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Executive Overview

Many retailers may never know their real return or exchange rates from store, ecommerce, or omnichannel transactions. Are you one of them?

The standard reporting systems tend to underrepresent the financial impact of returns. Until you understand the full impact retail returns have on your business, you cannot devise a strategy to achieve an optimal return rate. By optimizing your return and exchange rates, you can clear a path to improve your net sales, gross margins, and profits.

The Old Return Rate Calculation

Calculating return rates seems straightforward, but many ecommerce, POS, and retail ERP reporting systems greatly oversimplify the formula. Their method for calculating return rate is to add up all pure return transactions and divide that negative number into net sales. (A pure return is a transaction simply returning an item or items—no exchanges or refunds.)

Every return transaction offers a potential positive consumer experience, but it also represents an added cost and a loss of revenue. Exchanges cost money in restocking fees, in employee time, and in possible damage to goods.

Using this old method of calculating return rates, retailers exclude exchanges from their calculations. They assume that exchanges show no impact on their business because no money changes hands and, on the surface, they have no apparent effect on net sales.

Every return transaction offers a potential positive consumer experience, but it also represents an added cost and a loss of revenue. Exchanges cost money in restocking fees, in employee time, and in possible damage to goods. The new “real return rate” method of calculation includes the value from pure returns as well as the value from all exchange transactions, whether they are positive exchanges, negative exchanges, or even exchanges. From an operational point of view, this makes much more sense because it more precisely quantifies the impact of all returns on the retailer’s business. (If you are unfamiliar with the differences among these exchange types, check out our list of definitions at the end of this blog.)

The Real Return Rate Includes Exchange Transactions

Retailers today are beginning to think of returns as merchandise that flows back into the store or ecommerce distribution center, and gross sales as merchandise that flows out of the store or ecommerce DC. The outbound transactions are very different from the inbound, and the metrics you use should not obscure that fact by netting out the individual pieces, as often happens in exchange transactions. In fact, whether a return or exchange, there is a consumer on your site or at your store, allowing you the chance to interact and influence future sales.

To achieve the real return rate, you need to include the total dollars from pure returns and the total dollars from all returned items involved in exchange transactions (positive, negative, and even exchanges).

If the return rate is not calculated this way, a large portion of the merchandise returns are completely ignored. The logic behind this is simple; every item returned adds to cost and lost revenue.

For example, under the old way, if 100% of your returns were exchange transactions, you would have a return rate of 0%. Shouldn’t the formula reflect the actual activity?

The Impact on Your Business

When you first use this new, realistic calculation, you may suffer a moment of sticker shock. When retailers make the transition, they may see a return rate increase of 50% to 150%! This new view, however, offers you benefits such as:

  • More visibility into item return trends and patterns, creating better merchandise and consumer service intelligence.
  • Greater understanding of shopper behavior within and across channels, such as buy-online-return-in-store (BORIS), enhancing CRM analysis.
  • Stronger ability to spot and prevent return fraud and abuse.
  • Improved capability to reduce return rate and thereby keep more revenue (net sales).

Fraud and abuse issues can exist in both exchange and return transactions. Exchanges could be popular among fraudsters since exchanges are often scrutinized less. Many retailers assume that exchanges are “safer” transactions than returns. Since return fraud can be perpetrated in many transactions, the fraud prevention tools you use and the method by which you calculate return rate must account for exchanges, too.

Example: The Hidden Cost of Returns

A footwear retailer realized that the company was losing 1% of sales each year to fraudulent and abusive merchandise returns. Executives saw an opportunity to improve overall profitability by tracking return transactions, reducing refund amounts, creating incremental revenue, and delivering positive consumer service during returns.

When items that get returned within exchange transactions are unexpectedly hidden, you lose opportunities to rescue sales, provide better consumer service, prevent loss, and more.

Think of it this way: A consumer returns one pair of shoes that have been worn but are not defective, and the consumer demands a refund. The selling price of the shoes was $100 per pair; the cost of the shoes was $60 per pair; the gross margin was $40 per pair; and the net profit was $10 per pair. The store processing the return must write off this pair of shoes. How many new pairs of shoes do you think that retailer will have to sell to make up for the loss on the write off? The answer is 10. The revenue on 10 pairs would be $1,000, which would cover the cost of the shoes ($600) plus the lost margin ($400) and the operating expenses ($300) and yield the original 10% net profit.

When items that get returned within exchange transactions are unexpectedly hidden, you lose opportunities to rescue sales, provide better consumer service, prevent loss, and more.

The vast majority of returns are initiated by consumers who repeatedly shop at a given store, website, or catalog. More than 75% of all shoppers don’t return purchases; however, the remaining 25% of acceptable returns can be costly. Assuming a 40% gross margin and a $100 item retail price for 20 items, a retailer would achieve zero profit at a 20% return rate. Are you one of those retailers?

Example: Analysis of 10 Major Retailers

Appriss Retail tallied up real return rates for 10 different retailers. All were found to be underestimating their return rate—one by as much as 150%. They had an average return rate discrepancy of more than 80%. For a retailer doing $10 billion in annual revenue, that results in additional costs of more than $462 million.

Knowing Accurate Return Rate Leads to Better Decisions

While the effect of returns on the retailer’s store or ecommerce financial statements may seem negligible when using traditional calculation methods, they are significant. Failure to accurately account for all types of returns using a consumer-based, operational perspective can have dramatic consequences in terms of underestimating costs and in terms of the marketing repercussions of failing to maintain or grow a loyal consumer base. To improve store performance, retailers need to have a clear picture their true profits and losses including their true retail return rate.

Definitions—What Are Retail Returns Types?

Pure return (return)–This transaction only includes returned items, and no items are purchased or exchanged. The consumer will receive a refund in this type of transaction.

Even exchange–This transaction includes returned items and purchased items that are exactly equal in value so that the total transaction amount is zero. No tender will be exchanged in this type of transaction.

Negative exchange–This is a transaction that includes returned items and purchased items where the price of the returned items exceeds the price of the purchased items. The consumer will receive money back in this type of transaction.

Positive exchange–This is a transaction that includes returned items and purchased items where the price of the purchased items exceeds the price of the returned items. The consumer will owe money to the retailer in this type of transaction.

 

Appriss Retail’s Verify® return authorization addresses merchandise returns regardless of sales channel and preserves sales revenue.

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Author

David Speights, PhD, Chief Data Scientist, Appriss Inc.

David Speights, PhD, chief data scientist at Appriss, Inc., is the lead author of “Essentials of Modeling and Analytics: Retail Risk Management and Asset Protection” which was co-authored by Daniel M. Downs, PhD, and Adi Raz, PhD. He resides in California, USA, and speaks internationally on the intersection of artificial intelligence and retail.

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