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Sales data analysis with explanation and regional overview & Predictive Analysis Explained

The document outlines an analysis of sales data for a multinational company conducted by business analyst Subhankar Basak, utilizing tools like Excel and R Studio. It covers steps including data simplification, creating pivot tables for revenue analysis by country and order type, and presenting findings through charts. Key insights highlight the United States as the highest revenue contributor, suggestions for improvement in underperforming regions like Sweden and Denmark, and the importance of training for better resource utilization.

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ANALYSIS OF SALES & PREDICTION MODEL
HELLO! I am Subhankar Basak Business Analyst Here I will show you how to analyse sales data. You can find me @ Linkedin
About the Data Used in this Analysis Example This data is downloaded from IBM Website to download the data click the below link. https://www.ibm.com/communities/analytics/watson-analytics-blog/sales-products-sample-data/ TOOLS USED FOR DATA ANALYSIS Microsoft Excel R Studio TOOLS FOR PREPARATION OF SLIDE Microsoft Power Point. Slide Templates from Slide Carnival
“Sales Data for Multinational Company can be overwhelming at first, however when we start simplyfying the data we get Information.
Overviewing Data Sample Lets start analyzing Sales Data for Vital Information 1
Take a close look at the Data Sample Retailer country Order method type Retailer type Product line Product type Product Year Quarter Revenue Quantity Gross margin United States Fax Outdoors Shop Camping Equipment Cooking Gear TrailChef Deluxe Cook Set 2012Q1 2012 59628.66 489 0.34754797 United States Fax Outdoors Shop Camping Equipment Cooking Gear TrailChef Double Flame 2012Q1 2012 35950.32 252 0.4742745 United States Fax Outdoors Shop Camping Equipment Tents Star Dome 2012Q1 2012 89940.48 147 0.35277197 United States Fax Outdoors Shop Camping Equipment Tents Star Gazer 2 2012Q1 2012 165883.4 1 303 0.28293788 United States Fax Outdoors Shop Camping Equipment Sleeping Bags Hibernator Lite 2012Q1 2012 119822.2 1415 0.29145017 88,475 11 144 21 DATA ROWS DATA COLUMNS PRODUCTS COUNTRIES
Now this Raw Data is useless in terms of any decision making, however we as a Business Analyst will work with the data to find out crucial information about the revenue share through analysis.  At first we are creating a Pivot Table in MS Excel,  Row Level – we have “Countries”  Column level - we have “Order Type” & “Products”  Values – we have “Revenue” i.e. Sum of Revenue. Sum of Revenue Column Labels E-mail Fax Mail Sales visit Specia l Telephone Web Grand Total Row Labels United States 613855.53 9630394.82 4346724.58 38737642.45 24077 95.14 41463959.53 553610588.5 650810960.5 Japan 13960470.39 821487.69 3795285.41 9940937.8 36456 5.5 7876782.26 244905969.6 281665498.6 China 4453684.39 757563.02 10664498.65 11359416.7 221588053.8 248823216.5 Canada 31862614.15 195549.5 2392936.69 9180026.32 12262855.8 190993682.4 246887664.9 France 253033.53 3651122.85 16698553.17 20570160.62 178350772 219523642.2 PREVIEW PIVOT TABLE
UNDERSTANDING PIVOT TABLE DATA From the Above table we get; 1. Revenue Total of each Countries 2. The segregation of the Revenue from differnent Order Types. 3. Total Revenue of each Countries. This is an important information, however we are not done yet we cannot show this pivot table in our presentation to the Management. We need graphs. Sum of Revenue Column Labels E-mail Fax Mail Sales visit Special Telephone Web Grand Total Row Labels United States 613855.53 9630394.82 4346724.58 38737642.45 2407795.14 41463959.53 553610588.5 650810960.5 Japan 13960470.39 821487.69 3795285.41 9940937.8 364565.5 7876782.26 244905969.6 281665498.6 China 4453684.39 757563.02 10664498.65 11359416.7 221588053.8 248823216.5 Canada 31862614.15 195549.5 2392936.69 9180026.32 12262855.8 190993682.4 246887664.9 France 253033.53 3651122.85 16698553.17 20570160.62 178350772 219523642.2 United Kingdom 1955831.77 1279532.73 458316.87 10345992.69 521382.36 5487268.82 199174762.4 219223087.6 Germany 17197904.03 118768.81 5674772.23 8132036.03 5358461.21 164291721.1 200773663.4 Korea 3381359.76 7944762 570265.85 5643233.32 141034511.6 158574132.5 Singapore 4619801.07 761688.62 1422191.4 5341842.49 3432525.85 140683764.7 156261814.2 Italy 13470599.28 2041782.44 5114211.56 289214.47 3928010.4 129786955 154630773.1 Lets go over to the next slide
DATA PRESENTATION USING DONUT CHART STATES TOTAL REVENUE United States 650810960.5 Japan 281665498.6 China 248823216.5 Canada 246887664.9 France 219523642.2 United Kingdom 219223087.6 Germany 200773663.4 Korea 158574132.5 Singapore 156261814.2 Italy 154630773.1 Finland 152221983.4 Netherlands 148936395.6 Mexico 139369605 Spain 136763768.7 Austria 113366615.7 Australia 109299969.1 Brazil 109065671.4 Switzerland 100731875.5 Belgium 96958569.91 Sweden 80635525.26 Denmark 47898531.93 TOTAL REVENUE, United States, 650810960.5, 17% TOTAL REVENUE, Japan, 281665498.6, 7% TOTAL REVENUE, China, 248823216.5, 7% TOTAL REVENUE, Canada, 246887664.9, 7% TOTAL REVENUE, France, 219523642.2, 6% TOTAL REVENUE, United Kingdom, 219223087.6, 6% TOTAL REVENUE, Germany, 200773663.4, 5% TOTAL REVENUE, Korea, 158574132.5, 4% TOTAL REVENUE, Singapore, 156261814.2, 4% TOTAL REVENUE, Italy, 154630773.1, 4% TOTAL REVENUE, Finland, 152221983.4, 4% TOTAL REVENUE, Netherlands, 148936395.6, 4% TOTAL REVENUE, Mexico, 139369605, 4% TOTAL REVENUE, Spain, 136763768.7, 4% TOTAL REVENUE, Austria, 113366615.7, 3% TOTAL REVENUE, Australia, 109299969.1, 3% TOTAL REVENUE, Brazil, 109065671.4, 3% TOTAL REVENUE, Switzerland, 100731875.5, 3% TOTAL REVENUE, Belgium, 96958569.91, 3% TOTAL REVENUE, Sweden, 80635525.26, 2% TOTAL REVENUE, Denmark, 47898531.93, 1% United States Japan China Canada France United Kingdom Germany Korea Singapore Italy Finland Netherlands Mexico Spain Austria Australia Brazil Switzerland Belgium Sweden Denmark
DATA PRESENTATION USING DONUT CHART TOTAL REVENUE, United States, 650810960.5, 17% TOTAL REVENUE, Japan, 281665498.6, 7% TOTAL REVENUE, China, 248823216.5, 7% TOTAL REVENUE, Canada, 246887664.9, 7%TOTAL REVENUE, France, 219523642.2, 6% TOTAL REVENUE, United Kingdom, 219223087.6, 6% TOTAL REVENUE, Germany, 200773663.4, 5% TOTAL REVENUE, Korea, 158574132.5, 4% TOTAL REVENUE, Singapore, 156261814.2, 4% TOTAL REVENUE, Italy, 154630773.1, 4% TOTAL REVENUE, Finland, 152221983.4, 4% TOTAL REVENUE, Netherlands, 148936395.6, 4% TOTAL REVENUE, Mexico, 139369605, 4% TOTAL REVENUE, Spain, 136763768.7, 4% TOTAL REVENUE, Austria, 113366615.7, 3% TOTAL REVENUE, Australia, 109299969.1, 3% TOTAL REVENUE, Brazil, 109065671.4, 3% TOTAL REVENUE, Switzerland, 100731875.5, 3% TOTAL REVENUE, Belgium, 96958569.91, 3% TOTAL REVENUE, Sweden, 80635525.26, 2% TOTAL REVENUE, Denmark, 47898531.93, 1% United States Japan China Canada France United Kingdom Germany Korea Singapore Italy Finland Netherlands Mexico Spain Austria Australia Brazil Switzerland Belgium Sweden Denmark Our Analysis from the Chart: Top 4 Countries for most revenue shares are:  United States – 17%  Japan, China, Canada – 7% Bottom 2 Countries for least revenue shares are:  Sweden – 2%  Denmark – 1% Now we got information. “ Decision Time going in depth into Sweden & Denmark,” Lets go over to the next slide
DECISION TIMEGo deep into Sweden & Denmark and conclude improvements.
PIVOT TABLE ARRANGEMENTS --------------------------- We arranged the pivot table to get the total revenue of each Retailer type, our goal is to see each retailers performance to find scope for improvement. FOCUSING ON SWEDEN & DENMARK WE ALALYSE Sum of Revenue Column Labels E-mail Fax Sales visit Telephone Web Grand Total Row Labels Sweden 3880829.46 4205772.95 8082854.82 64466068.03 80635525.26 Department Store 16315108.7 16315108.7 Direct Marketing 78042.73 770136.51 848179.24 Eyewear Store 5464185.46 5464185.46 Golf Shop 1503872.49 1503872.49 Outdoors Shop 3051204.78 1102964.04 7901582.71 13654610.64 25710362.17 Sports Store 829624.68 3102808.91 103229.38 23463321.26 27498984.23 Warehouse Store 3294832.97 3294832.97 Denmark 175517.78 3070840.1 44652174.05 47898531.93 Department Store 15684025.18 15684025.18 Direct Marketing 806544.82 541924.67 1348469.49 Equipment Rental Store 1303052.22 1303052.22 Eyewear Store 7502647.55 7502647.55 Golf Shop 2479368.37 2479368.37 Outdoors Shop 2074894.91 9589527.09 11664422 Sports Store 189400.37 4702061.82 4891462.19 Warehouse Store 175517.78 2849567.15 3025084.93 Grand Total 3880829.46 175517.78 7276613.05 8082854.82 109118242.1 128534057.2
PRESENTATION FOR SWEDEN & DENMARK RETAILER WISE REVENUE Total, Sweden Department Store, 16315108.7 Total, Sweden Direct Marketing, 848179.24 Total, Sweden Eyewear Store, 5464185.46 Total, Sweden Golf Shop, 1503872.49 Total, Sweden Outdoors Shop, 25710362.17 Total, Sweden Sports Store, 27498984.23 Total, Sweden Warehouse Store, 3294832.97 Total, Denmark Department Store, 15684025.18 Total, Denmark Direct Marketing, 1348469.49 Total, Denmark Equipment Rental Store, 1303052.22 Total, Denmark Eyewear Store, 7502647.55 Total, Denmark Golf Shop, 2479368.37 Total, Denmark Outdoors Shop, 11664422 Total, Denmark Sports Store, 4891462.19Total, Denmark Warehouse Store, 3025084.93 Row Labels Sum of Revenue Sweden 80635525.26 Department Store 16315108.7 Direct Marketing 848179.24 Eyewear Store 5464185.46 Golf Shop 1503872.49 Outdoors Shop 25710362.17 Sports Store 27498984.23 Warehouse Store 3294832.97 Denmark 47898531.93 Department Store 15684025.18 Direct Marketing 1348469.49 Equipment Rental Store 1303052.22 Eyewear Store 7502647.55 Golf Shop 2479368.37 Outdoors Shop 11664422 Sports Store 4891462.19 Warehouse Store 3025084.93 Grand Total 128534057.2
PRESENTATION FOR SWEDEN & DENMARK RETAILER WISE REVENUE Total, Sweden Department Store, 16315108.7 Total, Sweden Direct Marketing, 848179.24 Total, Sweden Eyewear Store, 5464185.46 Total, Sweden Golf Shop, 1503872.49 Total, Sweden Outdoors Shop, 25710362.17 Total, Sweden Sports Store, 27498984.23 Total, Sweden Warehouse Store, 3294832.97 Total, Denmark Department Store, 15684025.18 Total, Denmark Direct Marketing, 1348469.49 Total, Denmark Equipment Rental Store, 1303052.22 Total, Denmark Eyewear Store, 7502647.55 Total, Denmark Golf Shop, 2479368.37 Total, Denmark Outdoors Shop, 11664422 Total, Denmark Sports Store, 4891462.19 Total, Denmark Warehouse Store, 3025084.93 Analysis Report From this chart we can conclude that; In Sweden our poor performers are; 1. Direct Marketing Team 2. Golf Shop In Denmark our poor performers are; 1. Equipment Rental Store 2. Direct Marketing Team Now we found out which retailer are our poor performers, So we will know move deep into each of this retailer to check how well they are utilizing resource.
SWEDEN POSSIBLE CAUSE & SUGGESTED IMPROVEMENTS EXPLAINED Analysis Report for SWEDEN Here we conclude; Direct Marketing Team & Golf Shop are not utilizing all of the resource and are the weakest of all in generating Revenue. Moreover; Department Store, Eyewear Store, Warehouse Store are also not utilizing resource to the fullest thus Sweden is the overall poor performer. Possible Cause: Lack of knowledge. [Note this is a general conclusion for better result feedback from retailers are mandatory.] Suggested Improvements: Training in Email, Sales Visit & Telephonic Marketing for Retailers in Sweden. Retailer country Sweden Sum of Revenue Column Labels Row Labels E-mail Sales visit Telephone Web Grand Total Department Store 16315108.7 16315108.7 Direct Marketing 78042.73 770136.51 848179.24 Eyewear Store 5464185.46 5464185.46 Golf Shop 1503872.49 1503872.49 Outdoors Shop 3051204.78 1102964.04 7901582.71 13654610.64 25710362.17 Sports Store 829624.68 3102808.91 103229.38 23463321.26 27498984.23 Warehouse Store 3294832.97 3294832.97 Grand Total 3880829.46 4205772.95 8082854.82 64466068.03 80635525.26
DENMARK POSSIBLE CAUSE & SUGGESTED IMPROVEMENTS EXPLAINED Analysis Report for SWEDEN Here we conclude; Direct Marketing Team & Equipment Rental Store are not utilizing all of the resource and are the weakest of all in generating Revenue. Moreover; Department Store, Eyewear Store, Warehouse Store, Golf Shop, Outdoor Shop & Sports Store are also not utilizing resource to the fullest thus Denmark is the overall poor performer. Possible Cause: Lack of knowledge. [Note this is a general conclusion for better result feedback from retailers are mandatory.] Suggested Improvements: Training in Email, Sales Visit & Telephonic Marketing for Retailers in Denmark. Retailer country Denmark Sum of Revenue Column Labels Row Labels Fax Sales visit Web Grand Total Department Store 15684025.18 15684025.18 Direct Marketing 806544.82 541924.67 1348469.49 Equipment Rental Store 1303052.22 1303052.22 Eyewear Store 7502647.55 7502647.55 Golf Shop 2479368.37 2479368.37 Outdoors Shop 2074894.91 9589527.09 11664422 Sports Store 189400.37 4702061.82 4891462.19 Warehouse Store 175517.78 2849567.15 3025084.93 Grand Total 175517.78 3070840.1 44652174.05 47898531.93
DENMARK POSSIBLE CAUSE & SUGGESTED IMPROVEMENTS EXPLAINED Analysis Report for All Countries Retailers We can safely comment that all other Countries retailers are utilizing all the resource however training can be a scope for Improvements Retailer country (All) Sum of Revenue Column Labels Row Labels E-mail Fax Mail Sales visit Special Telephone Web Grand Total Department Store 21891035.28 3387670.1 6770557.83 24259359.44 330808.09 36252434.25 569553845.9 662445710.8 Direct Marketing 1821356.46 12870.04 417580.97 9626910.75 1738879.43 40749479.96 54367077.61 Equipment Rental Store 75009.5 118768.81 532122.09 5018669.64 20856.64 1325558.36 36343206.23 43434191.27 Eyewear Store 453745.4 1312644.59 6236725.59 3392836.54 160322203.1 171718155.3 Golf Shop 4866719.76 5626272.5 1655897.9 10013141.1 1278483.46 21213341.34 366216312.8 410870168.9 Outdoors Shop 25233882.49 20513926.14 7161324.06 93319892.46 9432540 52046910.24 1059662257 1267370732 Sports Store 29547732.2 11599044.5 3089877.73 43924810.12 799923.46 26105030.15 854814341.7 969880759.9 Warehouse Store 550766.86 175517.78 2384483.29 16722603.8 1582789.85 20117210.39 150802797.1 192336169 Grand Total 84440247.95 41434069.87 23324488.46 209122112.9 13445401.5 162192200.7 3238464444 3772422965
“This brings us to the end of our Analysis, and finding out the poor performer and also helped in understanding the SCOPE FOR IMPROVEMENTS
NEXT CONCEPTHow well are the product lines are performing throughout the globe.
COUNTRY WISE PRODUCT LINES PERFORMANCE Denmark Sweden Belgium Switzerland Brazil Australia Austria Spain Mexico Netherlands Finland Italy Singapore Korea Germany
COUNTRY WISE PRODUCT LINES PERFORMANCE Denmark Sweden Belgium Switzerland Brazil Australia Austria Spain Mexico Netherlands Finland Italy Singapore Korea Germany United Kingdom France Canada China Japan United States From this chart it is clear that the “Outdoor Protection” product line is our poor performer globally. OUR GOAL Finding out ways to increase revenue globally from the product line “Outdoor Protection” Lets Begin.
ANALYSING OUTDOOR PROTECTION PRODUCT LINE The above headers are provided in client’s data, along that client informed that they invest 5% of difference of Revenue & Cost of Good Sold for marketing. [ *Note this is a hypothetic statement.] For that information we would calculate the required Cost of Goods Sold (COGS) & Marketing Cost. Calculation for COGS: Retailer country Order method type Retailer type Product line Product type Product Year Quarter Revenue Quantity Gross margin Formula Gross Margin = Revenue - COGS X 100 Revenue COGS = Revenue - Gross Margin x Revenue 100 According to the above formula we will calculate the COGS and marketing cost.
ANALYSING OUTDOOR PROTECTION PRODUCT LINE Now to start with, create a new excel file with the data for “Outdoor Protection” product line. To do checklist:  Remove rows with Value in Revenue as zero “0”.  Remove all the “Non quantitative” column from the file, such as Retailer country , Order method type , Retailer type , Product line , Product type , Product , Year , Quarter. Now that we are left with Revenue , Quantity , Gross margin , COGS , Marketing Cost, *NB : COGS & Marketing Cost are calculated see previous slide. From here we will do regression analysis using R Studio.
ANALYSING OUTDOOR PROTECTION PRODUCT LINE data <- read.csv("D:R - BIG DATAAnalysisSales Data WAOutdoorProtectionProduct.csv", header=T) # For Regression analysis we need Quantitative Value only # Hence deleting the other columns. data <- data[,-(1:8)] # For regression analysis we need to check the following; # Normality should hold, # There should be no Multi colinearity # Data should be Homoscedacity # There should be no Autocorrelation # NB. To make our life easy in R the linear regression code gives output # where there is a p-value determining significance, # here we will not go into all the above checks we will simply work with # considering p-value and i will explain it along side. lm.mod <- lm(Revenue~., data = data) summary(lm.mod) R CODE R CONSOLE OUTPUT Call: lm(formula = Revenue ~ ., data = data) Residuals: Min 1Q Median 3Q Max -1.012e-05 -1.560e-08 -6.000e-09 2.200e-08 1.039e-05 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.891e-08 4.154e-08 -4.550e-01 0.64900 Quantity 8.607e-11 3.242e-11 2.655e+00 0.00794 ** Gross.margin 3.980e-08 6.521e-08 6.100e-01 0.54165 COGS 1.000e+00 1.789e-11 5.590e+10 < 2e-16 *** Marketing.Cost 2.000e+01 2.489e-10 8.034e+10 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 9.581e-07 on 8349 degrees of freedom Multiple R-squared: 1, Adjusted R-squared: 1 F-statistic: 1.16e+23 on 4 and 8349 DF, p-value: < 2.2e-16 Note the p-value and the “*” beside p-value. From the above we should remove Gross Margin as it is of no significance.
ANALYSING OUTDOOR PROTECTION PRODUCT LINE # In addition we are writing the following code to generate linear model. lm.mod <- lm(Revenue~Quantity+COGS+Marketing.Cost, data = data) summary(lm.mod) R CODE R CONSOLE OUTPUT Call: lm(formula = Revenue ~ Quantity + COGS + Marketing.Cost, data = data) Residuals: Min 1Q Median 3Q Max -1.012e-05 -1.520e-08 -6.100e-09 2.250e-08 1.039e-05 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 5.245e-09 1.265e-08 4.150e-01 0.67848 Quantity 8.520e-11 3.238e-11 2.631e+00 0.00852 ** COGS 1.000e+00 1.676e-11 5.968e+10 < 2e-16 *** Marketing.Cost 2.000e+01 2.342e-10 8.539e+10 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 9.581e-07 on 8350 degrees of freedom Multiple R-squared: 1, Adjusted R-squared: 1 F-statistic: 1.547e+23 on 3 and 8350 DF, p-value: < 2.2e-16 Note the p-value and the “*” beside p-value. No we find all the variables are significant. So lets create the linear equation Y=mx1+mx2+mx3+b Y = Predicted Revenue m = Slope or the estimate from console result. x1, x2, x3 = Quantity, COGS, Marketing.Cost b = Intercept; Hence the formula; by replacing ‘m’ with the coefficient. Y = 8.52*x1 + 1*x2 + 2*x3 + 5.25
Prediction Revenue Now that we have our regression equation Y = 8.52*x1 + 1*x2 + 2*x3 + 5.25 Here ‘Y’ being the revenue. We need the substitute the x1, x2 & x3 with respective values to determine revenue, where x1 – Quantity. x2 – COGS, x3 – Marketing cost Lets Analyze for situation where, Quantity produced = 100 unit, COGS = 100 & MC= 100; Y=8.52*100+1*100+2*100+5.25 Y=1157 Now changing 1 unit in Quantity, and considering all other remain constant Y=8.52*101+1*100+2*100+5.25 Y=1166 We have a increase in Revenue with 1 unit change in Production.
Data Analysis - Conclusion Based on Multiple Linear regression analysis we can make vital Decision for betterment of the Business. With this we conclude our presentation. Thank you for your time Subhankar Basak

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Sales data analysis with explanation and regional overview & Predictive Analysis Explained

  • 1.
    ANALYSIS OF SALES& PREDICTION MODEL
  • 2.
    HELLO! I am SubhankarBasak Business Analyst Here I will show you how to analyse sales data. You can find me @ Linkedin
  • 3.
    About the DataUsed in this Analysis Example This data is downloaded from IBM Website to download the data click the below link. https://www.ibm.com/communities/analytics/watson-analytics-blog/sales-products-sample-data/ TOOLS USED FOR DATA ANALYSIS Microsoft Excel R Studio TOOLS FOR PREPARATION OF SLIDE Microsoft Power Point. Slide Templates from Slide Carnival
  • 4.
    “Sales Data forMultinational Company can be overwhelming at first, however when we start simplyfying the data we get Information.
  • 5.
    Overviewing Data Sample Letsstart analyzing Sales Data for Vital Information 1
  • 6.
    Take a closelook at the Data Sample Retailer country Order method type Retailer type Product line Product type Product Year Quarter Revenue Quantity Gross margin United States Fax Outdoors Shop Camping Equipment Cooking Gear TrailChef Deluxe Cook Set 2012Q1 2012 59628.66 489 0.34754797 United States Fax Outdoors Shop Camping Equipment Cooking Gear TrailChef Double Flame 2012Q1 2012 35950.32 252 0.4742745 United States Fax Outdoors Shop Camping Equipment Tents Star Dome 2012Q1 2012 89940.48 147 0.35277197 United States Fax Outdoors Shop Camping Equipment Tents Star Gazer 2 2012Q1 2012 165883.4 1 303 0.28293788 United States Fax Outdoors Shop Camping Equipment Sleeping Bags Hibernator Lite 2012Q1 2012 119822.2 1415 0.29145017 88,475 11 144 21 DATA ROWS DATA COLUMNS PRODUCTS COUNTRIES
  • 7.
    Now this RawData is useless in terms of any decision making, however we as a Business Analyst will work with the data to find out crucial information about the revenue share through analysis.  At first we are creating a Pivot Table in MS Excel,  Row Level – we have “Countries”  Column level - we have “Order Type” & “Products”  Values – we have “Revenue” i.e. Sum of Revenue. Sum of Revenue Column Labels E-mail Fax Mail Sales visit Specia l Telephone Web Grand Total Row Labels United States 613855.53 9630394.82 4346724.58 38737642.45 24077 95.14 41463959.53 553610588.5 650810960.5 Japan 13960470.39 821487.69 3795285.41 9940937.8 36456 5.5 7876782.26 244905969.6 281665498.6 China 4453684.39 757563.02 10664498.65 11359416.7 221588053.8 248823216.5 Canada 31862614.15 195549.5 2392936.69 9180026.32 12262855.8 190993682.4 246887664.9 France 253033.53 3651122.85 16698553.17 20570160.62 178350772 219523642.2 PREVIEW PIVOT TABLE
  • 8.
    UNDERSTANDING PIVOT TABLEDATA From the Above table we get; 1. Revenue Total of each Countries 2. The segregation of the Revenue from differnent Order Types. 3. Total Revenue of each Countries. This is an important information, however we are not done yet we cannot show this pivot table in our presentation to the Management. We need graphs. Sum of Revenue Column Labels E-mail Fax Mail Sales visit Special Telephone Web Grand Total Row Labels United States 613855.53 9630394.82 4346724.58 38737642.45 2407795.14 41463959.53 553610588.5 650810960.5 Japan 13960470.39 821487.69 3795285.41 9940937.8 364565.5 7876782.26 244905969.6 281665498.6 China 4453684.39 757563.02 10664498.65 11359416.7 221588053.8 248823216.5 Canada 31862614.15 195549.5 2392936.69 9180026.32 12262855.8 190993682.4 246887664.9 France 253033.53 3651122.85 16698553.17 20570160.62 178350772 219523642.2 United Kingdom 1955831.77 1279532.73 458316.87 10345992.69 521382.36 5487268.82 199174762.4 219223087.6 Germany 17197904.03 118768.81 5674772.23 8132036.03 5358461.21 164291721.1 200773663.4 Korea 3381359.76 7944762 570265.85 5643233.32 141034511.6 158574132.5 Singapore 4619801.07 761688.62 1422191.4 5341842.49 3432525.85 140683764.7 156261814.2 Italy 13470599.28 2041782.44 5114211.56 289214.47 3928010.4 129786955 154630773.1 Lets go over to the next slide
  • 9.
    DATA PRESENTATION USINGDONUT CHART STATES TOTAL REVENUE United States 650810960.5 Japan 281665498.6 China 248823216.5 Canada 246887664.9 France 219523642.2 United Kingdom 219223087.6 Germany 200773663.4 Korea 158574132.5 Singapore 156261814.2 Italy 154630773.1 Finland 152221983.4 Netherlands 148936395.6 Mexico 139369605 Spain 136763768.7 Austria 113366615.7 Australia 109299969.1 Brazil 109065671.4 Switzerland 100731875.5 Belgium 96958569.91 Sweden 80635525.26 Denmark 47898531.93 TOTAL REVENUE, United States, 650810960.5, 17% TOTAL REVENUE, Japan, 281665498.6, 7% TOTAL REVENUE, China, 248823216.5, 7% TOTAL REVENUE, Canada, 246887664.9, 7% TOTAL REVENUE, France, 219523642.2, 6% TOTAL REVENUE, United Kingdom, 219223087.6, 6% TOTAL REVENUE, Germany, 200773663.4, 5% TOTAL REVENUE, Korea, 158574132.5, 4% TOTAL REVENUE, Singapore, 156261814.2, 4% TOTAL REVENUE, Italy, 154630773.1, 4% TOTAL REVENUE, Finland, 152221983.4, 4% TOTAL REVENUE, Netherlands, 148936395.6, 4% TOTAL REVENUE, Mexico, 139369605, 4% TOTAL REVENUE, Spain, 136763768.7, 4% TOTAL REVENUE, Austria, 113366615.7, 3% TOTAL REVENUE, Australia, 109299969.1, 3% TOTAL REVENUE, Brazil, 109065671.4, 3% TOTAL REVENUE, Switzerland, 100731875.5, 3% TOTAL REVENUE, Belgium, 96958569.91, 3% TOTAL REVENUE, Sweden, 80635525.26, 2% TOTAL REVENUE, Denmark, 47898531.93, 1% United States Japan China Canada France United Kingdom Germany Korea Singapore Italy Finland Netherlands Mexico Spain Austria Australia Brazil Switzerland Belgium Sweden Denmark
  • 10.
    DATA PRESENTATION USINGDONUT CHART TOTAL REVENUE, United States, 650810960.5, 17% TOTAL REVENUE, Japan, 281665498.6, 7% TOTAL REVENUE, China, 248823216.5, 7% TOTAL REVENUE, Canada, 246887664.9, 7%TOTAL REVENUE, France, 219523642.2, 6% TOTAL REVENUE, United Kingdom, 219223087.6, 6% TOTAL REVENUE, Germany, 200773663.4, 5% TOTAL REVENUE, Korea, 158574132.5, 4% TOTAL REVENUE, Singapore, 156261814.2, 4% TOTAL REVENUE, Italy, 154630773.1, 4% TOTAL REVENUE, Finland, 152221983.4, 4% TOTAL REVENUE, Netherlands, 148936395.6, 4% TOTAL REVENUE, Mexico, 139369605, 4% TOTAL REVENUE, Spain, 136763768.7, 4% TOTAL REVENUE, Austria, 113366615.7, 3% TOTAL REVENUE, Australia, 109299969.1, 3% TOTAL REVENUE, Brazil, 109065671.4, 3% TOTAL REVENUE, Switzerland, 100731875.5, 3% TOTAL REVENUE, Belgium, 96958569.91, 3% TOTAL REVENUE, Sweden, 80635525.26, 2% TOTAL REVENUE, Denmark, 47898531.93, 1% United States Japan China Canada France United Kingdom Germany Korea Singapore Italy Finland Netherlands Mexico Spain Austria Australia Brazil Switzerland Belgium Sweden Denmark Our Analysis from the Chart: Top 4 Countries for most revenue shares are:  United States – 17%  Japan, China, Canada – 7% Bottom 2 Countries for least revenue shares are:  Sweden – 2%  Denmark – 1% Now we got information. “ Decision Time going in depth into Sweden & Denmark,” Lets go over to the next slide
  • 11.
    DECISION TIMEGo deepinto Sweden & Denmark and conclude improvements.
  • 12.
    PIVOT TABLE ARRANGEMENTS --------------------------- We arrangedthe pivot table to get the total revenue of each Retailer type, our goal is to see each retailers performance to find scope for improvement. FOCUSING ON SWEDEN & DENMARK WE ALALYSE Sum of Revenue Column Labels E-mail Fax Sales visit Telephone Web Grand Total Row Labels Sweden 3880829.46 4205772.95 8082854.82 64466068.03 80635525.26 Department Store 16315108.7 16315108.7 Direct Marketing 78042.73 770136.51 848179.24 Eyewear Store 5464185.46 5464185.46 Golf Shop 1503872.49 1503872.49 Outdoors Shop 3051204.78 1102964.04 7901582.71 13654610.64 25710362.17 Sports Store 829624.68 3102808.91 103229.38 23463321.26 27498984.23 Warehouse Store 3294832.97 3294832.97 Denmark 175517.78 3070840.1 44652174.05 47898531.93 Department Store 15684025.18 15684025.18 Direct Marketing 806544.82 541924.67 1348469.49 Equipment Rental Store 1303052.22 1303052.22 Eyewear Store 7502647.55 7502647.55 Golf Shop 2479368.37 2479368.37 Outdoors Shop 2074894.91 9589527.09 11664422 Sports Store 189400.37 4702061.82 4891462.19 Warehouse Store 175517.78 2849567.15 3025084.93 Grand Total 3880829.46 175517.78 7276613.05 8082854.82 109118242.1 128534057.2
  • 13.
    PRESENTATION FOR SWEDEN& DENMARK RETAILER WISE REVENUE Total, Sweden Department Store, 16315108.7 Total, Sweden Direct Marketing, 848179.24 Total, Sweden Eyewear Store, 5464185.46 Total, Sweden Golf Shop, 1503872.49 Total, Sweden Outdoors Shop, 25710362.17 Total, Sweden Sports Store, 27498984.23 Total, Sweden Warehouse Store, 3294832.97 Total, Denmark Department Store, 15684025.18 Total, Denmark Direct Marketing, 1348469.49 Total, Denmark Equipment Rental Store, 1303052.22 Total, Denmark Eyewear Store, 7502647.55 Total, Denmark Golf Shop, 2479368.37 Total, Denmark Outdoors Shop, 11664422 Total, Denmark Sports Store, 4891462.19Total, Denmark Warehouse Store, 3025084.93 Row Labels Sum of Revenue Sweden 80635525.26 Department Store 16315108.7 Direct Marketing 848179.24 Eyewear Store 5464185.46 Golf Shop 1503872.49 Outdoors Shop 25710362.17 Sports Store 27498984.23 Warehouse Store 3294832.97 Denmark 47898531.93 Department Store 15684025.18 Direct Marketing 1348469.49 Equipment Rental Store 1303052.22 Eyewear Store 7502647.55 Golf Shop 2479368.37 Outdoors Shop 11664422 Sports Store 4891462.19 Warehouse Store 3025084.93 Grand Total 128534057.2
  • 14.
    PRESENTATION FOR SWEDEN& DENMARK RETAILER WISE REVENUE Total, Sweden Department Store, 16315108.7 Total, Sweden Direct Marketing, 848179.24 Total, Sweden Eyewear Store, 5464185.46 Total, Sweden Golf Shop, 1503872.49 Total, Sweden Outdoors Shop, 25710362.17 Total, Sweden Sports Store, 27498984.23 Total, Sweden Warehouse Store, 3294832.97 Total, Denmark Department Store, 15684025.18 Total, Denmark Direct Marketing, 1348469.49 Total, Denmark Equipment Rental Store, 1303052.22 Total, Denmark Eyewear Store, 7502647.55 Total, Denmark Golf Shop, 2479368.37 Total, Denmark Outdoors Shop, 11664422 Total, Denmark Sports Store, 4891462.19 Total, Denmark Warehouse Store, 3025084.93 Analysis Report From this chart we can conclude that; In Sweden our poor performers are; 1. Direct Marketing Team 2. Golf Shop In Denmark our poor performers are; 1. Equipment Rental Store 2. Direct Marketing Team Now we found out which retailer are our poor performers, So we will know move deep into each of this retailer to check how well they are utilizing resource.
  • 15.
    SWEDEN POSSIBLE CAUSE& SUGGESTED IMPROVEMENTS EXPLAINED Analysis Report for SWEDEN Here we conclude; Direct Marketing Team & Golf Shop are not utilizing all of the resource and are the weakest of all in generating Revenue. Moreover; Department Store, Eyewear Store, Warehouse Store are also not utilizing resource to the fullest thus Sweden is the overall poor performer. Possible Cause: Lack of knowledge. [Note this is a general conclusion for better result feedback from retailers are mandatory.] Suggested Improvements: Training in Email, Sales Visit & Telephonic Marketing for Retailers in Sweden. Retailer country Sweden Sum of Revenue Column Labels Row Labels E-mail Sales visit Telephone Web Grand Total Department Store 16315108.7 16315108.7 Direct Marketing 78042.73 770136.51 848179.24 Eyewear Store 5464185.46 5464185.46 Golf Shop 1503872.49 1503872.49 Outdoors Shop 3051204.78 1102964.04 7901582.71 13654610.64 25710362.17 Sports Store 829624.68 3102808.91 103229.38 23463321.26 27498984.23 Warehouse Store 3294832.97 3294832.97 Grand Total 3880829.46 4205772.95 8082854.82 64466068.03 80635525.26
  • 16.
    DENMARK POSSIBLE CAUSE& SUGGESTED IMPROVEMENTS EXPLAINED Analysis Report for SWEDEN Here we conclude; Direct Marketing Team & Equipment Rental Store are not utilizing all of the resource and are the weakest of all in generating Revenue. Moreover; Department Store, Eyewear Store, Warehouse Store, Golf Shop, Outdoor Shop & Sports Store are also not utilizing resource to the fullest thus Denmark is the overall poor performer. Possible Cause: Lack of knowledge. [Note this is a general conclusion for better result feedback from retailers are mandatory.] Suggested Improvements: Training in Email, Sales Visit & Telephonic Marketing for Retailers in Denmark. Retailer country Denmark Sum of Revenue Column Labels Row Labels Fax Sales visit Web Grand Total Department Store 15684025.18 15684025.18 Direct Marketing 806544.82 541924.67 1348469.49 Equipment Rental Store 1303052.22 1303052.22 Eyewear Store 7502647.55 7502647.55 Golf Shop 2479368.37 2479368.37 Outdoors Shop 2074894.91 9589527.09 11664422 Sports Store 189400.37 4702061.82 4891462.19 Warehouse Store 175517.78 2849567.15 3025084.93 Grand Total 175517.78 3070840.1 44652174.05 47898531.93
  • 17.
    DENMARK POSSIBLE CAUSE& SUGGESTED IMPROVEMENTS EXPLAINED Analysis Report for All Countries Retailers We can safely comment that all other Countries retailers are utilizing all the resource however training can be a scope for Improvements Retailer country (All) Sum of Revenue Column Labels Row Labels E-mail Fax Mail Sales visit Special Telephone Web Grand Total Department Store 21891035.28 3387670.1 6770557.83 24259359.44 330808.09 36252434.25 569553845.9 662445710.8 Direct Marketing 1821356.46 12870.04 417580.97 9626910.75 1738879.43 40749479.96 54367077.61 Equipment Rental Store 75009.5 118768.81 532122.09 5018669.64 20856.64 1325558.36 36343206.23 43434191.27 Eyewear Store 453745.4 1312644.59 6236725.59 3392836.54 160322203.1 171718155.3 Golf Shop 4866719.76 5626272.5 1655897.9 10013141.1 1278483.46 21213341.34 366216312.8 410870168.9 Outdoors Shop 25233882.49 20513926.14 7161324.06 93319892.46 9432540 52046910.24 1059662257 1267370732 Sports Store 29547732.2 11599044.5 3089877.73 43924810.12 799923.46 26105030.15 854814341.7 969880759.9 Warehouse Store 550766.86 175517.78 2384483.29 16722603.8 1582789.85 20117210.39 150802797.1 192336169 Grand Total 84440247.95 41434069.87 23324488.46 209122112.9 13445401.5 162192200.7 3238464444 3772422965
  • 18.
    “This brings usto the end of our Analysis, and finding out the poor performer and also helped in understanding the SCOPE FOR IMPROVEMENTS
  • 19.
    NEXT CONCEPTHow wellare the product lines are performing throughout the globe.
  • 20.
    COUNTRY WISE PRODUCTLINES PERFORMANCE Denmark Sweden Belgium Switzerland Brazil Australia Austria Spain Mexico Netherlands Finland Italy Singapore Korea Germany
  • 21.
    COUNTRY WISE PRODUCTLINES PERFORMANCE Denmark Sweden Belgium Switzerland Brazil Australia Austria Spain Mexico Netherlands Finland Italy Singapore Korea Germany United Kingdom France Canada China Japan United States From this chart it is clear that the “Outdoor Protection” product line is our poor performer globally. OUR GOAL Finding out ways to increase revenue globally from the product line “Outdoor Protection” Lets Begin.
  • 22.
    ANALYSING OUTDOOR PROTECTIONPRODUCT LINE The above headers are provided in client’s data, along that client informed that they invest 5% of difference of Revenue & Cost of Good Sold for marketing. [ *Note this is a hypothetic statement.] For that information we would calculate the required Cost of Goods Sold (COGS) & Marketing Cost. Calculation for COGS: Retailer country Order method type Retailer type Product line Product type Product Year Quarter Revenue Quantity Gross margin Formula Gross Margin = Revenue - COGS X 100 Revenue COGS = Revenue - Gross Margin x Revenue 100 According to the above formula we will calculate the COGS and marketing cost.
  • 23.
    ANALYSING OUTDOOR PROTECTIONPRODUCT LINE Now to start with, create a new excel file with the data for “Outdoor Protection” product line. To do checklist:  Remove rows with Value in Revenue as zero “0”.  Remove all the “Non quantitative” column from the file, such as Retailer country , Order method type , Retailer type , Product line , Product type , Product , Year , Quarter. Now that we are left with Revenue , Quantity , Gross margin , COGS , Marketing Cost, *NB : COGS & Marketing Cost are calculated see previous slide. From here we will do regression analysis using R Studio.
  • 24.
    ANALYSING OUTDOOR PROTECTIONPRODUCT LINE data <- read.csv("D:R - BIG DATAAnalysisSales Data WAOutdoorProtectionProduct.csv", header=T) # For Regression analysis we need Quantitative Value only # Hence deleting the other columns. data <- data[,-(1:8)] # For regression analysis we need to check the following; # Normality should hold, # There should be no Multi colinearity # Data should be Homoscedacity # There should be no Autocorrelation # NB. To make our life easy in R the linear regression code gives output # where there is a p-value determining significance, # here we will not go into all the above checks we will simply work with # considering p-value and i will explain it along side. lm.mod <- lm(Revenue~., data = data) summary(lm.mod) R CODE R CONSOLE OUTPUT Call: lm(formula = Revenue ~ ., data = data) Residuals: Min 1Q Median 3Q Max -1.012e-05 -1.560e-08 -6.000e-09 2.200e-08 1.039e-05 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.891e-08 4.154e-08 -4.550e-01 0.64900 Quantity 8.607e-11 3.242e-11 2.655e+00 0.00794 ** Gross.margin 3.980e-08 6.521e-08 6.100e-01 0.54165 COGS 1.000e+00 1.789e-11 5.590e+10 < 2e-16 *** Marketing.Cost 2.000e+01 2.489e-10 8.034e+10 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 9.581e-07 on 8349 degrees of freedom Multiple R-squared: 1, Adjusted R-squared: 1 F-statistic: 1.16e+23 on 4 and 8349 DF, p-value: < 2.2e-16 Note the p-value and the “*” beside p-value. From the above we should remove Gross Margin as it is of no significance.
  • 25.
    ANALYSING OUTDOOR PROTECTIONPRODUCT LINE # In addition we are writing the following code to generate linear model. lm.mod <- lm(Revenue~Quantity+COGS+Marketing.Cost, data = data) summary(lm.mod) R CODE R CONSOLE OUTPUT Call: lm(formula = Revenue ~ Quantity + COGS + Marketing.Cost, data = data) Residuals: Min 1Q Median 3Q Max -1.012e-05 -1.520e-08 -6.100e-09 2.250e-08 1.039e-05 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 5.245e-09 1.265e-08 4.150e-01 0.67848 Quantity 8.520e-11 3.238e-11 2.631e+00 0.00852 ** COGS 1.000e+00 1.676e-11 5.968e+10 < 2e-16 *** Marketing.Cost 2.000e+01 2.342e-10 8.539e+10 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 9.581e-07 on 8350 degrees of freedom Multiple R-squared: 1, Adjusted R-squared: 1 F-statistic: 1.547e+23 on 3 and 8350 DF, p-value: < 2.2e-16 Note the p-value and the “*” beside p-value. No we find all the variables are significant. So lets create the linear equation Y=mx1+mx2+mx3+b Y = Predicted Revenue m = Slope or the estimate from console result. x1, x2, x3 = Quantity, COGS, Marketing.Cost b = Intercept; Hence the formula; by replacing ‘m’ with the coefficient. Y = 8.52*x1 + 1*x2 + 2*x3 + 5.25
  • 26.
    Prediction Revenue Now thatwe have our regression equation Y = 8.52*x1 + 1*x2 + 2*x3 + 5.25 Here ‘Y’ being the revenue. We need the substitute the x1, x2 & x3 with respective values to determine revenue, where x1 – Quantity. x2 – COGS, x3 – Marketing cost Lets Analyze for situation where, Quantity produced = 100 unit, COGS = 100 & MC= 100; Y=8.52*100+1*100+2*100+5.25 Y=1157 Now changing 1 unit in Quantity, and considering all other remain constant Y=8.52*101+1*100+2*100+5.25 Y=1166 We have a increase in Revenue with 1 unit change in Production.
  • 27.
    Data Analysis -Conclusion Based on Multiple Linear regression analysis we can make vital Decision for betterment of the Business. With this we conclude our presentation. Thank you for your time Subhankar Basak