SQL

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Big Data Analysis Project

Group #18

Aaron Fuhrman

Jessica Demuro

Wilson Tapia

AJW Consultants, Inc.

The Pennsylvania State University

12/9/14

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Big Data Analysis Project

Aaron Fuhrman

Jessica Demuro

Wilson Tapia

AJW Consultants, Inc.

The Pennsylvania State University

12/9/14

Table of Contents

1.)

Introduction………………………………………………………………………………….2

2.) Methods………………………………………………………………………………….5

2.1) Moving Average and Exponential Smoothing………………………………....3

2.2) Regression Analysis…………….………………………………………………4

2.3) K-Means Clustering…………………………….……………………………...4

2.4) Regression……………………………………………………………………. 7

3) Results……………………………………………………………………………………5

Introduction:

This assignment uses predictive analytics methods to analyze the given transaction data

and make meaningful recommendations for the store manager to improve sales. Predictive analytics calculations including moving average, exponential smoothing, linear regression and

kmeans clustering are used and included in this report. There is a wide variety of data included

in the transaction data that varies from week to week.

The first calculation in this analysis is a moving average and exponentia l smoothing

calculation which calculates the prediction error for weeks 10 through 20 of the given data. The

Mean Squared Error, Mean Absolute Deviation, and Tracking Signal are then calculated from the

given data. The next calculation involves a regression analysis of the given transaction data. Lastly, the kmeans clustering algorithm is used to cluster the customers in the database based on

their buying patterns. The data is then analyzed and used to make a final conclusion about the

relationships existing among the customers and transaction data.

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Problem Description:

The customer base and transaction data is to be analyzed in order to develop appropriate strategies to increase sales. To do this, a series of predictive analytic methods will be used. Some

of these methods include moving average and exponential smoothing, regression and kmeans

calculations. After these tests are performed, they will be analyzed in order to make

recommendations to the management.

Methodology:

A variety of predictive analytics methods were used in this project. They are described in

detail below.

2.1 Moving Average and Exponential Smoothing

This part of the database analysis entailed the forecast of the items being sold and also the

error of this forecast compared to the actual value for weeks 1020. More specifically, we had to

calculate the Mean Absolute Deviation (MAD), Mean Square Error (MSE) and the Tracking

Signal (TS). To obtain these numbers we had to first create a forecast of the data by using two

methods. One was a moving average and the other exponential smoothing. For the moving

average we used an 3pt moving average which created a prediction for the following week. To

create this forecast we calculated the average of the previous three weeks and used that number as the prediction for the next. This method was repeated until we received a forecast for weeks 12

through 20. Theoretically, the more points this moving average contains, the more accurate it

becomes. As a subset, we chose cereal and cat food to represent the process. The exponential

smoothing uses an alpha value, the forecast and the actual value for the previous week to predict

the next. One important piece of information to note is that the forecast must be integers. You

will always round up to the next whole integer if a decimal was present. The reason behind this is

because you cannot purchase half or a piece of an item so a full item must be purchased to fill the void. What the alpha value does in the exponential smoothing is give a weight to how much the

previous actual value affects the forecast for the new value. In this case we chose an alpha value

of .2. This alpha value was chosen arbitrarily with the constraint that it had to be between 0 and

1. We used the results with the actual values to create a graph and 2 Tables that used the weeks

and the amount of units bought (Figures A1 thought A6 and Tables 14)

Using the forecasted values and the actual values, we were able to obtain the MAD, MSE,

and TS. The MAD measures the parameters of the data. In other words by how much does the

forecast deviate from the actual values without regard to sign. The Mean Square Error measures the difference between the forecast and the actual values. Last we have the Tracking Signal

which measures the bias in the forecast. The MAD was calculated by taking the differences

between the actual and forecast and summing all this differences without regard to sign, in other

words, taking the absolute value of each difference before adding it to the rest.. Lastly, the

differences were divided by the number of inputs ,or averaged, and this gave you your MAD. The

MSE was calculated in a similar manner, except instead of taking the absolute value of these

numbers, they were squared and then again averaged. For the last item, the TS was calculated but

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summing all of the differences between the actual values and forecasted values and dividing this sum by the MAD of that respective tem. When translated to excel, the main functions used were

ABS, ROUNDUP, sqr, and other basic mathematical computations.

2.2 Regression Analysis

Using all 104 weeks of the transaction data for the top 10 items, pivot tables were created

in excel. Once the top 10 items where obtained, a regression analysis was done in minitab which summarized the ANOVA table, correlation coefficient, Rsq, Fvalues, fitted models and the

regression equation. A table was created that compared the different item types and the R square

value. For question #3 a pivot table was used. However we use only 60 weeks of the data and the

top three items sold. Regression was done in Minitab to obtain the regression equation. In

question #4 we used this regression equation and plugged the range of weeks (6180) into the

equation. Different values were then obtained.

2.3 kMeans Clustering

The kmeans clustering algorithm was used to cluster the customers based on their buying

patterns. The information obtained was used to identify any correlation between the customer’s

buying patterns and demographic information. Three different sets of two items were chosen and

clustered with k=3. First, the items of eggs and ice cream (items 12 and 13) were chosen. A pivot

table was then made in Excel to display each customer ID with the units of eggs and ice cream purchased. The data was then pasted into the sample kmeans Excel spreadsheet provided, which

then calculated the centroids based on the designated amount of clusters. In the first calculation

containing eggs and ice cream, the items were also clustered with k=4,5, and 6 as well.

2.4 Regression

Figure 2.1: Top 10 selling items over two years (104 weeks)

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This screenshot was created in microsoft excel with a pivot table by using the “ item

type” as columns the “weeks” as rows and the sum of units bought as the value, it shows

the top 10 items that where sold the most in the 104 weeks

Table 2.1: Shows the ranking of the top 10 item type sold with the total of their unit

bought in the 2 years.

Item Type Ranking Total Volume

17 1 7776

6

12 2 6879

3 3 6254

8 4 5165

5 5 4961

2 6 4006

4 7 3930

13 8 3538

9 9 3382

11 10 2365

The graphs below represent the time series plot of the top 10 items sold and their units

bought in the 104 weeks.

Graph 2.1: Time series plot of the Weeks vs Number of units bought on item 17

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The regression analysis was performed using minitab . Minitab summarized for us the analysis of variances, ANOVA table, correlation coefficient, Rsq, Fvalues, fitted models and the regression

equation

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The Simple Linear Regression Analysis of each one of the items against the week are shown

below. This was done by carrying out a detailed Analysis of Variance which shows values like

degrees of freedom, correlation coefficient, Pvalues, Fvalues and the Regression Equation.

They were all done using Minitab.

Regression Analysis: 2 versus Weeks

Analysis of Variance

Source DF Adj SS Adj MS FValue PValue Regression 1 745.1 745.1 2.70 0.104 Weeks 1 745.1 745.1 2.70 0.104 Error 102 28196.8 276.4 Total 103 28942.0

Model Summary S Rsq Rsq(adj) Rsq(pred) 16.6265 2.57% 1.62% 0.00%

Coefficients

Term Coef SE Coef TValue PValue VIF Constant 43.20 3.28 13.15 0.000 Weeks 0.0892 0.0543 1.64 0.104 1.00

Regression Equation

Y = 43.20 0.0892 Weeks

Fits and Diagnostics for Unusual Observations

Obs 2 Fit Resid Std Resid 40 73.00 39.63 33.37 2.02 R 44 0.00 39.28 39.28 2.37 R 45 0.00 39.19 39.19 2.37 R 46 0.00 39.10 39.10 2.36 R 47 0.00 39.01 39.01 2.36 R 49 0.00 38.83 38.83 2.35 R 50 0.00 38.74 38.74 2.34 R 51 0.00 38.65 38.65 2.34 R

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52 0.00 38.56 38.56 2.33 R 101 72.00 34.19 37.81 2.31 R

R Large residual



Source DF Adj SS Adj MS FValue PValue Regression 1 1042 1042 0.71 0.402 Weeks 1 1042 1042 0.71 0.402 Error 102 150216 1473 Total 103 151258

Model Summary

S Rsq Rsq(adj) Rsq(pred) 38.3758 0.69% 0.00% 0.00%

Coefficients


Regression Equation

Y = 65.67 0.105 Weeks


Std Obs 3Fit Resid Resid 2 167.00 65.46 101.54 2.70 R 22 250.00 63.35 186.65 4.91 R 80 167.00 57.23 109.77 2.89 R 96 156.00 55.55 100.45 2.66 R

R Large residual

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Model Summary


Coefficients


Regression Equation Y = 38.84 0.0200 Weeks


Std Obs 4Fit Resid Resid 98 104.00 36.88 67.12 3.32 R

R Large residual



Source DF Adj SS Adj MS FValue PValue Regression 1 2430 2429.9 5.65 0.019 Weeks 1 2430 2429.9 5.65 0.019

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Error 102 43872 430.1 Total 103 46302

Model Summary


Coefficients


Regression Equation

Y = 56.15 0.1610 Weeks


Obs 5 Fit Resid Std Resid 44 0.00 49.07 49.07 2.38 R 45 0.00 48.91 48.91 2.37 R 46 0.00 48.75 48.75 2.36 R 47 0.00 48.59 48.59 2.35 R 49 0.00 48.27 48.27 2.34 R 50 0.00 48.10 48.10 2.33 R 51 0.00 47.94 47.94 2.32 R 52 0.00 47.78 47.78 2.32 R 84 88.00 42.63 45.37 2.21 R 88 87.00 41.99 45.01 2.20 R

R Large residual



Source DF Adj SS Adj MS FValue PValue Regression 1 7345 7345.1 14.97 0.000 Weeks 1 7345 7345.1 14.97 0.000

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Error 102 50042 490.6 Total 103 57387

Model Summary


Coefficients


Regression Equation

Y = 64.36 0.2799 Weeks


Obs 8 Fit Resid Std Resid 44 0.00 52.04 52.04 2.36 R 45 0.00 51.76 51.76 2.35 R 46 0.00 51.48 51.48 2.34 R 47 0.00 51.20 51.20 2.32 R 49 0.00 50.64 50.64 2.30 R 50 0.00 50.36 50.36 2.28 R 51 0.00 50.08 50.08 2.27 R 52 0.00 49.80 49.80 2.26 R

R Large residual



Source DF Adj SS Adj MS FValue PValue Regression 1 2660 2660.0 12.48 0.001 Weeks 1 2660 2660.0 12.48 0.001 Error 102 21742 213.2 Total 103 24402

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Model Summary


Coefficients


Regression Equation

Y = 41.36 0.1685 Weeks


Obs 9 Fit Resid Std Resid 9 69.00 39.85 29.15 2.03 R 27 76.00 36.82 39.18 2.71 R 44 0.00 33.95 33.95 2.34 R 45 0.00 33.78 33.78 2.33 R 46 0.00 33.61 33.61 2.31 R 47 0.00 33.45 33.45 2.30 R 49 0.00 33.11 33.11 2.28 R 50 0.00 32.94 32.94 2.27 R 51 0.00 32.77 32.77 2.26 R 52 0.00 32.60 32.60 2.24 R 88 57.00 26.54 30.46 2.11 R

R Large residual




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Model Summary


Coefficients


Regression Equation

Y = 24.49 0.0334 Weeks


Std Obs 11 Fit Resid Resid 88 102.00 21.56 80.44 5.40 R 101 52.00 21.12 30.88 2.09 R 104 67.00 21.02 45.98 3.11 R

R Large residual




Model Summary


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Coefficients


Regression Equation

Y = 65.58 + 0.011 Weeks


Std Obs 12 Fit Resid Resid 27 154.00 65.87 88.13 2.21 R 38 152.00 65.99 86.01 2.15 R 41 148.00 66.02 81.98 2.05 R 77 147.00 66.41 80.59 2.02 R 81 171.00 66.45 104.55 2.63 R 96 219.00 66.61 152.39 3.85 R

R Large residual




Model Summary


Coefficients

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Regression Equation

Y = 39.27 0.1001 Weeks


Std Obs 13Fit Resid Resid 1 82.00 39.17 42.83 2.46 R 9 83.00 38.37 44.63 2.55 R 101 82.00 29.16 52.84 3.03 R

R Large residual




Model Summary


Coefficients


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Regression Equation Y = 76.24 0.028 Weeks


Obs 17 Fit Resid Std Resid 44 0.00 75.01 75.01 2.37 R 45 0.00 74.98 74.98 2.37 R 46 0.00 74.95 74.95 2.37 R 47 0.00 74.92 74.92 2.37 R 49 0.00 74.87 74.87 2.36 R 50 0.00 74.84 74.84 2.36 R 51 0.00 74.81 74.81 2.36 R 52 0.00 74.78 74.78 2.36 R 104 136.00 73.32 62.68 2.01 R

R Large residual

Table 2.2: Correlation coefficient and Adjusted correlation coefficient of the top 10 item

types sold.

Item Type R^2 Adj_R^2

17 0.07% 0.00%

12 2.83% 1.88%

3 0.01% 0.00%

8 0.45% 0.00%

5 10.90% 10.03%

2 12.80% 11.94%

4 5.25% 4.32%

13 0.09% 0.00%

9 0.69% 0.00%

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11 2.57% 1.62%

The correlation coefficients above are shown in percentage.

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3) Figure 3.1: Top 3 selling items over two years (60 weeks)

In order to perform question #3 team constructed a pivot table in excel from the transactional

data. In the pivot table we used Item Type as a Column, Weeks as Rows, and Sum of units bough

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in the . From this we obtained that the top values where 17,12 & 3 with a grand total of 4487, 3815, and 3526 respectably. We used Minitab to obtain “The Fitted Line Plot” and the

regression analysis for the top three weeks. From this analysis we were able to obtain the desire

equations for question#4. We also perform an OLS regression analysis in excel as well.

Grand Total 3526 3815 4487 11828

Row Labels 3 12 17

Grand

Total

Graph 3.1: Fitted Line Plot the first 60 weeks vs. number of units sold for item 17


The regression equation is

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Y = 85.11 + 0.0423 Weeks

S = 22.5467 RSq = 0.1% RSq(adj) = 0.0%


Source DF SS MS F P

Regression 1 27.0 27.017 0.05 0.819

Error 50 25417.7 508.353

Total 51 25444.7




Y = 76.18 0.1012 Weeks

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S = 33.5337 RSq = 0.3% RSq(adj) = 0.0%


Source DF SS MS F P

Regression 1 154.6 154.61 0.14 0.712

Error 50 56225.4 1124.51

Total 51 56380.1




Y = 81.59 0.4956 Weeks

S = 38.3690 RSq = 4.8% RSq(adj) = 2.9%

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Source DF SS MS F P

Regression 1 3711.2 3711.19 2.52 0.119

Error 50 73608.9 1472.18

Total 51 77320.1

The fitted line charts and the regression analysis was performed for the top 3 items sold in the

first 60 weeks (butter, snacks and eggs). The regression analysis performed in minitab provides

us the ANOVA table, the regression equation and the Rsq and AdjRsq

4)

For question #4 our group predicted the total weekly sales quantity for weeks 61 through

80. For this question we used the solution from question #3 and copy the equation for item 17, 12

and 3. Since we have the equation as you can observe from above we replace the numbers 61 through 80 in “Weeks” to predict the sales for each week. For this question Microsoft Excel was

used.

TABLE 4.1: The forecasted sales from week 61 to 80 for Item 17

Weeks Formula for item 17 Answer

61 Y = 85.11 + 0.0423 Weeks 87.6903

62 Y = 85.11 + 0.0423 Weeks 87.7326

63 Y = 85.11 + 0.0423 Weeks 87.7749

64 Y = 85.11 + 0.0423 Weeks 87.8172

65 Y = 85.11 + 0.0423 Weeks 87.8595

66 Y = 85.11 + 0.0423 Weeks 87.9018

67 Y = 85.11 + 0.0423 Weeks 87.9441

68 Y = 85.11 + 0.0423 Weeks 87.9864

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69 Y = 85.11 + 0.0423 Weeks 88.0287

70 Y = 85.11 + 0.0423 Weeks 88.071

71 Y = 85.11 + 0.0423 Weeks 88.1133

72 Y = 85.11 + 0.0423 Weeks 88.1556

73 Y = 85.11 + 0.0423 Weeks 88.1979

74 Y = 85.11 + 0.0423 Weeks 88.2402

75 Y = 85.11 + 0.0423 Weeks 88.2825

76 Y = 85.11 + 0.0423 Weeks 88.3248

77 Y = 85.11 + 0.0423 Weeks 88.3671

78 Y = 85.11 + 0.0423 Weeks 88.4094

79 Y = 85.11 + 0.0423 Weeks 88.4517

80 Y = 85.11 + 0.0423 Weeks 88.494



61 Y = 76.18 0.1012 Weeks 70.0068

62 Y = 76.18 0.1012 Weeks 69.9056

63 Y = 76.18 0.1012 Weeks 69.8044

64 Y = 76.18 0.1012 Weeks 69.7032

65 Y = 76.18 0.1012 Weeks 69.602

66 Y = 76.18 0.1012 Weeks 69.5008

67 Y = 76.18 0.1012 Weeks 69.3996

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68 Y = 76.18 0.1012 Weeks 69.2984

69 Y = 76.18 0.1012 Weeks 69.1972

70 Y = 76.18 0.1012 Weeks 69.096

71 Y = 76.18 0.1012 Weeks 68.9948

72 Y = 76.18 0.1012 Weeks 68.8936

73 Y = 76.18 0.1012 Weeks 68.7924

74 Y = 76.18 0.1012 Weeks 68.6912

75 Y = 76.18 0.1012 Weeks 68.59

76 Y = 76.18 0.1012 Weeks 68.4888

77 Y = 76.18 0.1012 Weeks 68.3876

78 Y = 76.18 0.1012 Weeks 68.2864

79 Y = 76.18 0.1012 Weeks 68.1852

80 Y = 76.18 0.1012 Weeks 68.084



61 Y = 81.59 0.4956 Weeks 51.3584

62 Y = 81.59 0.4956 Weeks 50.8628

63 Y = 81.59 0.4956 Weeks 50.3672

64 Y = 81.59 0.4956 Weeks 49.8716

65 Y = 81.59 0.4956 Weeks 49.376

66 Y = 81.59 0.4956 Weeks 48.8804

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67 Y = 81.59 0.4956 Weeks 48.3848

68 Y = 81.59 0.4956 Weeks 47.8892

69 Y = 81.59 0.4956 Weeks 47.3936

70 Y = 81.59 0.4956 Weeks 46.898

71 Y = 81.59 0.4956 Weeks 46.4024

72 Y = 81.59 0.4956 Weeks 45.9068

73 Y = 81.59 0.4956 Weeks 45.4112

74 Y = 81.59 0.4956 Weeks 44.9156

75 Y = 81.59 0.4956 Weeks 44.42

76 Y = 81.59 0.4956 Weeks 43.9244

77 Y = 81.59 0.4956 Weeks 43.4288

78 Y = 81.59 0.4956 Weeks 42.9332

79 Y = 81.59 0.4956 Weeks 42.4376

80 Y = 81.59 0.4956 Weeks 41.942

The calculations were done using Microsoft Excel and the regression equations were

performed by question #3 (Minitab).

Discussion from regression:

A Ranking was made from the top 10 items bought in the retail store the total volume of the item

17 which is the most sold item was 7776 units and the lowest item 11 sold 2365 in two years.

A time series was performed for each item type and it was observed that from week 4449

and 5255 there were no units bought in the retail store, this probability means that store was

closed during those weeks or it was in maintenance.

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Rsquared is a statistical measure of how close the data are to the fitted regression line. It

is also known as the coefficient of determination, or the coefficient of multiple determination for

multiple regression is measured in percentage between 0100. From the data observe the item

with a greater R^2 is item 2 which is around %12.80 and item 5 which is %10.9 usually is better

to take a look at the adjusted Rsquared which has less errors and is more exact. However from

this data we can conclude that most of this items have low regression items which is not good for

the data and because of this the fitted values will be off..

The top 3 items bought for week 160 are 17,12 and 3. The quantity of units bought are

4487, 3815 and 3526. The fitted value did not show any trend in the plot. After obtaining the

regression equation we predicted the total weekly sales quantity for weeks 6180 for each item

and we then obtained from the results that the top item sold was 17, then 12 and 3 respectively.

Question 5:

Kmeans clustering was performed for three sets of items. As mentioned above in the

methods section, eggs and ice cream were the first set of items chosen. Using excel, the data was clustered separately into three, four, five and six clusters and the centroid of each cluster was

then found. The resulting scatter plot of eggs and ice cream with k=3 is shown below in figure

5.1 . As shown below, cluster 1 represents the customers that purchase a moderate amount of

these items. Cluster 2 represents the customers that purchase low volumes of eggs and ice cream

and cluster 3 contains the customers that purchase these items in abundance.

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Figure 5.1: K means clustering for Eggs vs. Ice cream for K=3


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Figure 5.2 shows the K means clustering for eggs vs ice cream for k=4. Cluster number 1 represents the customers that bought a moderate amount of eggs and ice cream. Cluster 2

represents a smaller cluster of customers that bought a more moderate amount of ice cream but a

much larger amount of eggs. Cluster 3 represents customers that bought a minimum of each item

and cluster 4 represents the customers that bought a more moderate amount of eggs but a very

high amount of ice cream.

Figure 5.3: K means clustering for Eggs vs. Ice cream for k=5


The same analysis was repeated for the Pizza vs Snacks example as well and the results

are shown below in figure 5.5. Pizza and snacks were chosen together because they are very similar and they are two items that often go together as for parties etc. Cluster 1 as shown in blue

represents the customers that purchased a low volume of pizza and snacks. Cluster 2 as shown in

red represents the customers that purchased a high volume of snacks and pizza, whereas cluster 3

represents the customers that purchased a moderate amount of these items. Pizza is represented

on the x axis and snacks on the y axis.

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Figure 5.5: K Means clustering for Pizza vs. Snacks for k=3

Figure 5.6: K means clustering for BBQ vs Cereal for K=3

BBQ and cereal were chosen as two random items that would not normally go together.

Cluster 1 in blue represents customers who bought a low volume of both items. Cluster 2 in red

represents customers who bought a low volume of BBQ (xaxis) and a higher volume of cereal

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(located on the yaxis). Cluster 3 in green represents customers who bought a moderate amount of both items.

Question 6:

Out of the 494 families in the data and the ones that responded, it was found that only 9

families did not have any TV’s in their homes. This is a mere 2% of the data and is important for

advertisement purposes. If 98% of your customers have a television in their homes, the chance of

catching their attention through a television advertisements would most likely be the greatest.

The average income of the data was equivalent to 6 which represents the $25,000$35,000 range.

This range is lower on the income scale and it is an important thing to acknowledge from the

store owner’s perspective. In order to be successful, it is important to know information about

your customers and the financial background they are coming from. Figure 6.1 below is a bar

graph that shows the distribution of the customers in the database according to their income

number which is found in the data dictionary, as you can see the majority of the customers fall in

between income range 6 and 7.

The countifs function was used on the demographics data in Excel to calculate the number

of households with dogs and cats. It was found that 169 households had one or more dogs and 79

had one or more cats. Those totals together are only equal to about half of the families in the

database which can be important to store owners because maybe money can be saved by cutting

back on animal sections in the store etc. It was also found that 488 of the families had at least one

child, that is merely 99% of the customers. Every household that had a cat or dog also had at least

one child. This is important for many purposes such as advertising methods, sales, and coupon

distribution.

As far as magazine subscriptions, it was found that the highest amount of subscriptions

was 125 for Reader’s Digest and the sma llest was 12 for Cosmopolitan, followed by 13 for

Glamour. Not a lot of information can be obtained from this information besides the fact that

advertisements through magazines would not be the best option for the store because not enough

of the customers have subscriptions.

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Figure 6.1 Distribution of customers by income range

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Contents

1Table of Contents

1.) Introduction……………………………………………………………………………….37

2) Summary………….………………………………………………………...……………...37

3.) Methods…………………………………………………………………………………...38

2.1) Description of Data………………...…………………………………………….38

2.2) Database and Table Schema………………………………….…………………..38

2.3) Queries……………………………………………………….…………………..42

3.) Results……………………………………………………………………………………42

3.1) Description……………………………...……………………………………….4260

4.) Discussion………………………………………………………………………………..61

4.1) Gender………………………………………………………………………….62

4.2) Income………………………………………………………………………….63

4.4) Weeks of the year………………………………………………………………...63

5.) Conclusion……………………………………………………………………………….66

6.) Appendix: Query Results and Graphs……………………………………...……………6671 1 Introduction

By using Microsoft Access the group will be able to provide an essential data analysis of

the store for the transaction and demographics data, in order to make effective recommendations

about areas for additional research and effective marketing and management strategies. With this

new database the retail store will be able to make better decisions for the growth of their

business.

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The group will analyze the data from the transaction and the demographics tables and draw conclusions by pointing out the correlated items to obtain predictions. The trends will be

shown in graphics later on. Finally the group will advise the retail store on some

recommendations for improvement

Microsoft Access will be the method that the group will use to analyze the data. The first

objective is to create the tables in order to do the relationships. The demographics and transaction

data will be broken down into four tables, which are

∙ Demographics

∙ Transaction

∙ ItemType

∙ CouponID

Each table is conformed by a primary key and foreign keys to make relationships within

the tables, there will not be redundancy in any of this tables, our group made sure to put time and

effort in order to apply organized tables to link relationships.

From this relationships queries will be performed to obtain a sense strategy the company

should apply to increase the sales. The idea of creating queries is to obtain a presentable and

organized data in order to run reports for the retail store in an efficient manner.

Twenty queries will be generated in Microsoft Access, after the data is organized the

most influential categories will be exported to Microsoft Excel to highlight the key components

and trends in the data. After obtaining the results it will be easy from the team to obtain

conclusions and recommendations for the retail store.

Given the variety and amount of data, A number of analysis and visualization techniques where done in order to generate conclusions. The goal is to take the wide variety of useless data

and transformed into a meaningful one. By carefully creating queries by visualization techniques

it will be easier to obtain conclusions since they will be able to connect each other

Summary

The objective of this assignment was to analyze the transaction and customer demographic

data given in the project description, in order to draw conclusions by performing queries on the given data and be able to give recommendations and predict future trends. Microsoft Access, and

Microsoft Excel were used in order to visualize and evaluate the data. Using these methods, the

most useful data was considered and these observations were used to formulate strategies to

improve the store business strategy.

In the demographics tables many attributes were examined in great detail such as income,

gender, work hours, and ethnicity. A relationship was made with the transaction data in order to

understand the patterns. It was found that income, work hours , gender and weeks are key

attributes that have a large impact on purchasing items from the store.

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2. Methods

2.1 Description of Data

From this report two sets of data are given which are the transaction and the

demographics data. The transaction data has relevant information, the primary Key in the table is the customer ID, This will allow the data analyst to view the transaction made by the customer

including the week and the day that he made this purchase.

The customer ID listed in the transaction table is located in the demographics table as well. By having this link the retail store can obtain personal information about the specific

customer such as ethnicity, family size, income, education level, among others.

The analyst can use the demographics data to analyze the customer base and profile of the customer which will allow the manager to obtain a better knowledge of the group that is coming

to the retail store and what items are their buying and in what date is the transaction being made.

With this information the analyst can observe behaviors and trends that customers have with their

personal information. From this, a variety of conclusions can be done.

2.2 Access Database and Table Schema

Tables were provided from the data received from our retail stored from this project

concerning the transaction and the demographics data. The team focused on efficiency while

creating these tables by adding the minimum number of tables and attributes. The concise tables

are: Transaction, Demographics, ItemType and CouponID. Each of them has their own primary

key, after doing the relationship among the tables it was easy to create queries and reports. With

this results it is very helpful to analyzing trends and relationships.

1.) Demographics table: This table captures all of the customer demographic data

provided. The primary key is the unique customer ID number. The table also

contains info about the customer’s family, income, education level and

subscriptions to magazines.

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Figure 1: Demographics Table

2.) Transaction table: The transaction table uses the unique transaction ID as the

primary key. It organizes the information contained in each individual customer

transaction. Information contained in this table includes the type and number of

the item being purchased, the day and week of the purchase and coupon usage.

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Figure 2: Transaction Table

3.) Coupon table: The coupon table uses a unique Coupon ID as the primary key. This

table contains information about the coupons available to the customers and their

dollar value.

Figure 3: Coupon Table

4.) Item table: The item table uses the unique item type as the primary key. The data

dictionary includes 24 item types unique to each value.

Figure 4: Item Table

After the creation of the database tables, the relationships amongst them were made and defined.

The referential integrity was enforced amongst them and is shown below in Figure 4. The

primary keys of each table are included in the other tables as foreign keys.

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Figure 4: Database Schema

2.3 Queries

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A total of 20 queries were executed in Microsoft Access in order to analyze the

relationships among the various data categories. A wide range of the data categories were

considered and the most important ones were included in the queries. For example, it was

decided that the education level of customers was not a key area of concern, since the items listed

in the database are generic and widely used by the general public and most likely are not affected

by education level. The results of these queries, along with the SQL code produced are displayed

below.

3.0 Results

1.) This query displays the sum of the units bought for each item in descending order

SQL code:

SELECT ItemType.Description, Sum(Transaction.[Units Bought]) AS [SumOfUnits Bought]

FROM ItemType INNER JOIN [Transaction] ON ItemType.[Item Type] = Transaction.[Item Type]

GROUP BY ItemType.Description

ORDER BY Sum(Transaction.[Units Bought]) DESC;

Description:

The first query shown below is a calculation of the total number of units sold per item.

This table shows that snacks are the most sold item and pills are the least sold item. This

information is important to store management and vital for their success. It is important to have a

large enough inventory of these products to satisfy the customer demand. The placement of these products in the store can be important for advertisement purposes such as leading customers to

buy other products.

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Figure 5

2.) This query displays the total sum of units bought from each day of the week

SQL code:

SELECT Sum(Transaction.[Units Bought]) AS [SumOfUnits Bought], Transaction.Day

FROM [Transaction]

GROUP BY Transaction.Day;

Description:

The second query shown below shows the total number of items that were bought on each

day. The data shows that the most items were purchased on day 5 and 6 which represents Friday

and Saturday. Using the countifs function in excel, it was found that only 2 out of the 495 given

families had both the male and female parent unemployed. This means that about 99% of the

families in the data have at least one family member who is employed, making activities such as shopping more convenient on the weekend. This data indicates the store will be the busiest on

Fridays and therefore should have extra employees for stocking shelves and check out lines in

order to keep the customers satisfied.

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Figure 6

3.) This query counts the number of families of size 4, 5 and 6 with cable TV.

SQL code:

SELECT Count(Demographics.[Cable TV]) AS [CountOfCable TV], Demographics.[Family

Size]

FROM Demographics

GROUP BY Demographics.[Family Size], Demographics.[Cable TV], Demographics.[Family

Size]

HAVING (((Demographics.[Cable TV])=1) AND ((Demographics.[Family Size])>=4));

Figure 7

Description:

This query provides a count of families of size 4, 5 and 6 that have cable TV. The results

show that 16 families of 6 or more people, 24 families of 5 people and 59 families of 4 people

have cable TV. These totals refer strictly to the larger family size indicating that there is most

likely a variety of aged people living in the households. This information could be useful to

television marketers who are looking to attract a specific age range of people within these larger

families.

4.) This query displays the days of the week in descending order based upon when part

time (working less than 35 hours a week) women shop the most SQL code:

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SELECT Transaction.Day, Sum(Transaction.[Units Bought]) AS [SumOfUnits Bought], Demographics.[Female Work Hours]

FROM CouponID INNER JOIN (Demographics INNER JOIN [Transaction] ON

Demographics.[Customer ID] = Transaction.[Custome ID]) ON CouponID.CouponID =

Transaction.CouponID

GROUP BY Transaction.Day, Demographics.[Female Work Hours]

HAVING (((Demographics.[Female Work Hours])=3))


Description:

The fourth query shown below shows the days of the week that the women who work part

time tend to shop on the most. The results show the majority of them tend to do their shopping

towards the end of the week, with the most shopping done on Saturdays. This is similar to the

results shown above in figure 6, which shows the most items being sold on Friday and Saturday. These results relate back to the importance of having extra staff on the weekends in order to keep

the customers satisfied and in and out of the store in a timely fashion.

Figure 8

5.) This query displays the days of the week in descending order based upon when part

time (working less than 35 hours a week) men shop the most

SQL code:

SELECT Transaction.Day, Sum(Transaction.[Units Bought]) AS [SumOfUnits Bought],

Demographics.[Male Work Hours] FROM Demographics INNER JOIN [Transaction] ON Demographics.[Customer ID] =

Transaction.[Custome ID]

GROUP BY Transaction.Day, Demographics.[Male Work Hours]

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HAVING (((Demographics.[Male Work Hours])=3))


Description:

The fifth query shown below shows the days of the week that the men who work part time

tend to shop the most. The male work hours column that is equal to 2 refers back to the data dictionary which shows a value of 2 equaling work hours of less than 35 hours a week. The

results show that the majority of part time working men tend to do their shopping towards the end

of the week on days 5 and 6. In this regard, the data displays the same results as shown above for

part time working women. The analysis also showed the major difference in parttime working

males who shopped compared to women. As shown below in figure X, the most shopping was

done on day 6 by 366 male citizens in comparison with the 1750 female shoppers on day 6. This

information is important to the store owner because it tells them about the shopping crowd they

will be expecting.

Figure 9

6.) This query displays the top 5 customer ID’s of the people who purchased the most

ice cream

SQL code:

SELECT TOP 5 Transaction.[Custome ID], Transaction.[Item Type], Sum(Transaction.[Units Bought]) AS [SumOfUnits Bought]

FROM [Transaction]

GROUP BY Transaction.[Custome ID], Transaction.[Item Type]

HAVING (((Transaction.[Item Type])=13))


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Description:

The sixth query shown below shows the top 5 customers that bought the most of item 13

which is ice cream. It shows the most ice cream bought by a single customer was equal to 90.

This information can be used to analyze specific demographic information about these customers

and to form any relationship among them that could increase sales in the future. This information

can be evaluated for any item and is also important for the distribution of coupons to increase the

chance of the best customer’s returning to the store. The customers will in turn be rewarded for

their top purchases.

Figure 10

7.) This query displays the items in order from the most to least purchased for

customers that make an income of $15,00$20,000.

SQL code:

SELECT Count(ItemType.[Item Type]) AS [CountOfItem Type], ItemType.Description,

Sum(Transaction.[Units Bought]) AS [SumOfUnits Bought], Demographics.Income

FROM ItemType INNER JOIN (Demographics INNER JOIN [Transaction] ON

Demographics.[Customer ID] = Transaction.[Custome ID]) ON ItemType.[Item Type] =

Transaction.[Item Type]

GROUP BY ItemType.Description, Demographics.Income

HAVING (((Demographics.Income)=4))


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Figure 11 below shows the most purchased items by customers within the income range of 1520,000

Description:

The query number #7 shows The most purchased item by customers who make between

$15,000 and $20,000 is eggs, followed by snacks and butter. Furthermore, since the total number

of transactions for each item is fairly close to the total number of each item purchased, these

customers are more likely to only buy one item at a time. Encouraging “buying in bulk” by offering discounts when customers purchase more than one item could increase the number of

items bought per transaction.

8.) This query displays the number of items sold in order from highest to lowest for

customers with an income range of greater than $75,000

SQL Code:









Description:

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Query number 8 shows the most purchased item for customers with an income range greater than $75,000 is cat food. This information may be useful to analyze other important

demographic information about the customers in this income range such as number of pets. The

following top selling items are snacks, cereal and crackers which are common to all of the

income ranges. This shows that these common items are most likely immune to income

discrepancies and are purchased by a majority of the customers.

Figure 12 below shows the most purchased items by customers within the income range of

greater than 75,000

9.) This query displays the number of items sold in order from highest to lowest for

customers with an income range of $55,000$65,000.

SQL Code:



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Description:

As shown in Figure 13 below, for customers that make an income between

$55,00065,000 eggs were the top selling item followed by snacks and butter. These are common, everyday grocery items, the same that were observed to be purchased very frequently

by many other customer demographics, including lower income groups. This suggests that these

items may be immune to income discrepancies and are widely consumed by households within a

wide range of financial situations.

Figure 14

10.) This query displays the day of the week that retired women shop the most

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SQL Code:

SELECT Transaction.Day, Count(Demographics.[Female Work Hours]) AS [CountOfFemale

Work Hours], Demographics.[Female Work Hours]

FROM Demographics INNER JOIN [Transaction] ON Demographics.[Customer ID] =


GROUP BY Transaction.Day, Demographics.[Female Work Hours]

HAVING (((Demographics.[Female Work Hours])=4))

ORDER BY Count(Demographics.[Female Work Hours]) DESC;

Description:

The results below show that the majority of retired women shop on Fridays and Saturdays. This is similar to the shopping patterns found where it shows the most items are sold

at the store on Fridays and Saturdays.

11.) This query displays the days of the week that retired men shop the most

SQL Code:

SELECT Transaction.Day, Count(Demographics.[Male Work Hours]) AS [CountOfMale Work

Hours], Demographics.[Male Work Hours]

FROM Demographics INNER JOIN [Transaction] ON Demographics.[Customer ID] =


GROUP BY Transaction.Day, Demographics.[Male Work Hours]

HAVING (((Demographics.[Male Work Hours])=4))

ORDER BY Count(Demographics.[Male Work Hours]) DESC;

Description:

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The table below shows the days in which retired men go shopping the most in order from highest to lowest. The results show that the majority of retired men go shopping on Fridays

which is the same as the results of retired women.

12.) This query shows how the sales for one particular item, pizza, vary from

week to week. (Appendix 1)

SQL code:

SELECT Count(Transaction.[Item Type]) AS [CountOfItem Type], Transaction.Week,


FROM [Transaction]

GROUP BY Transaction.Week, Transaction.[Item Type], Transaction.[Item Type]

HAVING (((Transaction.[Item Type])=16))

ORDER BY Count(Transaction.[Item Type]) DESC;

Explanation:

This query showed how the sales of one particular item, in this case pizza, vary from

week to week. In this case, the sales don’t fluctuate too much among each other for any of the given weeks. The sales of pizza are pretty steady as shown which means there should be a

constant inventory of it in the store.

13.) This query shows an average of each unit bought for families with children.

An average value of units bought is given for values 18, which the data dictionary indicates

are all families with children. As expected, the numbers increase as the number of children

in the family increases. The results are shown in Appendix I. SQL Code:

SELECT Demographics.Children, Transaction.[Item Type], Avg(Transaction.[Units Bought])

AS [AvgOfUnits Bought]


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Demographics.[Customer ID] = Transaction.[Custome ID]) ON ItemType.[Item Type] = Transaction.[Item Type]

GROUP BY Demographics.Children, Transaction.[Item Type]

HAVING ((Not (Demographics.Children)=0));

14. This query shows the number of coupons and of what origin, that each ethnicity

used

SQL Code:

SELECT Demographics.Ethnicity, Count(CouponID.CouponOrigin) AS CountOfCouponOrigin,

CouponID.CouponOriginFROM ItemType INNER JOIN (Demographics INNER JOIN

(CouponID INNER JOIN [Transaction] ON CouponID.CouponID = Transaction.CouponID) ON


Transaction.[Item Type]GROUP BY Demographics.Ethnicity, CouponID.CouponOrigin;

Description:

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This query shows the number of coupons that each ethnicity used. As the data dictionary states the ethnicity value of 1 represents white people and coupon origin 19 represents ActNow.

As the figure above shows for example, there was one person from the white ethnic group that

used a coupon from ActNow. This information could be very important to the store owner in

order to target specific demographic areas with specific coupons. This information also shows

black people (ethnicity value 2) are the largest ethnic group that used zero coupons with 4271

people using zero. This information could also be useful to target these people with more

coupons and try to increase the store’s sales.

15. Top items that people of Ethnicity number 3 bought (Hispanic)

SQL Code:

SELECT ItemType.Description, Demographics.Ethnicity, Sum(Transaction.[Units Bought]) AS

[SumOfUnits Bought]



GROUP BY ItemType.Description, Demographics.Ethnicity

HAVING (((Demographics.Ethnicity)=3))


Description:

The results shown below show that the top purchased item by people of ethnicity number

, which according to the data dictionary is Hispanic, was cereal which was followed by snacks

and detergents.

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16. Top purchased items by ethnicity number 2 (Blacks)

SQL Code:

SELECT ItemType.Description, Demographics.Ethnicity, Sum(Transaction.[Units Bought]) AS

[SumOfUnits Bought]



GROUP BY ItemType.Description, Demographics.Ethnicity

HAVING (((Demographics.Ethnicity)=2))


Description:

The results shown below show that the top purchased item by people of ethnicity number

2, which according to the data dictionary is Black, was eggs which was followed by snacks and

butter.

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17.) This query displays the total number of items sold in the weeks of the summer

SQL Code:

SELECT ItemType.Description, Transaction.Week, Sum(Transaction.[Item Type]) AS

[SumOfItem Type]

FROM ItemType INNER JOIN [Transaction] ON ItemType.[Item Type] = Transaction.[Item Type]

WHERE (((Transaction.Week)>=26 And (Transaction.Week)<=38)) OR

(((Transaction.Week)>=78 And (Transaction.Week)<=90))

GROUP BY ItemType.Description, Transaction.Week

ORDER BY Transaction.Week, Sum(Transaction.[Item Type]) DESC;

Description:

This query shows that the customer’s buying behaviors are very similar to the buying

behaviours they exhibit throughout the rest of the year. The top selling item as shown below is

snacks which is common among all of the evaluations. The following top selling items are eggs,

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crackers and ice cream. This informati n shows that it may be beneficial to the company to offer

sales on these items throughout the weeks of summer.

(rest displayed in access file!)

18.) This query displays the number of items sold in order from highest to lowest

for customers with an income range of greater than $45,000$55,000

SQL Code:


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FROM ItemType INNER JOIN (Dem graphics INNER JOIN [Transaction] ON






Description:

Query 18 shows the top selling item for customers with an income range between

$45,000 and $55,000 was cat food followed by snacks, butter, eggs and cereal.

19.) This query shows the number of each item that was sold per day for days 17.

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SQL Code:

SELECT Transaction.Day, ItemType.Description, Sum(Transaction.[Units Bought]) AS

[SumOfUnits Bought]

FROM ItemType INNER JOIN (Coup nID INNER JOIN [Transaction] ON

CouponID.CouponID = Transaction.CouponID) ON ItemType.[Item Type] = Transaction.[Item

Type]

GROUP BY Transaction.Day, ItemType.Description;

Description:

The results from query 18 shown below show the sum of each item purchased on days 17. For example as shown in figure X below, 181 units of bacon were purchased on Monday in

comparison to 193 units of bacon sold on Tuesday. The sum of units of ice cream bought

significantly increases on days 5 and 6 which is Friday and Saturday. This makes sense because

its the weekend and people will have a greater chance of wanting ice cream on weekends for

activities such as party’s etc. This variety in purchases shown can be important to store owners to

predict sales and inventory.

o

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20. This query shows the top 5 items sold on day 6 (Saturday)

SQL code:

SELECT Top 5 Transaction.Day, ItemType.Description, Sum(Transaction.[Units Bought]) AS

[SumOfUnits Bought]

FROM ItemType INNER JOIN (CouponID INNER JOIN [Transaction] ON

CouponID.CouponID = Transaction.CouponID) ON ItemType.[Item Type] = Transaction.[Ite m Type]

GROUP BY Transaction.Day, ItemType.Description

HAVING (((Transaction.Day)=6))


Description:

As shown below in figure X, the top 5 items bought on Saturdays are snacks, eggs, butter,

cereal and cook. This information can be very important to store store owners to predict inventory

and sales. This information can also help in the planning of item sales which would draw in

additional customers to the store.

2.5 Discussion:

Once the database, queries and graphs were created, important trends were observed from the data. A summary of the data analysis is provided in 4 main categories

∙ Gender

∙ Income

∙ Week of the year

∙ Work hours

Work hours: The different work hours among the customers in the database were examined for any

potential buying patterns or trends. It was found that the most popular day to shop for part time men

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and women was on Saturdays. Although the number of women part time shoppers greatly surpassed

the number of males with a count of 1750 compared to 366, it was found that both genders preferred to do their shopping on Saturdays.

As shown in query 10 and 11, the same calculation was done for retired males and females

and the results showed that the most popular day to shop for both genders was on Friday. In this

case, the amount of Friday shoppers for both male and females nearly doubled the Saturday

shoppers but Saturdays were still the second busiest day to shop. All of these results show that the

stores are the busiest on the weekends. This concept is very important for store managers to

recognize in order to have the proper staff on hand to keep customers happy and to have a fully

stocked inventory to avoid running out of items.

Gender

Specific trends related to gender were discovered when analyzing the queries our group

created, which lead us to many important conclusions. The female customers in the data bought a

much higher amount of items compared to men making them the bigger overall shoppers in the

database. From the given query results we can see the comparison between male and female part

time workers and their shopping habits (Units bought vs Hours of work). This particular example shows that in total on Saturday’s the women bought 1750 items while men just bought 366 items.

This difference in gender is surprisingly high.

The same pattern was not present when we analyzed the shopping patterns of retired males

and females. From the queries obtained we can observe that the retired men spent a similar

amount of time shopping as women and the total of their items bought were also very similar. In

other words, retired men shop as equal as retired female. We also found that the most frequent

day that retired individuals go shopping is on Friday and then Saturday. This may be the result of

some kind of sales that may occur in the shop on these days. The total number of items retired males bought on Fridays was 4232 in comparison to 4297 for retired females.

Income

After finalizing the queries and the results, the group came to the conclusion that

customers income did infact have a large influence on the customers buying behavior. At first

we can see from query #7 that customers with a low income, between $15,000$20,000 most

commonly bought item was eggs followed by snacks and butter. The count of the item purchased was also very close to the sum of units bought, indicating that the majority of these customers

buy each item one at time. One way to possibly increase these numbers is to increase the

coupons distributed for these items to these specific demographic regions.

As a commonality item among most of the data, item number 17 which was snacks

seemed to be the most popular. However, the most purchased item within each income range

differed slightly. From the lowest income group to the highest income group, the most frequently

purchased items were as follows: snacks, cat food, eggs, cereal, and cat food. Although the

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distinct reason for these observations is not clear, more research may be beneficial to the store.

Knowing that these are the most frequently purchased items, management at the retail store could

make some rearrangements to the store layout. For example, they could place these items towards

the front of the store so that customers could find them easily, or they could place these items in

the back of the store so that customers would have to walk past many different items on their

way, and potentially buy some items that they weren’t looking for. Overall, customer income does have an affect on customer’s overall buying behavior at the store, both with the coupons

they use and the items they purchase.

Weeks of the year:

Another factor that was analyzed from the data was how the sales of certain items changed

from week to week. One of our queries (number 12), showed the different sales from one week to

another for item number 16 which was pizza. As the results given in Appendix I show, regardless

of season or week, the sales in pizza did not differ much from one another. There were one or two fluctuating weeks in the whole results but this fluctuation could have been a result of a birthday

party or another special occasion that happens once or twice a year. There were no major

fluctuations in pizza sales from one week to another.

The group also analyzed the top selling items in the weeks of the summer and it was found

that snacks were the highest selling item followed by eggs and crackers. These items are

commonality items that were found in many of our analysis as the top sellers. From one week to

another there were slight changes within the units of these items purchased but nothing drastic. This means that the store does not need to make any special preparations for the summer season

and can save money that may have been spent here and spend it elsewhere.

Figure A1: Sum of Units bought by day of the week

This pie chart shows the percentage of items bought by day. From this chart we can clearly

tell that the most sales occur on fridays and thursdays. This is very useful in determining when the

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perfect day would be to restock the store. This information was obtained from a query created in

access showing the relation of the amount of items bought by each day.

Figure A2: Graphs showing Ice cream and Soft Drinks during the summer

The two graphs shown here were created by finding the relation of items sold during the summer. I then specifically selected Ice Cream and Soft Drinks as my 2 choices for comparison

because they both seem to have a similar pattern over the summer. For the most part they tend to have

similar spikes around the s me time periods of the 1st and 2nd summers. This information can help

stores prepare and plan their inventory for the these time periods to optimize sales.

Figure A3: Pie Chart of percentage of items sold by item type

This Pie chart can also help the stores prepare their stock. They can use this to rank how popular an item type is. The more popular the item type is, tells you that you should consider

having a wider range of this specific type. You can try stocking different flavors or different brands.

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Also for the items that are not so popular, you can consider either advertising it better so that it sells

more, or even slowly decrease the amount of this item you stock on the shelves.

Figure A4: Graph of the Items most bought by people with Income 10 Stores should design their stores and inventory around the customers of the area. If there is

a vast number of a specific demographic for example this figure, taking the items that they mostly

buy can give insight on how to design your store around your customers. This idea optimizes

revenue and helps gain loyal customers.

Figure A5

These two pie charts show the percentage of items bought by males and females, respectively,

who work over 35 hours. Using this information you can better understand your customers needs

and cater to them. For example, since most males that work over 35 hrs shop on a wednesday, you

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can then make small changes that would make the shop a more suitable environment for that

customer without losing the other customers interest completely.

Conclusion:

This project served as great practice in taking a large quantity of meaningless data,

analyzing, and then transforming it into meaningful information.The first part of the project was

used to see how reliable this data is. The calculations from the MAD and MSE show that this

data is in fact reliable. The method prefered would be the exponential smoothing with alpha=0.2

because the MAD values are lower for the ES than the 3pt moving average for both chosen items.

Also the TS was reasonably low so there also seemed to be very little bias. Many relationships

were established among the various categories of data in order to observe basic patterns and trends. The queries created in Access proved to be one of the most essential parts of this project.

The queries allowed the analyst to view only specific and desirable attributes of the data and

make conclusions about the data from these results. This also allows the analyst to obtain very

specific information bout the data and to observe how specific attributes interact with one

another.

Another crucial takeaway from this project is the importance of data visualization. The use

of various charts and graphics to display data makes patterns and trends a lot easier to interpret. In

conclusion, data analysis as a whole can be complicated but it is absolutely vital in order to make valuable and correct conclusions from large volumes of data.

Appendix I:

Table A1: 3 Point Moving average for cat food

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Table A22: Exponential smoothing for cat food (alpha=0.2)

Figure A6: Graph Exponential Smoothing for cat food (alpha=0.2)

Table A3: 3 Point Moving average for cereal

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Table A4: Exponential smoothing for cereal (alpha=0.2)

Figure A7: Graph Exponential Smoothing for cereal (alpha=0.2)

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Full query results located in Access file

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Roles

Wilson: Moving Average and Exponential Smoothing, Query graphs,

Jessica: Kmeans graphs and question 6 (open ended question), Queries and descriptions

Aaron: Regression, Queries and descriptions

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