of our study. A questionnaire was
designed containing these 18 items given as under:

i.    Allotment of Reward Points based on                  x.    Exchange Facilities
      Spending                                             xi.   Separate Queuing / Billing Counters
ii. Periodic Discounts                                     xii. Ability of the Service Personnel to
iii. Home Delivery                                               Recognize without the Card
iv. Invitation for Special Events                          xiii. Membership Charges
v. Redemption Vouchers                                     xiv. Renewal Fees
vi. Special Offer / Gifts on Birthdays,                    xv. Carrying the Plastic in the Wallet
      Anniversaries                                        xvi. Process of Redemption of Reward Points
vii. Periodic Information Catalogues                       xvii. Regular Mails
viii. Gift Hampers                                         xviii.Feedback through phone calls
ix. Personalized Services / Special Treatment

The respondents were also asked whether they would like to become member of a new loyalty program, by
carrying one more plastic in their wallet. The question was dichotomic in nature, requesting for either ‘yes’
or ‘no’ as reply.

3. Findings

The distribution of the demographic parameters of the respondents, including gender, age group, income
group, 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                                          72.4
The sample represents a typical middle and upper middle class of an urban society of Kolkata who are
mostly patrons of large retail stores and chains in the city. The distribution of retail loyalty cards held by
the 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                             0.8


The responses from the survey of 250 respondents revealed that around 70 percent of the members of the
loyalty 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
             friends


The responses collected from the data revealed that only six parameters out of 18 had an average score of
3.5 and more, which means that only 5 parameters were relatively liked by the respondents. These are
Periodic discounts, Special Offer/Gifts on Birthdays/Anniversaries, Personalized Services/Special
Treatments, Exchange Facility, Separate Queuing/Billing Counters. The parameters which were disliked
with a score of less than 2 were Process of Reward Point Redemption, Feedback through Phone Calls and
Regular Mails.

An exploratory factor analysis was performed on the items and 7 factors could be extracted which
explained 58 percent of the total variance contributed by all the 18 items. A Bartlett’s Test of Sphericity
confirmed 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 factors
thus 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 factor
loadings 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
 Forms
Extraction Method: Principal Component Analysis
Rotation Method: Varimax with Kaiser Normalization
a. Rotation Converged in 8 Iterations.

ANOVA study on the factor scores, thus extracted, revealed that none of these factors were dependent on
any of the demographic characters viz. age, gender, income, profession, etc. The findings revealed, that the
respondents, who frequently visited modern shopping malls and also possessed retail loyalty program
memberships could not be differentiated in terms of demographic characteristics. At the same time, it is
interesting to note from the table 5 that there are significant differences between means of two groups of
respondents (who had desired to become a loyalty member and who did not) with respect to only 3 factors
out of 7 extracted factors. These were HDSE, CF and PS. Difference of mean were significant for these
cases even at 5 percent level or less. Thus these three factors may be considered to be determinant factors
for 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 Desired
Gift Offer                                -.0434609                  .0685518                   .389
Discount, Exchange and Special            -.0190555                  .0300567                   .706
Queue
Charges & Fees                            -.10.14718                 -.2321641                  .003
Feedback & Mails                          -.0391900                   .0618152                  .438
Home Delivery & Special Events                 .1471890               1.0041909                  .042
Personalized Service                          -.1155583                .1822724                  .021
Failure of Recognition / Redemption            .0439590               -.0693375                  .384

Analysis of the dataset further revealed that a vast majority of 61.2 percent of the respondents did not wish
to 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 individual
respondent and a binary logistic regression was performed. The dependent variable was ‘desire to become a
member by carrying one more plastic’ (yes / no) and the independent variables were the perceived factor
scores of the offerings. In stepwise regression procedures, it was interesting to note that only 3 factors out
of 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 the
model. Estimate of 2 log-likelihood of the step 3 model was 314.99. The model could predict 64.4
observations 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            0.000

Table 7 illustrates the B values (Regression Coefficient) and their reliability in the above mentioned
regression models. The Wald values and their significance are quite satisfactory in terms of acceptance of
the models. The sensitivity of the variables in the models to the odds of the output variable may be viewed
from Exp (B) column of table 5. A value more than 1 indicates a positive impact to the odds while a value
less than 1 indicates a negative impact. According to this, personalized services had a high positive
marginal impact on desire to have a new membership. Home delivery had also a similar but less intensive
impact. 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        0.609
a. Variable(s) entered on step 1: PS
b. Variable(s) entered on step 2: HDSE
c. Variable(s) entered on step 3: DESQ
Further to the above classification analysis using logistic regression, the dataset was further tested using
discriminant 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 66
percent of the observations correctly. The standardized canonical discriminant function coefficients are
illustrated in the table 8.




Table 8: Standardized Canonical Discriminant Function Coefficient

                                                 Function
                                                     1
Gift Offer                                        - 0.200
Discount, Exchange and Special                    - 0.088
Queue
Charges & Fees                                     0.468
Feedback & Mails                                   0.180
Home Delivery & Special Events                     0.667
Personalized Service                              - 0.527
Failure of Recognition / Redemption               - 0.202

It 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 the
members. Similar to the previous logistic model, Personalized Service had the maximum positive impact
on ‘desire to have a new loyalty membership by carrying a plastic card’.

4. Managerial Implications and Direction of Future Research

This study may be taken as an exploratory initiative to identify customer groups who would like to be
member of a retail loyalty program. The study clearly indicates that the benefits of a retail loyalty program
are availed of by the family and not just by the individual who is the member of the program. Further the
members could not be differentiated by means of demographic characteristics, implying they belong to the
same demographic segment. The study clearly demonstrates the importance of personalized service and
special treatment as the predominant factor followed by home delivery, invitation to special events which
influence customer’s intention to opt for a loyalty program. It also revealed that customers are more
interested in intangible personalised services than tangible benefits like gifts, discounts etc. It was
interesting to note that customers were averse to charges like renewal and membership fees. Failure of
recognition by the retail sales person and difficulty in point redemption were factors which created negative
perception.

The most revealing aspect of the study was that a vast majority of around 62 percent of the present loyalty
program members were unwilling or reluctant to become a member of a new retail loyalty program by
carrying one more plastic in their wallet. This implies that retailers need to find out an alternative to the
present system of a plastic loyalty card. Further research needs to be undertaken in this direction if loyalty
programs are to be made successful.

While planning for a new loyalty program, retailers need to have a clear insight of shopper likings and
disliking with respect to their loyalty program. This is particularly relevant as some of the techniques
adopted by the retailers may create a negative perception amongst the members of the loyalty program. It
may be finally recommended that retailers need to be clear about the relative importance of various factors
which makes a retail loyalty program successful.

This research is limited to the holders of retail loyalty programs in the city of Kolkata, where membership
of a loyalty program meant holder of a plastic card. The research is only act as an indicator towards certain
factors 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 customer
expectations 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.44-5

Kalokota, 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.47-55

Miranda M.J.; Konya, L. and Havrila, I. (2004): “Shoppers’ satisfaction levels are not the only key to store
loyalty”, Marketing intelligence and Planning”, Vol.23, No.2, pp.220-232

Noordhoff, C.; Pauwels, P. and Schroder, O.G. (2004): “The effect of customer card programs – A
comparative study in Singapore and The Netherlands”, International Journal of Service Industry
Management, Vol.15, No.4, pp.351-364

Stauss, B.; Schmidt, M. and Schoeler, A. (2005): “Customer frustration in loyalty programs”, International
Journal of Service Industry Management, Vol.16, No.3, pp.229-252

Sudman, 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

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User Perception of the Retail Loyalty Programs in the City of Kolkata, India

  • 1. 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 atishc@spjimr.org And Dr. Kalyan Sengupta Professor of IT and Systems, IISW&BM, Kolkata, India kalyansen2002@yahoo.co.uk The authors would like to acknowledge the contributions made by two of the students of the class of 2007 of ICFAI Business School, Kolkata namely Shri Sanmitra Sarkar and Shri Debojyoti Banerjee who were involved throughout the course of a live project for a leading retail group of India based on which this paper is written.
  • 2. User Perception of the Retail Loyalty Programs in the City of Kolkata, India Abstract Loyalty programs are being increasingly used as CRM tactics. Recent studies have questioned the fate of loyalty 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 shopper expectations while designing a loyalty program and the relative importance of various factors which makes the loyalty program successful. The current research addresses the issue of identifying the factors which are critical to the success of a retail loyalty program in the Indian context. Keywords – Loyalty, loyalty program, shopper perception, CRM 1. Introduction During the past decade, loyalty programs have been intensively experimented throughout the globe mostly to create a new generation of CRM tactics as was evident from ample experiences including Japanese retailing, US airlines and hotels, French banks, UK groceries and so forth (Brown, 2000; Kalokota and Robinson, 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 30 percent) 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) observed from empirical researches that loyalty in repeat purchases is a result of passive acceptance of brands rather than from positive efforts to improve customer attitudes. A recent study (Noordhoff et. al, 2004) questioned the fate of loyalty programs in the long run. Store customers of Netherlands and Singapore were compared in terms of behavioral and attitudinal loyalty with respect to loyalty cards. It was concluded from the study that efficacy of store loyalty programs appeared to diminish with an increasing number of alternative card programs in the market. It also diminished with the habituation of customer with these cards. While the sustainability of loyalty scheme is in question, the marketers need to be clear about relative importance of data 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 extremely essential. Organizational and regular feedback from the marketplace may extract customers’ latent needs in some ongoing manner. A well designed loyalty scheme could be considered as a useful instrument for continuous tracking of customers, which may enable a successful CRM and hence a sustainable loyalty improvement system. The present study aims to identify factors which influence customer likings and disliking with respect to retail loyalty programs in the city of Kolkata, India. 2. Methodology The experiment performed for the current research was a spot survey where respondents were any existing loyalty card holders. The idea was to assess the perception of existing loyalty program members who could share their experiences of such membership in terms of attitude towards the benefits offered by the firms through such program. The consumer survey was based on simple random sampling. Target population was those who visited established shopping centers in the city of Kolkata. The sample frame consisted of existing large shopping malls where customers were interviewed as they left the centers. Randomized selection 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 of ICFAI Business School, Kolkata were trained for conducting the survey. They intercepted a total of 528 respondents in six shopping malls and asked about willingness of the respondents to participate in the survey. Out of these shoppers, only 278 respondents agreed to fill in the forms. Ultimately a set of 250 fully filled in forms could be collected and this formed our sample for the study.
  • 3. The survey form contained a list of potential parameters for which the respondents indicated their likings or disliking on a five point scale. Initially a list of 34 such parameters were considered and discussed with an expert group, consisting of two senior managers of two retail stores, two regular retail consumers and a Professor of Marketing of a reputed Business School. After a long brain storming of two hours a group of 18 parameters came out to be realistic and relevant for the purpose of our study. A questionnaire was designed containing these 18 items given as under: i. Allotment of Reward Points based on x. Exchange Facilities Spending xi. Separate Queuing / Billing Counters ii. Periodic Discounts xii. Ability of the Service Personnel to iii. Home Delivery Recognize without the Card iv. Invitation for Special Events xiii. Membership Charges v. Redemption Vouchers xiv. Renewal Fees vi. Special Offer / Gifts on Birthdays, xv. Carrying the Plastic in the Wallet Anniversaries xvi. Process of Redemption of Reward Points vii. Periodic Information Catalogues xvii. Regular Mails viii. Gift Hampers xviii.Feedback through phone calls ix. Personalized Services / Special Treatment The respondents were also asked whether they would like to become member of a new loyalty program, by carrying one more plastic in their wallet. The question was dichotomic in nature, requesting for either ‘yes’ or ‘no’ as reply. 3. Findings The distribution of the demographic parameters of the respondents, including gender, age group, income group, 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
  • 4. 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 72.4 The sample represents a typical middle and upper middle class of an urban society of Kolkata who are mostly patrons of large retail stores and chains in the city. The distribution of retail loyalty cards held by the 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 0.8 The responses from the survey of 250 respondents revealed that around 70 percent of the members of the loyalty 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 friends The responses collected from the data revealed that only six parameters out of 18 had an average score of 3.5 and more, which means that only 5 parameters were relatively liked by the respondents. These are Periodic discounts, Special Offer/Gifts on Birthdays/Anniversaries, Personalized Services/Special Treatments, Exchange Facility, Separate Queuing/Billing Counters. The parameters which were disliked with a score of less than 2 were Process of Reward Point Redemption, Feedback through Phone Calls and Regular Mails. An exploratory factor analysis was performed on the items and 7 factors could be extracted which explained 58 percent of the total variance contributed by all the 18 items. A Bartlett’s Test of Sphericity confirmed 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 factors thus 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 factor loadings are shown in table 4.
  • 5. 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 Forms Extraction Method: Principal Component Analysis Rotation Method: Varimax with Kaiser Normalization a. Rotation Converged in 8 Iterations. ANOVA study on the factor scores, thus extracted, revealed that none of these factors were dependent on any of the demographic characters viz. age, gender, income, profession, etc. The findings revealed, that the respondents, who frequently visited modern shopping malls and also possessed retail loyalty program memberships could not be differentiated in terms of demographic characteristics. At the same time, it is interesting to note from the table 5 that there are significant differences between means of two groups of respondents (who had desired to become a loyalty member and who did not) with respect to only 3 factors out of 7 extracted factors. These were HDSE, CF and PS. Difference of mean were significant for these cases even at 5 percent level or less. Thus these three factors may be considered to be determinant factors for 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 Desired Gift Offer -.0434609 .0685518 .389 Discount, Exchange and Special -.0190555 .0300567 .706 Queue Charges & Fees -.10.14718 -.2321641 .003 Feedback & Mails -.0391900 .0618152 .438
  • 6. Home Delivery & Special Events .1471890 1.0041909 .042 Personalized Service -.1155583 .1822724 .021 Failure of Recognition / Redemption .0439590 -.0693375 .384 Analysis of the dataset further revealed that a vast majority of 61.2 percent of the respondents did not wish to 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 individual respondent and a binary logistic regression was performed. The dependent variable was ‘desire to become a member by carrying one more plastic’ (yes / no) and the independent variables were the perceived factor scores of the offerings. In stepwise regression procedures, it was interesting to note that only 3 factors out of 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 the model. Estimate of 2 log-likelihood of the step 3 model was 314.99. The model could predict 64.4 observations 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 0.000 Table 7 illustrates the B values (Regression Coefficient) and their reliability in the above mentioned regression models. The Wald values and their significance are quite satisfactory in terms of acceptance of the models. The sensitivity of the variables in the models to the odds of the output variable may be viewed from Exp (B) column of table 5. A value more than 1 indicates a positive impact to the odds while a value less than 1 indicates a negative impact. According to this, personalized services had a high positive marginal impact on desire to have a new membership. Home delivery had also a similar but less intensive impact. 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 0.609 a. Variable(s) entered on step 1: PS b. Variable(s) entered on step 2: HDSE c. Variable(s) entered on step 3: DESQ Further to the above classification analysis using logistic regression, the dataset was further tested using discriminant analysis. Desire to have a new membership (yes / no) was the dependent variable, while the 7
  • 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 66 percent of the observations correctly. The standardized canonical discriminant function coefficients are illustrated in the table 8. Table 8: Standardized Canonical Discriminant Function Coefficient Function 1 Gift Offer - 0.200 Discount, Exchange and Special - 0.088 Queue Charges & Fees 0.468 Feedback & Mails 0.180 Home Delivery & Special Events 0.667 Personalized Service - 0.527 Failure of Recognition / Redemption - 0.202 It 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 the members. Similar to the previous logistic model, Personalized Service had the maximum positive impact on ‘desire to have a new loyalty membership by carrying a plastic card’. 4. Managerial Implications and Direction of Future Research This study may be taken as an exploratory initiative to identify customer groups who would like to be member of a retail loyalty program. The study clearly indicates that the benefits of a retail loyalty program are availed of by the family and not just by the individual who is the member of the program. Further the members could not be differentiated by means of demographic characteristics, implying they belong to the same demographic segment. The study clearly demonstrates the importance of personalized service and special treatment as the predominant factor followed by home delivery, invitation to special events which influence customer’s intention to opt for a loyalty program. It also revealed that customers are more interested in intangible personalised services than tangible benefits like gifts, discounts etc. It was interesting to note that customers were averse to charges like renewal and membership fees. Failure of recognition by the retail sales person and difficulty in point redemption were factors which created negative perception. The most revealing aspect of the study was that a vast majority of around 62 percent of the present loyalty program members were unwilling or reluctant to become a member of a new retail loyalty program by carrying one more plastic in their wallet. This implies that retailers need to find out an alternative to the present system of a plastic loyalty card. Further research needs to be undertaken in this direction if loyalty programs are to be made successful. While planning for a new loyalty program, retailers need to have a clear insight of shopper likings and disliking with respect to their loyalty program. This is particularly relevant as some of the techniques adopted by the retailers may create a negative perception amongst the members of the loyalty program. It may be finally recommended that retailers need to be clear about the relative importance of various factors which makes a retail loyalty program successful. This research is limited to the holders of retail loyalty programs in the city of Kolkata, where membership of a loyalty program meant holder of a plastic card. The research is only act as an indicator towards certain factors about which customers may have particular liking and disliking. Further research may be
  • 8. undertaken to ascertain these factors. Research may also be undertaken to investigate customer expectations 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.44-5 Kalokota, 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.47-55 Miranda M.J.; Konya, L. and Havrila, I. (2004): “Shoppers’ satisfaction levels are not the only key to store loyalty”, Marketing intelligence and Planning”, Vol.23, No.2, pp.220-232 Noordhoff, C.; Pauwels, P. and Schroder, O.G. (2004): “The effect of customer card programs – A comparative study in Singapore and The Netherlands”, International Journal of Service Industry Management, Vol.15, No.4, pp.351-364 Stauss, B.; Schmidt, M. and Schoeler, A. (2005): “Customer frustration in loyalty programs”, International Journal of Service Industry Management, Vol.16, No.3, pp.229-252 Sudman, 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