Drivers of Customer Retention: An Introspection Into Indian Retail Customers

Abstract

There is a wide variety of choices for the modern retail customer including multiple retail formats. The success of the retail establishments has a great reliance of customer retention, which is an essential attribute to achieve profitability. This study takes in to consideration to extract the factors responsible for customer retention which in turn assists in increasing the customer base. The prime objective of the study is to ascertain the influence of customer satisfaction, switching costs and customer loyalty on customer retention. Whereas, the second one is to explore the effect of demographic factors on customer retention. The sample size of this study was 600 respondents who were chosen for the full-fledged study. The statistical techniques used for final analysis were structural equation modelling and regression. The findings subsequent to the statistical analysis and interpretation concluded that customer loyalty, customer satisfaction and switching cost have the strongest effect on customer retention in retails. Customer satisfaction alone is not every time an indicator of customer loyalty. A loyal customer will spread positive word of mouth to other prospective customers about the retail. Occupation of respondent has a major influence on customer retention dimensions.

Keywords

Retailing Customer Satisfaction Switching Cost Customer Loyalty Customer Retention India

Introduction

Retailing has taken a long time to come to the present shape of diversity and development. Customer retention is the focus area of modern marketing which leads to value addition in growth and profitability of the retails. In the competitive retail landscape, the success of the retail relies on customer retention as it is the major link to accomplish profitability. In the present business setting, which is highly market oriented and frequently changing, retailers should be clear about their understandings and should be flexible towards the conduct of consumers in choosing and buying different goods and services for taking care of their needs (Roy et al., 2017). Retaining a customer is less expensive than bringing a new customer to the retail. And according to Gallo (2014), ‘acquiring a new customer tends to cost the retailer 5–25 times more rather than retaining an existing one’. And this virtue of customer retention is mostly a result of customer satisfaction and loyalty.

The study undertakes the challenge of exploring the determinants of customer loyalty and retention and ultimately helps to gain more retail customers. Ramakrishnan (2006) defines customer retention as a very significant marketing goal which blocks customers from going towards the competitor. Customer retention thus plays a major role in the success of the business. Through retention activity, enterprises are trying their level best to retain their existing customers (Mostert et al., 2009). Fluss (2009) explains that in the competitive world the competitors will always try to give more feasible deals to attract customers. The retails should be very conscious about their competitors because retaining a customer is less expensive when compared to acquisition. ‘A 2% increase in customer retention has the same effect as decreasing costs by 10%’ (Murphy & Murphy, 2002).

Research studies in retail management has emerged in India just in 2008 and lot of related areas have not surpassed the infancy stage. There are very less studies exhaustively dealing with customer loyalty and retention in Indian retails. Ang and Buttle (2006) were of the opinion that ‘a 5% increase in customer retention can generate an increase in customer net present value of between 25% and 95% across a wide range of business environment’. These complexities give scope for the study of the determinants of customer loyalty and retention in the new age retails. The earlier studies gave more emphasis on customer loyalty. There is a dearth of related literature available in the Indian context. There have been separate studies concentrating on customer satisfaction, loyalty and retention. These are the reasons for concluding this title, ‘Drivers of Customer Retention: An introspection into Indian Retail Customers’. The number of empirical studies in the Indian context is very less, integrating the variables of retail customer satisfaction, loyalty and retention which is the research gap identified. On account of these, the proposed study may contribute to literature by filling the gaps.

The prime objective of this study is to empirically validate a model linking customer satisfaction, customer loyalty and customer retention with select antecedents. An analysis was undertaken to find out the effect of customer satisfaction, switching costs and customer loyalty on customer retention. The study also examined the influence of demographic factors in to the variables. The research study used the technique of path analysis to test the hypothesized model. The study has been planned and structured in succeeding sections as explained further. The next section on literature review and research hypothesis development explores the previous literature, multiple constructs responsible for influencing customer satisfaction, loyalty and retention were identified. The hypotheses which were formulated also led to the development of the conceptual model. The subsequent sections explain the methods used for data collection and analysis of the collected data. The results are interpreted and discussed in the study. The study is finally concluded along with the limitations of the study, and the scope for future research in the last section.

Literature Review and Research Hypothesis Development

The behaviour of the consumer is complex when associated with organized retail, and specifically retails. Customer retention is the vital factor for the success of any retails. Ramakrishnan (2006) defines customer retention as a very significant marketing goal which blocks customers from going towards the competitor. As explained by Tsai et al. (2006) customer retention was occurring because of limitation based on motivator switching costs or stimulation based on aspiration such as satisfaction or quality. Buttle (2004) proved that customer retention was an indicator of growth, prosperity and profitability. The more the customers are retained by way of planned retention activities and the more they continue, the revenue of the organization increases (Terry, 2005). A 2% increase in customer retention was reaping the same returns as if reducing costs by 10%, and the situation changes from industry to industry (Murphy & Murphy, 2002).

In the opinion of Murali et al. (2016), the term customer satisfaction also explains the degree of customers observed performance as against the level of involvement expected. Satisfaction was a noteworthy antecedent in securing customer retention (Day, 1994; Gil et al., 2006). Ranaweera and Prabhu (2003) add to the research that ‘it is a held belief that the more satisfied the customers are, the greater is their retention’. The retention of satisfied internal and external customers and stake holders are the building blocks to continued existence and growth for the organization. It had been argued that the more satisfied customers had a chance of greater retention (Dean, 2004). Customer satisfaction becomes an essential virtue for the company to take care of the current customer and ultimately retain them (Guo et al., 2009). Satisfaction ultimately leads to improved retention and thereby increased acquisition which happens through word-of-mouth recommendations in the positive format (Wiles, 2007). Customer satisfaction is an important antecedent of customer retention (Lee et al., 2001; Ranaweera & Prabhu, 2003). Customer satisfaction’s influence on customer retention had strong literature back up from Parasuraman et al. (1988), Cronin et al. (2000) and Gil et al. (2006). Because of the reasons stated, the following hypothesis is being recommended:

H₁: Customer satisfaction has a positive relationship with customer retention.

Customer satisfaction along with customer loyalty had a strong binding on the significant events in modern retailing, a market known for its steady growth and excessive competition (Eskildsen et al., 2004). In addition to this Han and Hyun (2018) emphasize that customer satisfaction contributes an enjoyable feeling with the customer in the process of consumption and thereby fulfilment of needs. Meesala and Paul (2018) affirm that one of the strong and influential antecedents of customer loyalty is undoubtedly customer satisfaction. In a study carried out by Kamath (2009), it was found out that there was a close connection between consumer satisfaction and loyalty. The research studies had dealt with a wide range of factors which leads to customer loyalty, namely customer trust, customer satisfaction and shared values (Liu et al., 2011). Satisfaction had a positive and considerable influence on the loyalty (Ahmad, 2012; Bodet, 2008). There is a presence of a constructive relationship connecting customer satisfaction and loyalty (Akbar & Pavez, 2009; Guenzi et al., 2009). The customer satisfaction had direct and indirect relationship with loyalty (Zeng & Zhang, 2008). Various researches had explained satisfaction as one of the important antecedents of customer loyalty (Gummerus et al., 2004; Han & Hyun, 2018; Meesala & Paul, 2018; Murali et al., 2016; Ranaweera & Prabhu, 2003; Ulaga & Eggert, 2006; Vesel & Zabkar, 2009). This directs to compile the following hypothesis:

H₂: Customer satisfaction has a positive relationship with customer loyalty.

Customer loyalty had become a state of art marketing tool in the twenty-first century (Duffy, 2005). Customer loyalty creates repeated purchase intentions and ultimately behaviours with the same brand or establishment (Chen, 2012). In the words of Murali et al. (2016), loyalty occurs due to the benefits provided by the organization to the customers which motivate them to continue with the brand and even increase the frequency and quantity of purchases. Other than having a favourable attitude and getting in to making repeat purchases, loyal customers were believed to be adjustable with any prices charged by the company. Han and Hyun (2018) critically observe the benefits that loyal customers bring in to make each firm prosperous, which include consistent profits even with a minimal investment on marketing and sales promotion expenses. Customers were the basis of any organization’s sustainable growth, and particularly customer loyalty can be a good inspiration directing to profitability (Hayes, 2008). Customer loyalty in true sense happens when the customer eventually becomes a brand diplomat for supporting the organization, not expecting any extra benefits. Lin and Wu (2011) were of the belief that there was a strong relationship between customer retention and the brilliance offered by the services or products. There was a direct connecting link between customer retention and customer loyalty (Gerpott et al., 2001). Bolton et al. (2000) said that customer loyalty had a strong effect on customer retention. This directs to compile the following hypothesis:

H₃: Customer loyalty has a positive relationship with customer retention.

Risdianto (2017) as well as Samudro et al. (2019) had a consensus on the strong influence of switching barrier and how it is leading to customer retention. Additionally, Kim et al. (2004) explained similar relationship in the area of mobile telecommunication services, interestingly Danesh et al. (2012) reported the same findings operational in hypermarkets. Shin and Kim (2007) were of the opinion that ‘perceived switching cost rather than actual switching cost explains customer switching intention and affects the market outcome’. As an effect of this, apparent switching costs could be of great advantage to retain customers. Researchers had a novel idea to prevent displeasure by creating switching barriers which was a value addition to the service offered (Ranaweera & Prabhu, 2003). Switching barriers were influenced positively on customer retention and because of this, switching costs still is a valid reason to retain customers (Ranaweera & Prabhu, 2003). Switching costs has a relation with customer retention both directly and indirectly (Lee et al., 2001). Scholars had identified the important consequences of switching cost particularly from the propagated corporate customer retention programmes (Garbarino & Johnson, 1999; Morgan & Hunt, 1994). Switching cost was a pointer as well as a supplementary factor for customer retention (Chen & Hitt, 2002; Kim et al., 2004). Switching cost consists of every cost which directly or indirectly was involved in switching from one retail establishment to another. This helps us to formulate the following hypothesis:

H₄: Switching cost has a positive relationship with customer retention.

The hypothetical model draws a lot of inputs from the abstract theories. The relationship between the constructs which are connected in this model are well established and grounded from the related literature. The proposed model (Figure 1) displays the factors which will lead to loyalty and ultimately customer retention.

Demographics had an influence on the choice of retail store format regarding grocery retailing (Carpenter & Moore, 2006; Fox et al., 2004). Studies evaluating the moderating results of personal characteristics suggest a combination of results and point out both significant and insignificant effects (Evanschitzky, 2006; Homburg et al., 2003). Moderating factors which combine satisfaction and loyalty are gender, age and income, which are the demographic factors (Homburg & Giering, 2001). The retail format selection of the customer had significant relationship with the age of the customer, gender, occupation, education and monthly household income, family size and travel distance (Prasad & Aryasri, 2011). Mortime and Clarke (2010) explained that female grocery customers prioritized supermarket store features more important compared to male customers.

The higher retention is being appreciated by those who have a better educational background (Mirza et al., 2009). Recent developments were that scholars had started off considering personal and relational features as moderators. The demographic attributes are age, gender, whether married or not, income, working status of females, education, occupation and size of the family. Particularly, we distinguish the details of retail customers with respect to their gender, education, occupation and annual family income. Therefore, it is hypothesized that:

H₅: There is significant difference in customer retention across gender.

H₆: There is significant difference in customer retention across occupation.

H₇: There is significant difference in customer retention across education qualification.

H₈: There is significant difference in customer retention across annual family income.

Methods

Quantitative method is applied to forecast facts with quantifiable variables (Leedy & Ormrod, 2001). The research study has used random sampling for the purpose of data collection. The structured questionnaire has a total of 22 items. As explained by Hair et al. (2019), the ideal number of samples should be 5–20 times of the number of items. Efforts were made to collect the information from maximum respondents so that statistical tools can be carried out effectively. Finally, a sample of 600 respondents was chosen for the full-fledged study.

Sampling and Data Collection

The questionnaire was designed using Likert scale measurement to measure constructs for customer satisfaction, customer loyalty and customer retention. Items are being rated based on a scale which is a five-point one, and ranges from strongly disagree to strongly agree. The number of questionnaires distributed for the first phase was 250. The first phase data collection was done for the first pilot study and the responses received were 152. The questionnaires distributed in the second phase were 750 and the responses received were 522. The final data collection ended in collecting a sample size of 600 which is a combination of 112 taken from the first phase and 488 from the second phase from the respondents who are the Indian retail customers.

Measurement Development

The questions related to each construct are compiled from previous work-related literature. The summary of scale constituted questions adopted from various authorities for the following constructs. Customer satisfaction 6 (Anderson & Swaminathan, 2011), switching cost 3 (Wang & Wu, 2012), customer loyalty 6 (Eakuru & Mat, 2008) and customer retention 3 (Trasorras et al., 2009). The responses to the questions are recorded using a five-point Likert scale. Table 1 lists the profile of the respondents exclusively.

Table 1.

Profile of the Sample

		Frequency	Percentage
Occupation of the respondents	Student	195	32.5
	Employed	284	47.3
	Self-employed	42	7.0
	Household work	73	12.2
	Others	6	1.0
Gender representation of the respondents	Male Female	284 316	47.3 52.7
Educational qualification of the respondents	10th standard	7	1.2
	PUC	22	3.7
	Undergraduate	258	43.0
	Postgraduate	313	52.2
Annual income of respondents (₹)	Not applicable	38	6.3
	<1 lakh	34	5.7
	1–2 lakhs	108	18.0
	3–4 lakhs	103	17.2
	4–5 lakhs	317	52.8
	Total	600	100.0

Source: The author.

Notes: PUC = pre-university college.

Procedure

All of the statistical analyses were done using the software’s SPSS (v.20). The questionnaire was designed using Likert scale (1–5) measurement, which ranges from strongly disagree to strongly agree. Likert scaling is the most prevalently used scales among the market researchers to evaluate psychographic variables (Hair et al., 2007). Surveyed respondents were suggested to choose their level of retention in relation to the variables.

Inter-item correlation is the first step towards achieving data reliability, a confirmatory factor analysis (CFA) was conducted using AMOS 20.0 software and mainly administered to check the measurement model. Structural models identification and goodness-of-fit done to show an overall acceptable fit. The reliability was investigated, and the individual construct scale reliability was further tested. Convergent validity and discriminant validity were also found out, further to which, finally, the hypotheses were also tested.

Results

The objective of inter-item correlation is to check for redundancy, that is, the extent to which the items in the scale are assessing the same content. Values of items above 0.2 (20 items out of total 22 items) in Table 2 which were retained for full-fledged study. Questions namely customer satisfaction 3 (cust_satis_3) and customer satisfaction 5 (cust_satis_5) were dropped and not considered for the full-fledged study.

Table 2.

Inter-Item Correlation Test Results for Pilot Study 2

Question	Item Correlation	Question	Item Correlation	Question	Item Correlation
Satis_1	0.487	Loyal_3	0.538	Switch_cost_2	0.391
Satis_2	0.586	Loyal_4	0.531	Switch_cost_3	0.408
Satis_4	0.492	Loyal_5	0.478	Retent_1	0.539
Loyal_1	0.495	Loyal_6	0.465	Retent_2	0.516
Loyal_2	0.568	Switch_cost_1	0.420	Retent_3	0.538

Source: The author.

Note: Loyal= customer loyalty; Retent = customer retention; Satis = customer satisfaction; Switch_cost = switching cost.

CFA dropped 3 questions were customer loyalty 3 (loyal_3), customer loyalty 5 (loyal_5) and customer loyalty 6 (loyal_6).

Reliability and Validity

The initially available responses (n = 112) were analysed using SPSS (v.20) software, using Cronbach’s alpha, intra-class correlation and CFA. Reliability test assesses the internal consistency score of a measurement scale. A value of 0.920 (a value which is adequately above 0.7) shows that there is high reliability. The objective of structural equation modelling as a result is to identify a model which is over identified. The proposed model in this study is an over-identified model with positive degrees of freedom (627) drawn from the AMOS output.

The data on 600 respondents were entered into SPSS database for analysis and the results are indicated along with explanation. The results show the composite reliability of the constructs is greater than 0.70. Accordingly, atmosphere has a composite reliability value of trust (0.780), switching cost (0.772), customer loyalty (0.874), customer retention (0.792), commitment (0.734) and customer satisfaction (0.893). The composite reliability values reveal that most of the constructs are reliable.

Discriminant validity involves in testing statistically if two constructs differ. According to Hair et al. (2011), its being suggested that the ideal value according to average variance extracted for every construct has to be more than 0.50. Accordingly, square root of the average variance extracted value of trust are (0.711), switching cost (0.616), and customer loyalty (0.763), customer retention (0.698), commitment (0.659) and customer satisfaction (0.698). Accordingly, the findings show that the AVE values of all the constructs are greater than the square of the correlations between any two latent variables considered together, which is also an indicator that all the constructs possess discriminant validity.

Goodness of fit index obtained is 0.877 (Table 3), as against the recommended value of above 0.90. The adjusted goodness of fit index is 0.855, as against the recommended value of above 0.80 as well. The normed fit Index, relative fit index and comparative fit index are 0.787, 0.761 and 0.855, respectively, are close to the recommended level of 0.90. Root mean square error of approximation is 0.052 and is well below the recommended limit of 0.10. Hence, the model shows an overall acceptable fit. Accordingly, all the values of three fit measures namely absolute, incremental and parsimony are close to the prescribed norms of goodness of fit. The standardized regression weights (the beta coefficient) as part of path regression analysis results show statistical significance of relationship path between the factors.

Table 3.

Goodness-of-Fit Indices for Measurement Model

Fit Indices	Model Value
Absolute fit measures
Chi-square/df (χ2/df)	2.620
GFI (goodness of fit index)	0.877
RMSEA (root mean square error of approximation)	0.052
Incremental fit measures
AGFI (adjusted goodness of fit index)	0.855
NFI (normed fit index)	0.787
CFI (comparative fit index)	0.855
IFI (incremental fit index)	0.856
RFI (relative fit index)	0.761
Parsimony fit measures
PCFI (parsimony comparative of fit index)	0.762
PNFI (parsimony normed fit index)	0.702

Source: The author.

The percentage responses (Likert scale) for selected constructs in the form of bar chart falling under customer satisfaction, customer loyalty and customer retention measure is explained. Here diverging stacked bar chart is used instead of traditional vertical bar diagram. Diverging stacked bar chart is more appropriate for data captured on a Likert scale. In analysing the diagram, the percentages of responses who were favouring the statement are displayed on the right side and of those who opposed are indicated on the left of the zero line. There are enough respondents who does not agree or disagree, and because of this they are at the centre. The neutral group is ignored when the range possess an even number of options. In such event, what is mainly considered is the total percentage either to the right or even to the left of the zero line.

A diverging stacked bar chart pertaining to the customer retention factor is shown in Figure 2. It is observed that 55.2% of the respondents agreeing and 16.2% of them strongly agreeing that next time they will buy again from their current retail shop. Cumulatively, about 71% of respondents are in strong favour of the previous statement. On the other hand, 26.2% of the respondents remained neutral, while a mere 2.3% of them disagreed and none of them strongly disagreed that they would continue to be loyal to their favourite retail shops. Further, when asked on whether in the foreseeable future they will consider their current retail as part of their consideration set, it is observed that 51.8% of the respondents agreed and 13.7% of them strongly agreed. Cumulatively, about 65% of respondents seem to have strong acceptance that in future they will consider the current retail. Likewise, regarding to the third item under customer retention dimension, it is again observed from Figure 2 that 58.0% of the respondents agreeing and 11.5% of them strongly agreeing to the statement that they still continue to have purchasing relationship with retail shop. Cumulatively, about 69% of respondents are showing their strong acceptance that they would continue to better the purchase relationship with retail shops.

Diverging stacked bar chart pertaining to the overall customer loyalty is shown in Figure 3. It is observed that 45.0% of the respondents agreeing and 11.2% of them strongly agreeing that the retail shops, the customers have selected for purchase of products, have personal meaning to them. Cumulatively, about 56% of respondents are of strong opinion on the statement. On the other hand, 35.5% of the respondents remained neutral, while a mere 6.2% of them disagree and another 2.2% of respondents strongly disagree that the retail shops, that they have selected for purchase of products, have personal meaning to them, as part of customer loyalty. Cumulatively, about 53% of respondents are of strong opinion on the statement that ‘In the future, I would like to patronize the retail shop I have chosen’. Cumulatively, about 64% of respondents are of strong opinion on the statement that ‘I will recommend the retail shop I have chosen to persons I know’.

The diverging stacked bar chart pertaining to the overall customer satisfaction is shown in Figure 4. It is observed that 61.5% of the respondents agree and 15.0% of them strongly agree that the choice of purchase from their favourite retail shop is a wise decision. Cumulatively, about 76% of respondents are of the strong opinion to the statement definitely influencing the satisfaction level. On the other hand, 21.7% of the respondents remained neutral, while a mere 1.8% of them disagree and none of them strongly disagreeing that the choice of purchase from their favourite retail shop is a wise decision in terms of overall satisfaction. Further, it is observed that 58.3% of the respondents agreeing that they think they did the right thing by buying from this retail shop, and 20.9% of the respondents strongly agree to this statement. Cumulatively, about 79% of respondents are of strong opinion on the statement. Finally, it is again observed from Figure 4 that 57.8% of the respondents agreeing and 15.5% of them strongly agreeing that they are satisfied with their decision of purchasing commodities from retail shops. Cumulatively, about 73% of the respondents are of the strong opinion on statement that they are satisfied with their decision of purchasing products from the retail shops as part of customer satisfaction.

Figure 5 shows a diverging stacked bar chart pertaining to switching cost sub dimension of customer retention. It is observed that 46.2% of the respondents agreeing and 13.0% of them strongly agreeing that there would higher cost in terms of time and money in switching to another retail shop. Cumulatively, about 59% of respondents are of strong view of the above statement. On the other hand, more than one-third (33.7%) of the respondents were not in a position to conclude concretely, while a mere 5.5% of them disagree and another 1.7% strongly disagree that there would be higher cost in terms of time and money in switching to another retail shop.

Further, it is observed that 47.0% of the respondents agreeing and 13.5% of them strongly agreeing that in general, there would be very inconvenience to switch from one retail shop to another as they would have accustomed to the present mall for better satisfaction. On the other hand, 31.7% of the respondents remained neutral, while a mere 6.8% of them disagree and another 1.0% strongly disagree that there would be any inconvenience factor involved in case of switching from one retail shop to another and would have no adverse influence on satisfaction. Likewise, regarding to the third item under switching cost dimension, it is again observed from Figure 5 that 47.0% of the respondents agreeing and 13.5% of them strongly agreeing that it would take longer time and effort to switch to another retail shop. Cumulatively, about 60% of respondents are of strong view that it would indeed be difficult to switch in a shorter time period. On the other hand, 31.7% of the respondents not in a position to conclude concretely, while a mere 7.3% of them disagree and another 1.2% strongly disagree to the above statement on customer satisfaction.

Structural Model Hypotheses Testing

The approach followed in data analysis while using structural equation modelling is a confirmatory one rather going for an exploratory view. The standardized regression weights (the beta coefficient) which is a part of path regression analysis outcomes are depicted in Table 4. The single headed arrows reveal the casual relationships existing in the model, and the tail variable indicated by the arrow appears as the cause of the variable at that point. Accordingly, the standard loadings (also referred as regression coefficients) are given along with the Z-statistics and p-values in Table 4. The results of the regression equation are depicted in Figure 6 for better understanding of those hypotheses whether having statistical significance (p < .05).

The Z-value and p-value of the path from customer satisfaction to customer retention (Table 4) is 4.179 and 0.000, respectively. As the p-value (.000) is less than significant alpha value of 0.05, H₁ is accepted. Furthermore, the regression coefficient (β₁₁) of direct path between customer satisfaction and customer retention is 0.345 (Table 4). Customer satisfaction has a direct influence on customer retention. The results accept customer satisfaction has a positive relationship with customer retention.

Regression path coefficient of customer satisfaction and customer loyalty is not statistically significant (with a Z- value of 0.644 and p-value of .522 (Table 4) greater than alpha significance level of 0.05). The direct path regression coefficient (β₈) between customer satisfaction and customer loyalty is 0.098. Therefore, H₂ is rejected. Thus, the customer satisfaction is not directly leading to loyalty, which is a surprising result. Hence, the researcher explored further for indirect effects as well.

The Z-value and the p-value of the path from customer loyalty to customer retention (Table 4) is 4.560 and 0.000, respectively. As the p-value (.000) is less than significant alpha value of 0.05, H₃ is accepted, that customer loyalty has a positive relationship with customer retention, based on the results.

The p-value of the path from switching cost to customer retention (Table 4) is 0.000. As the p-value (.000) is less than significant alpha value of 0.05, H₄ is accepted. Furthermore, the regression co-efficient of the path switching cost to customer retention is positive and significant. It could be concluded that switching cost has a positive relationship with customer retention. This clearly indicates that any impediment to a customer’s changing of retails would have a direct influence (significantly) on customer retention.

Table 4.

Standardized Regression Weights for Customer Retention

	Standardized Coefficient	Standard Error	Z-Statistic	P-value
CSAT->CLOYAL	0.098 (β₈)	0.182	0.644	.522
CSAT->CRETN	0.345 (β₁₁)	0.081	4.179	.000*
SWCST->CRETN	0.476 (β₁₂)	0.044	8.128	.000*
CLOYAL->CRETN	0.385 (β₁₃)	0.070	4.560	.000*

Source: The author.

Notes: * significant at 5% level. CLOYAL = customer loyalty; CRETN = customer retention; CSAT = customer satisfaction; SWCST = switching cost.

From Table 5 independent t-test result, it is observed that there exist no distinction in mean values of male and female respondents for customer retention (t = −0.377; p = .707; p > .05) dimension which confirms that the H₅, there is a significant difference in customer retention across gender, is rejected. In other words, there is no statistical evidence to conclude that male customers are giving more importance to customer retention dimension in comparison to the female customers.

From Table 5 independent t-test result, it is observed that there is a significant difference in the mean scores of employed and unemployed respondents for customer retention (t = −3.991; p = .000; p < .05) dimension, and therefore the hypothesis H₆, there is significant difference in customer retention across occupation, is accepted. The mean score of employed (mean = 11.58) is slightly higher than unemployed (mean = 11.12) respondents. Therefore, it could be concluded that there is statistical evidence that employed respondents have higher customer retention scores compared to the unemployed.

Table 5.

Independent T-Test Result With Mean Scores Influence on Each Dimension

Dimension			Mean	SD	T-value	P-value
Customer Retention	Male	284	11.34	1.81	−0.377	.707
	Female	316	11.39	1.71
Customer Retention	Unemployed	274	11.12	1.84	−3.224	.001
	Employed	326	11.58	1.64

Source: The author.

Notes: * significant at 5% level. SD = standard deviation.

It is to be observed through one-way ANOVA output (Table 6), that there exists a variation in entire mean values of customer retention (F_(3,596) = 1.547; p = .148; p > .05) dimension among different levels of education. On account of these reasons the hypothesis H₇, there is significant difference in customer retention across education qualification, is rejected. Alternatively, mean values of customer retention do not vary extensively across different levels of educational status.

Table 6.

One-Way ANOVA Result for Customer Retention Dimension Across Levels of Educational Qualification

Dimension		Sum of Squares	df	Mean Square	F	Sig.
Customer retention	Between groups	24.253	3	8.084	1.547	0.148
	Within groups	1819.081	596	3.052
	Total	1843.333	599

Source: The author.

Note: * significant at 5% level.

It is noticeable through one-way ANOVA output (Table 7), that there exists no variation in overall mean value of customer retention ( F_(4,595) = 1.784; p = .130; p > .05) dimension among different levels of income group. On account of these reasons the hypothesis H₈, there is significant difference in customer retention across annual family income, is rejected. In other words, mean values of customer retention do not vary across different levels of income.

Table 7.

One-Way ANOVA Result for Customer Retention Dimension Across Levels of Income Group

Dimension		Sum of Squares	df	Mean Square	F	Sig.
Customer retention	Between groups	21.843	4	5.461	1.784	0.130
	Within groups	1821.490	595	3.061
	Total	1843.333	599

Source: The author.

Note: * significant at 5% level.

Discussions

The discussion is carried out for the important objectives related to the study, and the major concepts are discussed subsequently. Customer retention is of great relevance in modern times as it is one of the biggest contributors of profitability of retail industry. The result is apparent from the research data that customer satisfaction, loyalty and switching costs have a significant relation with customer retention. Forward looking organizations consider the retention of the customer as a very vital factor in their management and marketing decision making (Lariviere & Vandenpoel, 2005). Nowadays, success of any business concern depends heavily upon retention of the customer. Retention can be explained by the repeat purchase nature of the customer. Here as a result of the cordial relationship, the customer is even thinking on incremental basis to deal with the retail shop and subscribe to it for long. Customers strongly agree that next time they will buy again from their current retail shop. Perkins-Munn et al. (2005) found an exceptional relationship between repeat purchase intentions concluding to repurchase, as a result customer retention.

The influence of customer loyalty on customer retention has been empirically concluded from the research data. A loyal customer is not only a supporter of the retail but also has positive contributions concluding to retention. Bolton et al. (2000) said that customer loyalty has a powerful consequence in customer retention. Customer loyalty is undoubtedly a remarkable achievement for a retail shop and coupled with customer retention it becomes more productive. A noteworthy affiliation was present which was connected to loyalties consequence on customer retention (Trasorras et al., 2009). In this connection, a commonly established academic notion is that customer feedback from an existing customer is one of the vital metrics for retailers in obtaining customer retention (Blokdyk, 2017; De Haan et al., 2015).

According to results from the data analysis, customer satisfaction has a direct effect on customer retention. It is not essential that the customer needs to be loyal to achieve retention. If the customer is repeatedly purchasing from the retailer but not spreading word-of-mouth to the known people about the retailer then the customer is not loyal but there is customer retention. Satisfaction makes the customer contented to purchase from a preferred retail which will motivate for repeat purchase and thereby getting in to retention plans. Customer satisfaction has every possibility to persuade the purchasing conduct and a result of this is in the process customer retention instilled in the customer (Anderson & Srinivasan, 2003). Customer satisfaction influences customer retention (Cronin et al., 2000; Gil et al., 2006; Parasuraman et al., 1988). According to the findings of the research study, customer satisfaction alone is not a strong indicator of customer loyalty. Sivadas and Baker-Prewitt (2000) were of the opinion that ‘Satisfaction influences the likelihood of recommending a departmental store as well as repurchase but has no direct impact on loyalty’.

These days most of the retailers in India are practicing different marketing techniques to retain the customers. Membership cards in retails provide discounts and bonus points which operate like switching costs because the customer has to forego these benefits if they shift to other retail shop. From the study it could be concluded that switching cost positively influences the customer retention. Switching costs acts not only as a pointer but also as a supplementing factor for customer retention (Chen & Hitt, 2002; Kim et al., 2004). It would take longer time and effort to switch to another retail. Switching cost has a relation with customer retention both directly and indirectly (Lee et al., 2001; Ranaweera & Prabhu, 2003). From the results, it is obvious that gender difference has no influence on customer retention. The results show that different levels of annual family income have no significant impact on the dimensions of customer retention. The results show that customer retention dimensions vary based on occupation influence. Employed respondents have a greater tendency of agreeing that customer retention dimensions are influencing factors as compared to unemployed counterpart. The result also shows that educational levels of the respondents have no influence on customer retention. Both educated and uneducated respondents have the same view on the dimensions of customer retention.

Conclusions, Limitations and Future Research

In the modern era, the customer is dynamic and so are their preferences which persuade them to have a good shopping experience more than the price. In the competitive world just satisfying a customer is not enough; the retailer cannot expect that the customer will come again. Here the importance of customer loyalty and retention comes in to application. Competition prevailing in the retail sector has to seriously address the need for retaining customers. The findings of the study seek to provide the retails a clear understanding of the expectations of retail customers in India. The research study found that switching cost has the strongest effect on customer retention followed by customer loyalty and satisfaction. Retails should be prepared to help the customers beyond their expectations.

The success of any organization is mainly dependent on the retention of the customers. Buttle (2004) proved that the customer retention was an indicator of growth, prosperity and profitability. These days acquisition of new customer is very expensive compared to the expense involved in retention of the existing customer. The research results help the management of retails to decide where they can control expenses and where they should not compromise quality of the product and service. To put in perspective, customer retention involves a long-lasting customer commitment directed to a brand and sustaining such relationship because of positive perceptions and previous experiences (Boohene et al., 2013; Mohamed & Borhan, 2014). Not to forget that recent research has also explained that in today’s turbulent and dynamic business environment, customer retention poses a significant challenge for lot of organizations and due to this the future steps calls for ‘enhanced customer retention management’ (Ascarza, 2018).

The language English would have caused a communication barrier at least to a few who would have given better feedback in their regional languages. The research was constrained to the targeted customers only. Information would have come from the retailer about their task of building up suitable loyalty programmes. There are multiple areas in retailing which have unlimited scope for further research. A qualitative analysis has good scope in the research in order to get retail customers feedback as result of an in-depth analysis. This study is expected to provide in-depth recommendations about what they expect from the side of the customers based on multiple parameters. The research could take into consideration the retention strategies for different product categories and even for multiple store formats.

Footnotes

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author received no financial support for the research, authorship and/or publication of this article.

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