The Retail Equation (TRE) publishes the annual Consumer Returns in the Retail Industry report to shed light on return trends and, ultimately, help retailers compare and improve their business processes. We are pleased to extend the application of this report to a monthly analysis of merchandise return activity. Each month, TRE will release the TRE Return Index, which is a numerical estimation of the volume of returns compared to U.S. retail sales. Our goal is to provide retailers with a monthly overview of return trends, statistics, and key learnings.

Trend report observations from this month include:

2016 RETAIL RETURN TRENDS

The TRE September Return Index went down one percent since August.

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SEP TEMBER

Labor Day, September 5, was the peak day for returns. The Saturday prior held second place.

Returns at mall based retailers continued their decline for a third month with a 6.3 percent drop in September.

Wisconsin and Iowa joined the states with the highest returns while Oklahoma and New Mexico joined the list of ten states with the fewest number of returns.

TRE Return IndexVolume of Returns (Unit Adjusted) Compared to U.S. Retail Sales

150

142

134

126

118

110

102

94

86

78

70NOV2015

DEC2015

JAN2016

FEB2016

MAR2016

APR2016

MAY2016

JUN2016

JUL2016

AUG2016

SEP2016

OCT2016

Legend: — TRE Return Index --- TRE Return Index (estimated) — U.S. Monthly Retail Sales (unadjusted)

TRE Return Index = 76.5

Change from Prior Month = -1.0%

$282.9 B

Returns by Date/Time and Retailer Type

Returns by Day of Week

0

51

102

153

204

255

306

357

408

459

510

OCT2015

NOV2015

DEC2015

JAN2016

FEB2016

MAR2016

APR2016

MAY2016

JUN2016

JUL2016

AUG2016

SEP2016

0

26

52

78

104

130

156

182

208

234

260

NOV2015

DEC2015

JAN2016

FEB2016

MAR2016

APR2016

MAY2016

JUN2016

JUL2016

AUG2016

SEP2016

OCT2015

Return Index by Retail Category

Return Index by Retail Format

Many consumers did not make their returns on Sundays.

Labor Day, September 5, was the peak day for returns. The Saturday prior held second place.

Returns at mall based retailers continued to decline for the third consecutive month with a 6.3 percent drop in September.

The TRE Return Index for hard goods retailers increased 3.7 percent in September. All other retail categories decreased.

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30.00

38.75

47.50

56.25

65.00

SaturdayFridayThursdayWednesdayTuesdayMondaySunday

2 | 2016 RETA IL RETURN TRENDS

Legend: — Department Stores — Mix of Hard and Soft Goods Retailers — Hard Goods Retailers — Soft Goods Retailers

Legend: — Big Box Retailers — Mall Based Retailers — Strip Mall and Stand Alone Retailers

Returns by Day of Month

9/1/

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2016

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2016

9/4/

2016

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2016

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2016

9/7/

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2016

9/9/

2016

9/10

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6

9/11

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6

9/12

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6

9/13

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6

9/14

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6

9/15

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6

9/16

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6

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6

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6

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6

9/23

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9/26

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6 9/

30/2

016

Average

Returns by Geographic Location

Returns by State

Returns by Region

Wisconsin and Iowa joined the states with the highest returns while Oklahoma and New Mexico joined the list of ten states with the fewest number of returns.

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3 | 2016 RETA IL RETURN TRENDS

The southern states had the highest return volume in September.TRE Analys is

NORTHEASTTRE Return Index: 74.0Percent of total returns: 23.0%

SOUTHTRE Return Index: 69.8Percent of total returns: 34.7%

MIDWESTTRE Return Index: 77.4Percent of total returns: 21.0%

WESTTRE Return Index: 93.4Percent of total returns: 21.3%

10 states with lowest frequency of returns

Average states

10 states with highest frequency of returns

Methodology and Participating Retailers

The monthly TRE Retail Return Trend report is compiled by The Retail Equation by analyzing merchandise return transactions from retailers in the U.S. and Canada over a variety of retail segments, including many of the world’s largest, well-known big box, mass merchandise, department store, grocery/drug, and specialty retail merchants.

The Retail Equation would like to thank all of the retailers who regularly participate in this report. You will notice that no retailer names are mentioned, per TRE’s commitment to maintain confidentiality of each organization’s data.

The Retail Equation

The Retail Equation, an Appriss company, optimizes retailers’ revenue and margin by shaping behavior in every customer transaction. The company’s solutions use predictive analytics to turn each individual shopper visit into a more profitable experience. This yields immediate financial payback, increasing store comps by as much as two percent, with significant return on investment. The Software-as-a-Service applications operate in more than 34,000 stores in North America, supporting a diverse retail base of specialty apparel, footwear, hard goods, department, big box, auto parts, drug/pharmacy, grocery, and more.

PO Box 51373 Irvine, CA 92619-1373 USA +1 (888) 371-1616 www.TheRetailEquation.com

© October 2016. The Retail Equation, Inc., an Appriss company. All Rights Reserved. The Retail Equation logo is a trademark of The Retail Equation Incorporated. Patents, pending patents, trademarks, service marks and registered trademarks referenced herein are the property of The Retail Equation Incorporated, including but not limited to The Retail Equation, Verify Return Authorization, Verify-1, Verify-2, Verify-3, Receipt Verification, Change for Charity, Return Rewards, Purchase Rewards and Patents 6,016,480, 7,455,226, 8,025,229, 8,355,946, 8,356,750, 8,561,896, and 8,583,478.

Proprietary and Confidential. TRE3015-09

Should you change your return policy for the winter holiday shopping season? A majority of retailers — 75.4 percent according to the National Retail Federation* — make no adjustments. That means that three quarters of retailers are missing an opportunity to convert returners to long-term customers. Remember, most holiday returns are gifts. Therefore, in most cases, the consumer who took advantage of the pre-holiday discounts, extended store hours, and valuable services in order to make the purchase is not the same person standing in line to make a return or exchange. The consumer in line provides a new opportunity for conversion, which is most likely to happen if the process is pleasant, fast, and accommodating. These three factors are addressed at the beginning of the shopping season by making the return policy more lenient, and they are supported throughout the peak return season by using technologies that ensure speed and accuracy.

* ”2015 Consumer Returns in the Retail Industry,” by the National Retail Federation, p.4.

RETURNS IN FOCUS