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 July Return Index was down 6.5 percent to 79.6.

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JULY

The only category with increased returns in July was Mixed Goods (up 8.7 percent). Soft Goods showed the sharpest decrease with volumes down 17.7 percent compared to June’s figures.

Traffic at the return desk was steady Monday through Thursday.

During July, consumers in the Midwest seem to have been the most satisfied with their purchases; that region had the lowest percentage of returns nationwide.

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

150

142

134

126

118

110

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94

86

78

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

TRE Return Index = 79.6

Change from Prior Month = -6.5%

$286.4 B

Returns by Date/Time and Retailer Type

Returns by Day of Week

0

51

102

153

204

255

306

357

408

459

510

AUG2015

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78

104

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156

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Return Index by Retail Category

Return Index by Retail Format

Traffic at the return desk was steady Monday through Thursday.

Independence Day, July 4, is often a business day for retail chains, although many shorten their hours. This is the probable reason that this day’s return rates were well below average but not as disruptive as the major holidays.

Returns at mall based retailers plummeted 18.1 percent in July, more than compensating for the spike they experienced in June. Other formats remained steady.

The only category with increased returns in July was Mixed Goods (up 8.7 percent). Soft Goods showed the sharpest decrease with volumes down 17.7 percent compared to June’s figures.

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16.25

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65.00

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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

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Average

Returns by Geographic Location

Returns by State

Returns by Region

In July, Oklahoma replaced Nevada in the list of 10 states with the lowest percentage of returns per store. The states with the lowest percentages (in decreasing order) were Mississippi, Maine, and Wyoming.

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3 | 2016 RETA IL RETURN TRENDS

During July, consumers in the Midwest seem to have been the most satisfied with their purchases; that region had the lowest percentage of returns nationwide.TRE Analys is

NORTHEASTTRE Return Index: 77.2Percent of total returns: 23.1%

SOUTHTRE Return Index: 72.7Percent of total returns: 34.9%

MIDWESTTRE Return Index: 80.5Percent of total returns: 20.8%

WESTTRE Return Index: 96.5Percent of total returns: 21.2%

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

© August 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. TRE3015-07

Do you stop predicting consumer behavior too soon?

For many retailers, the merchandising system uses predictive analytics to project demand, set purchase quantity, and distribute the supply. This is an excellent starting point, but it takes place weeks or months in advance, and it focuses on particular consumer segments, not individuals. In practice, consumers alter their behavior continually. In order to keep profits high and maximize the value of each consumer, retailers need to take advantage of solutions that predict behavior in real-time as well. These solutions can anticipate individual consumer purchases or fraudulent behavior. Through customized, one-to-one messaging to consumers you can impact their behavior during their shopping trips.

RETURNS IN FOCUS