Mining Loyalty Card Data for Increased Competitiveness:        Case of a leading Retail Store of Kolkata, India           ...
1. Introduction and BackgroundDuring the past decade, loyalty programs have been intensively experimented throughoutthe gl...
2. MethodologyComputerized billing data for a well known life-style retail chain was gathered from twoof its retail points...
A k-means cluster analysis of the entire dataset (including both the shops) revealed fivereasonable clusters of customers ...
indicating significance level of 000. The model did not however, include other    demographic variables like age, marital ...
Table 3: Important Demographic profiles of two shopsa. Gariahat         Marital Status            Frequency           Perc...
The shopping behavior of customers of two different outlets of the chain was some whatdifferent because of location factor...
Coefficients   a,b                                U nstan dardize d            Standardized                               ...
Figure 1: Neural Modeling using Clementine 9.0The estimated relative importance of the input variables is varied in nature...
Figure 2: Results of Neural Modeling using Clementine 9.04. Managerial ImplicationsAnalyses of billing database and custom...
more discount or bargain the customers are offered more is the revenue. So these twofactors are critically important for t...
References:Brown, S.A. (2000): Customer Relationship Management, John Wiley & Sons, Toronto.Dasgupta S (2005): Who’s Afrai...
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Mining Loyalty Card Data for Increased Competitiveness: Case of a leading Retail Store of Kolkata, India

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Mining Loyalty Card Data for Increased Competitiveness:
Case of a leading Retail Store of Kolkata, India

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Mining Loyalty Card Data for Increased Competitiveness: Case of a leading Retail Store of Kolkata, India

  1. 1. Mining Loyalty Card Data for Increased Competitiveness: Case of a leading Retail Store 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(Paper Presented at the Conference on Global Competitiveness at IIM-Kozhikode, 25-26 March, 2006) 1
  2. 2. 1. Introduction and BackgroundDuring the past decade, loyalty programs have been intensively experimented throughoutthe globe mostly to create a new generation of CRM tactics (Brown, 2000; Kalokota andRobinson, 1999; Field, 1997). It was evident from ample experiences including Japaneserelating, US airlines and hotels, French banks, UK groceries and so forth. In India it wasobserved that Shoppers’ Stop, a leading retail chain, managed to achieve 60 percent of itssales from repeat customers (as against the Indian average of 30 percent) by virtue of itshighly pushed loyalty programs.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 purchase markets isresulted from passive acceptance of brands rather than from positive efforts to improvecustomer attitudes. A recent study (C. Noordhoff et. al, 2004) expressed the fate ofloyalty programs in the long run. Store customers of Netherlands and Singapore werecompared in terms of behavioral and attitudinal loyalty with respect to loyalty cards. Itwas concluded from the study that efficacy of store loyalty programs appeared todiminish with an increasing number of alternative card programs in the market. It alsodiminished with the habituation of customer with these cards. While the sustainability ofloyalty 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 isextremely essential. Organizational and regular feedback from the marketplace mayextract customers’ latent needs in some ongoing manner. A well designed loyaltyscheme could be considered as a useful instrument for continuous tracking of customers,which may enable a successful CRM and hence a sustainable loyalty improvementsystem. The present study will address these issues in the Indian context with respect to aleading retail chain in India. 2
  3. 3. 2. MethodologyComputerized billing data for a well known life-style retail chain was gathered from twoof its retail points in the city of Kolkata, India. Transactions of only loyalty card holderswere collected for a period from 1st August 2004 to 28th February 2005, constituting334093 bills information lines for different items purchased. 12990 customers withloyalty cards took part in the purchase process. The transaction dataset was merged withthe customer profile dataset for generating knowledge on purchase behaviors by way ofclassifications and associations. The entire dataset was cleaned to avoid null data andoutliers. Preprocessing like data generalization, aggregation and relevancy analyses wereperformed on the dataset and finally, a relevant as well as compact dataset was extracted.From the above dataset, attempts were made to rightly estimate the measures of purchasevalues on individual customers by using various demographic factors indicated in theloyalty card profile of the customers and also the behavioral patterns like frequency,recency, etc.The two shops of the chain located at two different locations of the city were analyzedseparately in order to compare the buying patterns in the two different locations.Step wise regression models were adopted to investigate relationships of value purchasedwith the given input variables as discussed earlier. It was also interesting to apply anartificial neural network model for the same purpose. A multilayer perception model waschosen using eight different input variables against a single output variable – value ofpurchase of a customer. In a further analysis, based on CART classification model, itwas attempted to investigate the behavior of purchase by extracting a rule set from thedata as prepared and pre-processed for our models.3. Results and Discussions 3
  4. 4. A k-means cluster analysis of the entire dataset (including both the shops) revealed fivereasonable clusters of customers in the system. Cluster centers and cluster size of eachgroup revealed a sharp peak of the customer pyramid (table 1). The top 2 levels of thepyramids constituted only 2.4 percent of the total customers, who spent heavily duringthe seven months under study. The mean values of these two clusters were Rs.79360 andRs.36780 respectively against the average of only Rs.6500 for the whole of thecustomers. It was found that only top 2.4 percent of the total customers contributednearly 15 percent of the total revenue and also top 11.5 percent of the customerscontributed to 41 percent of the revenue.Table 1: Customer Value Pyramid Cluster Percentage of Total Customers Average Value Purchase (Rs.) 1 0.22 79360 2 2.15 36780 3 9.16 18765 4 27.24 8770 5 61.23 2400Source: Billing dataIt was thus important for the organization to identify typical characteristics of high valuecustomers so that proper CRM could be implemented in the most effective way. In orderto identify such behaviors three different approaches were adopted. Regression model,CART decision tree model and Neural Network model were performed on the data set forclassification and prediction purposes.A step wise regression model confirmed (R Square value of 0.52) the fact that amount ofpurchase by a customer was strongly and positively related to frequency of visit to theshop and discount-amount the customer enjoyed from the shop. However, recency of acustomer was also positively related with low intensity. The standardize Beta-coefficients were 0.528, 0.350 and 0.035 respectively. It was also interesting to note thatthe dummy variables gender (female = 1) and type (non-bengali = 1) had negative lowimpacts on the customer revenue. The final regression model had high F-value of 2799 4
  5. 5. indicating significance level of 000. The model did not however, include other demographic variables like age, marital status etc. Table 2: Stepwise Regression – Both Shops Coefficients a Unstandardized Standardized Coefficients CoefficientsModel B Std. Error Beta t Sig.1 (Constant) 1097.858 77.199 14.221 .000 Frequency 1626.298 16.902 .649 96.219 .0002 (Constant) 836.337 70.442 11.873 .000 Frequency 1276.345 16.826 .510 75.857 .000 bargain 1.753 .034 .345 51.324 .0003 (Constant) 1076.600 77.169 13.951 .000 Frequency 1291.442 16.908 .516 76.382 .000 bargain 1.745 .034 .343 51.195 .000 Gender -750.892 99.560 -.047 -7.542 .0004 (Constant) 614.180 117.528 5.226 .000 Frequency 1326.465 18.178 .530 72.973 .000 bargain 1.773 .034 .349 51.445 .000 Gender -722.547 99.606 -.045 -7.254 .000 Recency 4.502 .864 .036 5.213 .0005 (Constant) 763.921 130.619 5.848 .000 Frequency 1322.731 18.229 .528 72.562 .000 bargain 1.777 .034 .350 51.524 .000 Gender -722.889 99.583 -.045 -7.259 .000 Recency 4.321 .866 .035 4.989 .000 Type -252.771 96.306 -.016 -2.625 .009 a. Dependent Variable: BILL_VALUE_sum_sum In order to compare characteristics of the two different shops – one located at Camac Street and the other at Gariahat, it was observed that the demographic pattern of customers were more or less the same in these two shops, excepting Gariahat shop which had a high Bengali patronage whereas the Camac Street shop which had less than half as Bengali population (table 3). 5
  6. 6. Table 3: Important Demographic profiles of two shopsa. Gariahat Marital Status Frequency Percent Unmarried 644 34 Married 1268 66 Total 1912 100 Gender Frequency Percent Male 1155 60 Female 752 40 Total 1907 100 Type Frequency Percent Bengali 1426 75 Non Bengali 481 25 Total 1907 100b. Camac Street Marital Status Frequency Percent Unmarried 3626 33 Married 7411 67 Total 11037 100 Gender Frequency Percent Male 6879 62 Female 4142 38 Total 11021 100 Type Frequency Percent Bengali 4924 45 Non Bengali 6096 55 Total 11020 100 6
  7. 7. The shopping behavior of customers of two different outlets of the chain was some whatdifferent because of location factor. Two sets of regression models were performed forthe two outlets. It was found that only three variables (frequency, bargain and recency)could explain amount of purchase in case of Gariahat shop whereas two extra variables(gender and type) were necessary to predict the purchase value of customers for theCamac Street shops. The R-square value was 0.522 for the first shop and was 0.524 forthe second. The coefficients of the independent variables and their significance arepresented in the table 4 for both the models.Table 4: Regression outputs of Outlets Coefficientsa,b Unstandardized Standardized Coefficients Coefficients Model B Std. Error Beta t Sig. 1 (Constant) 898.196 162.659 5.522 .000 Frequency 1479.870 38.259 .663 38.680 .000 2 (Constant) 718.403 151.115 4.754 .000 Frequency 1244.466 37.877 .558 32.855 .000 bargain 1.767 .100 .300 17.693 .000 3 (Constant) 46.099 256.662 .180 .857 Frequency 1302.100 41.770 .584 31.173 .000 bargain 1.824 .101 .310 18.029 .000 Recency 6.148 1.900 .060 3.236 .001 a. Dependent Variable: BILL_VALUE_sum_sum b. SHOP_CODE = GariahatA further analysis on the two outlets showed that the top cluster of the customer group ofCamac Street had average purchase value of Rs.75760 during the period under study,while it was Rs.44380 at the Gariahat outlet. It is interesting to note that for both theshops, frequency of visits and the amount of bargain earned were the two most importantfactors for total amount of purchase. 7
  8. 8. Coefficients a,b U nstan dardize d Standardized C oefficients C oefficients M del o B Std. Error Beta t Sig. 1 (Con stant) 1153.63 7 85.959 13.421 .000 F uen req cy 1644.66 0 18.602 .648 88.414 .000 2 (Con stant) 863.243 78.436 11.006 .000 F uen req cy 1281.52 8 18.573 .505 68.999 .000 barga in 1.745 .037 .347 47.433 .000 3 (Con stant) 1119.01 5 85.901 13.027 .000 F uen req cy 1297.37 8 18.659 .511 69.531 .000 barga in 1.737 .037 .346 47.319 .000 G der en -803.974 111.388 -.048 -7.218 .000 4 (Con stant) 683.233 129.567 5.273 .000 F uen req cy 1329.87 3 19.998 .524 66.500 .000 barga in 1.762 .037 .351 47.497 .000 G der en -776.810 111.454 -.047 -6.970 .000 Recency 4.300 .958 .034 4.490 .000 5 (Con stant) 881.868 146.781 6.008 .000 F uen req cy 1324.89 5 20.066 .522 66.026 .000 barga in 1.766 .037 .351 47.586 .000 G der en -774.438 111.419 -.047 -6.951 .000 Recency 4.089 .960 .032 4.259 .000 T e yp -311.117 108.154 -.019 -2.877 .004 a. Dependen Variab BILL_ t le: VALUE_sum sum _ b. SH P_CO E = C ac Stree O D am tArtificial Neural Network (ANN) Model A Neural Network based model was tried on the data with bill amount as output variable and eight input variables namely frequency, gender code, recency, shop code, type (Bengali or non-bengali), age, bargain, marital state. It was found that the model code estimate with 96 percent accuracy, using 1:3 neurons Hidden Layers. A further analysis of the model reveals that mean error of estimate was Rs.27 (figure 1 and 2). 8
  9. 9. Figure 1: Neural Modeling using Clementine 9.0The estimated relative importance of the input variables is varied in nature, where bargainbeing the most important factor, followed by frequency and recency. The least importantfactors were shop code, type of customer and marital status of customers.It is interesting to note that in both the models (regression and ANN) both bargain andfrequency were important parameters while frequency was more important as judged byregression model unlike ANN model. Recency was important to both the models.Marital status was however considered to be not important in both the models. 9
  10. 10. Figure 2: Results of Neural Modeling using Clementine 9.04. Managerial ImplicationsAnalyses of billing database and customer profile no doubt reveals precious knowledgeon customer purchase behavior which may be suitably used to formulate realisticmarketing programs to improve revenue and market share. In this particular situation weexperience that more frequently the people visit the shop, more is the revenue. Also 10
  11. 11. more discount or bargain the customers are offered more is the revenue. So these twofactors are critically important for the chain to increase its sales. Marketing investmentsshould align with such findings and discoveries.Recency though has a low but positive impact on the amount of sales, it is extremelyuseful to maintain a low average recency for the customers and management actions maybe devised to target, follow-up and encourage those customers whose recency values areabove the expected threshold.Loyalty cards generate a large amount of valuable customer data which enable to trackand monitor customers in the most effective way to enhance sustainability. One mayconclude that loyalty programs thus become means for earning valuable customerinformation to shape up appropriate market mix at a humble cost of reward points. 11
  12. 12. References:Brown, S.A. (2000): Customer Relationship Management, John Wiley & Sons, Toronto.Dasgupta S (2005): Who’s Afraid of Wal-Mart?, Business Standard (India), Dec 4,2005Field, 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.Miranda M.J.; Konya, L. and Havrila, I. (2004): “Shoppers’ satisfaction levels are not theonly key to store loyalty”, Marketing intelligence and Planning”, Vol.23, No.2,pp.220-232Noordhoff, C.; Pauwels, P. and Schroder, O.G. (2004): “The effect of customer cardprograms – A comparative study in Singapore and The Netherlands”, InternationalJournal of Service Industry Management, Vol.15, No.4, pp.351-364Stauss, B.; Schmidt, M. and Schoeler, A. (2005): “Customer frustration in loyaltyprograms”, International Journal of Service Industry Management, Vol.16, No.3,pp.229-252Uncles, M. D.; Grahame, R. D. and Kathy, H. (2003): “Customer loyalty and customerloyalty programs”, Journal of Consumer Marketing, Vol.20, No.4, pp.294-316Malley, L.O’ (1998): “Can loyalty schemes really build loyalty?”, Marketing Intelligenceand Planning, Vol.16, No.1, pp.47-55 12

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