Understanding the data
Column
Chart A
5,009Chart B
9,994The company sells products that are classified into these various sub-categories. As the histogram indicates, paper and binders have been the two most popular items during the past four years.
Row
Objective
This is a simple data set of US superstore from 2014-2018, obtained from Kaggle. The column names reveal what type of information is revealed in the data set. Using high level metrics we see how successful the company has been in generating sales and the total profit earned over the past four years. I will analyze specific attributes on a high level and won’t examine the attributes on an annual basis, with the objective to explore recomendations the company can implement to earn 15% gross profit over the next four years. [1] "Row.ID" "Order.ID" "Order.Date" "Ship.Date"
[5] "Ship.Mode" "Customer.ID" "Customer.Name" "Segment"
[9] "Country" "City" "State" "Postal.Code"
[13] "Region" "Product.ID" "Category" "Sub.Category"
[17] "Product.Name" "Sales" "Quantity" "Discount"
[21] "Profit" Chart C
2,297,201Chart D
286,397Chart E
12%Demographics
Column
Chart 1 - Note:Texas is considered a “central” region in the data set.
Chart 2
Row
Map 1 - Disclaimer: There aren’t any sales associated with Alaska or Hawaii so these states have been omitted from the charts.
Map 2
Sale Analytics
Column
Customers clearly tend to make multiple orders.
Chart A
793B
13Row
Gross profit percentages were calculated manually. Based on the information from the data set, product profits varied with each order so the average gross profit percentage was calculated for each product.
The following quantiles represent the distribution of product quantities sold over the past four years. Any products sold at a frequency over the 3rd quantile will be referred to as fast moving.
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.000 3.000 5.000 5.367 7.000 19.000 Conclusion
Row
Recommendations
- Make an effort to attratct more sales in states that have potential for growth. These states should already have a history of providing positive profit figures.
- Market a specific set of products. These products should have a history of fast moving sales and provide at least 15% gross profit.
- Hopefully with a number of new customers, there will be an increase in sales as we noted earlier that most if not all customers are repeat customers.
Row
As we seen on the demographics tab, even though there are states that signficiantly purchase more products compared to the rest of the other regions, a great number of these states actually contribute negative margins as a whole, Texas being the largest contributor. Ideally the goal should be to implement efforts to grow sales in states where there is room for growth. States in green represent states that experienced less than 1,000 sales over the past four years. As we noted early, a handful of states contributed negative margins and we won’t want to grow sales in those states since customers tend to purchase non-profitable products.
If the company wants to reach the desired 15% gross profit percentage during the next four years, they should sell products that contain gross profit percentages equal to or greater than 15%. We identified the products that are fast moving, which are represented as the 3rd quantile (see third tab), and meet the objectives criteria. In order to filter through this large amount of potential products, lets choose products that are generally sold at a higher selling price and are over the 3rd quantile to maximize our potential revenue.
Min. 1st Qu. Median Mean 3rd Qu. Max.
4.0 18.0 38.0 130.8 125.0 2355.0 [1] "printing all products that meet our desired criteria..." Pro_ID GP% Qty_Sold Avg_Price
56 FUR-CH-10000454 17 12 886
60 FUR-CH-10000665 16 9 440
90 FUR-CH-10002335 16 9 771
152 FUR-FU-10000308 19 7 226
218 FUR-FU-10002253 30 7 150
242 FUR-FU-10002937 46 8 337
285 FUR-FU-10004017 21 7 181
291 FUR-FU-10004091 25 8 140
312 FUR-FU-10004864 21 13 141
388 OFF-AP-10000576 21 7 287
440 OFF-AP-10002945 29 8 1413
482 OFF-AR-10000380 25 10 162
537 OFF-AR-10001953 17 7 153
818 OFF-BI-10004318 20 8 404
831 OFF-BI-10004600 24 8 998
847 OFF-EN-10000056 40 8 194
998 OFF-LA-10003930 46 8 415
1021 OFF-PA-10000100 46 8 212
1045 OFF-PA-10000357 43 12 178
1067 OFF-PA-10000675 46 9 174
1081 OFF-PA-10000994 45 9 310
1084 OFF-PA-10001033 43 11 130
1096 OFF-PA-10001289 42 8 160
1137 OFF-PA-10001878 43 7 189
1163 OFF-PA-10002254 42 7 126
1213 OFF-PA-10003302 43 7 157
1306 OFF-ST-10000585 18 7 448
1307 OFF-ST-10000604 22 7 140
1313 OFF-ST-10000675 20 7 161
1343 OFF-ST-10001490 23 13 626
1344 OFF-ST-10001496 20 10 659
1348 OFF-ST-10001526 24 8 719
1356 OFF-ST-10001837 22 8 155
1378 OFF-ST-10002615 24 9 731
1383 OFF-ST-10002974 22 7 171
1386 OFF-ST-10003208 23 12 644
1389 OFF-ST-10003306 24 8 575
1412 OFF-ST-10004340 22 10 230
1461 TEC-AC-10000109 16 9 165
1477 TEC-AC-10000736 34 8 202
1478 TEC-AC-10000844 30 8 295
1480 TEC-AC-10000892 31 7 270
1498 TEC-AC-10001465 24 9 132
1504 TEC-AC-10001606 29 7 257
1509 TEC-AC-10001838 41 7 731
1518 TEC-AC-10002049 27 15 917
1532 TEC-AC-10002402 21 8 270
1536 TEC-AC-10002567 27 9 222
1541 TEC-AC-10002800 30 9 180
1544 TEC-AC-10002926 40 8 176
1548 TEC-AC-10003033 26 10 1082
1570 TEC-AC-10003832 23 18 622
1580 TEC-AC-10004353 23 7 158
1583 TEC-AC-10004469 40 10 157
1587 TEC-AC-10004571 36 9 260
1605 TEC-CO-10001449 33 8 2355
1606 TEC-CO-10001571 38 7 629
1609 TEC-CO-10002095 34 7 1271
1614 TEC-CO-10004202 24 7 694
1695 TEC-PH-10000441 17 8 343
1710 TEC-PH-10000984 23 7 711
1720 TEC-PH-10001336 18 9 338
1732 TEC-PH-10001557 18 10 359
1739 TEC-PH-10001750 16 8 173
1755 TEC-PH-10002103 20 9 351
1786 TEC-PH-10002726 40 7 159
1797 TEC-PH-10003012 22 10 443
1844 TEC-PH-10004531 23 7 149