Segmenting Customers for Effective CRM – A Data MiningApproach using Billing and Loyalty Card Data of a Leading           ...
IntroductionThe past decade witnessed many changes in the approach towards marketing fromtransaction orientation, marketin...
relationship. They suggested that the strategy development process requires a focus on theorganizations business strategy ...
marketers need to be clear about relative importance of data collection and rewarding loyalcustomers for achieving sustain...
3. Data cleaning and Merging process was the most serious and time consuming task for   the project. In depth inspection o...
4. Cleaning and validation of the data set was a rigourous process. The data sets of bills   and customers were then merge...
Findings1. Basic Distributions: Demographic and other basic distributions are listed in Table 1.   It was found that two t...
In the loyalty card population as high as 72 percent were married. It was observed thatmaximum sales occurred during Janua...
2. Customer Pyramid: It is interesting to note that out of 12990 loyalty customers, only   420 (3.3%) customers purchased ...
Table 4: Effect of Demographic and other Characters on Customer Value                                       Variable Teste...
4. Customer Value Characteristics: The longitudinal purchase data of customers were   quite useful to explore customer beh...
Table 6: Stepwise Regression – Both ShopsModel                      Unstandardized Coefficient    Standardized          t ...
this case two demographic variables (viz. gender and race type) were also entered in themodel, though impact of these vari...
Table 8: Stepwise Regression - Camac Street ShopModel                     Unstandardized Coefficient   Standardized       ...
regression model unlike ANN model. Recency was important to both the models. Maritalstatus was however considered to be no...
K-Means ClusterA K-Means cluster analysis was performed on the data set with eight variables namely:value of purchase, fre...
It is interesting to note that cluster 2 consisted of the maximum number of members, all   whom were Bengalis and married....
Managerial ImplicationsIt was observed that the form for collection of customer data by the retail store had certainshort ...
The customer data may also be effectively utilized to segment shoppers and specific tacticsemployed to develop these segme...
References:Brown, S.A. (2000): Customer Relationship Management, John Wiley & Sons, Toronto.Dasgupta, S. (2005): “Who‟s af...
Narver, J.C. and Slater, S.F. (1990): “The Effect of a Market Orientation on BusinessProfitability”, Journal of Marketing,...
Zablah, A.R.; Danny N.B. and Wesley J.J. (2003): “Customer Relationship Management:An Explication of Its Domain and Avenue...
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Segmenting Customers for Effective CRM – A Data Mining Approach using Billing and Loyalty Card Data of a Leading Retail Chain of Kolkata, India

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CRM – A Data Mining Approach using Billing and Loyalty Card Data of a Leading Retail Chain of Kolkata, India

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Segmenting Customers for Effective CRM – A Data Mining Approach using Billing and Loyalty Card Data of a Leading Retail Chain of Kolkata, India

  1. 1. Segmenting Customers for Effective CRM – A Data MiningApproach using Billing and Loyalty Card Data of a Leading Retail Chain 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 1
  2. 2. IntroductionThe past decade witnessed many changes in the approach towards marketing fromtransaction orientation, marketing orientation (Narver and Slater, 1990; Kohli andJaworski, 1990), and mass customization to establishment of long term relationships withcustomers (Webster 1992; Morgan and Hunt, 1994). Gronroos (1995) and Gummesson(1987) strongly suggested that marketing is to establish, maintain and enhance relationshipswith customers and other partners, at a profit, so that the objectives of the parties involvedare met. This could only be achieved through mutual exchange and fulfillment of promisesbetween the concerned parties. Eventually, customer relationship became the focus anddominant paradigm of marketing.It has also been suggested by many researchers that CRM is a philosophically relatedoffspring to relationship marketing (Zablah, Danny, and Wesley, 2003). The AcademicCommunity often uses the terms “Relationship Marketing” and CRM interchangeably(Parvatiyar and Sheth 2001). Payne and Frow, 2005 reviewed the various definitions ofCRM and proposed the following definition “CRM is a strategic approach that is concernedwith creating improved share holder value through the development of appropriaterelationship with key customers and customer segments. CRM unites the potential ofrelationship marketing strategy and IT to create profitable, long term relationship withcustomer and key stake holders. CRM provides enhanced opportunities to use data andinformation to both understand customers and co-create value with them. This requires across functional integration of processes, people, operations and marketing capabilities thatis enabled through information, technology and applications.”It may be observed from the above definition that the emphasis is on identification of keycustomers and customer segments and to develop appropriate long term strategicrelationships to create a sustainable profit. It also emphasized on the need to integrate crossfunctional processes. Payne and Frow, (2005) identified five key cross functional CRMprocesses and emphasized the fact that CRM activities involve collecting and intelligentlyusing customer data to build a consistently superior customer experience and customer 2
  3. 3. relationship. They suggested that the strategy development process requires a focus on theorganizations business strategy and its customer strategy. Customer strategy involvesexamining the existing and potential customer base and identifying which forms ofsegmentation are most appropriate.This paper aims to segment customers of a retail chain based on their observable storespecific characteristics like user status, usage frequency, store loyalty and patronage(Wedel and Kamakura, 2000). The customer data of loyalty card holders and OLTP data ofbills were examined to classify customers for effective segmentation. The two retail outlets(at the city of Kolkata) of a leading national level retail chain in India were used for thepurpose of the study.Loyalty Programs as a CRM tacticsDuring the past decade, loyalty programs have been intensively experimented throughoutthe globe mostly to create a new generation of CRM tactics as was evident from ampleexperiences including Japanese retailing, US airlines and hotels, French banks, UKgroceries and so forth (Brown, 2000; Kalokota and Robinson, 1999; Field, 1997). In Indiait was observed that Shoppers‟ Stop, a leading retail chain, managed to achieve 60 percentof its sales from repeat customers (as against the Indian average of 30 percent) by virtue ofits 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 ofpassive acceptance of brands rather than from positive efforts to improve customerattitudes. A recent study (Noordhoff et. al, 2004) questioned the fate of loyalty programs inthe long run. Store customers of Netherlands and Singapore were compared in terms ofbehavioral and attitudinal loyalty with respect to loyalty cards. It was concluded from thestudy that efficacy of store loyalty programs appeared to diminish with an increasingnumber of alternative card programs in the market. It also diminished with the habituationof customer with these cards. While the sustainability of loyalty scheme is in question, the 3
  4. 4. marketers need to be clear about relative importance of data collection and rewarding loyalcustomers 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 loyalty schemecould be considered as a useful instrument for continuous tracking of customers, whichmay enable a successful CRM and hence a sustainable loyalty improvement system. Thepresent study will address these issues in the Indian context with respect to a leading retailchain in India.MethodologyOLTP database of bills along with the customer database can generate enormous amount ofbusiness intelligence in the customer process. Sequential steps applied for the purpose ofunderstanding customer characteristics and identification of key parameters influencingsales, were as follows:1. Copies of billing data from two retail points during the period August 2004 to February 2005, from the billing OLTP system of the retail chain were collected. The entire data was dumped into a single ASCII data file, using fixed format space delimited structure. There were altogether 59 different fields of different widths. Total number of records were 3,34,093, each indicating a bill item.2. Customer loyalty-card data was collected in a separate ASCII file. Data included demographic and psychographic characteristics of customers, as collected on a form, while registering individual customer with a loyalty card. There were 27 fields in the file of different width. Total number of records was 12,990, each indicating one loyalty card customer. 4
  5. 5. 3. Data cleaning and Merging process was the most serious and time consuming task for the project. In depth inspection of the data, so acquired, revealed a number of serious difficulties, some of which may be listed below: a) Name fields of customers were not properly designed. There were a number of sub- fields like Title, First Name, Family Name, Final Name, etc. However, there was no instruction manual available to fill in these fields. Frequent irregularies were found in the data, as the sales persons while filling up forms, used their own discretions, as and when required. b) Address fields also created confusion (building, road, area, city, pin code, phone, mail, call mailing, etc. were the name of the fields) resulting in wrong data in the records. c) Date of birth, Date of marriage, etc. were variable length string fields in the data- file. Conversion of such data items into proper data fields required special treatments. d) Attributes of customers in the loyalty card form were also subject to confusion. For example, race of the customer had options like Bengali, Marwari, Christians, Muslims and others. Apparently, the options are not mutually exclusive – some are origin of birth indicators and some are religion indicators. For the purpose of clarity, the field was divided in two major groups – Bengali and Non-Bengali. e) No indication of family income or economic status of Loyalty Card customer was available. f) Brands and sub-brands of items had no codification – these were only string fields. g) Product category field was totally absent. For the purpose of our analysis, we generated a field, indicating product category. 5
  6. 6. 4. Cleaning and validation of the data set was a rigourous process. The data sets of bills and customers were then merged together, finally for mining and further analyses. (Figure 1).Figure 1: Cleaning, Validation and Merging of Data Cleaning, Recoding and Preprocessed Bill-data Validation Data Process Data Set Merge For Procedure Mining Cleaning, Customer Recoding and Preprocessed Profile Validation Data Data Process5. It was necessary to estimate the customer value pyramid for our data set in order to classify customers based on purchase value. Such customer classification was of critical importance for a successful implementation of CRM.6. The customer group was then segmented considering a few key behaviors of the customers such as frequency, recency, bargain, average amount of purchase, etc. 6
  7. 7. Findings1. Basic Distributions: Demographic and other basic distributions are listed in Table 1. It was found that two third of card holders were married for both the shops. Male population in the loyalty program was 57 percent on average. Gariahat shop was dominantly patronized by Bangalis (75 percent), while the Camac Street shop had 55 percent Non-Bengalis. The Camac Street shop is located in the central business district where population mix is highly heterogeneous. Membership status is gives to a customer, depending on amount of purchase during the time of registration. Volume of five star customers was only 6 percent while 84 percent of total card holders had one-star status. Camac Street shop was much larger than the other one and is situated at the central business area. This shop contributed for 87 percent of the loyalty members and the rest 13 percent by the other. Table 1: Distribution of Basic Parameters Parameter Percentage Shop-wise Members Camac Street 87 Gariahat 13 Membership Status One Star 84.4 Three Star 9.4 Five Star 5.8 Gender Male 57.6 Female 42.4 Marital Status Married 71.8 Unmarried 28.2 Hour-wise Bills 12 – 4 PM 34.2 4 PM – 7 PM 36.7 7 PM – 8 PM 29.1 7
  8. 8. In the loyalty card population as high as 72 percent were married. It was observed thatmaximum sales occurred during January and February, when the shop offered its annualsale (up to 50 percent discount). October is the festive month of the city and consequentlysale was considerably high. It was noted that daily volume of sales (bill items) increased during afternoons and highest during the evenings. Nearly 30 percent of sales occurred during a single hour in the evening (7 - 8 pm), while during the early afternoon (12 - 4 pm) the volume per hour was merely 8-9 percent. Out of all the items sold in the shops, shirts, trousers, salwar-kameez, gift articles, shirts ad stationeries were the high selling items. Accessories, cosmetics, kids and infant apparels, linens etc. were also sold in abundant. It was identified from the sales data that out of nearly 350 brands in the shop, 34 had sales less than 5 pieces during the study period. 102 brands had sales 5-10 pieces and 176 brands has sales 11-50 pieces. Only 10 brands had frequent sales of 4000 pieces or more (table 2). Table 2: Sale of Top Ten Brands During the Study Period (Brands sold more than 4000 pieces)* Brand Percent Sold RARE 27.2 ESKEE 12.1 LINCON 10.1 JOHN 9.6 MILT 9.2 RTKML 8.6 ARC 6.3 FRAAJILE 6.2 BALOON 5.4 TONMIL 5.2 TOTAL 100.0 * Brand names camouflaged 8
  9. 9. 2. Customer Pyramid: It is interesting to note that out of 12990 loyalty customers, only 420 (3.3%) customers purchased more than INR 25,000 during a period of seven months. These customers were most valuable patrons of the shops who might be considered to be offered greater care and attention. The lowest category of the customers, buying only less than INR5000 during the period, constituted 57 percent of the customer volume (table 3). Nearly 16 percent customers, constituting the middle of the pyramid, bought high values (between INR 10,000 to 25,000) and might be considered for up-gradation. Table 3: Customer Value Pyramid Value Purchased Frequency of Percent Cumulative Percent (INR) Customers 50K above 49 0.4 0.4 25K – 50K 371 2.9 3.2 15K – 25K 863 6.6 9.9 10K – 15K 1264 9.7 19.7 5K – 10K 2964 22.8 42.5 Less 5K 7449 57.3 100.0 Total 12960 99.83. Customer Value and Behaviors: Customer tracking and identifying their buying behaviors was an important issue of the present paper. Initially the demographic characters of the loyalty card customers were considered and these were related to the customer pyramid. The estimated p-values of 2 tests and ANOVA test confirmed that only membership status and marital status of card-holders had significant bearing on the amount of purchase. Married customers appeared to buy more values, while Five- Star customers also purchased more amount than others (Table 4). The other demographic characters like age, sex and race did not show any impact on the customer value even at 10 percent level of significance. 9
  10. 10. Table 4: Effect of Demographic and other Characters on Customer Value Variable Tested P-Value Membership Status 0.00 Chi Square Test Sex 0.17 Marital Status 0.00 Bengali (race) 0.27 ANOVA Age 0.33In order to take appropriate marketing decisions, it was important to estimate certainactivity patterns of the customers related to different categories of purchase values. Theactivity parameters tested for the purpose were frequency, bargain amount, recency, andaverage amount of purchase. Bargain amount was a derived variable, indicating differencebetween MRP and bill value. It was evident from the ANOVA analyses that all thesevariables, viz. bargain, recency, frequency and average amount of purchase were dependenton value category of the customer pyramid (table 5). A further post hoc analysis explainsthat the highest valued customers enjoyed maximum bargain amount, consistently loweringwith value categories of customers (table 4). Frequency of visit to the shop was again aninteresting observation to reveal that higher the values of customers, more frequent thevisits were during the study period. Also in case of average amount of purchase, one couldargue from the tests that, higher the values of the customers, higher the average amount ofpurchase per visit. These interesting facts converge to some double benefit concept of thecustomers. If the shop can succeed to bring in their customers more frequently, therewould be definite benefit of higher amount of average sales, resulting in higher customervalue at the end of the period. Table 5: ANOVA Tables for Activity Based Variables df Mean Square F Sig. Bargain Between Groups 5 1495998635.1 827.206 0.000 Within Groups 12923 1808496.854 Total 12928 Recency Between Groups 5 1213752.323 347.555 0.000 Within Groups 12903 3492.264 Total 12908 Frequency Between Groups 5 10022.528 1784.242 0.000 Within Groups 12941 5.617 Total 12946 Avg. amt Between Groups 5 2841779561.7 789.478 0.000 Within Groups 12941 3599569.312 Total 12946 10
  11. 11. 4. Customer Value Characteristics: The longitudinal purchase data of customers were quite useful to explore customer behaviors during the period of study. Demographic and activity based variables were used to demonstrate value purchased by the customers. A series of regression models and artificial neural network models were applied to perform such causal analysis. In all the predictive models value purchased during the period was the dependent variable. Independent variables were available demographic and activity variables, viz., frequency, recency, bargain, age, gender, marital status, shop code, race type etc. A step-wise regression model on the data set confirmed (R Square value of 0.52) the fact that amount of purchase by a customer was strongly and positively related to frequency of visit to the shop and bargain-amount (difference between MRP and bill value) the customer enjoyed from the shop (table 6). However, recency of a customer was also positively related with much lower impact. The standardize Beta-coefficients were 0.528, 0.350 and 0.035 respectively. However, t-values of the entire regression coefficient showed high significance, pleading robustness of all the models. It was interesting to note that the dummy variables gender (female = 1) and type (non-bengali = 1) had negative low impacts on the customer revenue. These results indicated that male members purchased more than the female members. Also Bengalis purchased more amounts than non-bengalis. The final regression model had high F-value of 2799 indicating significance level of 000. The model did not however, include other demographic variables like age, marital status etc. 11
  12. 12. Table 6: Stepwise Regression – Both ShopsModel Unstandardized Coefficient Standardized t Sig. Coefficient B Std. Error Beta 1 (Constant) 1097.858 77.199 14.221 0.000 Frequency 1626.298 16.902 0.649 96.219 0.000 (Constant) 836.337 70.442 11.873 0.000 2 Frequency 1276.345 16.826 0.510 75.857 0.000 Bargain 1.753 0.034 0.345 51.324 0.000 (Constant) 1076.600 77.169 13.951 0.000 3 Frequency 1291.442 16.908 0.516 76.382 0.000 Bargain 1.745 0.034 0.343 51.195 0.000 Gender -750.892 99.560 -0.047 -7.542 0.000 (Constant) 614.180 117.528 5.226 0.000 Frequency 1326.465 18.178 0.530 72.973 0.000 4 Bargain 1.773 0.034 0.349 51.445 0.000 Gender -722.547 99.606 -0.045 -7.254 0.000 Recency 4.502 0.864 0.036 5.213 0.000 (Constant) 763.921 130.619 5.848 0.000 Frequency 1322.731 18.229 0.528 72.562 0.000 5 Bargain 1.777 0.034 0.350 51.524 0.000 Gender -722.889 99.583 -0.045 -7.259 0.000 Recency 4.321 0.866 0.035 4.989 0.000 Type -252.771 96.306 -0.016 -2.625 0.009Dependent Variable: Total bill value of a customerIn order to compare characteristics of the two different shops – one located at Camac Streetand the other at Gariahat. Regression models were applied to perform such causal analysis.In all the predictive models value purchased during the period was the dependent variable.Independent variables were available demographic and activity variables, viz., frequency,recency, bargain, age, gender, marital status, shop-code, race, type, etc.Table 7 illustrates the regression models for the Gariahat shop. It is interesting to observethat only activity based variables (viz. frequency, bargain and recency) were theindependent variables chosen by the models and demographic variable was entered.Importance of the activity variables was similar to the previous models for both shops.This indicates that basic buying behaviors are to a large extent similar in two cases.The results of the Camac Street shop (which is located is a central commercial area of thecity), although indicated a similar relationships in terms of activity based variables. But in 12
  13. 13. this case two demographic variables (viz. gender and race type) were also entered in themodel, though impact of these variables was merely marginal.Thus the shopping behavior of customers of two different outlets of the chain were mostlythe same and marginally different because of location factor. Two sets of regression modelsconfirmed that only three variables (frequency, bargain and recency) could explain amountof purchase in case of Gariahat shop whereas two extra variables (gender and type) werealso useful to predict the purchase value of customers for the Camac Street shop. Table 7: Stepwise Regression - Gariahat ShopModel Unstandardized Coefficient Standardized t Sig. Coefficient B Std. Error Beta 1 (Constant) 898.196 162.659 5.522 0.000 Frequency 1479.870 38.259 0.663 38.680 0.000 (Constant) 718.403 151.115 4.754 0.000 2 Frequency 1244.466 37.877 0.558 32.855 0.000 Bargain 1.767 0.100 0.300 17.693 0.000 (Constant) 46.099 256.662 0.180 0.000 3 Frequency 1302.100 41.770 0.584 31.173 0.000 Bargain 1.824 0.101 0.310 18.029 0.000 Recency 6.148 1.900 0.060 3.236 0.001Dependent Variable: Total bill value of a customerShop – Code: GariahatA further analysis on the two outlets showed that the top cluster of the customer group ofCamac Street had much higher average purchase value of INR 75760 during the periodunder study, while it was INR 44380 at the Gariahat outlet. It was interesting to note thatfor both the shops, frequency of visits and the amount of bargain were the two mostimportant factors for total amount of purchase. 13
  14. 14. Table 8: Stepwise Regression - Camac Street ShopModel Unstandardized Coefficient Standardized t Sig. Coefficient B Std. Error Beta 1 (Constant) 1153.637 85.959 13.421 0.000 Frequency 1644.660 18.602 0.648 88.414 0.000 (Constant) 863.243 78.436 11.006 0.000 2 Frequency 1281.528 18.573 0.505 68.999 0.000 Bargain 1.745 0.037 0.347 47.433 0.000 (Constant) 1119.015 85.901 13.027 0.000 3 Frequency 1297.378 18.659 0.511 69.531 0.000 Bargain 1.737 0.037 0.346 47.319 0.000 Gender -803.974 111.388 -0.048 -7.218 0.000 (Constant) 683.233 129.567 5.273 0.000 Frequency 1329.873 19.998 0.524 66.500 0.000 4 Bargain 1.762 0.037 0.351 47.497 0.000 Gender -776.810 111.454 -0.047 -6.970 0.000 Recency 4.300 0.958 0.034 4.490 0.000 (Constant) 881.868 146.781 6.008 0.000 Frequency 1324.895 20.066 0.522 66.026 0.000 5 Bargain 1.766 0.037 0.351 47.586 0.000 Gender -774.438 111.419 -0.047 -6.951 0.000 Recency 4.089 0.960 0.032 4.259 0.000 Type -311.117 108.154 -0.019 -2.877 0.004Dependent Variable: Total bill value of a customerShop – Code: Camac StreetArtificial Neural Network (ANN) ModelA Neural Network based Multi-layered Perception model was tried on the data withcustomer bill amount as predicted 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 could generate a prediction with 96 percent accuracy, using 1:3neurons Hidden Layers.The 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 by 14
  15. 15. regression model unlike ANN model. Recency was important to both the models. Maritalstatus was however considered to be not important in both the models.Figure 2: Results of Neural Modeling using Clementine 9.0 15
  16. 16. K-Means ClusterA K-Means cluster analysis was performed on the data set with eight variables namely:value of purchase, frequency of visit, race, age, time of store visit, marital status, genderand store location. It was observed that the model could generate six clusters as shown infigure 3 below.Figure 3: Results of K-Means Clustering using Clementine 9.0 16
  17. 17. It is interesting to note that cluster 2 consisted of the maximum number of members, all whom were Bengalis and married. The value of purchase and the frequency of visit for this segment was also the highest and all of them happened to shop at the Camac Street Store. On the other hand cluster 3 comprised of members who were all non-Bengalis and all of whom were male. It may be further observed that the members were mostly young and middle aged, frequented the stores between ten to fifteen times over the period of eight months and shopped during the evenings. The details of the six customer clusters are given in Table 9 below. Table 9: Customer Clusters and their AttributesCluster 1 2 3 4 5 6No. of Members 2295 2808 2646 2046 2454 2124Value of purchase 4800 6600 5900 6000 4200 5600(Rs.)Freq. of visit 10 15 13 13 11 15Race (Bengali %) 40 100 0 60 100 0Age Young- Young- Young- Young- Young- Young- middle middle middle middle middle middleTime of store visit Late Eve. Eve. Eve. Eve. Eve. Eve.Marital Status 0 100 100 0 92 97(Married %)Gender (Male %) 97 58 100 50 52 0Store (Camac 68 100 81 83 0 80St.%) It may be noted that that the variables considered were those which were available in the „Application Form‟ designed by the shop management. 17
  18. 18. Managerial ImplicationsIt was observed that the form for collection of customer data by the retail store had certainshort comings owing to design problems which created confusion amongst those filling upthe same. Vital data regarding demographic and psychographic profile of the customerscould not be collected as the same was not included in the form. Non-availability of astandard instruction manual created further complications for the front line sales force. Thefront line sales force was also not adequately trained for the purpose. These points out theimportance of proper design of the instrument for data collection and also need foradequate training of the front line sales personnel who plays an important role in theprocess of data collection.Analyses of billing database and customer profile enabled classification of customers andidentification of key customers of the stores based on purchase value. The categorization ofcustomers and creation of a customer pyramid is vital in designing effecting strategies foreach of the customer groups. This would also enable identification of opportunities forupward migration in the pyramid and retention strategies.Factors influencing sales for the customer groups revealed that factors like frequency ofvisit and bargain or discount offered to customers had a strong positive impact on sales.This implies that it is critical for the store to make people visit the store more frequently.Also sales promotions are critical for the store to increase sales than other forms ofpromotion. Marketing investments may be aligned based on the above findings.Recency had a low but positive impact on the amount of sales. It was extremely useful tomaintain a low average recency for the customers and management actions may be devisedto target, follow-up and encourage those customers whose recency values are above theexpected threshold. Also those customers who had not visited the store in the recent pastmay be tracked to ascertain the reasons for not patronizing the store. 18
  19. 19. The customer data may also be effectively utilized to segment shoppers and specific tacticsemployed to develop these segments.Directions of Future ResearchThis research was conducted based on the data of the actual purchases made by themembers of the loyalty card program over a period of time. However, the original memberof the loyalty card program may not always be the buyer as the card is more of a familycard. No research was undertaken to study the attitude of the buyers who actually used theloyalty card, which might reveal valuable information. Such researches may be undertakenin the future.In this case the customer was segmented based on observable store specific parameters.Further research may be undertaken to segment the customers based on cultural,demographic and socio-economic variables using the customer data as collated in themembership forms of the holders of the loyalty cards.Further studies may be carried out to track the movement of brands in the store. Researchmay be undertaken to analyze the market basket both on product categories as well asbrands. It may also be of interest to study the choice of product categories as well as brandsfor each of the customer groups which may be used to design appropriate promotionalstrategies.ConclusionLoyalty programs can become means for earning valuable customer information to shapeup appropriate marketing strategies. Loyalty cards as is evident can generate a sizeableamount of valuable customer data which enables to track and monitor customers in aneffective way in the process of co-creation of value, which is a crucial component of CRM.It may enable the organization to focus on the most profitable customers and customerssegments. 19
  20. 20. 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.44-5Gronroos, C. (1995): “Relationship Marketing: The Strategy Continuum”, Journal ofAcademy of Marketing Science, vol.23, pp.252-254Gummesson, E. (1987): “The New Marketing: Developing Long-Term InteractiveRelationships”, Long Range Planning, vol.20, no. 4, pp.10-20.Kalokota, R. and Robinson, M. (1999): “e-Business”, Addison-Wesley, Reading, MA.Kohli, A.K. and Jaworski, B.J. (1990): “Market Orientation: The Construct, ResearchPropositions and Managerial Implications”, Journal of Marketing, vol.54, no. 2, pp.1-18.Malley, L.O‟ (1998): “Can loyalty schemes really build loyalty?”, Marketing Intelligenceand Planning, Vol.16, No.1, pp.47-55Miranda 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.220-232Morgan, R.M. and Hunt S.D. (1994): “The Commitment – Trust Theory of RelationshipMarketing,” Journal of Marketing, Vol.58, pp.20-38. 20
  21. 21. Narver, J.C. and Slater, S.F. (1990): “The Effect of a Market Orientation on BusinessProfitability”, Journal of Marketing, vol.54, no.4, pp.20-35.Noordhoff, C.; Pauwels, P. and Schroder, O.G. (2004): “The effect of customer cardprograms – A comparative study in Singapore and The Netherlands”, International Journalof Service Industry Management, Vol.15, No.4, pp.351-364Parvatiyar, A. and Sheth J.N. (2001): “Conceptual Framework of Customer RelationshipManagement,” in Customer Relationship Management – Emerging Concepts, Tools andApplications, Jagdish N. Sheth, Atul Parvatiyar, and G. Shainesh, eds. New Delhi, India:Tata/McGraw-Hill, pp.3-25Payne A. and Frow P. (2005): “A Strategic Framework for Customer RelationshipManagement”, Journal of Marketing, American Marketing Association, Vol.69, pp.167-176.Stauss, 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.294-316Webster F.E. (1992): “The Changing Role of Marketing in the Corporation”, Journal ofMarketing, Vol.56, pp.1-17.Wedel M. and Kamakura W. (2000): “Market Segmentation – Conceptual andMethodological Foundations”, International Series in Quantitative Marketing, SecondEdition. 21
  22. 22. Zablah, A.R.; Danny N.B. and Wesley J.J. (2003): “Customer Relationship Management:An Explication of Its Domain and Avenues for Further Inquiry,” in RelationshipMarketing, Customer Relationship Management and Marketing Management: Co-Operation-Competition-Co-Evolution, Michael Kleinaitenkamp and Michael Ehret, eds.Berlin: Freie Universitat Berlin, pp.115-24. 22
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