I had to analyze and visualize the given data and come up with answers to the questions asked in the case study competition. The only tools allowed were Excel, Tableau, and Power BI, and I used the first two for coming up with the answers

1. Presented by-
Biswadeep Ghosh Hazra
Xavier Institute of Management, Bhubaneswar
(XIMB)
ACCS Case Solution

2. Data-Visualized
Sales-Target shows the scenario that countries, irrespective
of the Month overestimated their target. There were a few
exceptions- Canada (February, April); France
(September); Mexico (February). However, Profit and
Sales-Target are not interdependent as a high profit does not
directly mean a high/low Sales-Target
Highest profit by country and month-
Canada-June-$332,921 || France-October-$481,554
Germany- December-$408,424 || Mexico-June-$271,730
USA-May-$234,706
The graphs show that there is a strong correlation between
Target & Sales and also between Profit & Sales. Germany
leads all other countries in terms of Profit, Sales & Target.
There is seasonality in the data with possible monthly
variations.

3. Data-Visualized (Contd..)
The bar graph shows the cumulative profit levels for each
Country, each Product and each Segment. The Enterprise
segment is mostly making losses and is one of the Segment
that must be discontinued by the countries. All six products
under the Enterprise segment is making losses in USA and in
countries like Canada and France they have little success at
profit making. The Midmarket segment fares poorly (albeit
having positive profits). Channel Partners follow suit. The
two most profitable segments are- Government & Small
Businesses.
The bar graph shows the cumulative sales for each Country,
each Product and each Segment. The Channel Partners
and Midmarket Segments as seen are really low on sales and
the Enterprise section (which had poor profits- negative)
has good sales. This might lead to losses/reduced profits
for the countries over the months. Small Businesses and
Government have good sales and they definitely contribute
to the majority of the profits made by the countries. Thus, it
can be said with certainty that the Enterprise Segment if
removed can lead to better profits for the countries

4. Anomalies in Data (Quest for the True Outliers)
Using the above criteria, there were 35 outliers with respect to Sales
and a staggering 79 outliers with respect to Profit. However as
multiple countries were involved with different Segments and
Products, the standardization using Z scores seemed to be a better
method
Two methodologies were followed-
1. Finding outliers by Quartile method (Sales and
Profit)
2. Standardizing the Sales and Profit Columns and
finding entries that are 2.68 Std. Devs. away
from the mean (on either side)
The 2nd method was followed through as it was
more statistically sound
Sales Quartile 1 Quartile 3 IQR Upper Bound Lower Bound
$16,257.30 $2,72,888.00 $2,56,630.70 $6,57,834.05 -$3,68,688.75
Profit Quartile 1 Quartile 3 IQR Upper Bound Lower Bound
$2,807.20 $23,718.48 $20,911.28 $55,085.40 -$28,559.72
The table shows the Population Std.
Dev., Mean, Minimum and Maximum
value & Range for Sales and Profit
respectively. For both Sales & Profit, any
observations that lie outside 2.68 standard
deviation is considered to be an outlier.
This is because for a normal distribution,
till 2 standard deviations, 95.44% of the
datapoints are covered & till 2.68 Std. Dev.
99.6319% of the data points are covered.
The rest can be thought of as outliers
Population Standard Deviation $2,41,304.83
Mean $1,75,830.66
Minimum Value $1,655.08
Maximum Value $11,59,200.00
Range $11,57,544.92
Outlier 2.68
Population Standard Deviation $42,823.66
Mean $24,790.93
Minimum Value -$38,046.25
Maximum Value $2,62,200.00
Range $3,00,246.25
Outlier 2.68
Sales
Profit

5. Anomalies in Data (Quest for the True Outliers)
From the table above, the outliers can be seen. Z Score based on Sales has 15 outliers and Z Score based on profit has 15 outliers. The outliers
based on Z scores of Sales and Profit are combined together with a simple AND function and only the outliers common with both Sales and Profit
are taken into consideration. There are a combined of 8 outliers. Thus, only 8 data points are removed. The reason is to avoid reducing too many
datapoints by labelling them as outliers. 2 datapoints from each country are identified as outliers decreasing per country count from 105 to 103 each
Segment Country Product Sales Profit Date Target Date(String) Sales-Target Outlier(Sales) Outlier(Profit) Z score(Sales) Z Score(Profit) Z Score(Filter)-Sales Z Score(Filter)-Profit COMBINED
Government France Amarilla 9,62,500.00₹ 2,47,500.00₹ 1February2014 11,55,000.00₹ February -1,92,500.00₹ TRUE TRUE 3.260 5.201 REJECT REJECT TRUE
Government United States ofAmerica Paseo 11,59,200.00₹ 2,62,200.00₹ 1July2014 15,33,525.00₹ July -3,74,325.00₹ TRUE TRUE 4.075 5.544 REJECT REJECT TRUE
Government United States ofAmerica VTT 8,84,205.00₹ 1,54,385.00₹ 1August 2014 8,25,258.00₹ August 58,947.00₹ TRUE TRUE 2.936 3.026 REJECT REJECT TRUE
Government France Amarilla 9,36,138.00₹ 1,88,378.00₹ 1September2014 9,36,138.00₹ September -₹ TRUE TRUE 3.151 3.820 REJECT REJECT TRUE
Government Germany Velo 9,86,811.00₹ 2,38,791.00₹ 1October2014 9,76,741.50₹ October 10,069.50₹ TRUE TRUE 3.361 4.997 REJECT REJECT TRUE
Government Germany VTT 9,86,811.00₹ 2,38,791.00₹ 1October2014 8,76,046.50₹ October 1,10,764.50₹ TRUE TRUE 3.361 4.997 REJECT REJECT TRUE
Government Canada Carretera 9,78,236.00₹ 2,36,716.00₹ 1December2014 8,68,434.00₹ December 1,09,802.00₹ TRUE TRUE 3.325 4.949 REJECT REJECT TRUE
Government Canada Paseo 9,78,236.00₹ 2,36,716.00₹ 1December2014 11,27,966.00₹ December -1,49,730.00₹ TRUE TRUE 3.325 4.949 REJECT REJECT TRUE
Assumptions
1. Reducing only the common outliers will be sufficient in improving the overall quality of data
2. Any datapoint that lie outside 2.68 Std. Devs. from the mean can be treated as an outlier
3. Since dates for the data (day wise) are not specifically mentioned, the outliers are assumed to be
any random day from the month mentioned
4. Forecast done is done on an Average basis since all dates within a month are essentially
duplicates of one another (from raw data). So average for the entire month is taken into
consideration
5. There exists considerable seasonality in the data given (evident from the observations too)
Excel
sheet (1)
containing
insights
Excel
sheet (2)
containing
additional
insights

6. Improvements after removing outliers
Sales Profit Target
Mean 163324.8699 Mean 21686.19101 Mean 185291.1626
Standard Error 9723.055928 Standard Error 1532.906703 Standard Error 11200.4037
Median 35585.6 Median 9370.8 Median 41171.76
Mode 32670 Mode 0 Mode 7497
Standard Deviation 221079.2871 Standard Deviation 34854.67158 Standard Deviation 254670.68
Sample Variance 48876051176 Sample Variance 1214848131 Sample Variance 64857155270
Kurtosis 1.697694327 Kurtosis 4.471152683 Kurtosis 2.726451767
Skewness 1.577388922 Skewness 2.092261584 Skewness 1.729032813
Range 1036427.42 Range 224453.75 Range 1341297.89
Minimum 1655.08 Minimum -38046.25 Minimum 1601.11
Maximum 1038082.5 Maximum 186407.5 Maximum 1342899
Sum 84438957.75 Sum 11211760.75 Sum 95795531.07
Count 517 Count 517 Count 517
Confidence Level(95.0%) 19101.64371 Confidence Level(95.0%) 3011.505632 Confidence Level(95.0%) 22003.99982
Sales Profit Target
Mean 175830.6567 Mean 24790.92905 Mean 198275.5049
Standard Error 10541.4504 Standard Error 1870.760188 Standard Error 11992.97558
Median 36340 Median 9495.84 Median 42861
Mode 32670 Mode 0 Mode 7497
Standard Deviation 241534.9719 Standard Deviation 42864.50085 Standard Deviation 274793.5919
Sample Variance 58339142672 Sample Variance 1837365433 Sample Variance 75511518154
Kurtosis 2.046953669 Kurtosis 8.402700099 Kurtosis 2.937493848
Skewness 1.651336885 Skewness 2.658629887 Skewness 1.769517183
Range 1157544.92 Range 300246.25 Range 1531923.89
Minimum 1655.08 Minimum -38046.25 Minimum 1601.11
Maximum 1159200 Maximum 262200 Maximum 1533525
Sum 92311094.75 Sum 13015237.75 Sum 104094640.1
Count 525 Count 525 Count 525
Confidence Level(95.0%) 20708.6953 Confidence Level(95.0%) 3675.111228 Confidence Level(95.0%) 23560.21871
Sales Profit Target
Sales 1
Profit 0.8071764 1
Target 0.98153222 0.760232007 1
By comparing the Descriptive Statistics and the Correlation both before and after removal of outliers, the insights are-
Correlation between i) Sales & Profit have dropped from .81 to .77 ii) between Target & Sales have remained the same at 0.981 iii)
between Target & Profit have dropped from .76 to .72 [This can be attributed to the fact that outliers had more leverage on overall data]
The Standard Error and Skewness has decreased for Sales, Profit and Target (4.8%, 21.4%, 2.3% decrease for Skewness)
Kurtosis of Normal Distribution is 3 and Kurtosis of Profit has been reduced from 8.40 to 4.47 – a significant improvement although
Kurtosis for Sales has reduced from 2.04 to 1.7 (17% decrease)
Sales Profit Target
Sales 1
Profit 0.766305216 1
Target 0.98159378 0.718017448 1
Before removing outliers After removing outliers

7. Growth (Sales)
Sales
January February March April May June July August September October November December Grand Total
Canada $11,86,256.49 $14,82,165.98 $8,11,132.50 $15,93,562.95 $7,83,941.67 $27,25,979.40 $21,09,549.29 $9,52,043.04 $9,38,647.61 $22,15,924.48 $9,52,833.26 $20,03,257.44 $1,77,55,294.11
France $15,44,720.75 $5,74,938.46 $15,59,748.75 $13,32,862.70 $10,42,776.97 $16,29,183.98 $11,48,065.08 $7,79,802.09 $8,17,054.99 $33,79,661.56 $11,23,994.59 $23,89,929.20 $1,73,22,739.11
Germany $8,74,935.11 $13,47,335.87 $4,79,509.59 $13,94,813.46 $13,17,483.00 $16,30,025.24 $16,09,549.75 $10,46,755.17 $12,55,161.90 $14,47,965.32 $6,17,106.50 $22,83,342.44 $1,53,03,983.35
Mexico $16,55,822.85 $15,97,700.42 $9,46,494.56 $10,26,911.49 $11,16,760.07 $22,10,094.40 $9,26,957.94 $10,78,756.00 $10,22,441.26 $18,55,574.30 $11,23,522.84 $16,33,894.72 $1,61,94,930.85
United States of America $13,46,026.49 $13,32,890.66 $17,89,974.47 $16,16,624.48 $19,49,249.35 $13,23,610.80 $11,49,598.13 $11,23,061.12 $14,29,253.48 $15,03,072.26 $15,66,757.01 $17,31,892.10 $1,78,62,010.34
MoM Growths
January February March April May June July August September October November December
Canada - 24.94% -45.27% 96.46% -50.81% 247.73% -22.61% -54.87% -1.41% 136.08% -57.00% 110.24%
France - -62.78% 171.29% -14.55% -21.76% 56.24% -29.53% -32.08% 4.78% 313.64% -66.74% 112.63%
Germany - 53.99% -64.41% 190.88% -5.54% 23.72% -1.26% -34.97% 19.91% 15.36% -57.38% 270.01%
Mexico - -3.51% -40.76% 8.50% 8.75% 97.90% -58.06% 16.38% -5.22% 81.48% -39.45% 45.43%
United States of America - -0.98% 34.29% -9.68% 20.58% -32.10% -13.15% -2.31% 27.26% 5.16% 4.24% 10.54%
The growth in cumulative sales per country is shown in the table
above. October & December are the months where sales in all
countries grows in positive. The table beside shows the highest
sales growth % for respective countries and the month of growth.
France in the month of September-October clearly has the best
growth. June-July is the time period when all five countries have
negative growth followed by July-August when all countries except
Mexico have negative growth. Thus June to August will be a slump
Country Sales Growth
(Highest)
Month of highest growth
Canada 248% May-June
France 314% September-October
Germany 270% November-December
Mexico 98% May-June
USA 34% February-March
Average Sales Growth %- Canada (35), France (39),
Germany (37), Mexico (10), USA (4)

8. Growth (Profit)
Profit
January February March April May June July August September October November December Grand Total
Canada $1,38,762.99 $2,50,474.98 $83,898.50 $2,39,706.45 $80,499.67 $3,32,921.40 $2,53,011.29 $1,64,931.04 $1,24,092.61 $2,34,494.48 $71,780.26 $2,77,551.44 $22,52,125.11
France $2,47,508.75 $75,658.46 $1,31,492.75 $1,36,497.20 $1,56,518.97 $3,33,537.98 $1,31,731.08 $91,657.09 $1,47,461.99 $4,81,553.56 $1,44,743.59 $4,55,449.20 $25,33,810.61
Germany $59,908.11 $1,91,747.87 $84,851.59 $1,77,399.46 $2,02,718.00 $3,07,875.24 $1,24,518.75 $97,093.17 $2,20,121.90 $1,42,313.32 $67,615.50 $4,08,424.44 $20,84,587.35
Mexico $2,50,287.85 $2,15,689.42 $1,73,589.56 $1,70,988.49 $1,54,197.07 $2,71,730.40 $1,06,159.94 $1,93,822.00 $1,56,265.26 $2,50,624.30 $1,15,977.84 $2,55,520.72 $23,14,852.85
United States of America $1,17,560.99 $1,67,476.66 $1,96,034.47 $2,05,392.98 $2,34,706.35 $2,27,688.80 $46,244.63 $89,178.12 $1,86,812.48 $1,95,418.26 $2,04,483.01 $1,55,388.10 $20,26,384.84
MoM Growths
January February March April May June July August September October November December
Canada - 80.51% -66.50% 185.71% -66.42% 313.57% -24.00% -34.81% -24.76% 88.97% -69.39% 286.67%
France - -69.43% 73.80% 3.81% 14.67% 113.10% -60.50% -30.42% 60.88% 226.56% -69.94% 214.66%
Germany - 220.07% -55.75% 109.07% 14.27% 51.87% -59.56% -22.03% 126.71% -35.35% -52.49% 504.04%
Mexico - -13.82% -19.52% -1.50% -9.82% 76.22% -60.93% 82.58% -19.38% 60.38% -53.72% 120.32%
United States of America - 42.46% 17.05% 4.77% 14.27% -2.99% -79.69% 92.84% 109.48% 4.61% 4.64% -24.01%
Country Profit Growth
(Highest)
Month of highest growth
Canada 314% May-June
France 227% September-October
Germany 504% November-December
Mexico 120% November-December
USA 109% August-September
The growth in cumulative profit per country is shown in the table
above. The table beside shows the highest profit growth % for
respective countries and the month of growth. Ideally, sales
growth and profit growth should go hand in hand but there is a
deviation here. For Mexico and USA, the highest increase in
profit happened during a time when sales increase (in % terms)
was higher than average but not the highest. Maybe, this was due
to selling less profitable items or loss making items. All months
had at least one country with negative profit growth %, with July
being the worst (similar to sales trend) as all five countries
recorded negative growthAverage Profit Growth %- Canada (61), France (43), Germany
(73), Mexico (15), USA (17)

9. Sales Profit Target
Sales 1
Profit 0.765799 1
Target 0.981524 0.717368 1
CANADA
Sales Profit Target
Sales 1
Profit 0.765602 1
Target 0.981498 0.717118 1
GERMANY
Sales Profit Target
Sales 1
Profit 0.766305 1
Target 0.981594 0.718017 1
FRANCE
Sales Profit Target
Sales 1
Profit 0.765602 1
Target 0.981498 0.717118 1
MEXICO
Sales Profit Target
Sales 1
Profit 0.765602 1
Target 0.981498 0.717118 1
USA
Sales Profit Target
Sales 1
Profit 0.766305 1
Target 0.981594 0.718017 1
OVERALL
Correlations
Overall, there is very little difference between
correlations of different countries and the overall
correlations between Sales, Profit and Target. France
has the highest correlation for all- between Profit &
Sales, Target & Sales & Target & Profit. This is
exactly same as the overall correlation for all countries
combined. For the lowest correlations, multiple
countries share the same spot
Forecasts
Date Profit (Actual) Profit (Estd.)
1 December 2014 $23,129.29 $23,129.29
1 January 2015 $16,573.95
1 February 2015 $15,931.80
1 March 2015 $15,289.65
1 April 2015 $14,647.50
1 May 2015 $14,005.36
1 June 2015 $13,363.21
1 July 2015 $12,721.06
1 August 2015 $12,078.91
1 September 2015 $11,436.76
1 October 2015 $10,794.61
1 November 2015 $10,152.46
1 December 2015 $9,510.31
CANADA
Date Profit (Actual) Profit (Estd.)
1 December 2014 $32,532.09 $32,532.09
1 January 2015 $33,875.65
1 February 2015 $24,700.47
1 March 2015 $29,266.91
1 April 2015 $35,765.57
1 May 2015 $26,590.39
1 June 2015 $31,156.83
1 July 2015 $37,655.48
1 August 2015 $28,480.30
1 September 2015 $33,046.74
1 October 2015 $39,545.40
1 November 2015 $30,370.22
1 December 2015 $34,936.66
FRANCE
Date Profit (Actual) Profit (Estd.)
1 December 2014 $29,173.17 $29,173.17
1 January 2015 $21,884.77
1 February 2015 $22,069.24
1 March 2015 $22,253.70
1 April 2015 $22,438.16
1 May 2015 $22,622.62
1 June 2015 $22,807.08
1 July 2015 $22,991.54
1 August 2015 $23,176.01
1 September 2015 $23,360.47
1 October 2015 $23,544.93
1 November 2015 $23,729.39
1 December 2015 $23,913.85
GERMANY
Date Profit (Actual) Profit (Estd.)
1 December 2014 $11,099.15 $11,099.15
1 January 2015 $12,032.00
1 February 2015 $11,366.90
1 March 2015 $10,701.80
1 April 2015 $10,036.71
1 May 2015 $9,371.61
1 June 2015 $8,706.51
1 July 2015 $8,041.42
1 August 2015 $7,376.32
1 September 2015 $6,711.22
1 October 2015 $6,046.13
1 November 2015 $5,381.03
1 December 2015 $4,715.93
USA

10. Forecasts
Date Profit (Actual) Profit (Estd.)
1 December 2014 $18,251.48 $18,251.48
1 January 2015 $15,760.79
1 February 2015 $14,469.32
1 March 2015 $13,177.85
1 April 2015 $11,886.38
1 May 2015 $10,594.92
1 June 2015 $9,303.45
1 July 2015 $8,011.98
1 August 2015 $6,720.52
1 September 2015 $5,429.05
1 October 2015 $4,137.58
1 November 2015 $2,846.11
1 December 2015 $1,554.65
MEXICO
Effect of removal of the Enterprise Segment
Profit Growth
after removing
Enterprise-
Canada (2%),
France (2.3),
Germany (1),
Mexico (4),
USA (6.63)
January February March April May June July August September October November December
Canada $1,57,426.74 $2,52,854.98 $81,681.00 $2,37,850.20 $86,668.42 $3,68,538.90 $2,39,683.79 $1,61,469.79 $1,27,636.36 $2,29,471.98 $90,747.76 $2,63,906.44 $22,97,936.36
France $2,52,477.50 $66,638.46 $1,34,474.00 $1,31,192.82 $1,69,057.72 $3,27,635.48 $1,31,731.08 $1,07,799.59 $1,56,578.24 $5,24,271.06 $1,38,203.59 $4,52,231.70 $25,92,291.24
Germany $87,601.86 $1,92,756.62 $84,851.59 $1,56,301.96 $1,97,028.00 $3,03,950.24 $1,37,692.50 $1,35,139.42 $2,27,948.15 $1,72,150.82 $75,205.50 $4,04,159.44 $21,74,786.10
Mexico $2,55,135.35 $2,35,376.92 $1,79,159.56 $1,70,988.49 $1,56,754.57 $2,68,420.40 $99,323.69 $2,01,522.00 $1,69,452.76 $2,93,744.30 $1,21,459.09 $2,55,520.72 $24,06,857.85
UnitedStatesofAmerica $1,21,903.49 $1,74,364.16 $1,99,774.47 $2,31,234.23 $2,70,256.35 $2,22,236.30 $50,778.38 $75,073.12 $1,98,782.48 $1,97,570.76 $2,28,353.01 $1,90,350.60 $21,60,677.34
As seen earlier, the bar graph shows that there are no segments with negative profits.
The overall profit scenario has improved with USA making the most of the removal
of the Enterprise segment as all of its products were loss making in the Enterprise
segment. However, the Month on Month growth exhibits varying trends, maybe due
to the seasonality of the datapoints present. A total of 75 datapoints pertaining to
the Enterprise Segment were removed in addition to the 8 outliers before.
The forecasts are in line with the
current profit levels of the countries.
Since profits are an important source
of information for any organization,
only profit forecasts are shown here,
detailed forecasts of both Profits and
Sales are present in the Excel sheets
shared above. Forecasts are done via
Exponential Smoothing which is
always useful for time-series data
which have no particular pattern

11. THANK YOU