2. COUNT OF ORDER ID’S BY CATEGORY / SUPER-CATEGORY
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
20000
Category Level Count of Order Id's
Women's Fashion
Mobiles & Tablets
Men's Fashion
Home & Kitchen
Electronics
3. WEEKLY SEASONAL ADJUSTED RETURN RATES
15.06%
14.92% 14.92%
16.42%
0.140
0.145
0.150
0.155
0.160
0.165
0.170
1 2 3 4
ReturnRate
Period 1 = Week of 12/13/15
Period 2 = Week of 12/20/15
Period 3 = Week of 12/27/15
Period 4 = Week of 1/3/2016
Seasonal-Adjusted Return Rate
Return Rate %
• Returns Increased from
14.92% in Week 3 to 16.42%
in Week 4
4. RETURNS VS. TOTAL ORDERS
5939 5402
4170 4814
39430
36218
27945
29316
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
Week of
12/13/15
Week of
12/20/15
Week of
12/27/15
Week of
1/3/2016
Sum of Returns
Count of OrderID
• Returns from Week 3 to Week 4 Continue to
Increase with a Surge in Orders.
• This affirms our Hypothesis that there is a
coincidence with an Increase in Return Rates,
along with an Increase in Orders after the
Holidays.
5. CALL-OUT OF RETURN RATE BY SUPER-CATEGORY
Women's
Fashion
Mobiles &
Tablets
Men's
Fashion
Home &
Kitchen
Electronics
Week of 12/27/15 14.99% 15.42% 14.90% 15.06% 14.38%
Week of 1/3/2016 18.07% 16.25% 14.58% 14.93% 15.05%
0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
14.00%
16.00%
18.00%
20.00%
ReturnRate
Supercategory
Supercategory Level Change in Return Rate Week 3 vs. Week 4
Week of 12/27/15
Week of 1/3/2016
• The Women’s Fashion Super-
category exhibited the
largest increase in Return
Rates from Week 3 (14.99%) to
Week 4 (18.07%)
• Women’s Fashion was
followed by Mobiles and
Tablets and Electronics, as
Super-categories that also
showed an increase.
6. CALL-OUT OF CATEGORY RETURN RATES WITHIN
WOMEN’S FASHION SUPER-CATEGORY
13.34%
15.98%
15.08%
15.59%
14.74%
15.02%
15.28%
13.00%
13.14%
14.29%
14.90%
17.87%
18.46%
23.41%
0.00% 5.00% 10.00% 15.00% 20.00% 25.00%
DRESS MATERIALS
BOOTS
JACKETS & COATS
SKIRTS
SUNGLASSES
TOPS & TEES
JEANS
Average Return Rate by Women's Fashion Categorie(s)
Week of 1/3/2016
Week of 12/27/15
• Continuing to Isolate Return
Rates Differentials between
Week 3 to Week 4
• Jeans outpaced all other
Categories within Women’s
Fashion; showing an increase
in Return Rate from 15.28% in
Week 3, to 23.41% in Week 4
(an 8.13% increase.
• Tops & Tees (3.43% higher)
and Sunglasses (3.12% higher),
also were other notable
Categories.
7. RETURN CODE MAPPING
Return Code Return Reason
RT01 Bought by mistake
RT02 Better price available
RT03 Performance or quality not adequate
RT04 Incompatible or not useful
RT05 Product damaged but shipping box OK
RT06 Item arrived too late
RT07 Missing parts or accessories
RT08 Both product and shipping box damaged
RT09 Wrong item was sent
RT10 Item defective or doesn’t work
RT11 Received extra item I didn’t buy(no refund needed)
RT12 No longer needed
RT13 Didn’t approve purchase
RT14 Inaccurate website description
150
144
180
165
353
317
333
369
330
343
325
352
148
661
154
178
185
195
325
352
353
354
361
368
373
393
485
738
0 100 200 300 400 500 600 700 800
RT05
RT12
RT11
RT13
RT02
RT06
RT07
RT04
RT01
RT14
RT10
RT08
RT09
RT03
RT05 RT12 RT11 RT13 RT02 RT06 RT07 RT04 RT01 RT14 RT10 RT08 RT09 RT03
Week of 1/3/2016 154 178 185 195 325 352 353 354 361 368 373 393 485 738
Week of 12/27/15 150 144 180 165 353 317 333 369 330 343 325 352 148 661
Ranked Return Code Mapping by Quantity of Returns
• Performance or Quality (RT03) is the main Return Code (RC) driver in terms of
absolute returns at 738.
• Wrong Item Sent (RT09) is the main RC driver in terms of Percentage increase from
Week 3 to Week 4 at 227.7% (485-148).
• Items arriving too late (RT06), does not appear to be as big a driver of Returns, at
11.65% increase.
8. SUMMARY OF RETURN REASONS RANKED
501%
58.3%
38.5%
33.8%
29.6%
25.7%
22.1%
20.6%
16.4%
14.8%
2.5%
1.6%
.7%
-0.6%
0 50 100 150 200 250 300 350 400
RT09
RT03
RT10
RT08
RT07
RT14
RT01
RT04
RT06
RT02
RT11
RT12
RT13
RT05
Return Code Ranking by Percentage Increase from Week 3 - Week 4
Week of 1/3/2016 Week of 12/27/15
Top 10 Return Reasons:
1. Wrong Item was sent (RT09)
2. Performance or Quality not adequate (RT03)
3. Item defective or doesn’t work (RT10)
4. Both product and shipping box damaged (RT08)
5. Missing part or accessories (RT07)
6. Inaccurate website description (RT14)
7. Bought by mistake (RT01)
8. Incompatible or not useful (RT04)
9. Item arrived too late (RT06)
10. Better price available (RT02)
9. EFFECT ON RETURN CODE BY STOCKOUT DAYS
0
20
40
60
80
100
120
140
160
180
200
RT01 RT02 RT03 RT04 RT05 RT06 RT07 RT08 RT09 RT10 RT11 RT12 RT13 RT14
StockoutDays
Return Code
Effect on Return Codes by Stockout Days
Week of 12/27/15
Week of 1/3/2016
• RC RT09 (wrong item was
sent), had the most negative,
dramatic effect due to
Stockout. The absolute
increase in the number of
returns from Week 3 – Week 4
increased from 33 to 127
• RC RT03 (performance or
quality not adequate)
continues to be a leading
indicator as well, with an
increase from 155 – 186 Week
3-Week 4.
• RC RT07 (Missing Parts or
Accessories) rounds out the
Top 3 largest Return Code by
Stockout Days.
10. SUMMARY ROOT CAUSE ANALYSIS AND RECOMMENDATIONS
• The Women’s Fashion Super-category led the way with the largest total sales volume of 58,925 total items. The Tops
& Tees category within women’s fashion, accounted for 30.7% of all orders within that Super-category, and was
second to Jeans with the highest Average Return rate.
• “Performance or Quality not Adequate” is the leader in Absolute Returns.
• Perform Quality Assurance analysis on Website Product Descriptions to include features such as material
composition, highlighting key technological features (such as moisture-wicking), and verification of size charts.
• Review Supply Chain partners’ product availabilities for higher quality, comparable items
• “Wrong Item Sent” is the leader in largest percentage increase in return rates.
• Review Product SKU’s to verify that the website product matches vendors item numbers.
• Review shipping system for accuracy to verify that operating system SKU matches website SKU’s
• Require supply chain partners to perform continuous improvement initiatives to reduce “mispicks”
