BUSN20019 Professional Project For Retail Supermarket Data Set
Answer:
Introduction
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.
Literature Review
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.
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.
Methods
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
Finding and Data analysis
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).
Descriptive Analysis
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).
Correlation
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 | ||||||
Compare Means Analysis
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 |
Factor Analysis
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. | |
Discussion
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.
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.
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