Financial Forecasting Analysis

This assignment focuses on financial forecasting analysis. It presents a series of financial data points across different time periods, likely representing revenue, expenses, or other key performance indicators. Students are tasked with analyzing these trends and generating forecasts for future periods. The provided data spans multiple years and includes figures in various categories, suggesting a comprehensive assessment of financial planning and projection capabilities.

MARKETING ANALYTICS

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................1
1 Profitable and less profitable product lines in business.......................................................1
2 Difference in seasonality pattern across different channels and product lines....................2
3 Forecast of sales level and profit margin for three year time period for product line and
channel....................................................................................................................................5
4 Estimation of performance for three years and recommendation.....................................10
CONCLUSION..............................................................................................................................12
REFERENCES..............................................................................................................................13
APPENDIX....................................................................................................................................14

INTRODUCTION
Analytics is the one of growing domain across the globe. Today industry is valued at billion of amount and number of firms
entering in to this industry is increasing at rapid rate. There are number of reasons due to which analytics industry grow at such huge
rate. One of them is that there are number of factors that firm are facing in its business. By using analytic tools, reasons due to which
problem comes in existence are identified and solved. In the current research report, marketing related data is analysed by using varied
tools. Regression tool is applied on data set in order to explore relationship between different variables and to identify solution of the
problem. Apart from this, forecasting is also done for sales and profit across different channel of distribution and product lines. At end
of the report, conclusion section is prepared and on that basis recommendation is made. It can be said that analytics is the one of the
important tool that is used to analyse data set and to identify solution of the business problem.
1 Profitable and less profitable product lines in business
Cantaso Ltd is the one of the well-known company across the globe. Currently, firm is operating its business in the retail sector
and is offering wide variety of products and services to the customers. In order to identify most profitable and loss making regions
descriptive statistics method and data exploration method is used. Under data exploration, some computations are done and in
descriptive analysis mean and standard deviation is computed for each variable. On this basis, regions that are highly profitable and
loss making are identified. In the report, data of mentioned firm is analysed by using analytic tool. Main objective is to identify
product lines that are highly and less profitable for the business firm. On analysis of data it is identified that most profitable regions for
the firm are North America where profit of 4150637381 is made followed by Europe and China where sales of 1100780705 and
931195377 is made. In areas where low amount of profit earned are Turkmenistan where profit of only 19428 is earned followed by
Switzerland and Slovenia where profit of 11667273 and 11869520 is earned. It can be said that there is huge difference between profit
amounts that is earned on profitable less profitable nations. North America is the region from where huge amount of sale is made.
Mentioned region accounts about more than 70% of overall profit of the business firm. thus, it need to focus more on North America,
Europe and China to earn good amount of profit in its business. China is another market from where good amount of profit can be
1

earned. As per facts, in the mentioned region profit of about 931195377 is earned. There is huge population of mentioned region and
consumption power of relevant people is increasing consistently. Thus, China can be attractive market for the firm.
It is also very important to evaluate the standard deviation in profitability of nations to measure risk that is associated with
relevant region. It can be seen that standard deviation in case of China is very high in comparison to other markets and profitability in
the mentioned region is increasing consistently which reflect that profit in Chinese market for the firm is increasing rapidly in
comparison to developed markets. This, reflects that it is the best time for making investment in China. North America however is the
one of main source of earning of good amount of profit in business but there are few nations where less amount of profit is earned but
same increase at fast pace and one of them is Pakistan where profit of 36269422 is earned but its standard deviation is 1094675.90
which is lower than same of North America and Europe STDEV which is 125264671 and 33222162 respectively.
Canada and Japan are the other nations on which company can focus to expand its market. Profit earned on Canadian and
Japanese market is 128453817 and 133500470. Standard deviation of both markets is 3876887 and 4029045. It can be observed that
standard deviation of Japan is higher than other markets but less then North America and Europe. Thus, Japan can be considered as
one of lucrative area for doing business. In case of Canada, also standard deviation is high which are 3876887 that is less then North
America, Europe, China and Japan. It can be said that Canada apart from Japan is the location where good amount of profit can be
gained.
2 Difference in seasonality pattern across different channels and product lines
Seasonality is the factor that has huge influence on the firm profitability. It can be seen from the chart that is in appendix that
there is difference in seasonality pattern across different quarters of 2007, 2008 and 2009. It can be observed that due to seasonality
factor the firm in the North America and Europe market bears heavy amount of loss in Q1 2007. In case of Australia, low profit is
gained and across all nations, same level of profit is earned. It can be said that there is similarity in seasonal pattern across the nations
but in case of North America and Europe there is difference in terms of fluctuation that is observed in profit earning in Q1 of 2007 and
in further quarters in comparison to other nations. In Q1 of 2008 different trend is observed and suddenly huge plunge is observed in
2

profitability of North America and Europe market. Again, same trend is observed across all nations there is stability in earning of
profit but in case of North America, Europe and Australia, China and India there are different trends in terms of fluctuation in gained
profit. Overall, in these nations, profit is moving in same direction but rate of change in profit amount in comparison to previous
quarter profit amount vary. It can be said that there is difference in seasonality pattern across these mentioned nations in terms of
earning of profit.
3

It can be said on the basis of above chart that profit amount earned plunged by huge raneg from Q1 of 2008 to last quarter of
2008 in case of North America, Europe and Chinese market in comparison to other nations but increasing and decreasing trend was
same across all these countries. It can be said that there is high degree of similarity in seasonality pattern across regions but there is
devuation in terms of earning of profit.
4

Image given above reflect that in case of store profit is fluctuating at rapid pace. Seasonality factor is same across all channels of
distribution. Inverse trend in terms of earning of profit is not observed across all channels of distribution but rate of deviation in profit
amount vary across these channels of distribution.
3 Forecast of sales level and profit margin for three year time period for product line and channel
In retail business it is very important to identify number of factors that have heavy impact on sales performance of the business
firm. Seasonality is the one of the common factors whose impact is observed on sales in retail business. Thus, it is very important to
identify seasonal factors or months in which sales decline significantly so that according to situation changes can be made in the
inventory. On analysis of figures, it can be observed that in second quarter for upcoming three years sales will decline. This is one of
the seasonal factor that lead to decline in sales.
5

Projected profit across regions
It is predicted that profit will increast at very huge rate in case of Europe and China market. Profit will remain flat in North
America market. In case of Australia and Russia sales growth will low for upcoming quarters of 2010,2011 and 2012.
Profit forecast across channels
6

It can be seen from the chart that higher amount of profit will be earned in all quarters for years 2010,2011 and 2012 in case of stores
relative to online and other channel of distribution. At second number good amount of profit is expected to be earned in case of online
channel of distribution. It can be said that store and online will be channel of distribution through which there is probability of earning
of large amount of profit in business.
Sales across different regions
7

It is predicted that sales in North America will increase at sharp rate followed by Iran and Japan. In other nations that are revealed in
chart sales growth rate will remain moderate or low.
8

It is predicted that sales will grow consistently in the nations Pakistan, Syria, Thailand and South Korea. Whereas, in other
nations that are displayed in chart sales will increase at moderate or at slow rate.
Sales across different channel of distribution
9

It is predicted that company must focus on its stores and online channel of distribution to make maximum amount of sales in the
business relative to other channels.
4 Estimation of performance for three years and recommendation
In order to estimate the performance of the specific business it is very important to understand the market. Firm is currently
operating in retail sector and is offering electronic products. Currently, economic conditions of the nations are uncertain and in future
10

time period same may become worse. Number of steps is taken by the government to ensure that economy is on growth track but this
does not mean that uncertainty comes to end (Retail economics, 2017). This is because still major global economies are not in good
condition and if recession comes in existence it will severely affect business performance. All these things will lead to decline in the
firm performance and elevation in unemployment rate. It can be assumed that in the upcoming time period sales will increase at
moderate rate across all nations in which firm is currently operating its business. There is at least moderate probability that retail firm
will earn good amount of profit in its business. In the market North America, China, Japan and few other nations in upcoming three
years there is high probability of earning good amount of profit in business. There are some areas where firm need to work and in
respect to same recommendation is given below.
 In terms of HR it is suggested to the business firm that it must not increase its workforce and must focus on online and store
channel of distribution because through same huge amount of profit is made in the business. Through catalog and re-sellers,
less amount of sale is generated in the business. Due to this reason employee, cost burden is increasing on the firm. Hence, it is
recommended that firm must not increase its workforce in retail segment and must work with current employees. It is also
recommended that efforts must be made to make the best use of these employees in retail shop so that burden of employee cost
can be reduced in the business.
 It is also recommended that firm must try to increase its workforce in its online channel of distribution but in limited manner.
This is because that in online retail business less number of employees are required. Thus, small addition in current workforce
that is doing online-related work will prove beneficial for the firm. Managers must ensure that they are taking only those
candidates as an employee that have strong knowledge of relevant domain.
 Firm must also focus on its advertisement and under this focus must be on products and retail stores. This is because through
online channel of distribution already good amount of sales is made in the business. Through offline and other mode of
distribution at same rate gain is not made. Due to this reason in advertisement firm must show its retail shops and their
11

attraction. Apart from this, items that will be available on their retail stores must also be revealed so that customer think that all
required items are available under single roof.
CONCLUSION
On the basis of above discussion it is concluded that in upcoming years good amount of profit will be generated across most of
nations in which firm is currently operating its business. There is almost similarity in terms of seasonality across analysed nations.
Profit may fluctuate at sharp rate in American, Europe, China and Japan market. There is huge significance of analytics for the
business firms as there are many facts that are hidden in data set. Analytic is the tool that helps one in identifying trends that are
hidden in data set. There are number of analytic tools that can be used for analysis of data. It is concluded that online platform is place
from where business firms are making huge amount of sales in their business. Through offline mode less sales are generated in the
business. Thus, it is very important to ensure that effective use of assets are made in retail stores so that more and more customers can
be attracted in shop and good amount of revenue can be earned in business.
12

REFERENCES
Books and Journals
Retail economics, 2017. [Online]. Available through:< http://www.retaileconomics.co.uk/>. [Accessed on 10th August 2017].
13

APPENDIX
Question 1
Profit STDEV
North America € 4,150,637,381
€
125,264,671
Europe € 1,100,780,706 € 33,222,162
Sweden €23630.66 € 355,546
Bhutan € 24,848,449 € 750,008
Canada € 128,453,817 € 3,876,887
China € 931,195,377 € 28,102,593
Italy € 44,088,274 € 1,330,643
Singapore € 20,417,480 € 616,229
Kyrgyzstan € 21,247,720 € 641,284
Japan € 133,500,470 € 4,029,045
Spain € 12,035,683 € 363,470
Sweden € 11,773,251 € 355,546
Pakistan € 36,269,422 € 1,094,676
Russia € 56,923,928 € 1,717,986
Spain € 12,035,683 € 363,470
Thailand € 29,908,840 € 902,740
Syria € 37,693,112 € 1,258,753
Australia € 63,687,538 € 1,922,069
Question 2
Seasonality pattern
Additive seasonal index for 2007 Additive seasonal index for 2008
Q1 2017 Q2 2007 Q3 2007 Q4 2007 Q1 2008 Q2 2008 Q3 2008 Q4 2008
North
America
-
14447585
5.4
-
14215027
6.8
-
14304682
8.1
4296729
60.3
-
74727259
.88
2600930.
447
30416870
.94
4170945
8.49
Europe -
14447585
-
14215027
-
14304682
4296729
60.3
-
74727259
2600930.
447
30416870
.94
4170945
8.49
14

5.4 6.8 8.1 .88
Australia
-
1358112.
14
-
1347252.
464
-
1342552.
146
4047916.
751
-
1022197.
615
360916.5
05
28896.31
5
632384.7
95
Bhutan
-
446877.5
157
-
443760.8
706
-
442277.0
22
1332915.
408
-
390402.5
675
169889.8
725
10120.66
25
210392.0
325
1069906.
165
1202050.
115
China
-
19874144
.1
-
19710740
.62
-
19673700
.05
5925858
4.77
-
15545394
.17
2413162.
945
1496862.
095
1163536
9.13
4535547.
444
7579934.
677
India
-
1395316.
226
-
1384978.
497
-
1381903.
878
4162198.
601
-
891546.7
025
107272.7
075
51812.04
75
732461.9
475 75351.58
374133.9
9
Iran
-
967162.7
656
-
960039.6
909
-
959141.8
575
2886344.
314
-
645205.0
175
148975.7
225
114005.8
125
382223.4
825
139610.1
6
171871.6
8
Japan
-
2741244.
054
-
2715535.
431
-
2701947.
764
8158727.
25
-
2550596.
353
-
499265.2
425
367493.2
175
2682368.
378
802164.0
375
2200280.
078
Kyrgyzsta
n
-
458880.4
752
-
454504.2
197
-
453886.8
4
1367271.
535
-
278269.0
225
111616.5
375
-
24082.60
25
190735.0
875
-
3440.507
5
149588.7
025
Pakistan
-
596744.1
65
-
593389.0
632
-
592418.5
675
1782551.
796
-
494633.5
425
56889.77
75
13491.75
75
424252.0
075
115999.4
8 269201.8
Russia
-
1530581.
952
-
1523714.
922
-
1526296.
673
4580593.
547
-
368752.8
625
458229.5
975
-
114059.3
425
24582.60
75
90650.79
75
-
139112.8
725
Singapore
-
428437.4
359
-
425543.2
338
-
424622.8
955
1278603.
565 -401270
140712.6
9 19661.52
240895.7
9 40024.21 221023.8
South
Korean
-
430221.0
-
427529.0
-
425778.5
1283528.
701
-
733220.9
-
442253.9
-
399930.3
1575405.
238
253246.4
225
134048.8
425
15

682 398 927 625 225 525
Syria
-
908827.3
744
-
904512.1
74
-
903975.1
894
2717314.
738
-
663580.6
875
325486.6
125
-
166364.0
575
504458.1
325 13726.82
242092.7
7
Taiwan
-
444020.8
464
-
440630.4
588
-
438075.5
225
1322726.
828
-
435399.6
8
166069.1
5 86164.1
183166.4
3
-
77967.64
75
123140.1
925
Thailand
-
433569.8
511
-
429798.6
718
-
428195.5
828
1291564.
106
-
704465.2
9
-
260304.9
-
301120.8
3
1265891.
02
118137.2
825
364789.3
925
Turkmenis
tan
-
968444.7
618
-
964468.4
39
-
959865.9
964
2892779.
197
-
562213.8
875
-
32369.32
75
41853.47
25
552729.7
425
239678.0
2
235696.3
8
Additive seasonal index for 2009
Q1 2009 Q2 2009 Q3 2009 Q4 2009
-47294589.85 9518586.451 15244192.19 22531811.21
-47294589.85 9518586.451 15244192.19 22531811.21
-342507.8025 -100638.8125 216325.3475 226821.2675
-1154422.065 -1117534.215 1069906.165 1202050.115
-9838860.973 -2276621.149 4535547.444 7579934.677
-217521.44 -231964.13 75351.58 374133.99
-333267.6 21785.76 139610.16 171871.68
-1627976.263 -1374467.853 802164.0375 2200280.078
19190.5425 -165338.7375 -3440.5075 149588.7025
-225356.98 -159844.3 115999.48 269201.8
-277464.9325 325927.0075 90650.7975 -139112.8725
-213126.87 -47921.14 40024.21 221023.8
-211299.2775 -175995.9875 253246.4225 134048.8425
-164525.97 -91293.62 13726.82 242092.77
-101531.6675 56359.1225 -77967.6475 123140.1925
-340866.6075 -142060.0675 118137.2825 364789.3925
16

-272725.52 -202648.88 239678.02 235696.38
Seasonality pattern for channels
Seasonality index for channels for 2007 Seasonality index for channels for 2008
Q1 2017 Q2 2007 Q3 2007 Q4 2007 Q1 2008 Q2 2008 Q3 2008 Q4 2008
1
-
127,105,664.6075 71,158,795.7245
-
9,120,418.6375
65,067,287.520
5
-
60,098,089.6445
1,335,872.425
5
22,862,961.427
5
35,899,255.791
5
2 -23,372,701.1290 3,016,426.4070 9,018,768.0250
11,337,506.697
0
-
19,985,614.3900
6,464,883.006
0 6,280,723.5980 7,240,007.7860
3 -701,519.8700
-
14,019,741.7740
-
5,089,239.1080
19,810,500.752
0
-
10,972,488.9370 412,375.6870 4,558,856.0290 6,001,257.2210
4 -16,112,284.8465 1,255,267.4215 5,381,703.3155 9,475,314.1095
-
12,155,553.5265
4,865,878.045
5 2,295,520.3475 4,994,155.1335
Seasonality index for channels for 2009
Q1 2009 Q2 2009 Q3 2009 Q4 2009
-40,197,957.0935 6,653,387.6905 12,772,591.4785 20,771,977.9245
-21,023,686.5810 8,406,387.6530 7,731,726.3690 4,885,572.5590
-4,414,376.4410 1,371,956.5350 18,266.8890 3,024,153.0170
-7,096,632.7575 2,865,198.7605 2,471,600.7165 1,759,833.2805
Question 3
Forercast profit for regions
2010 2011
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
North
America
28981882
9.4
2897538
96.1
2896859
95.7 289614992.5
28954074
4.8
2894631
04.3
2893819
16
2892970
17.8
Europe
33257177
.54
1600805
84.5
2208423
71.3 203868622.9
32453905
2.5
3313987
99.2
3318557
01.1
3437461
45
Australia -
806878.8
2255035.
677
3143984.
369
4619225.339 2495015.7
89
6247827.
204
4783185.
944
6603069.
002
17

582
Russia
2164823.
688
1588222.
363
1796947.
343 2028737.696
3694994.1
82
3076365.
911
3576991.
22
3472675.
26
China
-
7677509.
133
3351624
7.21
4014852
2.97 65209375.93
34556421.
41
8062018
7.23
7079129
4.42
9855732
7.5
2012
Q1 Q2 Q3 Q4
289208240
2891154
05.5
2890183
28.9
2889168
16.4
390458239.6
3739909
20.5
3739304
55.3
3874026
23.8
4592092.819
5137139.
009
5700206.
712
6070724.
94
4333416.544
3906236.
042
4096953.
562
4239665.
541
59972054.74
7819916
1.72
9183875
7.09
1024650
91.4
Forecast profit for channels
2010 2011
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
1 279185715.6
289145127.
3
299455928.
4
310130516.
7 321181727.5
332622848.
7
344467637.
3
356730335.
5
2 138794659
145466043.
1
152445704.
5
159747888.
5 167387498.3
175380126.
2
183742084.
7
192490440.
1
3 46332054.14
47002666.8
4
47682765.0
3
48372482.9
1 49071956.52
49781323.8
6
50500724.8
8
51230301.4
8
4 76363557.52
77917969.8
8
79503134.2
9
81119659.1
4 82768164.86
84449284.1
4
86163662.2
1
87911957.0
5
18

2012
Q1 Q2 Q3 Q4
369425688.1 382568960 396175954.8 410263033.6
201643047.4 211218586.7 221236601.1 231717537.1
51970197.62 52720559.24 53481534.38 54253273.16
89694839.65 91512994.3 93367118.79 95257924.76
Forecast sales for channels
2010 2011
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
1 494250945.8
511689181.
9
529742676.
4 548433137 567783037.2
587815643.
6
608555043.
3
630026173.
7
2 245061383.1
256385401.
1
268232689.
7
280627428.
4 293594914.5
307161613.
9
321355215.
6
336204688.
1
3 82087469.08
83248558.3
4
84426070.6
7
85620238.3
7 86831297.02
88059485.5
3 89305046.2
90568224.7
5
4 135725308.8
138410459.
6
141148732.
7 143941179 146788870.3
149692899.
5
152654381.
1
155674451.
9
2012
Q1 Q2 Q3 Q4
652254851.7 675267805.2 699092705.3 723758199
351740338.4 367993874.2 384998468 402788825.5
91849270.38 93148435.8 94465977.32 95802154.85
158754270.9 161895020.2 165097905.1 168364155
Forecast sales for regions
2010 2011
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
North
America
511559653.
5
534935649.
1
559379823.
5
584940987.
7
611670183.
1
639620783.
6
668848601.
9
699412001.
2
Europe 807899.881 714435.911 631784.559 558694.942 494060.884 436904.183 386359.802 341662.777
19

4 8 2 6 7 3 4
Australia
11162449.8
3
11832047.4
8
12541812.0
2
13294152.9
5
14091624.2
6
14936933.1
9
15832949.3
4
16782714.4
7
Bhutan
8319537.24
4 9663787.96
11225239.4
6
13038986.5
1
15145794.4
3
17593015.2
9
20435652.1
7
23737595.4
4
China
177367209.
6
189607574.
2
202692663.
8
216680774.
1
231634223.
9
247619632.
6
264708217.
3
282976109.
5
India
11236288.2
9 11890556.6
12582921.7
5
13315602.0
4
14090944.9
5
14911434.6
5
15779699.9
5
16698522.7
3
Iran
7331140.59
5
7757222.80
6
8208068.70
1
8685117.53
3
9189892.20
4
9724004.12
5
10289158.3
6
10887159.0
8
Japan
31944284.8
9
36074176.1
6 40737997.1 46004776.4
51952467.0
4
58669100.0
9
66254087.6
6
74819694.2
8
Kyrgyzstan
3869839.24
4
4107136.00
2
4358983.68
7
4626274.55
6
4909955.57
8
5211031.78
9
5530569.85
4
5869701.84
7
Pakistan 8118495.07
9216796.30
6
10463679.9
6
11879246.8
3
13486317.0
2
15310797.8
6
17382101.4
8
19733619.0
1
Russia
5755953.84
1
5541672.66
3
5335368.69
1
5136744.94
9
4945515.52
2
4761405.13
5
4584148.76
2
4413491.24
4
Singapore
3860290.39
4
4112099.75
7
4380334.81
6
4666067.02
9
4970437.74
8
5294662.77
7
5640037.22
4 6007940.68
South Korean
7933720.07
9
9099813.59
2
10437298.8
4
11971366.8
9
13730911.3
6
15749072.6
4
18063862.0
8
20718877.9
1
Syria
8244383.03
7
9649224.08
3
11293449.7
3
13217851.0
7
15470169.9
7
18106283.5
1
21191590.2
1
24802632.4
9
Taiwan
3771854.93
3
4004738.47
9
4252000.82
5
4514529.75
3
4793267.85
9
5089215.93
7
5403436.57
3 5737057.96
Thailand
8304951.09
9
9502663.62
1
10873106.2
7
12441189.6
2
14235416.7
5
16288401.3
8
18637460.6
4
21325293.4
4
Turkmenistan
7519920.10
7
7961951.32
1
8429965.73
6 8925490.68 9450143.26
10005635.6
4
10593780.6
2
11216497.5
7
20

2012
Q1 Q2 Q3 Q4
731372011.6 764792452 799740057.5 836284612.9
302136.6425 267183.1899 236273.4171 208939.5207
17789452.81 18856581.97 19987724.61 21186720.67
27573058.73 32028246.9 37203293.61 43214511.86
302504695.1 323380976.4 345697959.6 369555069.7
17670846.87 18699787.64 19788641.74 20940897.8
11519915.3 12189446.99 12897891.53 13647510.51
84492698.47 95416269.26 107752085.1 121682727.1
6229629.256 6611627.249 7017049.182 7447331.401
22403258.89 25434057.91 28874875.09 32781179.24
4249186.921 4090999.277 3938700.602 3792071.663
6399842.729 6817308.814 7262006.496 7735712.109
23764126.4 27256963.73 31263176.24 35858219.5
29028995.58 33975530.01 39764952.8 46540891.96
6091277.946 6467368.341 6866679.476 7290645.058
24400756.58 27919752.82 31946247.04 36553428.9
11875818.67 12573895.58 13313006.4 14095563.17
21