Impact Of Digitalization Of Retail Operations On Business Organizations

Literature Review

This project aims to analysis the supermarket retail digitization to increase the consumer requirement awareness by using the SPSS data mining tool. This tool is used to provide the effective analysis for super market retail department.  The SPSS tool is the most popular tool to analysis the data sets. The SPSS is stands for statistical package for the social science and it is an application that can aid in data handling. It is used as data collection tool and it is used for analyzing and editing the all sorts of data. Here, we will analysis the retail supermarket data set to reduce the product waste. This analysis is used to increases the consumer requirement awareness. The retail supermarket data set is used to determine the product ordering and stocking activity to ensure the consumer requirement awareness. It is used to automate the operations and it allow to in-depth analysis of the customer data and retail data by using the digital technologies. The retail digital technology is used to provide the more efficient services and offers the customers requirement services. This process is used to increase the customer needs and expectations.  This analysis uses the four main methods to analysis the retail digitalization data sets to increase the consumer requirement awareness such as correlation, linear regression, compare means and factor analysis. The retailing digitalization process is used to provide the competitive advantage. This analysis will be done and discussed in detail. 

According to this paper (Pavlyushchenko, 2018), describes the Retail digitalization of financial services is used to increases the consumer requirement awareness due to growing the customer development and expectations of digital technologies and retail banking has started to applying the data mining model to reduces the product wastes. It is used to provide the effective marketing plan including the business model. This paper applies the deductive research approach and quantitative methods to analyze the customer attitudes to the direct banking. It collects the secondary data from internet sources, published materials and books. In current situation every business and industry is forced to adapt to the new digital society to provide the effective business process and it lose the competitive advantage. It is used to automate the operations and it allow to in-depth analysis of the customer data and retail data by using the digital technologies. This technology is used to provide the more efficient services and offers the customers requirement services. This process is used to increase the customer needs and expectations. It use the new business model, disruptive technologies factors to contributes the new business formation in the retail digitalization. It provides the consumer financial services by uses the core capability of the digital technologies. It utilizes the remote services tools such as mail, telephone retail and online shopping. This paper is used to provide a better understanding of the current digitalization trends in retail super market and it identify the causes of transformation in the retail industry towards the digital technologies. It investigates the existing and potential tendencies in retail industry. It wants to diversify the supermarket product line with direct banking products and it is used to establishing an efficient marketing strategy for retail industry.

Methods

This paper (RINTAMÄKI, 2018) is describes to managing the consumer value in retailing. The retailing is performs the transformation and propelled by changes in the roles of digitalization and customer value. This process is used to provide the competitive advantage. The consumer value is used to gaining the more relevance in explaining the digitalization and consumer behavior and it is redefining the boundaries of customer value and value creation management. It is increasingly the shaping the competitive advantage. This paper is used to conceptualize and model the management of customer value in retailing. It mainly investigate the three main aspects in management of consumer value in retailing like investigating the how digitalization transforms the consumer values in retail, identify the competitive consumer values dimensions in retail and measuring and modeling the key dimensions of consume value in retail. These three aspects are needs to identified and investigate the consumer value in retail.

This analysis uses the four main methods to analysis the retail digitalization data sets to increase the consumer requirement awareness (“SPSS – Statistical Package for the Social Sciences – Quick Overview”, 2018). The methods are listed in below.

• Descriptive Statistics
• Factor Analysis
• Linear Regression
• Compare Means

The SPSS analysis and findings are discussed in below (FIELD, 2018).

Do SPSS analysis by using below steps.

First open SPSS and load the data set. It is shown below (Provost & Fawcett, 2013).

Here, we will do descriptive analysis based on frequencies. It is shown below.

Frequencies

Statistics
	recepit	cost	itemno	fruitveg	dairy	meat
N	Valid	20	14	14	14	14	14
Missing	0	6	6	6	6	6
Mean	10.50	152.0014	30.57	6.71	2.86	5.21
Std. Error of Mean	1.323	34.26085	6.068	1.659	.636	1.570
Median	10.50	117.3400	22.50	5.00	2.00	3.50
Mode	1a	24.52a	17a	3a	1a	2
Std. Deviation	5.916	128.19237	22.705	6.207	2.381	5.873
Variance	35.000	16433.283	515.495	38.527	5.670	34.489
Range	19	486.93	76	22	9	23
Minimum	1	24.52	8	1	1	0
Maximum	20	511.45	84	23	10	23
Sum	210	2128.02	428	94	40	73
a. Multiple modes exist. The smallest value is shown

The graphs are illustrated in below (Borgogna, 2018). 

The correlation analysis is shown below (Witte, 2017).

Correlation between Cost and Fruit veg

Correlations
	cost	Fruit veg
cost	Pearson Correlation	1	.737**
Sig. (2-tailed)		.003
N	14	14
fruitveg	Pearson Correlation	.737**	1
Sig. (2-tailed)	.003
N	14	14
**. Correlation is significant at the 0.01 level (2-tailed).

Correlation between Cost and Dairy

Correlations
	cost	dairy
cost	Pearson Correlation	1	.933**
Sig. (2-tailed)		.000
N	14	14
dairy	Pearson Correlation	.933**	1
Sig. (2-tailed)	.000
N	14	14
**. Correlation is significant at the 0.01 level (2-tailed).

 Correlation between Cost and Meat  

Descriptive Statistics
	Mean	Std. Deviation	N
cost	152.0014	128.19237	14
meat	5.21	5.873	14

Correlations
	cost	meat
cost	Pearson Correlation	1	.889**
Sig. (2-tailed)		.000
N	14	14
meat	Pearson Correlation	.889**	1
Sig. (2-tailed)	.000
N	14	14
**. Correlation is significant at the 0.01 level (2-tailed).

Linear Regression 

The linear regression analysis is shown below (Graham, 2011). 

Variables Entered/Removeda
Model	Variables Entered	Variables Removed	Method
1	meat, fruitveg, dairyb	.	Enter
a. Dependent Variable: cost
b. All requested variables entered.

Variables Entered/Removeda
Model	Variables Entered	Variables Removed	Method
1	meat, fruitveg, dairyb	.	Enter
a. Dependent Variable: cost
b. All requested variables entered.

ANOVAa
Model	Sum of Squares	df	Mean Square	F	Sig.
1	Regression	190889.816	3	63629.939	27.978	.000b
Residual	22742.862	10	2274.286
Total	213632.678	13
a. Dependent Variable: cost
b. Predictors: (Constant), meat, fruitveg, dairy

The compare means analysis is shown below (“IBM SPSS Software | IBM Analytics”, 2018).

Case Processing Summary
	Cases
Included	Excluded	Total
N	Percent	N	Percent	N	Percent
cost  * fruitveg	14	70.0%	6	30.0%	20	100.0%
cost  * dairy	14	70.0%	6	30.0%	20	100.0%
cost  * meat	14	70.0%	6	30.0%	20	100.0%

 Cost vs. Fruitveg

Report
cost
fruitveg	Mean	Std. Deviation	Median
1	122.9300	.	122.9300
2	46.5200	31.11270	46.5200
3	120.1667	45.37388	113.4100
5	118.3333	55.27354	121.2700
6	90.8600	.	90.8600
10	255.6200	117.13931	255.6200
16	83.0000	.	83.0000
23	511.4500	.	511.4500
Total	152.0014	128.19237	117.3400

ANOVA Table
	Sum of Squares	df	Mean Square	F	Sig.
cost * fruitveg	Between Groups	(Combined)	188715.153	7	26959.308	6.492	.018
Linearity	116121.402	1	116121.402	27.961	.002
Deviation from Linearity	72593.751	6	12098.958	2.913	.109
Within Groups	24917.525	6	4152.921
Total	213632.678	13

Measures of Association
	R	R Squared	Eta	Eta Squared
cost * fruitveg	.737	.544	.940	.883

Cost vs. Dairy

Report
cost
dairy	Mean	Std. Deviation	Median
1	69.2325	30.23969	80.7750
2	104.9950	50.04017	94.8950
3	155.9333	28.58393	172.0800
4	113.4100	.	113.4100
5	338.4500	.	338.4500
10	511.4500	.	511.4500
Total	152.0014	128.19237	117.3400

ANOVA Table
	Sum of Squares	df	Mean Square	F	Sig.
cost * dairy	Between Groups	(Combined)	201743.223	5	40348.645	27.149	.000
Linearity	185779.587	1	185779.587	125.005	.000
Deviation from Linearity	15963.636	4	3990.909	2.685	.109
Within Groups	11889.456	8	1486.182
Total	213632.678	13

Measures of Association
	R	R Squared	Eta	Eta Squared
cost * dairy	.933	.870	.972	.944

Cost Vs. Meat

Report
cost
meat	Mean	Std. Deviation	Median
0	98.3000	104.34068	98.3000
2	88.4975	25.89028	84.7050
3	83.0000	.	83.0000
4	168.5400	.	168.5400
5	68.5200	.	68.5200
6	172.7900	.	172.7900
7	117.3400	5.55786	117.3400
10	338.4500	.	338.4500
23	511.4500	.	511.4500
Total	152.0014	128.19237	117.3400

ANOVA Table
	Sum of Squares	df	Mean Square	F	Sig.
cost * meat	Between Groups	(Combined)	200703.892	8	25087.987	9.702	.011
Linearity	169009.856	1	169009.856	65.362	.000
Deviation from Linearity	31694.036	7	4527.719	1.751	.278
Within Groups	12928.786	5	2585.757
Total	213632.678	13

Measures of Association
	R	R Squared	Eta	Eta Squared
cost * meat	.889	.791	.969	.939

The factor analysis is shown below (“Digital Technology and Bricks and Mortar Retail Store”, 2018).

Communalities
	Initial	Extraction
cost	1.000	.924
fruitveg	1.000	.706
dairy	1.000	.905
meat	1.000	.920
Extraction Method: Principal Component Analysis.

Total Variance Explained
Component	Initial Eigenvalues	Extraction Sums of Squared Loadings
Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	3.455	86.381	86.381	3.455	86.381	86.381
2	.382	9.559	95.939
3	.111	2.775	98.715
4	.051	1.285	100.000
Extraction Method: Principal Component Analysis.

Component Matrixa
	Component
1
cost	.961
fruitveg	.840
dairy	.951
meat	.959
Extraction Method: Principal Component Analysis.
a. 1 components extracted.

According to the data analysis on SPSS tool is used to provide the effective data analysis for retail digitalization data set based on super market board. This data set is used to provide the information about the super market product ordering and stocking. In SPSS analysis, we are using the descriptive analysis, factor analysis, linear regression, correlation and compare means (Bhowal & Barua, 2008).

In Descriptive Analysis, we will analysis the receipt no, dairy, meat, fruit veg, cost and item no. These are attributes in the retail super market data set. The descriptive statistics is used to provide the mean, standard deviation, median, mode, mode, and more. These are used to provide the statistics information about the retail super market. The statistics information’s are needs provide the product and stocking information and it used to reduce the product waste to increase the consumer requirement awareness.

Finding and Data analysis

In correlation analysis, we will analysis the correlation between the cost and fruit veg, correlation between cost and dairy and correlation between the cost and meat. In correlation between the cost and fruit veg, it provides the correlation between the cost and fruit veg. The cost has correlation is 1 and -737. The fruit veg has the correlation is -737 and 1. In correlation between the cost and dairy, it provides the correlation between the cost and dairy. The cost has correlation is 1 and -933. The fruit veg has the correlation is -933 and 1. In correlation between meat and cost, it provides the correlation between the meat and cost. The cost has correlation is 1 and -889. The fruit veg has the correlation is -889 and 1 (“How Technology Enhances The Customer Experience”, 2018).

In linear Regression, this analysis is used to provide the sum of squares, mean square for dependant variable and predictors. Here, we will use the dependant variable is cost and predictors is dairy, fruit veg and meat. The histogram graph is illustrated in above.

The compare means analysis is used to provide the effective comparison between the attributes based on dependent variable. Here, we will use the dependent variable is cost and attributes is meat, dairy and fruit veg. The comparing means analysis is used to determine the mean, standard deviation and median based on dependent variable. In Cost and Fruit veg compare means has the total mean is 152.0014, median is 117.3400 and standard deviation is 128.19237. In Cost and Dairy compare means has the total mean is 152.0014, median is 117.3400 and standard deviation is 128.19237. In Cost and Meat compare means has the total mean is 152.0014, median is 117.3400 and standard deviation is 128.19237 (“Digitizing the consumer decision journey“, 2018).

The compare means analysis is used to provide the effective comparison between the attributes based on dependent variable. Here, we will use the dependent variable is cost and attributes is meat, dairy and fruit veg. The comparing means analysis is used to determine the mean, standard deviation and median based on dependent variable. In Cost and Fruit veg compare means has the total mean is 152.0014, median is 117.3400 and standard deviation is 128.19237. In Cost and Dairy compare means has the total mean is 152.0014, median is 117.3400 and standard deviation is 128.19237. In Cost and Meat compare means has the total mean is 152.0014, median is 117.3400 and standard deviation is 128.19237 (“Impact Of Digitization On Consumers and Entrepreneurs | |”, 2018).

So, these analyses are used to provide the detail information about the retail product stocking and ordering activities. It is used to analysis the overall retail digitalization in retail super market and it used to increases the consumer requirement awareness. 

Conclusion 

This project aims to analysis the retail digitalization to increase the consumer requirement awareness by using the SPSS data mining tool. It is used to provide the effective analysis for super market retail department. Due to growing the customer development and expectations of digital technologies and retail banking has started to apply the data mining model to reduce the product wastes. It is used to provide the effective marketing plan including the business model. . It is used to provide the effective marketing plan including the business model. Here, we can analyze the retail supermarket data set to reduce the product waste. This analysis is used to increases the consumer requirement awareness. The retail supermarket data set is used to determine the product ordering and stocking activity to ensure the consumer requirement awareness (Witten, Frank, Hall & Pal, 2017). This analysis uses the four main methods to analysis the retail digitalization data sets to increase the consumer requirement awareness such as correlation, linear regression, compare means and factor analysis. The retailing digitalization process is used to provide the competitive advantage. The consumer value is used to gaining the more relevance in explaining the digitalization and consumer behavior and it is redefining the boundaries of customer value and value creation management. This analysis will be done and discussed in detail.

References

Bhowal, M., & Barua, P. (2008). Statistics. New Delhi: Asian Books.

Borgogna, A. (2018). Connecting with the customer: How airlines must adapt their distribution business model. Retrieved from https://www.strategyand.pwc.com/reports/connecting-with-the-customer

Digital Technology and Bricks and Mortar Retail Store. (2018). Retrieved from https://www.babson.edu/executive-education/thought-leadership/technology/Pages/digital-technology-bricks-and-mortar.aspx

Digitizing the consumer decision journey. (2018). Retrieved from https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/digitizing-the-consumer-decision-journey

FIELD, A. (2018). DISCOVERING STATISTICS USING IBM SPSS. [S.l.]: SAGE PUBLICATIONS.

Graham, A. (2011). Statistics. London: Hodder Education.

How Technology Enhances The Customer Experience. (2018). Retrieved from https://www.digitalistmag.com/customer-experience/2017/11/07/technology-enhances-customer-experience-05488417

IBM SPSS Software | IBM Analytics. (2018). Retrieved from https://www.ibm.com/analytics/data-science/predictive-analytics/spss-statistical-software

Impact Of Digitization On Consumers and Entrepreneurs | |. (2018). Retrieved from https://www.benergdigimark.com/digitization-consumers-entrepreneurs/

Pavlyushchenko, D. (2018). Retrieved from https://www.theseus.fi/bitstream/handle/10024/139929/Pavlyushchenko_Dmitriy.pdf?sequence=1&isAllowed=y

Provost, F., & Fawcett, T. (2013). Data science for business. Sebastopol: O’Reilly.

RINTAMÄKI, T. (2018). Managing Customer Value in Retailing. Retrieved from https://tampub.uta.fi/bitstream/handle/10024/98767/978-952-03-0077-7.pdf?sequence=3

SPSS – Statistical Package for the Social Sciences – Quick Overview. (2018). Retrieved from https://www.spss-tutorials.com/spss-what-is-it/

Witte, R. (2017). Statistics. New York: Wiley.

Witten, I., Frank, E., Hall, M., & Pal, C. (2017). Data mining. Amsterdam: Morgan Kaufmann.