Comprehensive Data Analysis Report for Sublime Delight Coffee Business

This report analyzes data from Sublime Delight, a coffee roasting and retail business. It begins with an introduction and methodology, using statistical tools to interpret data gathered from the company's records. Task 1 focuses on sales and profit analysis of external and internal customers, as well as mobile sales, utilizing tables and figures to illustrate key findings. Task 2 employs t-tests to compare service and coffee quality across different outlets, assessing for significant differences. Task 3 examines product proportions in January and December, comparing them to predictions and analyzing changes in the market mix. The report concludes with recommendations based on the analysis, offering insights into sales distribution, profit margins, and potential growth areas within the business. The analysis includes the application of the BADIR methodology for structured data interpretation and decision-making.

STATISTICS AND PROBABILITY

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................1
Background and context..................................................................................................................1
Methodology....................................................................................................................................1
TASK 1............................................................................................................................................2
Sales and profit of data analysis of external customers...............................................................2
.........................................................................................................................................................4
TASK 2............................................................................................................................................5
TASK 3............................................................................................................................................7
(a)Proportion of products in January month and comparison to prediciton................................7
(b)Proportion of products in December month and comparison to prediciton............................7
© Change in mix overall between January and December..........................................................8
(d) New market mix based on December data.............................................................................8
CONCLUSION AND REOCMENDATION..................................................................................9
Table 1Sales and profit of data of external customers.....................................................................2
Table 21Sales and profit of data of internal customers...................................................................3
Table 3Sales and profit of data for mobile......................................................................................3
Table 4Average percentage portion of Espesso, Mocha and Sublime in sales value for January
month...............................................................................................................................................7
Table 5Average percentage portion of Espesso, Mocha and Sublime in sales value for December
month...............................................................................................................................................7
Table 6Prediction by using moving average...................................................................................8
Figure 2Profit figure........................................................................................................................4
Figure 3Sales distribution of mobile, internal carts external customers..........................................4

INTRODUCTION
Data analysis is the one of the important approach that helps firm in making business
decisions. In data analysis there are number of tools and techniques that are used by the firms to
make business decisions. Coffee business is one of the common activity in most of European
nations as most of people like to take coffee. In the current report detail analysis of coffee related
data is done. In this regard, some of the statistical tools are applied on relevant facts and answers
of questions are identified and report on same is prepared. In this way entire research work is
carried out.
Background and context
Current data analysis is carried out on Sublime delight which is rapidly growing firm and
known for coffee rosting tasks. It is also coffee supplier and operating chain of outlets in its
business (Galarraga and Markandya, 2011). Entreprenuer is importing coffee bins and kept them
in different temperatures which make its business innovative in nature. Some of data was
gathered by its General manager in respect to the business and same is analyzed in current report
so that lots of hidden facts can be identified in respect to business. In this regard some of
important tests and other statistical approaches wll be used in research study.
Methodology
Gathered data is primary in nature which is collected from in house company records.
Data is properly arranged so as to ensure better analysis of facts and development of insights
about business. In this reagard some charting like histogram will be prepared and same will be
used to analyze data (Alvesson and Sköldberg, 2017). Apart from this, as per requirement
regresssion analysis or T test or any other alternative technique will be used in research study.
Along with this, in respect to mix proportion also relevant technique will be used. Thus, it can be
said that according to requirement relevant tools and methods will be used for data analysis.
BADIR methodology is given below. Buisness question: Business question are related to identifying sales distribution across
outlets and profit distribution across different chanels. Apart from this busienss question
is also related to identifying growth areas in business. Along with this, answer of question
whether there is significent difference between outlets in respect to service quality and
coffee quality.
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 Analysis plan: In order to answer these questions analysis plan is prepared and under this
data will be processed and by doing so profit value will be computed for all channels of
distribution. In this way, entire analysis will be done. Apart from this, T test will be
applied and significent difference will be identified in respect to multiple variables in
relevance to both outlets. In third section in order to do analysis simply percentage that
Espresso, Mocha and Sublime cover in entire sales will be determined. On this basis it
will be identified whether prediction actually happened some deviaton comes in results. Data collection: Data is gathered from company records which were earlier recorded by
company general manager (BADIR framework get from data to decisions, 2017).
Relevant data is taken in to consideration for analysis purpose. Derive insights: In this section obtained results will be interpreted and on that basis
conclusion will be formed in the report. Recommendations: On basis of conclusion formed recommendation will be made in
research report.
TASK 1
Sales and profit of data analysis of external customers
Table 1Sales and profit of data of external customers
Exter
nal
Custo
mers
Sales
units Cost Sales Prof
it
1 470
1033
0
20726.
8
1039
7
2 499
1097
5
20762.
51 9788
3 546
1064
3
22429.
78
1178
7
4 757
1286
4
29457.
06
1659
3
5 425 9341
18662.
35 9321
6 529
1031
8
22067.
09
1174
9
7 948
1469
7
35758.
08
2106
1
8 454 9986 19931. 9946
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96
9 560
1092
9
23276.
35
1234
8
10 575
1121
4
23939.
91
1272
5
11 436 9597
19279.
58 9682
12 530
1033
8
22337.
09
1199
9
13 583
1136
4
24393.
8
1303
0
14 728 12369
29074.
16
1670
5
15 434 9555
19635.
34
1008
0
16 440 9690
19870.
22
1018
0
17 391 8608
18012.
73 9405
Total
sales 389615
3896
15
5964
11
Table 21Sales and profit of data of internal customers
Interna
l
Carts Sales units Cost Sales Profit
1 1348.10002
20895.5
5 51227.8 30332
2
1399.09551
3
21685.9
8
53165.6
3 31480
3
1171.29675
6 18155.1
44509.2
8 26354
4
1328.38319
1
20589.9
4
50478.5
6 29889
5
1049.94075
7
16274.0
8
39897.7
5 23624
6 1506.05997
23343.9
3
57230.2
8 33886
7
1139.25897
3
17658.5
1
43291.8
4 25633
Total
20119
8
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Table 3Sales and profit of data for mobile
Mobile
s Sales units Cost Sales
Profi
t
1
842.119017
4
13052.8
4
33684.7
6
2063
2
2
999.090397
2 15485.9
38417.4
4
2293
2
3
726.781092
5
12355.2
8
30001.0
8
1764
6
Total
6120
9
External
Customers Internal
Carts Mobiles
0%
10%
20%
30%
40%
50%
60%
70%
80%
69%
23%
7%
Chart Title
Figure 1Profit figure
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External
Customers
Internal
Carts
Mobiles
0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%
47%
41%
12%
Chart Title
Figure 2Sales distribution of mobile, internal carts external customers
Interpretation
Image given above reflect that in case of mobile profit percentage is 7 and same in case
of internal cart is 23%. Apart from this, same percentage for external customer is 69%. This
meanns that from corporate sale less amount of profit is generated in the business and there are
few customers that purchase from firm warehouse. Majority of revenue comes from those that
are entities external to firm and are not corporate in nature. In case of sales it can be seen that
mobile cover 12% and same for internal carts is 41% followed percentage in case of external
customers is 47%. It can be said that sales in case of external customers and internal carts are
almost similar percentage is obtained on analysis. It can be seen from data that in internal cart
there are few customers but they make purchase in large quantity and due to this reason there is
similar percentage in case of mobile and internal carts.
Major growth areas are external customers where sales and margin both are high. At
second place, there is intenral carts where sales of huge amount is made but profit percentage is
moderate. Apart from this, in case of mobile there is less profit and less sales. Due to this reason
same can not be considered as growth area.
TASK 2
H0: There is no significcent mean difference between service quality across different outlets.
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H1: There is significcent mean difference between service quality across different outlets.
t-Test: Two-Sample Assuming Equal
Variances
Service
Service Service
Mean 4.15
3.81578947
4
Variance
0.79743589
7 1.5056899
Observations 40 38
Pooled Variance
1.14224376
7
Hypothesized Mean Difference 0
df 76
t Stat
1.38043263
7
P(T<=t) one-tail 0.08575011
t Critical one-tail
1.66515135
3
P(T<=t) two-tail
0.17150021
9
t Critical two-tail 1.99167261
Interpretation
Mean value in case of service for first outlet is 0.08>0.05 which means that there is no
significent mean difference between outlersn in terms of service quality. It can be observed that
in case of service quality for first outlet value is 4.15 and same is 3.81 for second outlet. 3.81 can
be considered as 4 and it can be said that there is not a big difference in service quality of both
outlets. On this basis it can be said that customer satisfaction level will be same in case of both
outlets in terms of service quality.
H0: There is no significent mean difference between quality of coffee across both outlets.
H1: There is significent mean difference between quality of food across both outlets.
t-Test: Two-Sample Assuming Equal
Variances
Coffee
Coffee Coffee
Mean 4.175
3.89473684
2
Variance
0.66089743
6
1.01564722
6
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Observations 40 38
Pooled Variance
0.83360457
1
Hypothesized Mean Difference 0
df 76
t Stat
1.35506603
6
P(T<=t) one-tail
0.08970529
8
t Critical one-tail
1.66515135
3
P(T<=t) two-tail
0.17941059
6
t Critical two-tail 1.99167261
Interpretation
Table in respect to application of T test is given above and this reflect that there is no
significent mean differene between both outlets in respect to quality of coffee as value of level of
significence 0.08>0.05. Thus, it means that quality of coffee given to customers is almost same
in both outlets. Mean value in case of first outlet is 4.17 and same for second outlet is 3.89. As
per rules 3.89 can be rounded to 4 and and like seen above it can be said that there is no big
difference in mean value of both variables. This again proved that there are same level of quality
of coffee in both outletsd and customers areequally satisfied.
TASK 3
(a)Proportion of products in January month and comparison to prediciton
Table 4Average percentage portion of Espesso, Mocha and Sublime in sales value for January
month
Jan Espresso Mocha Sublime
1 0.28 0.16 0.56
2 0.34 0.13 0.59
3 0.34 0.28 0.53
4 0.50 0.22 1.03
5 0.25 0.19 0.50
6 0.63 0.22 0.25
7 0.63 0.28 1.25
8 0.16 0.28 0.53
9 0.09 0.38 0.72
10 0.34 0.16 0.69
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11 0.16 0.34 0.56
12 0.13 0.13 0.88
13 0.34 0.19 0.66
14 0.44 0.19 0.88
15 0.28 0.16 0.53
16 0.06 0.28 0.50
17 0.25 0.13 0.44
Average 31% 22% 65%
Interpretation
It can be seen from table that average percentage in case of Espresso is 31% and same for
Mocha is 22% as well as 65% for Sublime. Same percentage was predicted and its value was
30% for Espresso and 10% for Mocha is 22% as well as 60% for Sublime. Hence, it can be said
that exactly results not match but there is small deviation in results.
(b)Proportion of products in December month and comparison to prediciton
Table 5Average percentage portion of Espesso, Mocha and Sublime in sales value for December
month
Dec Espresso Mocha Sublime
1 0.46 0.27 0.27
2 0.29 0.27 0.48
3 0.31 0.31 0.54
4 0.69 0.33 0.13
5 0.25 0.50 0.15
6 0.10 0.10 0.92
7 0.73 0.38 0.90
8 0.73 0.04 0.19
9 0.31 0.27 0.60
10 0.31 0.33 0.58
11 0.31 0.35 0.21
12 0.48 0.50 0.15
13 0.25 0.29 0.71
14 0.71 0.33 0.52
15 0.48 0.31 0.02
16 0.25 0.50 0.21
17 0.27 0.19 0.35
Averag
e 41% 31% 41%
Interpretation
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It can be seen from table that average is 41% for Espresso, 31% for Moche and same
percentage is 41% for Sublime. On other hand, prediction was that 30% of Espresso will be
supplied to external entities and 10% will be sold to Mocha as well as 60% for Sublime will be
sale to same sort of entity. It is clear that results deviate moderately from predicted results and
prediction does not hold for December month.
© Change in mix overall between January and December
It is clear that mix of products changed from January to December as percentage of
Espresso was 31% and same was 22% for Moche and 65% in case of Sublime. On other hand, in
case of December month percentage of Espresso become 41% and same for Moche is 31%.
Similarly, 41% is covered by Sublime. Hence, it can be observed that change comes in mix
between both months.
(d) New market mix based on December data
Table 6Prediction by using moving average
Espresso Mocha Sublime
#N/A #N/A #N/A
#N/A #N/A #N/A
0.35 0.28 0.43
0.43 0.31 0.38
0.42 0.38 0.27
0.35 0.31 0.40
0.36 0.33 0.65
0.52 0.17 0.67
0.59 0.23 0.56
0.45 0.22 0.46
0.31 0.32 0.47
0.37 0.40 0.31
0.35 0.38 0.35
0.48 0.38 0.46
0.48 0.31 0.42
0.48 0.38 0.25
0.33 0.33 0.19
42% 16.38% 42%
Intepretation
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On basis of above table it can be said that for Espresso proportion in bix mix supplied
outside will be nearby to 42% and same in case of Mocha is 16.38%. Apart from this, in case of
Sublime same percentage predicted is 42%. Hence, it can be said that any big change will not be
observd in distribution percentage in upcoming time period.
CONCLUSION AND REOCMENDATION
On the basis of above discussion it is concluded that there is significent importance of
data analysis methods for the firms because it help them in identifying lots of facts which
underpin management decision making process. It is also identified that before data analysis it is
very important to process same in proper manner so that appropriate data can be generated and
same can be used for analysis purpose. It is recommended that firm must not solely belive on
forcasted results. It must also make use of real time analytics and data visualization softwares in
order to make sound business decisions.
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