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

Despite fluctuations within segments, the industry-wide TRE Return Index was the same in September and October.

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1

2

3

OC TOBER

Mixed goods retailers enjoyed an 11.6 percent dip in returns during October.

Trick-or-treat was more important to consumers than making returns on Halloween. The question is: Will November 5 have unusually high returns as “renters” return their spooky gear?

Wyoming, Maine, and Mississippi were the states with the fewest returns.

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

150

142

134

126

118

110

102

94

86

78

70DEC2015

JAN2016

FEB2016

MAR2016

APR2016

MAY2016

JUN2016

JUL2016

AUG2016

SEP2016

OCT2016

NOV2016

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

TRE Return Index = 76.5

Change from Prior Month = -0.02%

$285.6 B

Returns by Date/Time and Retailer Type

Returns by Day of Week

0

51

102

153

204

255

306

357

408

459

510

NOV2015

DEC2015

JAN2016

FEB2016

MAR2016

APR2016

MAY2016

JUN2016

JUL2016

AUG2016

SEP2016

OCT2016

0

26

52

78

104

130

156

182

208

234

260

DEC2015

JAN2016

FEB2016

MAR2016

APR2016

MAY2016

JUN2016

JUL2016

AUG2016

SEP2016

OCT2016

NOV2015

Return Index by Retail Category

Return Index by Retail Format

On average, Saturdays had 24 percent more returns than Sundays.

Trick-or-treat was more important to consumers than making returns on Halloween. The question is: Will November 5 have unusually high returns as “renters” return their spooky gear?

Returns in each retail format changed by less than one percent since September.

Mixed goods retailers enjoyed an 11.6 percent dip in returns during October.

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0.0

17.5

35.0

52.5

70.0

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

10/1

/201

6

10/2

/201

6

10/3

/201

6

10/4

/201

6

10/5

/201

6

10/6

/201

6

10/7

/201

6

10/8

/201

6

10/9

/201

6

10/1

0/20

16

10/1

1/20

16

10/1

2/20

16

10/1

3/20

16

10/1

4/20

16

10/1

5/20

16

10/1

6/20

16

10/1

7/20

16

10/1

8/20

16

10/1

9/20

16

10/2

0/20

16

10/2

1/20

16

10/2

2/20

16

10/2

3/20

16

10/2

4/20

16

10/2

5/20

16

10/2

6/20

16

10/2

7/20

16

10/2

8/20

16

10/2

9/20

16

10/3

0/20

16

10/3

1/20

16

Average

Returns by Geographic Location

Returns by State

Returns by Region

Wyoming, Maine, and Mississippi were the states with the fewest returns.

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3 | 2016 RETA IL RETURN TRENDS

The largest percentage of returns were made in the South, but the states with the largest increases in returns were out West: Montana, Oregon, and Washington.

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NORTHEASTTRE Return Index: 74.8Percent of total returns: 23.2%

SOUTHTRE Return Index: 69.5Percent of total returns: 34.5%

MIDWESTTRE Return Index: 78.2Percent of total returns: 21.1%

WESTTRE Return Index: 92.0Percent of total returns: 21.1%

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

© November 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. TRE3015-10

Artificial Intelligence (AI) is in the press, but what is it, and can it apply to retail asset protection? AI encompasses a variety of applications where intelligent machines are programmed to make automatic decisions when presented with complex data. The AI emerging in the retail sector is not the type that allows machines to think on their own; instead, it allows systems to solve specific problems.

How can AI help retail? It can automatically learn new types of employee or consumer fraud and abuse, detecting and preventing that emerging threat. AI can determine shifts in social media that are related to increases in shrink and reported in-store incidents, and it can forecast shrink as new events in and around the store unfold.

Humans are not being replaced. The growth in AI supports their decisions with better information collected within a fast-moving retail environment.

RETURNS IN FOCUS