Corporate Strategy: Key to Elevated Customer Satisfaction

Problem Faced By The Organization

According to the below graph sales percentage of Lidl has been continuously decreasing according to the below statistics in past 2 years. From January 2018 sales of Lidl is decreasing constantly (Swaffield, Evans and Welch, 2018). There are various reasons because of which Lidl is facing this issue. One of the main reasons because of which this issue is being faced by Lidl is decreasing customer Satisfaction because of poor customer service. Many customers of Lidl are focusing on switching to other brand even through quality of products of Lidl is high and they offer it in a quite low price but still customer satisfaction level of Lidl is getting affected and main reason of this issue is poor customer service which is provided to their customers.

Figure 1 Change of Sales percentage at Lidl in UK

(Source: Sales percentage of Lidl in UK. 2019)

Aim of the study

Main aim of this study is “To explore the perspectives of implementing the strategies that will enhance the customer satisfaction and improve upon the overall sales of Lidl”.

Research Objectives

Main research objectives of this study are:

• To identify how the customer satisfaction can be enhanced in Lidl.
• To investigate the impact of customer satisfaction on sales.
• To recommend areas for future research.

Research Question

Main research question of this study is: “How can Lidl enhance it customer satisfaction?”

LITERATURE REVIEW

Enhance customer satisfaction

According to the view of Iglesias, Markovic and Rialp, (2019) Customer satisfaction is one of the most important factors which is required by the organizations to focus on as it can impact their business in many different ways. There are various factors because of which customer satisfaction of an organization is affected but some of the main factors that can impact satisfaction level of customers majorly are poor quality of products, high process of the products, poor service which is provided to customers and many more. Organizations should make it their first and foremost priority to enhance their customers satisfaction level. As the customer enters their store or super market they should focus on ways to build or strengthen their relationship between them and their customers. It can help organizations especially retail sector organization not only to enhance their sales but can also help them to gain competitive advantage and increase their market share in the current market in which they operate. Iglesias, Markovic and Rialp, (2019) further elaborates that customer satisfaction can only be enhanced by providing them with excellent products within their desired price range, providing them with excellent service and fulfilling needs, requirements and desires of their customers. There are many more ways to improve customer satisfaction Level such as: focusing on building customer centric culture within an organization where customers are given first priority and most of the decisions taken by the organizations are based on customer’s choice. Another way to improve satisfaction level of customers is by providing them with personalized customer service by focusing on improving customer engagement strategy. This can help retail sector organizations to improve their operational strategy as well.

As per the view of Otto, Szymanski and Varadarajan, (2019) In order to improve satisfaction level of customers it is important for organizations like Lidl to understand customer satisfaction in a detailed manner because Lidl is a retail sector global super market whose annual sales and profit indirectly depends upon customer satisfaction level. Otto, Szymanski and Varadarajan, (2019) further explains that there are many theories that can help an organization to understand customer behaviour and ways in which their behaviour affects their shopping experience and satisfaction level. One of the most commonly used theory that can be used to explain customer satisfaction is expectancy-value concept. This, expectancy-value theory helps in explain an individual’s attitude towards various objects and actions. As per this theory, expectancy represents idea that most of the individuals do not choose to do a task where they fail or when they fail. Value represents different reasons or beliefs that an individual has to get engaged in a task. So according to this theory customers might only buy a product if they find it fruitful and beneficial and if they think that buying or using this product will have positive outcome on them. Their beliefs regarding the product or services helps them to get engaged with the product. this theory helps in explaining customers attitude towards buying a product and reasons that can affect their decision making for buying a product.

Impact of customer satisfaction on sales

As per the view of Garton, Murphy and O’Halloran, (2016) satisfaction of customers directly/indirectly affects the overall sales of a product or service. Lower customer satisfaction level can affect overall sales of products or services of an organization which can impact their sales and product of an organization. Organizations are required to bring changes within their strategies that they use for sales of their products or services that they provide to their customers as it can affect overall sales of that organization. Other than these strategies especially retail sector organizations should focus on building strategies that can help them to retain their customers by enhancing their satisfaction level. This is majorly because higher satisfaction level means higher customer retaining rate and higher sales of products. Garton, Murphy and O’Halloran, (2016) further elaborates that retaining customers can also help the organization to lower their cost of new customers. Many times, due to continuously decreasing customer satisfaction organizations have to suffer loss or decrease in overall sales or profit. Debenhams sales hit by changing the shopping habit of customer in marketplace. As per theory, it has found that supply chain increasing and excluding sales at store open for less than years. In this way, it also responding to the shopping habit with customer, those who are spending more of disposable.

As elaborated by Wang, Lu and Tan, (2018) explained that, there are many ways through which organizations can increase their customer satisfaction level in order to decrease its negative impact on sales of an organization. One of the mast basic way through which satisfaction level of an organization’s customers can be increased or enhanced is by enhancing their customer experience level. Lidl is facing lower customer satisfaction level which is affecting their overall sales and one of the major reasons because of which they are facing this issue is because of poor customer service. Wang, Lu and Tan, (2018) further explains that Lidl offers excellent product quality, offers products in lowest possible price but the only factor which they failed to fulfil or provide it to customers is proper and excellent service. Only because of poor customer service, Lidl’s customer satisfaction level was decreasing which was directly/indirectly affecting their overall sales.

Summary

From the above literature review it is clear that customer satisfaction is one of the most important factors which is required to be focused on especially for retail sector organizations as for these organizations satisfaction level of customers can affect their sales and overall profit in many ways. It has also been identified that there are many factors that can affect expectation level of their customers. By fulfilling desires, requirements, expectations of customers organizations can enhance their customers satisfaction and expectation level. This chosen topic will help the retail sector organizations to understand the ways in which customer satisfaction impact overall sales of an organization. Main gap in this literature review is that this does not helps in addressing ways in which gaps in service quality can be fulfilled and customers expectation level can be reached.

METHODOLOGY

Research technique

There are two main types of research techniques: qualitative and quantitative. In qualitative technique non-numerical data is used where as in quantitative technique numerical data is used for analysis. Qualitative research method is a term that uses for existing theoretical approach for further case study expansion. It is the most suitable method for collecting and gathering large number of data related case study.

On the other hand, quantitative method is based on the mathematical concepts that help for identifying the accurate result or outcome.

For this research quantitative technique has been used as it is quite simpler and easy to define, use and identify.  It has justified that quantitative method provide better finding result and is useful for analysing research problem effectively.

Data collection

In this study, both primary and secondary research has been done. For secondary research, data will be collected from books, journals, online portals, articles, case studies related to problem, and the

organization’s annual report (Haar, Staniland and McGhee, 2018). Most of the secondary research data is available freely on various kinds of online and offline sources. Secondary data has helped the researcher to build a base for this research study and has also helped in enhancing knowledge about the current study in a much-detailed manner. Primary research data has been collected by the researcher from customers of Lidl. All the primary data has been collected with the help of a survey that consisted of closed-ended questions. These closed-ended survey questions helped the researcher collect all quantitative and numerical values for statistical analysis.

Data analysis

There are two types of data analysis: thematic and statistical data analysis. Thematic data analysis is done in qualitative research studies, whereas statistical data analysis is done in quantitative research study (Kebritchi, McCaslin and Rominger, 2019). As it is a quantitative research study in which all the data has been collected with the help of observations, in-depth exploration of things and ways in which things happened. All the primary data collected has been analysed with the help of statistical method using various kinds of mathematical methods as well.

For this quantitative data analysis, the SPSS tool has been used. SPSS is one of the most commonly used tool that helps researchers to analyse complex statistical data as well.

Sampling

For this research study, the probability sampling technique has been used because in probability sampling all the members of the population has a known chance of participating in the study (Elman, Gerring and Mahoney, 2016). In probability sampling, all the respondents has been chosen or selected randomly with the help of simple random sampling method. sample chosen for this study is customers of Lidl organizations and sample size of this study is 30 customers. Chosen respondents of the study has been asked to participate in a survey that consists of closed-ended questions. It has justified that simple random sampling method is reliable in terms of cost and time. in this way, researcher has used in the members selections.

Research Approach

There are two main types of research approaches: inductive and deductive research approach (Gray, Grove and Sutherland, 2016). deductive research approach uses hypothesis in order to confirm or reject research process, whereas in inductive approach, objectives and research questions are formed that are required to be achieved during the research process. For this research study, an inductive approach has been used, as in this generation, new theory has been done based on data collected.

DATA ANALYSIS 

Statistics
Â		youbuyourproductevenifpricegetshigher	recommendproducttofriendsfamilyorothers
N	Valid	20	20
	Missing	0	0
Mean		1.6000	1.6000
Median		2.0000	2.0000
Mode		2.00a	2.00
Std. Deviation		.50262	.50262
Variance		.253	.253

 

Multiple modes exist. The smallest value is shown

Frequency Table

satisfiedwiththeproductofcompany
Â		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	1.00	10	50.0	50.0	50.0
	2.00	10	50.0	50.0	100.0
	Total	20	100.0	100.0	Â

 

welldidourproductmetyourneeds
Â		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	1.00	3	15.0	15.0	15.0
	2.00	4	20.0	20.0	35.0
	3.00	6	30.0	30.0	65.0
	4.00	7	35.0	35.0	100.0
	Total	20	100.0	100.0	Â

 

wouldyouratethevalueformoneyofproduct
Â		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	1.00	4	20.0	20.0	20.0
	2.00	9	45.0	45.0	65.0
	3.00	7	35.0	35.0	100.0
	Total	20	100.0	100.0	Â

 

ourproductqualityisbetterthancompetitors
Â		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	1.00	10	50.0	50.0	50.0
	2.00	10	50.0	50.0	100.0
	Total	20	100.0	100.0	Â

 

featureyoulikedmostlyinourproduct
Â		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	1.00	4	20.0	20.0	20.0
	2.00	8	40.0	40.0	60.0
	3.00	8	40.0	40.0	100.0
	Total	20	100.0	100.0	Â

 

youbuyourproductevenifpricegetshigher
Â		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	1.00	8	40.0	40.0	40.0
	2.00	12	60.0	60.0	100.0
	Total	20	100.0	100.0	Â

 

recommendproducttofriendsfamilyorothers
Â		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	1.00	8	40.0	40.0	40.0
	2.00	12	60.0	60.0	100.0
	Total	20	100.0	100.0	Â

Regression

Descriptive Statistics
Â	Mean	Std. Deviation	N
satisfiedwiththeproductofcompany	1.5000	.51299	20
welldidourproductmetyourneeds	2.8500	1.08942	20

 

Correlations
Â		satisfiedwiththeproductofcompany	welldidourproductmetyourneeds
Pearson Correlation	satisfiedwiththeproductofcompany	1.000	-.330
	welldidourproductmetyourneeds	-.330	1.000
Sig. (1-tailed)	satisfiedwiththeproductofcompany	.	.078
	welldidourproductmetyourneeds	.078	.
N	satisfiedwiththeproductofcompany	20	20
	welldidourproductmetyourneeds	20	20

 

Model Summaryb
Model	R	R Square	Adjusted R Square	Standard . Error of the Estimate	Change Statistics
					R Square Change	F Change	df1
1	.330a	.109	.059	.49759	.109	2.194	1

 

Model Summaryb
Model	Change Statistics		Durbin-Watson
	df2	Sig. F Change
1	18a	.156	1.930

 

a. Predictors: constant, well-did our product meet your needs

b. Dependent Variable: satisfied with the product of company

 

ANOVAa
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	.543	1	.543	2.194	.156b
	Residual	4.457	18	.248	Â	Â
	Total	5.000	19	Â	Â	Â

 

a. Dependent Variable: satisfiedwiththeproductofcompany

b. Predictors: constant, well-did our product meet your needs

  

Coefficientsa
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	1.942	.319	Â	6.095	.000
	welldidourproductmetyourneeds	-.155	.105	-.330	-1.481	.156

 

a. Dependent Variable: satisfiedwiththeproductofcompany

 

Residuals Statisticsa
Â	Minimum	Maximum	Mean	Std. Deviation	N
Predicted Value	1.3215	1.7871	1.5000	.16909	20
Residual	-.63193	.67849	.00000	.48432	20
Standard. Predicted Value	-1.056	1.698	.000	1.000	20
Std. Residual	-1.270	1.364	.000	.973	20

 

a. Dependent Variable: satisfiedwiththeproductofcompany

Interpretation 

From above table, it is interpreted that the significant value is P= =.159, that is less than P= 0.05. So the null hypothesis is rejected. there is no difference in mean and significant value.

Regression

Descriptive Statistics
Â	Mean	Std. Deviation	N
wouldyouratethevalueformoneyofproduct	2.1500	.74516	20
ourproductqualityisbetterthancompetitors	1.5000	.51299	20

 

Correlations
Â		wouldyouratethevalueformoneyofproduct	ourproductqualityisbetterthancompetitors
Pearson Correlation	wouldyouratethevalueformoneyofproduct	1.000	.069
	ourproductqualityisbetterthancompetitors	.069	1.000
Sig. (1-tailed)	wouldyouratethevalueformoneyofproduct	.	.387
	ourproductqualityisbetterthancompetitors	.387	.
N	wouldyouratethevalueformoneyofproduct	20	20
	ourproductqualityisbetterthancompetitors	20	20

 

Model Summaryb
Model	R	R Square	Adjusted R Square	Standard . Error of the Estimate	Change Statistics
					R Square Change	F Change	df1
1	.069a	.005	-.051	.76376	.005	.086	1

 

Model Summaryb
Model	Change Statistics		Durbin-Watson
	df2	Sig. F Change
1	18a	.773	1.513

 

a. Predictors: (Constant), our product quality is better than competitors.

b. Dependent Variable: wouldyouratethevalueformoneyofproduct

  

ANOVAa
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	.050	1	.050	.086	.773b
	Residual	10.500	18	.583	Â	Â
	Total	10.550	19	Â	Â	Â

 

a. Dependent Variable: wouldyouratethevalueformoneyofproduct

b. Predictors: (Constant), our product quality is better than competitors.

 

Coefficientsa
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	2.000	.540	Â	3.703	.002
	ourproductqualityisbetterthancompetitors	.100	.342	.069	.293	.773

 

a. Dependent Variable: wouldyouratethevalueformoneyofproduct

  

Residuals Statisticsa
Â	Minimum	Maximum	Mean	Std. Deviation	N
Predicted Value	2.1000	2.2000	2.1500	.05130	20
Residual	-1.20000	.90000	.00000	.74339	20
Standard. Predicted Value	-.975	.975	.000	1.000	20
Std. Residual	-1.571	1.178	.000	.973	20

 

a. Dependent Variable: wouldyouratethevalueformoneyofproduct

Interpretation

By interpreting the table, it is stated that the significant value P =.773 is greater than P = 0.05. thus, null hypothesis is accepted. It means that there is difference between mean and significant value.

Regression

Descriptive Statistics
Â	Mean	Std. Deviation	N
recommendproducttofriendsfamilyorothers	1.6000	.50262	20
featureyoulikedmostlyinourproduct	2.2000	.76777	20

  

Correlations
Â		recommendproducttofriendsfamilyorothers	featureyoulikedmostlyinourproduct
Pearson Correlation	recommendproducttofriendsfamilyorothers	1.000	-.055
	featureyoulikedmostlyinourproduct	-.055	1.000
Sig. (1-tailed)	recommendproducttofriendsfamilyorothers	.	.410
	featureyoulikedmostlyinourproduct	.410	.
N	recommendproducttofriendsfamilyorothers	20	20
	featureyoulikedmostlyinourproduct	20	20

  

Model Summaryb
Model	R	R Square	Adjusted R Square	Standard. Error of the Estimate	Change Statistics
					R Square Change	F Change	df1
1	.055a	.003	-.052	.51563	.003	.054	1

 

Model Summaryb
Model	Change Statistics		Durbin-Watson
	df2	Sig. F Change
1	18a	.819	1.678

 

a. Predictors: (Constant), feature you like most in our product

b. Dependent Variable: recommend product to friends, family, or others

 

ANOVAa
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	.014	1	.014	.054	.819b
	Residual	4.786	18	.266	Â	Â
	Total	4.800	19	Â	Â	Â

 

a. Dependent Variable: recommend product to friends, family, or others

b. Predictors: (Constant), feature you like most in our product

 

Coefficientsa
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	1.679	.358	Â	4.688	.000
	featureyoulikedmostlyinourproduct	-.036	.154	-.055	-.232	.819

 

a. Dependent Variable: recommend product to friends, family, or others

 

Residuals Statisticsa
Â	Minimum	Maximum	Mean	Std. Deviation	N
Predicted Value	1.5714	1.6429	1.6000	.02742	20
Residual	-.64286	.42857	.00000	.50188	20
Standard. Predicted Value	-1.042	1.563	.000	1.000	20
Std. Residual	-1.247	.831	.000	.973	20

 

a. Dependent Variable: recommend product to friends, family, or others

Interpretation:

It can be evaluated that significant value is P= =.819, which is more than

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