5.2 Customers Types for Grocery Shopping Scenario


 Pierce Lewis
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1 CHAPTER 5: RESULTS AND ANALYSIS Introduction This chapter relates to the data analysis of the data collected through the survey and the interpretations are mentioned so that meaningful recommendations and conclusions can be drawn. The analysis was performed using the various data mining techniques like: (1) Two Step cluster analysis this technique is used for identifying clusters of customers based on their homogeneous groupings drawn from an, otherwise, set of heterogeneous customer data base; (2) Chi square test this technique is intended to test how likely it is that an observed distribution is due to chance. It is also called a "goodness of fit" statistic; (3) Factor analysis this technique is used for data preprocessing and for reducing the data to a manageable level which can be used for further analysis such as modeling and suitable interpretation; and (4) Multiple regression analysis this predictive data mining modeling technique is used to predict the dependent variable on the basis of the independent variables. All the data mining techniques used in this research study have been based on the use of IBM s SPSS (Statistical Package for Social Sciences), version Customers Types for Grocery Shopping Scenario The data mining technique used here is twostep cluster analysis. The TwoStep Cluster Analysis procedure is an exploratory tool designed to reveal natural groupings (or Clusters) within a data set that would otherwise not be apparent. The algorithm employed by this procedure has several desirable features that differentiate it from traditional Clustering techniques. These features are as follows: Handling of categorical and continuous variables: By assuming variables to be independent, a joint multinomialnormal distribution can be placed on categorical and continuous variables. 99
2 Automatic selection of number of Clusters: By comparing the values of a modelchoice criterion across different Clustering solutions, the procedure can automatically determine the optimal number of Clusters. Scalability: By constructing a Cluster Features (CF) tree that summarizes the records, the TwoStep algorithm can be used to analyze large data files. The two step clustering method is a scalable cluster analysis algorithm designed to handle very large data sets. It has two steps: a) Pre cluster the cases into many small sub clusters b) Cluster the sub clusters resulting from pre cluster step into the desired number of clusters. Table 5.1: AutoClustering No. of Clusters Schwarz's Bayesian Criterion (BIC) BIC Change(a) Ratio of BIC Changes(b) Ratio of Distance Measures(c) In the table 5.1: a shows the changes are from the previous number of clusters in the table. b shows the ratios of changes are relative to the change for the two cluster solution. c shows the ratios of distance measures based on the current number of clusters against the previous number of clusters. 100
3 Here, automated cluster selection has been used where number of clusters can be found using the Schwarz Bayesian Criterion (SBIC) where I stands for information ( vide Table 5.1). In statistics, the Bayesian information criterion (BIC) or Schwarz criterion (also SBC, SBIC) is a criterion for model selection, among a class of parametric models with different numbers of parameters. The number of clusters at which the schwarz criterion BIC becomes small and the change in BIC between adjacent numbers of clusters is small, has been shown. The algorithm selected two clusters which can also be determined from Table 5.2. As seen from Figure 5.1, Cluster1 has 120 cases, while Cluster has 152 cases. A total of 272 cases out of the total respondents 352 were clustered. 22.7% cases (80 nos. were excluded. Table 5.2: Cluster Distribution N % of Combined % of Total Cluster 1 Cluster % 34.1% % 43.2% Combined Excluded Cases Total % 77.3% % % As per figure 5.1, the clusters are graphically represented in the form of a pie chart with the larger pie showing cluster 1 and the smaller pie showing cluster 2. Cluster Size TwoStep Cluster Number 1 2 Figure 5.1: Graphical Representation of Cluster Distribution 101
4 As per the Table 5.3, it can be observed that Cluster 1 primarily consists of Store Non loyal shoppers, whereas, Cluster 2 consists of store loyal shoppers. Table 5.3: Store Loyalty amongst Surveyed Customers. STORE LOYALTY Cluster 1 Fr eq % Fr eq % Fr eq % Fr eq % Fr eq % Fr eq % Fr eq % Cluster 2 Combined (1 depicts lowest loyalty, while 7 show highest loyalty) The two clusters (identified in the table 5.2) have been named on the basis of the level of loyalty exhibited vide table 5.3 and thus have been named as follows: Cluster1: Store nonloyals Cluster2: Store loyals 5.3 Profiling for Various Customer Types Identified As the clusters are formed, now for the objective number 2, the profiling of each cluster needs to be done. The profiling has been done based on the following demographic and behavioral variables: Demographic variables included in the study are as follows: (A) Gender (B) Age (C) Occupation (D) Education (E) Income Shopping Behavior variables included in the study are as follows: (F) Shop Alone or Shop with someone ; (G) Expenditure on the shopping category (monthly) and (H) Number of shopping trips (shopping frequency). 102
5 Cluster Profiling: A) Gender variable: Table 5.4: Profiling of Cluster by Gender Cluster 1 Cluster 2 Combined Male Female Frequency Percent Frequency Percent % % % % % % Gender is one of the variables considered for profiling of the two clusters or segments of shoppers that have emerged. Table 5.4 shows that Men are more likely to be within the Store Nonloyals cluster (Cluster 1) as compared to women. Women seem more inclined to remain loyal (Cluster 2) to the same store especially in the context of grocery shopping The graphic representation of gender distribution as per figure 5.2 also shows that the distribution of males and females is different for the clusters visàvis the overall distribution of gender. Figure 5.3 validates the difference in distribution of the expected frequency from that of the observed frequency using the chisquare method (used for categorical variables). For categorical variables, SPSS calculates a chisquare value that compares the observed distribution of values of a variable within a cluster to the overall distribution of values. Figure 5.3 is a plot of the chisquare statistic for gender. Within each cluster, the observed distribution is compared to an expected distribution based on all cases. Large values of the statistic for a cluster indicate that the distribution of the variable in the cluster differs from the overall distribution. The critical value line that is drawn provides some notion of how dissimilar each cluster is from the average. If the absolute value of the statistic for a cluster is greater than the critical value, the variable is probably important in distinguishing that cluster from the others. In this case, it can be observed that Gender is an important variable in differentiating both the clusters from the overall distribution. 103
6 Within Cluster Percentage of gender 1 gender 1 2 Cluster 2 Overall Percent within Cluster Figure 5.2: Within Cluster Percentage of Gender (Gender Codes 1: Male; 2: Female) gender Bonferroni Adjustment Applied Critical Value Test Statistic 2 Cluster ChiSquare Figure 5.3: ChiSquare Gender B) Age variable: Table 5.5: Profiling of Cluster by Age >40 Freq % Freq % Freq % Cluster 1 Cluster 2 Combined
7 Within Cluster Percentage of age 1 age Cluster 2 Overall Percent within Cluster Figure 5.4: Within Cluster Percentage of age (Age Codes  1: yrs; 2: yrs; 3: 40 and above) Age is one of the variables considered for profiling of the two clusters that have emerged. Across different age categories, it seems inconclusive to distinguish the clusters on the basis of age because the critical value line exceeds the test statistic (figure 5.5). In other words, the chisquare statistic indicates that age is an insignificant variable when it comes to profiling the clusters (Table 5.5; Figures 5.4 & 5.5). age Bonferroni Adjustment Applied Critical Value Test Statistic 2 (N) Cluster 1 (N) ChiSquare (N) Nonsignificant Figure 5.5: Chisquare Age 105
8 C) Occupation variable: Table 5.6: Profiling of Cluster by Occupation Businessperson Selfemployed Service Homemaker Student Retired Fr Fre Fre Fre Fr Fr eq % q % q % q % eq % eq % Cluster 1 Cluster 2 Combined Occupation is another variable considered for profiling of the two clusters or segments of shoppers that have emerged. Table 5.6 shows that Selfemployed individuals, those in Service and Students are more likely to be within the store nonloyals cluster (Cluster 1). Homemakers and Retired individuals seem more inclined towards grocery store loyalty, as seen from their likelihood of being present in significant numbers in Cluster 2. Within Cluster Percentage of occupation 1 occupation Cluster 2 Overall Percent within Cluster Figure 5.6: Within Cluster Percentage of occupation (Occupation Codes  1: Business Person; 2: Selfemployed; 3: Service; 4: Homemaker; 5: Student; 6: Retired) 106
9 The graphic representation of Occupational distribution as per figure 5.6 also shows that the distribution of various occupations is different for the clusters visàvis the overall distribution for occupation. Figure 5.7 validates the difference in distribution of the expected frequency from that of the observed frequency using the chisquare method (used for categorical variables). By observing the critical value line in figure 5.7, it can be stated that occupation is an important variable in distinguishing both clusters from each other. occupation Bonferroni Adjustment Applied Critical Value Test Statistic 2 Cluster ChiSquare Figure 5.7: Chisquare Occupation D) Education: Table 5.7: Profiling of Cluster by Education Undergraduates Graduates PostGraduates Frequency % Frequency % Frequency % Cluster 1 Cluster 2 Combined Education is another demographic variable considered for profiling of the two clusters that have emerged. Table 5.7 shows that higher educated individuals (Post Graduates and Graduates) are more likely to be within cluster 1. Less educated shoppers (Undergraduates) are more likely to fall in cluster
10 The graphic representation of Education distribution (figure 5.8) also shows that the distribution of various occupations is different for the clusters visàvis the overall distribution for Occupation. Within Cluster Percentage of education 1 education Cluster 2 Overall Percent within Cluster Figure 5.8: Within Cluster Percentage of Education (Education Codes 1: Undergraduates; 2: Graduates; 3: Post Graduates) education Bonferroni Adjustment Applied Critical Value Test Statistic 2 Cluster ChiSquare Figure 5.9: Chi Square Education 108
11 Figure 5.9 validates the difference in distribution of the expected frequency from that of the observed frequency using the chisquare method (used for categorical variables). By observing the critical value line in figure 5.9, it can be stated that Education variable is important in distinguishing both clusters from each other. E) Income variable: Table 5.8: Profiling of Cluster by Income <10,000 10,000 20,000 20,00130,000 >30,000 Freq % Freq % Freq % Freq % Cluster 1 Cluster 2 Combined Monthly Household Income (MHI) is another demographic variable considered for profiling of the two clusters or segments of shoppers that have emerged. Table 5.8 shows that higher income individuals (MHI more than Rs 20,000) are more likely to be within the store nonloyalty cluster (Cluster 1). Shoppers with lesser incomes (MHI Less than Rs 20,000) are inclined towards maintaining store loyalty, as seen from their likelihood of being in Cluster 2. Within Cluster Percentage of MHI 1 MHI Cluster 2 Overall Percent within Cluster Figure 5.10: Within Cluster Percentage of MHI (Income Codes (Rs.) 1: less than 10000; 2: to 20000; 3: to 30000; 4: more than
12 The graphic representation of Income distribution (figure 5.10) also shows that the distribution of various Income categories is different for the clusters visàvis the overall distribution for MHI. Figure 5.11 validates the difference in distribution of the expected frequency from that of the observed frequency using the chisquare method (used for categorical variables). By observing the critical value line in figure 5.11, it can be stated that the MHI variable is important in distinguishing both clusters from each other. MHI Bonferroni Adjustment Applied Critical Value Test Statistic 2 Cluster ChiSquare Figure 5.11: Chi Square Income F) Shopwith variable: Table 5.9: Profiling of Cluster by Shopwith With Someone Alone Frequency Percent Frequency Percent Cluster 1 Cluster 2 Combined Shopwith someone or alone is one of the three behavioral variables considered for profiling of the two clusters or segments of shoppers that have emerged. Table 5.9 and figure 5.12 and 5.13 show that Shopping with someone or alone is not a significant variable to distinguish between Cluster 1 and Cluster
13 Within Cluster Percentage of shopwith 1 shopwith 1 2 Cluster 2 Overall Percent within Cluster Figurer 5.12: Within Cluster Percentage of Shopwith ( Shopwith codes 1: shop with someone; 2: shop alone) shopwith Bonferroni Adjustment Applied Critical Value Test Statistic 2 (N) Cluster 1 (N) ChiSquare (N) Nonsignificant Figure 5.13: Chi Square Shopwith G) Monthly Expenditure on Groceries variable: Expenditure per month is the last of the three behavioral variables considered for profiling of the two clusters that have emerged. Table 5.10 shows that individuals in the nonloyalty cluster (Cluster 1) spend less compared to shoppers represented in the store loyal cluster (Cluster 2). The graphical representation of Shopper Spend distribution as per figure 5.14 also shows that the distribution of various Shopping categories is different for the clusters visàvis the overall distribution for expenditure. 111
14 Table 5.10: Profiling of Clusters by Expenditure < >1000 Freq Percent Freq Percent Freq Percent Cluster 1 Cluster 2 Combined Within Cluster Percentage of spend 1 spend Cluster 2 Overall Percent within Cluster Figure 5.14: Within Cluster Percentage of Expenditure (Expenditure Codes (Rs.)  1: less than 500; 2: ; 3: more than 1000) spend Bonferroni Adjustment Applied Critical Value Test Statistic 2 Cluster ChiSquare Figure 5.15: Chi Square Expenditure Figure 5.15 validates the difference in distribution of the expected frequency from that of the observed frequency using the chisquare method (used for categorical 112
15 variables). By observing the critical value line in figure 5.15, it can be stated that the Expenditure variable is important in distinguishing both clusters from each other. H) Number of Shopping Trips variable: Table 5.11: Profiling of Clusters by Trips 14 trips/ month 59 trips/ month trips/ month >15 trips / month Freq % Freq % Freq % Freq % Cluster 1 Cluster 2 Combined The number of Shopping Trips is another behavioral variable considered for profiling of the two clusters that have emerged. Table 5.11 shows that individuals in the store nonloyal cluster (Cluster 1) make fewer shopping trips compared to shoppers represented in the store loyal cluster (Cluster 2). The graphic representation of Shopping Trips distribution (figure 5.16) also shows that the distribution of various Shopping categories is different for the clusters visàvis the overall distribution for Occupation. Within Cluster Percentage of trips 1 trips Cluster 2 Overall Percent within Cluster Figure 5.16: Within Cluster Percentage of trips (Number of Trips/ month Codes 1: 14 times; 2: 59 times; 3: times; 4: more than 15) 113
16 Figure 5.17 validates the difference in distribution of the expected frequency from that of the observed frequency using the chisquare method (used for categorical variables). By observing the critical value line (figure 5.17), it can be stated that the ShoppingTrips variable is important in distinguishing both clusters from each other. trips Bonferroni Adjustment Applied Critical Value Test Statistic 2 Cluster ChiSquare Figure 5.17: Chi Square Trips Figure 5.18: Relative Importance of Demographic and Behavioral Variables 114
17 In any cluster analysis based profiling; two things are most important. Firstly, to determine the relative importance of each variable used in the profiling and secondly, to measure the significance of each variable used in profiling. Figure 5.18 shows the relative importance of the various demographic and behavioral variables which is an output of TwoStep cluster analysis. Accordingly, Shopping trips is the most important variable for profiling customers based on loyalty followed by Education, MHI Monthly Household Income, Gender, Occupation, Expenditure, Shopwith and Age is the least important variable. In terms of significance of the variable in profiling the data, except for Age and Shopwith, all other variables are significant for profiling the clusters vide table Table 5.12: Summary of Demographic / Behavioral Variables Sample Distribution and Cluster Membership Demographic Sample Sample Cluster 1 Cluster 2 Significant Variable Freq. % NonLoyals Loyals for (%) (%) Profiling Gender Male YES Female Total Age NO 40 and above Total Occupation Businessperson SelfEmployed Service Homemaker YES Student Retired Total
18 Education Undergraduate Graduate Post Graduate Total Income/ mnth(rs) <10,000 10,001 to 20,000 20,001 to 30,000 >30,000 Total Behavioral Variables Spend < >1000 Total Shopwith Someone Alone Total Shopping Trips 14 trips 59 trips trips >15 trips Total YES YES Freq. % Cluster1 Cluster2 Significant for profiling YES NO YES The objective achieved here was to find the characteristics and the behavioral patterns of the various types of customers identified in the first objective. The two clusters: Store Loyals (Cluster 1) and Store Nonloyals (Cluster 2) have been profiled on the basis of demographic variables and shopping behavior variables. The profiles are described as follows: 116
19 Profile of Cluster 1 (StoreLoyals): Females Housewives / Retired Low Income Less Educated Spend more on groceries Make More trips for grocery shopping Profile of Cluster 2 (Store nonloyals): Males Service / Selfemployed / Students Higher Income Better Educated Spend less on grocery shopping Make fewer trips for grocery shopping 5.4 Customer Perceptions of Grocery Store Images Based on the customer perceptions of the grocery stores, the 23 store attributes have been taken vide table 5.13 from the CIRS Scale of Store Image (described in Chapter 4) Since these attributes are high in number, they need to be reduced to a more manageable level, so that a model with limited number of variables can be derived. To achieve this objective, the data mining technique i.e. Factor analysis using Principal Component Analysis with Varimax Rotation is used. The major two benefits of this data mining techniques are, firstly, they effectively reduce the dataset to a more manageable number of factors which are a combination of 2 or more attributes and secondly, the principal component analysis method gives exact factor scores, which can be used as a surrogate for the factors and can be further used for deriving a predictive model. In this case, since the dependent variable also called as the criterion variable (store loyalty) and the independent variables also called as the predictor variables (store attribute factors) are metric scaled, the predictive model used is the multipleregression analysis. 117
20 Table 5.13: Descriptive Statistics for Store Attributes Standard Store Attributes Mean Deviation REPUTATION QUALITY LAYOUT FRESH BRANDS PRIVATE LABELS LOWPRICES SCHEME LOCATION SALESMAN SERVICE RETURN VARIETY SPACIOUS CLEAN CHECKOUT DISPLAY EASYSEARCH LOYCARD PARKING DEBITCARD ONESTOPSHOPPING (OSS) AIRCONDITIONING (AC)
21 5.4.1 Adequecy of Data for Factor Analysis For checking the suitability of data for factor analysis, four recommended techniques have been applied: (a) Validation through Correlation Coefficient Matrix of Explanatory Variables (b) Validation through AntiImage Correlation Matrix (c) KaiserMeyerOlkin (KMO) measure of sampling adequacy (d) Bartlett s Test of Sphericity. (a) Validation through Correlation Coefficient Matrix of Explanatory Variables It is a lower triangle matrix showing simple correlations among all possible pairs of variables included in the analysis for the application of factor analysis. It is obligatory that the data matrix should have good correlations of visual inspection reveals no substantial number of correlations greater than 0.30 then factor analysis is probably inappropriate. A perusal of table 5.14 indicates that there are enough correlations between the variables greater than 0.30 indicating the suitability of data for application of factor analysis. (b) Validation through AntiImage Correlation Matrix The Antiimage matrices contain the negative partial covariances and correlations. They can give an indication of correlations which aren't due to the common factors. Small values in the table indicate that the variables are relatively free of unexplained correlations. Most or all values off the diagonal should be small (close to zero). Each value on the diagonal of the antiimage correlation matrix shows the Measure of Sampling Adequacy (MSA) for the respective item. The values less than 0.5 in the diagonal of antiimage correlation matrix indicate that variables do not fit with the structure of the other variables. The values in the table 5.15 (b), in the diagonal, marked a are all more than 0.5 thus indicating sampling adequacy and suitability of factor analysis. 119
22 Table 5.14: Correlation Matrix(a)
23 Table 5.14 (contd.) a is Determinant =
24 Table 5.15 (a): Antiimage Covariance Matrix
25 1.931( a) (a ) (a ) (a ) (a ) (a ) (a ) (a ) 9.758(a ) (a (a (a ( a) (a (a ( a) (a (a (a (a (a (a (a) a is the Measures of Sampling Adequacy(MSA) Table 5.15 (b): Anti Image Correlation Matrix 123
26 Table 5.16: KMO and Bartlett's Test KaiserMeyerOlkin (KMO) Measure of Sampling Adequacy Bartlett's Test of Sphericity Approx. ChiSquare Df Sig..000 c) KaiserMeyerOlkin (KMO) Measure of Sampling Adequacy It is an index used to examine the appropriateness of factor analysis. High value (between 0.5 and 1.0) indicate adequacy of data for the use of factor analysis [102]. The computed value of KMO statistics is (table 5.16) indicating the adequacy of data for factor analysis. This index compares the magnitudes of the observed correlation coefficient to the magnitudes of the partial correlation coefficients. Small values of the KMO statistic indicate that the correlations between pairs of variables cannot be explained by other variables and that factor analysis may not be appropriate vide Table d) Bartlett s Test of Sphericity It can be used to test the null hypothesis that the variables are uncorrelated in the population; in other words, the population correlation matrix is an identity matrix. In an identity matrix, all the diagonal terms are 1, and all offdiagonal are 0. The test statistics for sphericity is based on a chisquare transformation of the determinant of the correlation matrix. A large value of the test statistic will favor the rejection of the null hypothesis that the variables are uncorrelated in the population. The test statistics computed is ( 2) which is very high leading to the rejection of the hypothesis as mentioned earlier. Thus according to the Bartlett s test of Sphericity, factor analysis is appropriate for this data [102] vide Table
27 5.4.2 The Principal Component Analysis Method Using Varimax Rotation The principal component analysis (PCA) was used for extraction of factors through Varimax Rotation. The PCA is being used to reduce the number of attributes, wherein, several attributes converge to form a factor. In this case, the 23 stotre attributes are expected to converge into a lesser number of factors. The number of factors to be retained was based on (a) the Latent Route Criterion (Eigen Value Criterion), (b) Determination Based on Scree Plot, and (c) Determination Based on Percentage of Variance. (a) The Latent Rule Criterion (Eigen Value Criterion) In this criterion, only factors with eigen value greater than 1.0 are retained where an eigen value represents the total variance explained by each factor. After the factor analysis (vide Table 5.19), a total of six factors had eigen value 1.0. (b) Determination Based on Scree Plot A scree plot is a plot of the eigen values against the number of factors in order of extraction. The shape of the plot is used to determine the number of factors. The point at which there is a distinct break between the steep slope of factors and the gradual trailing of (known as the scree) indicates the true number of factors. A perusal of figure 5.19, which is a scree plot, indicates that the line starts flattening after the component number 6 (plotted on X axis). This suggests that there are a total of six factors after data reduction through factor analysis. (c) Determination Based on Percentage of Variance: In this approach the number of factors extracted is decided in such a way that they account for at least sixty percent of the cumulative percentage of variance. As per Table 5.18, the six factors identified based on the latent rule criterion and the scree plot account for a very comfortable cumulative percentage of variance. 125
28 Table 5.17: Communalities Store Attributes Initial Extraction REPUTATION QUALITY LAYOUT FRESH BRANDS PLs LOWPRICES SCHEME LOCATION SALESMAN SERVICE RETURN VARIETY SPACIOUS CLEAN CHECKOUT DISPLAY EASYSEARCH LOYCARD PARKING DEBITCARD OSS AC Extraction Method: Principal Component Analysis 126
29 Table 5.18: Total Variance Explained Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Component % of Cumulative % of Cumulative % of Cumulative Total Variance % Total Variance % Total Variance % Extraction Method: Principal Component Analysis 127
30 10 8 Eigen value Component Number Figure 5.19: Scree Plot An important output from factor analysis is the factor matrix, also called the factor pattern matrix (Table 5.19). The factor matrix contains the coefficients which express the standardize variables in terms of the factors. These coefficients, the factor loadings, represent the correlations between the factors and the variables. A coefficient with a large absolute value indicates that the factors and the variables are closely related. The coefficients of the factor matrix can be used to interpret the factors. Although the initial or unrotated factor matrix indicates the relationship between the factors and individual variables, it seldom results in factors that can be interpreted, because the factors are correlated with many variables. In this case, the factors have been rotated so that each factor has significant loadings (more than 0.40) ideally with not more than one variable. The method for rotation used here is the varimax procedure. This is an orthogonal method of rotation that minimizes the number of variables with high loadings on a factor, thereby enhancing the interpretability of the factors [102]. 128
31 Table 5.19: Component Matrix COMPONENTS/ FACTORS STORE ATTRIBUTES REPUTATION QUALITY LAYOUT FRESH BRANDS PLs LOWPRICES SCHEME LOCATION SALESMAN SERVICE RETURN VARIETY SPACIOUS CLEAN CHECKOUT DISPLAY EASYSEARCH LOYCARD PARKING DEBITCARD OSS AC Extraction Method: Principal Component Analysis; 6 components extracted. 129
32 The Table 5.20 and 5.21 describe the rotated component matrix. Only those variables were assigned a specific factor membership which had the highest loading on that factor subject to a minimum of The shortlisted variables have been highlighted in bold. Table 5.20: Rotated Component Matrix COMPONANTS/ FACTORS REPUTATION QUALITY LAYOUT FRESH BRANDS PLs LOWPRICES SCHEME LOCATION SALESMAN SERVICE RETURN VARIETY SPACIOUS CLEAN CHECKOUT DISPLAY EASYSEARCH LOYCARD PARKING DEBITCARD OSS AC Rotation Method: Varimax with Kaiser Normalization.Rotation converged in 7 iterations. 130
33 Table 5.21: ShortListed Attributes (Factor loadings only above 0.40) COMPONENTS/ FACTORS Shortlisted Attributes REPUTATION.826 QUALITY.817 LAYOUT.846 FRESH BRANDS.762 PLs.827 LOWPRICES.804 SCHEME LOCATION.907 SALESMAN.852 SERVICE.895 RETURN.921 VARIETY.752 SPACIOUS.645 CLEAN.905 CHECKOUT.697 DISPLAY.707 EASYSEARCH.855 LOYCARD.950 PARKING.911 DEBITCARD OSS.827 VALUE.519 The component transformation matrix (Table 5.22) describes the specific rotation applied to the factor solution. This matrix is used to compute the rotated factor matrix from the original (unrotated) factor matrix. If the offdiagonal elements are close to zero the rotation was relatively small. As per table 5.22 the offdiagonal value is which indicates that the number of rotations has been small. 131
34 Table 5.22: Factor Transformation Matrix Components/ Factors Extraction Method: PCA Rotation Method: Varimax with Kaiser Normalization LOYLCARD RETURN SERVICE SCHEME DEBITCARD REPUTATION QUALITY DISPLAYS LOWPRICES PLsOSS CHECKOUT SALESMAN EASYSEARCH LAYOUT Component 1 Component 3 Figure 5.20: Component Plot in Rotated Space 132
35 Table 5.23: Factor Score Coefficient Matrix STORE ATTRIBUTES COMPONENT/ FACTORS REPUTATION QUALITY LAYOUT FRESH BRANDS PLs LOWPRICES SCHEME LOCATION SALESMAN SERVICE RETURN VARIETY SPACIOUS CLEAN CHECKOUT DISPLAY EASYSEARCH LOYCARD PARKING DEBITCARD OSS AC Rotation Method: Varimax with Kaiser Normalization. Component Scores. 133
36 The Factor Score Coefficient Matrix (Table 5.23) shows the values used to compute factor scores. For each case, the factor score is computed by multiplying variable values by factor score coefficients. For principal component models, these give exact component/ factor scores. The Factor scores will then be used for regression analysis. Table 5.24: Factor Analysis of Grocery Store Attribute: Interpretation of Factors Factor F1: Store Ambience & Layout Eigen Value: % of Variance: Cumulative %: F2: Service and Loyalty Schemes Eigen Value: 2.98 % of Variance: Cumulative %: F3: Price and Quality Eigen Value: 1.99 % of Variance: 8.66 Cumulative %: F4: One Stop Shopping Eigen Value: 1.45 % of Variance: 6.31 Cumulative %: F5: Convenience Eigen Value: 1.14 % of Variance: 4.98 Cumulative %: F6: Salesman Eigen Value: 1.04 % of Variance: Cumulative %: Factor Statement Loading.846 This store has a well organized layout.645 This is a Spacious Store.904 This store is clean.697 This store has fast checkout.707 This store has good displays.855 In this store it is easy to search items.648 This store offers very good schemes & sales.895 This store has good service.921 In this store it is easy to return purchases.950 This store has attractive loyalty schemes.826 This store has a very good reputation.857 This store has good quality products.500 This store sells fresh products.804 This store has low prices.519 This store provides great value for money.762 This store stocks well known brands.827 This store s own products are of good quality.752 This store has a vast variety of products.827 This store offers everything under one roo.907 This store has a very convenient location.911 This store has good parking facilities.419 This store offers option to payby credit/debit card.852s This store has helpful salesmen 134
37 Table 5.25: Reliability Analysis of Factors Factor Reliability Statement Cronbach s alpha F1: Store.903 This store has a well organized layout Ambience & Layout This is a Spacious Store This store is clean This store has fast checkout This store has good displays In this store it is easy to search items F2: Service and Loyalty Schemes.938 This store offers very good schemes & sales This store has good service In this store it is easy to return purchases This store has attractive loyalty schemes F3: Price and.914 This store has a very good reputation Quality This store has good quality products This store sells fresh products This store has low prices This store provides great value for money F4: OneStop.912 This store stocks well known brands Shopping This store s own products are of good quality This store has a vast variety of products This store offers everything under one roof F5: Convenience.809 This store has a very convenient location This store has good parking facilities This store offers option to pay by credit/debit card F6: Salesman No estimate for This store has helpful salesmen single attribute. COMPOSITE RELAIBILITY All twenty three attributes 135
38 5.4.3 Internal Consistency Reliability Internal consistency reliability is used to assess the reliability of a summated scale where several items are summed to form a total score. In a scale of this type, each item measures some aspect of the construct measured by the entire scale, and the items should be consistent in what they indicate about the characteristic. This measure of reliability focuses on the internal consistency of the set of items forming the scale. The measure used here is the coefficient alpha or Cronbach s alpha, which is the average of all possible split half coefficients resulting from different ways of splitting the scale items. An alpha value of 0.6 or less indicates unsatisfactory internal consistency reliability. The Cronbach alpha value in the table 5.25 for all individual factors is well above 0.9, thus indicating a very strong reliability of the factors. The composite reliability of all the factors together consisting of all 23 attributes is which is a very strong indicator of reliability (vide Table 5.25). 5.5 Predictive Model for Store Loyalty In the third objective, the data mining technique used is the principal component analysis (PCA). The reason PCA is used to achieve the third objective is twinfold. Firstly, the original 23 attributes get reduced to a more manageable 6 factors/ dimensions which can further describe the consumer perception of grocery store images. Secondly, PCA enables computation of exact factor scores for each of the respondent for use in the subsequent development of a predictive model of customer store choice. Thus factor analysis has a capacity to transform the original data into a new data set with a reduced dimension space which can be used to calculate composite variables (factors) for further multivariate analysis such as multiple regression used in predictive modeling. In this study, factor scores (defined as composite scores estimated for each respondent on the derived factors) have been computed for each respondent where a factor score for the i th factor may be estimated as follows: 136
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