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1: Data Set 1
1.1: Introduction
The major area of emphasis in this report shall be provided on the key areas of statistical calculations concerned with an available data set. To further achieve suitable results and outcomes-oriented with the statistical calculations the additional emphasis on this report shall be further provided on the calculation of descriptive and regression statistical calculations to achieve suitable outcomes relating to the conclusion of the report. The data sets considered for the suitable calculation, analysis, and discussion for this particular report are based on the data sets for the EU referendum considering the socio-economic factors. The additional discussion and emphasis of this report shall be focused on a crucial mention of the predictive tools applicable for the suitable calculation of data sets. The crucial mention of the predictive tools shall further outline the various features, importance, advantages, and disadvantages of the selected predictive tool to further extract suitable evidence in this report. The additional emphasis of this report shall be further focused on a critical reflection for considering the key requirements of a data analytics project. This discussion shall be further revolving around the professional dynamics of an organization, whose primary objective and goal is to fulfil the needs and demands of its respective clients.
1.2: Key Data Features
To achieve suitable statistical exclamations, the primary emphasis of the data set for the EU referendum shall be focused on the calculation of descriptive statistics, which are mentioned as follows.
Descriptive Statistics
Electorate
Mean
121983.9423
Standard Error
4972.532913
Median
96760
Mode
102209
Standard Deviation
97059.97033
Sample Variance
9420637840
Kurtosis
53.76588074
Skewness
5.601606435
Range
1259156
Minimum
1799
Maximum
1260955
Sum
46475882
Count
381
Confidence Level (95.0%)
9777.125104
Table 1: Descriptive Statistics for Electorate
The above table demonstrates the various calculations of descriptive statistics in which the mean, median, and mode of the observations are calculated as 121983, 96760, and 102209 electorates.
Votes Cast
Mean
88076.56168
Standard Error
3257.919246
Median
72741
Mode
37975
Standard Deviation
63592.04673
Sample Variance
4043948407
Kurtosis
42.88572813
Skewness
4.928880305
Range
789099
Minimum
1424
Maximum
790523
Sum
33557170
Count
381
Confidence Level(95.0%)
6405.80658
Table 2: Descriptive Statistics for Votes Cast
The above table demonstrates the descriptive statistics for Votes cast in which the mean votes cast is calculated as 88076, while the median and mode of votes cast are calculated as 72741 and 37975 votes cast.
Valid Votes
Mean
88010.07349
Standard Error
3255.413965
Median
72714
Mode
#N/A
Standard Deviation
63543.14559
Sample Variance
4037731351
Kurtosis
42.92962405
Skewness
4.931265277
Range
788725
Minimum
1424
Maximum
790149
Sum
33531838
Count
381
Confidence Level(95.0%)
6400.880629
Table 3: Descriptive Statistics for Valid Votes
The above table demonstrates the descriptive statistics for valid votes in which the mean of valid votes is slated to be 88010 valid votes. Similarly, the median of the valid votes is slated to be 72714 valid votes.
Remain Votes
Mean
42314.74803
Standard Error
1826.405129
Median
33523
Mode
36762
Standard Deviation
35650.00589
Sample Variance
1270922920
Kurtosis
43.71551274
Skewness
4.966922156
Range
439904
Minimum
803
Maximum
440707
Sum
16121919
Count
381
Confidence Level (95.0%)
3591.125841
Table 4: Descriptive Statistics for Remain Votes
The above table demonstrates the descriptive statistics for remain votes in which the mean votes are calculated as 42314 votes. The median and mode of remain votes are calculated as 33523 and 36762 votes respectively.
Leave Votes
Mean
45695.32546
Standard Error
1601.78824
Median
37576
Mode
35224
Standard Deviation
31265.65913
Sample Variance
977541440.6
Kurtosis
27.98726763
Skewness
3.993978846
Range
348821
Minimum
621
Maximum
349442
Sum
17409919
Count
381
Confidence Level (95.0%)
3149.478201
Table 5: Descriptive Statistics for Leave Votes
As per the demonstrations of the above table it can be depicted that the mean of leave votes is calculated as 45695 votes. Subsequently, the median and mode of the leave votes are calculated as 37576 and 35224 respectively.
Median Age
Mean
40.5984252
Standard Error
0.219059372
Median
41
Mode
40
Standard Deviation
4.27586835
Sample Variance
18.28305015
Kurtosis
-0.002903845
Skewness
-0.45956062
Range
22
Minimum
29
Maximum
51
Sum
15468
Count
381
Confidence Level (95.0%)
0.430720303
Table 6: Descriptive Statistics for Median Age
In the context of the above table, the depiction of descriptive statistics for median age is being conveyed in which the mean, median and modal age of the observation have been calculated as 40,41 and 40 respectively. As per the explanations and demonstrations of Amrhein, Trafimow and Greenland (2019), the significance of an average and median age suggests that the majority of the voters in the UK are concentrated on middle-aged persons.
Percent Graduates
Mean
26.91406418
Standard Error
0.392496166
Median
25.70996337
Mode
#N/A
Standard Deviation
7.661219528
Sample Variance
58.69428466
Kurtosis
2.796299256
Skewness
1.224033124
Range
54.14939814
Minimum
14.21477655
Maximum
68.36417469
Sum
10254.25845
Count
381
Confidence Level (95.0%)
0.771736294
Table 7: Descriptive Statistics for Percent Graduates
The above table of descriptive statistics for percent graduates states that the Kurtosis and Skewness of the observation are calculated as 2.79 and 1.22 respectively. Moreover, the Standard deviation and the sample variance of the observation have been calculated as 7.66 and 58.69 respectively. As opined, narrated and illustrated by Pereira, Pellaux and Verloo (2018), the significance of 7.66 as a standard deviation further represents the data sets close to the mean, in which case the percentage of graduated voters is stated to revolve around the 26-30% mark.
Percent HiProf
Mean
10.25338244
Standard Error
0.195791057
Median
9.50274242
Mode
#N/A
Standard Deviation
3.82168896
Sample Variance
14.6053065
Kurtosis
4.700398516
Skewness
1.439861394
Range
31.45378056
Minimum
4.014746473
Maximum
35.46852703
Sum
3906.53871
Count
381
Confidence Level (95.0%)
0.384969529
Table 8: Descriptive Statistics for Percent HiProf
As per the demonstrations of the descriptive statistics of Percent HiProf, it can be observed that the range of the data set is slated to be 31.45.
Percent Born Outside UK
Mean
10.63536959
Standard Error
0.513104376
Median
7.174176927
Mode
#N/A
Standard Deviation
10.01539786
Sample Variance
100.3081944
Kurtosis
5.27743223
Skewness
2.31189262
Range
52.93171093
Minimum
2.151430945
Maximum
55.08314188
Sum
4052.075816
Count
381
Confidence Level(95.0%)
1.008879331
Table 9: Descriptive Statistics for Percent Born Outside UK
As depicted from the above table of descriptive statistics for Percent born outside the UK, the mean and median percentage of people born outside of the UK is considered to be 10 and 7% respectively. As per narrations and opinions of Prabheesh, Padhan and Garg (2020), this further demonstrates a minuscule percentage of people being born outside the UK and representing themselves as voters.
Percent Born LPR
Mean
1.175614156
Standard Error
0.0580326
Median
0.837097887
Mode
#N/A
Standard Deviation
1.13275116
Sample Variance
1.28312519
Kurtosis
8.94054331
Skewness
2.665851252
Range
7.501718746
Minimum
0.119303269
Maximum
7.621022015
Sum
447.9089934
Count
381
Confidence Level (95.0%)
0.114105226
Table 10: Descriptive Statistics for Percent Born LPR
From the above table of descriptive statistics for lawful permanent residents, the mean and median number of LPR is calculated as 1.18 and 0.84 respectively.
Econ Inactive
Mean
29.8861665
Standard Error
0.183555079
Median
29.76578726
Mode
#N/A
Standard Deviation
3.582852198
Sample Variance
12.83682987
Kurtosis
-0.227710034
Skewness
0.03723038
Range
21.29943917
Minimum
17.94081381
Maximum
39.24025298
Sum
11386.62944
Count
381
Confidence Level (95.0%)
0.360910827
Table 11: Descriptive Statistics for Econ Inactive
From the above table of descriptive statistics for Econ Inactive, it can be depicted that the standard deviation of 3.58 is generally considered to be situated close to the mean. Chaim and Laurini (2018) further expressed and stated that a standard deviation of 3 further represents that the Econ Inactive people are mostly situated in the 30-33 numerical bracket.
Unemployed
Mean
4.050482445
Standard Error
0.063254412
Median
3.897011066
Mode
#N/A
Standard Deviation
1.234676866
Sample Variance
1.524426963
Kurtosis
0.077908151
Skewness
0.672481229
Range
6.915021544
Minimum
1.10974106
Maximum
8.024762604
Sum
1543.233811
Count
381
Confidence Level (95.0%)
0.12437249
Table 12: Descriptive Statistics for Unemployed
The above table further states that the mean number of unemployed population is calculated as 4.
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.996029748
R Square
0.992075258
Adjusted R Square
0.991839019
Standard Error
8768.217981
Observations
381
ANOVA
df
SS
MS
F
Significance F
Regression
11
3.55147E+12
3.22861E+11
4199.457229
0
Residual
369
28369327584
76881646.57
Total
380
3.57984E+12
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
7492.402332
4814508734
1.55621E-06
0.99999875
-9467308015
9467323000
-9.467E+09
9467323000
X Variable 1
-32.84913075
18.19046618
-1.80584326
0.071757589
-68.61911104
2.92084954
-68.619111
2.92084954
X Variable 2
-3.01178E+12
5.17674E+12
-0.581790555
0.561063017
-1.31914E+13
7.16783E+12
-1.319E+13
7.16783E+12
X Variable 3
3.01178E+12
5.17674E+12
0.581790555
0.561063017
-7.16783E+12
1.31914E+13
-7.168E+12
1.31914E+13
X Variable 4
3.01178E+12
5.17674E+12
0.581790555
0.561063017
-7.16783E+12
1.31914E+13
-7.168E+12
1.31914E+13
X Variable 5
-305.9520486
197.0356683
-1.552774943
0.121334135
-693.4056687
81.50157145
-693.40567
81.50157145
X Variable 6
-788.3763941
223.979269
-3.519863234
0.000485765
-1228.812277
-347.9405109
-1228.8123
-347.9405109
X Variable 7
-372.0414929
368.8347364
-1.008694291
0.313782243
-1097.323139
353.2401531
-1097.3231
353.2401531
X Variable 8
444.7719753
125.6569311
3.539573755
0.000452001
197.6784757
691.8654749
197.678476
691.8654749
X Variable 9
-1867.914181
671.9204735
-2.779963187
0.005714995
-3189.187746
-546.640616
-3189.1877
-546.640616
X Variable 10
505.2300069
173.6559329
2.909373716
0.003840941
163.7506167
846.7093972
163.750617
846.7093972
X Variable 11
1538.609189
673.1355968
2.285734399
0.022836178
214.9461892
2862.272189
214.946189
2862.272189
Table 13: Regression Statistics for EU Referendum
From the above table of regression statistics for the EU referendum, it can be stated that the multiple R and R square value of the observation is calculated as 0.9960 and 0.9920 respectively. Moreover, the coefficient and standard error of the observation are slated to be 7492.402332 and 4814508734 respectively. As per narrations and observations of Yabansu et al. (2019), the significance of regression statistics further helps to define the level of dependency between multiple variables of the data set. Therefore, from the above table, it can be concluded that the level of dependency between Total voters to education and median age is considered highly dependent.
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1.3: Discussion on Predictive Tool
The selection of a predictive tool for suitably calculating the statistical components of an observation and data set is considered an important metric to further acquire requisite values from the data sets. Therefore, the selection of Statistical analysis, machine learning module and search engine optimisation (SEO). As per the statements and illustrations of Be?o (2021), the advantages of statistical analysis further include a data collecting organisation to have a large and vast data set of the EU referendum and extract feasible results in an easy and non-complex manner. Moreover, the implementation of statistical analysis as an important predictive tool can further help the organisation to conduct a detailed market survey of the number of voters and virtually estimate how much growth in the voters and electors list is expected.
The selection of a machine-learning module is further considered a beneficial and significant stage of a predictive tool. Hofmann, Lau and Kirchebner (2022), opined that the advantages of employing a suitable machine learning module further enable an organisation to identify necessary trends and paradigms of the voters based in a constituency. The various trends and paradigms of the voters can be further identified as the orientation of educated voters in an election as well as the concentration of the youth population in the elections. The selection of SEO as a predictive tool also creates a significant amount of favourable circumstances for an organisation. According to Seo (2020), the advantages of SEO mainly consist of the ability for an organisation to filter and select suitable EU referendum data to further understand how much percentage of the voters is unemployed. Necessary steps and actions can be further implemented by the concerned authorities thereafter to minimise the concentration of unemployed individuals in an election.
1.4: Reflection
The significance of a data analytics project is considered an area of utmost importance to get a basic overview of the number of voters present in a constituency as well as interpret the results accordingly through numerical calculations. As per the opinions and explanations of Baijens, Helms and Kusters (2020), the requirements of a data analytics project further include the conduct of three steps which are importing of data, data analysis and communication. In my opinion, the import of data is a critical function of the data analytics project to extract suitable information about the total number of voters in an electorate. I also feel that data analysis using statistical tools is another important metric to fetch suitable data results. I further feel that establishing suitable communication for the conveyance of data analysis is further important for ensuring the true numerical aspects of the imported data.
2: Data Set 2
2.1: Introduction
In this report, a detailed discussion on the key statistical implications shall be conducted and special adherence shall be further provided to the calculation of statistical figures. Majority of the calculations for the statistical implications shall be further conducted on the available data set which is related to customer purchases and pregnancy. This report shall further revolve around the numerical calculations of descriptive statistics and regression statistics and shall further provide a suitable discussion on the various predictive tools that could be applied by a particular organisation. A self-reflection on the various requirements of a data analytics project shall be further briefly discussed in this report.
2.2: Key Data Features
Descriptive Statistics
Pregnancy Test
Birth Control
Feminine Hygiene
Mean
0.075
Mean
0.14
Mean
0.141
Standard Error
0.008333333
Standard Error
0.010978184
Standard Error
0.011010915
Median
0
Median
0
Median
0
Mode
0
Mode
0
Mode
0
Standard Deviation
0.263523138
Standard Deviation
0.347160655
Standard Deviation
0.348195692
Sample Variance
0.069444444
Sample Variance
0.120520521
Sample Variance
0.12124024
Kurtosis
8.462662537
Kurtosis
2.323241804
Kurtosis
2.273689484
Skewness
3.231987188
Skewness
2.078123989
Skewness
2.066191206
Range
1
Range
1
Range
1
Minimum
0
Minimum
0
Minimum
0
Maximum
1
Maximum
1
Maximum
1
Sum
75
Sum
140
Sum
141
Count
1000
Count
1000
Count
1000
Confidence Level(95.0%)
0.016352845
Confidence Level(95.0%)
0.021542945
Confidence Level(95.0%)
0.021607174
Table 14: Descriptive Statistics for Pregnancy Test, Birth Control and Feminine Hygiene
From the above table of descriptive statistics for Pregnancy test, the mean, median and mode of the observation are slated to be 0. This further means that majority of the customers did not buy pregnancy kits recently. From the table of birth control, the mean, median and mode of the observation are also calculated as 0, meaning that the majority of the population did not purchase birth control kits recently (investopedia.com, 2022). The above table of feminine hygiene also presents mean, median and mode, meaning that the majority of the population did not buy feminine products recently.
Folic Acid
Prenatal Vitamins
Prenatal Yoga
Mean
0.106
Mean
0.128
Mean
0.018
Standard Error
0.009739551
Standard Error
0.010570134
Standard Error
0.004206387
Median
0
Median
0
Median
0
Mode
0
Mode
0
Mode
0
Standard Deviation
0.307991654
Standard Deviation
0.334256979
Standard Deviation
0.133017644
Sample Variance
0.094858859
Sample Variance
0.111727728
Sample Variance
0.017693694
Kurtosis
4.581399453
Kurtosis
2.980162965
Kurtosis
50.83369238
Skewness
2.563638165
Skewness
2.230292947
Skewness
7.261682229
Range
1
Range
1
Range
1
Minimum
0
Minimum
0
Minimum
0
Maximum
1
Maximum
1
Maximum
1
Sum
106
Sum
128
Sum
18
Count
1000
Count
1000
Count
1000
Confidence Level(95.0%)
0.019112325
Confidence Level(95.0%)
0.020742211
Confidence Level(95.0%)
0.008254368
Table 15: Descriptive Statistics for Folic Acid, Prenatal Vitamins and Prenatal Yoga
From the above table of descriptive statistics for Folic Acid, Prenatal vitamins and Prenatal Yoga the mean, median and mode are calculated as 0. This further signifies that majority share of the customers did not buy these products recently.
Body Pillow
Ginger Ale
Sea Bands
Mean
0.018
Mean
0.069
Mean
0.03
Standard Error
0.004206387
Standard Error
0.008018934
Standard Error
0.005397141
Median
0
Median
0
Median
0
Mode
0
Mode
0
Mode
0
Standard Deviation
0.133017644
Standard Deviation
0.25358096
Standard Deviation
0.170672579
Sample Variance
0.017693694
Sample Variance
0.064303303
Sample Variance
0.029129129
Kurtosis
50.83369238
Kurtosis
9.62089868
Kurtosis
28.51261882
Skewness
7.261682229
Skewness
3.406121085
Skewness
5.518659029
Range
1
Range
1
Range
1
Minimum
0
Minimum
0
Minimum
0
Maximum
1
Maximum
1
Maximum
1
Sum
18
Sum
69
Sum
30
Count
1000
Count
1000
Count
1000
Confidence Level(95.0%)
0.008254368
Confidence Level(95.0%)
0.015735886
Confidence Level(95.0%)
0.010591033
Table 16: Descriptive Statistics for Body Pillow, Ginger Ale and Sea Beds
From the above table of descriptive statistics for body pillow, ginger ale and sea beds the mean, median and mode are calculated as 0 respectively. Therefore, this signifies that the majority of the customers did not buy these products.
Stopped Buying Cigarettes
Cigarettes
Smoking Cessation Products
Mean
0.092
Mean
0.097
Mean
0.06
Standard Error
0.009144376
Standard Error
0.009363689
Standard Error
0.007513751
Median
0
Median
0
Median
0
Mode
0
Mode
0
Mode
0
Standard Deviation
0.289170572
Standard Deviation
0.296105857
Standard Deviation
0.237605674
Sample Variance
0.08361962
Sample Variance
0.087678679
Sample Variance
0.056456456
Kurtosis
6.006873157
Kurtosis
5.449903464
Kurtosis
11.79538488
Skewness
2.827518946
Skewness
2.727454425
Skewness
3.71103733
Range
1
Range
1
Range
1
Minimum
0
Minimum
0
Minimum
0
Maximum
1
Maximum
1
Maximum
1
Sum
92
Sum
97
Sum
60
Count
1000
Count
1000
Count
1000
Confidence Level(95.0%)
0.017944389
Confidence Level(95.0%)
0.018374755
Confidence Level(95.0%)
0.014744545
Table 17: Descriptive Statistics for Stopped Buying Cigarettes, Cigarettes and Smoking Cessation Products
The above table demonstrates that the mean, median and mode for stopped buying cigarettes, cigarettes and smoking cessation products is calculated as 0. This further signifies that the majority of the population in the households are continuing to smoke cigarettes.
Stopped Buying Wine
Wine
Maternity Clothes
Pregnant
Mean
0.13
Mean
0.123
Mean
0.131
Mean
0.5
Standard Error
0.01064017
Standard Error
0.010391293
Standard Error
0.010674875
Standard Error
0.0158193
Median
0
Median
0
Median
0
Median
0.5
Mode
0
Mode
0
Mode
0
Mode
1
Standard Deviation
0.336471712
Standard Deviation
0.32860155
Standard Deviation
0.337569182
Standard Deviation
0.500250188
Sample Variance
0.113213213
Sample Variance
0.107978979
Sample Variance
0.113952953
Sample Variance
0.25025025
Kurtosis
2.862017051
Kurtosis
3.292766964
Kurtosis
2.804331821
Kurtosis
-2.004012036
Skewness
2.203700753
Skewness
2.299170594
Skewness
2.190599724
Skewness
-5.79052E-18
Range
1
Range
1
Range
1
Range
1
Minimum
0
Minimum
0
Minimum
0
Minimum
0
Maximum
1
Maximum
1
Maximum
1
Maximum
1
Sum
130
Sum
123
Sum
131
Sum
500
Count
1000
Count
1000
Count
1000
Count
1000
Confidence Level(95.0%)
0.020879646
Confidence Level(95.0%)
0.020391265
Confidence Level(95.0%)
0.020947749
Confidence Level(95.0%)
0.031042867
Table 18: Descriptive Statistics for Stopped Buying Wine, Wine, Maternity Clothes and Pregnant
From the above table of descriptive statistics for stopped buying wine, wine and maternity clothes, the mean, median and mode of the observation are found to be zero. However, the mean, median and mode of pregnant population are slated to revolve around 0.5 to 1. This signifies that in every two households, 1 person is found to be pregnant.
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.672171463
R Square
0.451814475
Adjusted R Square
0.443457989
Standard Error
0.373195361
Observations
1000
ANOVA
df
SS
MS
F
Significance F
Regression
15
112.9536189
7.530241258
54.06751595
3.6614E-117
Residual
984
137.0463811
0.139274778
Total
999
250
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
0.413672294
0.018939149
21.84217941
1.51822E-86
0.37650653
0.450838058
0.37650653
0.450838058
X Variable 1
0.218058401
0.046541505
4.685246063
3.18916E-06
0.12672639
0.309390412
0.12672639
0.309390412
X Variable 2
-0.274472752
0.034666709
-7.917473492
6.51371E-15
-0.342501929
-0.206443574
-0.342501929
-0.206443574
X Variable 3
-0.245079751
0.034344935
-7.13583383
1.86482E-12
-0.312477486
-0.177682016
-0.312477486
-0.177682016
X Variable 4
0.34405342
0.039239498
8.768038156
7.86842E-18
0.267050703
0.421056136
0.267050703
0.421056136
X Variable 5
0.298086596
0.036121626
8.252302888
4.95531E-16
0.227202323
0.368970869
0.227202323
0.368970869
X Variable 6
0.318919562
0.089495211
3.563537737
0.000383451
0.143296155
0.494542969
0.143296155
0.494542969
X Variable 7
0.184546094
0.089416384
2.063895744
0.039289318
0.009077376
0.360014811
0.009077376
0.360014811
X Variable 8
0.226086097
0.047063221
4.803880632
1.79836E-06
0.133730281
0.318441913
0.133730281
0.318441913
X Variable 9
0.140434527
0.069965526
2.007196064
0.045001874
0.00313574
0.277733315
0.00313574
0.277733315
X Variable 10
0.159818146
0.041818284
3.821728963
0.000140837
0.077754879
0.241881414
0.077754879
0.241881414
X Variable 11
-0.164941603
0.040425204
-4.080167477
4.86467E-05
-0.244271122
-0.085612084
-0.244271122
-0.085612084
X Variable 12
0.165721439
0.051586516
3.212495273
0.001358593
0.06448921
0.266953668
0.06448921
0.266953668
X Variable 13
0.191146551
0.03600652
5.308664995
1.365E-07
0.12048816
0.261804943
0.12048816
0.261804943
X Variable 14
-0.207701535
0.036766736
-5.649169753
2.10997E-08
-0.279851759
-0.135551311
-0.279851759
-0.135551311
X Variable 15
0.240508913
0.035811449
6.715978306
3.15411E-11
0.170233324
0.310784502
0.170233324
0.310784502
Table 19: Regression Statistics for Customer Purchases and Pregnancy
From the above table of regression statistics, the resulting coefficients and standard error are calculated as 0.4136 and 0.1893 respectively. This further signifies that the level of dependency between variables such as being pregnant and changing lifestyle norms as well as buying suitable products is relatively low.
2.3: Discussion on Predictive Tool
Predictive tools are generally used by businessmen to predict their customer's rationale and potential audiences regarding their campaigns and promotions. As per the view of Anshari et al. (2019), predictive Tool regarding the Customer purchases and pregnancy refers to the electronic equipment through which the customer's pregnancy segments are measured. The predictive tools such as medicines, small equipment, reports, and machine models are used to ascertain the no. of customers who are getting pregnant by staying in a household based on recent customer purchases. Predictive tools are “Text Analysis, Real-Time Analysis, Statistical Analysis, Data Mining, and Optimization” (Bates et al. 2018). These tools are used to measure the customer's expectations and it also evaluates the time where the demand of related substances are increased or decreased.
Customer purchases the products such as Feminine Hygiene, Folic Acid, Prenatal Vitamins, Prenatal Yoga, Body Pillow, Ginger Ale, Sea-Bands, Smoking, and Cessation Products, Maternity Clothes, and Pregnancy tool kit. As stated by Chen et al. (2021), the demands and supply of these products are measured by the “Sales Reporting Tools”. Under these, there are various software and retorting tools that help in measuring the quantities that are sold in a fixed period of time. The sales tools are InsightSquared, MixPanel, Intercom, Klipfolio, HubSpot Reporting and DataBox. These tools are the business intelligence tools that help in acquiring the marketing and promotions segment in numerical (Guan et al. 2019). Under this, the account holder's details including the number are put to get the overall access to the products that are sold.
For example- at the time of pregnancy, the demand for intoxicating substances such as wine, and cigarettes are low. As per the author Guha et al. (2021), from the data given in the case study, it can be seen that the businessman has received less income from the four categories. Buying Cigarettes, Products and Stopped Buying Wine. However, the demand for the products such as Feminine Hygiene, Folic Acid, Prenatal Vitamins and Body Pillow rises. This is based on the same time this product is required by the customers (Jai et al. (2021). Therefore it can be seen that the customers are rational in nature. If a product is less in demand, then naturally the other substitute can have more demand than the product.
These reporting tools enable small pieces of information regarding each product with its metrics in a simple way. The CRM and sales tools help to identify the overall required demand and sales of a distinctive product. In a general manner, the Meister Task is the best tool to measure the customer purchaser in the household segment as well as the market segment. The sales tools show the required level of products from the specific category where the people need to attain their satisfaction. As opined by Nusraningrum and Gading (2021), this tool also promotes the reasonable rate segment, s the customers likely to buy good quality products at low prices. The marketing of the above mentioned products is done by the sales analytics where the pregnancy products are in demand when the customers are eager to buy those products in time of need.
These sales promotions are done by getting the information from the legal websites on the customer preferences. The avenues are collected in the form to get all the information that which products are required for the customers who are pregnant. According to the information, the products were made and the life cycle is totally dependent on the customer preferences. As mentioned by Pradeep et al. (2018), generally, customers from household aspects demand these products as they frequently like to conceive. However, in urban areas, the demand for these products creates a competitive opportunity where the customers are rational in nature. This increases the producer's interest to gain more profit by selling the products which are in demand.
The Self-service sites and online communities in the current times show the customer purchases in a brief way. This is because people are mainly spending their time on social sites and media, where they can freely share what they want. From there businessmen can ascertain the demand to a great extent. As per the view of McCarthy et al. (2019), people generally want innovative and cheap rates for the products they want. Therefore from the sales tools and through the social media the requirement and preferences of the customers are easily ascertained. The predictive tools and its software “gather and enrich contact data and customer information”. This is vital for the companies to enrich all the valuable and required data about their customers.
2.4: Reflection
In my opinion, the key requirements for a data analytics project to be effective in meeting the
Needs of the client are the use of predictive tools. In the second data set, the customer's purchases are determined by sales tools that reflect the exact demand of the customers. I think that predictive tools show the effectiveness of the demand where the customer purchases are clearly shown. From there, in my opinion, it can be stated that the demand for these products is low for a certain period. These data are collected to a great extent. As per my knowledge, it can be stated that the clients can give effective reviews if the product and its related data are accurate.
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