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Can Product Returns Make You Money?

S P R I N G 2 0 1 0 V O L . 5 1 N O . 3

R E P R I N T N U M B E R 5 1 3 1 6

J. Andrew Petersen and V. Kumar

SLOANREVIEW.MIT.EDU SPRING 2010 MIT SLOAN MANAGEMENT REVIEW 85

After a certain threshold, a customer’s rate of product returns actually correlates to an increase in the amount of his or her future purchases.

C U S T O M E R S E R V I C E

MANY COMPANIES SEE customers’ product returns as a major inconvenience and an eroder of profits. After all, product returns cost manufacturers and retailers more than $100 billion per

year, or an average loss per company of about 3.8% in profit.1 The electronics industry alone spends

some $14 billion annually on product returns through reboxing, restocking and reselling. And be-

cause only about 5% of products are returned as a result of defects, it appears that product returns

will remain an inevitable part of the customer-company relationship even as manufacturing con-

tinues to improve product quality.

For some companies, the solution has been to create product-return disincentives, such as lim-

ited time frames for returns (say, within 30 days after purchase), product customization that allows

returns only when the product is defective, and

nonrefundable purchase costs (shipping costs

or restocking fees, for example). But are these

practices, which reduce the costs and frequen-

cies of product returns, ideal for the bottom

line? Despite the company’s handling costs and

its revenues lost from refunds, the customer’s

ability to return products may have a positive

effect on his or her future purchases and actu-

ally increase long-term profits.

Several recent studies have in fact begun illu-

minating the potential benefits of allowing

customers to return products with impunity. This

research finds that when a company has a lenient

product-return policy, which allows customers to

return almost any product at any time, they are

more willing to make other purchases.2 The

knowledge that they can return a product reduces

the risk customers might perceive in purchas-

ing it in the first place. The studies also find that a

Marketers and sellers hate product returns, but smart companies aren’t passively accepting them as bitter pills to be swallowed. They’re managing product-return policies to maximize future profits. BY J. ANDREW PETERSEN AND V. KUMAR

Can Product Returns Make You Money? THE LEADING QUESTION

How can marketers manage product-return policies to maximize future profits?

FINDINGS Marketers can target and manage customers by taking information about both their purchase and return behaviors into account.

Lenient product- return policies yield more profits than strict product-return policies.

Managing product returns in an optimal way increases profits even during tougher economic times.

86 MIT SLOAN MANAGEMENT REVIEW SPRING 2010 SLOANREVIEW.MIT.EDU

C U S T O M E R S E R V I C E

satisfactory product return can provide another touch

point for building a successful buyer-seller relation-

ship.3 Reducing customer risk and increasing customer

satisfaction, across purchases and product returns

alike, can increase the number of future purchases and

thus raise the company’s revenue from sales.

In no way do these findings suggest that compa-

nies encourage customers to return products. But

they do show that product returns do not necessar-

ily drag down a company’s profits over time.

Our own research, on which this article is based,

extends these studies by exploring the trade-offs be-

tween the costs of product returns — particularly

when customers deem such experiences satisfactory

— and their long-term benefits to the company.4 Such

knowledge can be useful not only in understanding

the effect of product returns on future purchases and

profits but also in “managing” — not necessarily dis-

couraging — customer product-return behavior so as

to maximize profits. (See About the Research.)

What We Learned From Company 1 Our research addressed the following three questions:

1. What purchase and customer characteristics

lead to more (or fewer) product returns?

2. How can marketing managers strategically man-

age customers, using information about their

purchase and product-return behavior, to maxi-

mize company profits?

3. What is the trade-off between the cost of product re-

turns and the potential benefits that accrue through

positive, long-term customer-purchase behavior?

We obtained answers by analyzing six years of

purchase, product-return and marketing-commu-

nications data from “Company 1” — a large

national catalog retailer that sells apparel and ac-

cessories and is known to have a lenient return

policy. We used two different cohorts of customers,

the first including those who made their first pur-

chase in 1998 and the second including those who

made their first purchase in 1999. Customers in

both cohorts were similar in how many catalogs

they received, how many purchases they made and

how many products they returned per year.

Results of this analysis provided key insights

into the role that product returns play in the cus-

tomer-company relationship. As expected, the

more a customer purchased, the more products he

or she returned. But we also empirically tested the

effect of several purchase characteristics on prod-

uct-return behavior and found that each would

help to shift a customer’s rate of product returns ei-

ther higher or lower. (See “Purchase Characteristics

and Their Effect on Product Returns.”)

We also analyzed the consequences of a custom-

er’s product-return behavior both on the company’s

decision subsequently to send catalogs to that cus-

tomer and his or her actual purchase behavior

thereafter. We found that as a customer’s rate of

product returns increased (to a threshold):

■ The number of catalogs a customer received

decreased.

■ The customer’s amount of future purchases

increased.

This result elucidates the potential long-term

benefits of product-return behavior. It implies that

ignoring such behavior, or even trying to discourage

it directly by not marketing to customers who return

products, is a mistake. In fact, a moderate degree of

product returns by a customer could not only lead to

greater future purchases but also maximize profits.

We explore the latter possibility by analyzing the

trade-offs between the short-term costs and long-

term benefits of product returns. In the process, we

can also determine exactly what level of product re-

turn maximizes profits for Company 1.

We used the data from both cohorts of custom-

ers to simulate the impact that changes in customer

product-return behavior would have on company

profits. We first computed the discounted company

profit from this sample of customers, as follows:

Company Profit =

Purchase Value x Margin - Product Return Costs

- Marketing Costs

Discount Factor Based on Purchase Timing

We then allowed the overall percentage of the prod-

ucts returned by all customers both to increase and

decrease from the original percentage of product re-

turns, which was around 16%. Because the results from

both cohorts were similar, we include here only the

findings from Cohort 1. Based on the actual level of

ABOUT THE RESEARCH We conducted a study using six years of pur- chase, product-return and marketing communica- tions data from a large national catalog retailer that sells apparel and ac- cessories. The retailer is known to have a lenient return policy that allows customers to return prod- ucts at any time after the purchase, whether the products are defective or not. We determined the factors that led to increas- ing or decreasing product returns from customers, identified how each cus- tomer’s product-return behavior affected his or her future purchase be- havior and long-term value to the company, and then analyzed the trade-offs between the costs of product returns and the potential long- term benefits resulting from satisfactory product- return experiences. In addition, we used a sam- ple of customers from a second catalog retail company to run a six- month field experiment in which we optimally allo- cated resources to customers based on our new knowledge of the drivers and conse- quences of customer product-return behavior.

SLOANREVIEW.MIT.EDU SPRING 2010 MIT SLOAN MANAGEMENT REVIEW 87

product returns, the 1,572 customers in Cohort 1 yielded

a discounted profit of around $92,000 over six years. But

by varying under simulation the amount of product re-

turns per customer, we found the optimal percentage of

product returns that would maximize company profits

to be 13%, or a decrease in product returns of 3% from

the actual level. (See “Optimal Amount of Product Re-

turns to Maximize Profits,” p. 89.)

Note that the optimal rate of product returns is not

even close to 0%, which would be off the scale at the

left (corresponding to -16%). In fact, decreases in

product returns beyond 13% — that is, from -3% to-

ward -15% and beyond — decrease profits as well. At

1% product returns, or 15% below the current rate,

profit is around $64,000. However, it is important to

note that increases in product returns beyond a cer-

tain point significantly decrease profits. At 31%, or

15% above the current amount of product returns,

the company experiences negative profits of around

$21,000 from the Cohort 1 customers.

General Guidelines for Marketing-Resource Allocation These findings show that managers should embrace

customers’ product-return behavior and offer them a

satisfactory experience. Moreover, managers should

use the drivers shown in the table “Purchase Charac-

teristics and Their Effect on Product Returns” as levers

to help increase or decrease a customer’s product-

return behavior toward the ideal threshold — 13% in

the case of Company 1. We offer three general insights

to managers with regard to managing customers and

allocating marketing resources.

1 Consequences of Product Returns. This

company sent fewer catalogs to customers

who returned more products — a typical

response of companies that shy away from continu-

ing to invest in relationships with such customers.

However, customers who return more products

(up to a threshold) tend to purchase the most

products in the future. Thus, companies that send

fewer catalogs to customers who return products

are not optimally allocating resources. Instead, cat-

alog mailing s should be based on both the

customer’s purchase and product-return behavior

so as to maximize the future streams of revenue

from that customer. How can a manager determine

a resource-allocation strategy? By leveraging the

drivers of customer product-return behavior.

2 Drivers of Product Returns. If a customer has

been returning products too frequently — over

13% at Company 1 — managers can utilize

drivers of decreasing product returns to decrease the

customer’s likelihood of returning products. For in-

stance, a manager could send this customer discounts

on purchasing familiar products in more convenient

distribution channels. Suppose a customer is pur-

chasing women’s clothing from the company’s

brick-and-mortar retail store. The company could

send this customer a coupon to shop online that would

provide a discount in the women’s-clothing product

category. That could open up a new distribution chan-

nel, which has empirically been shown to increase a

customer’s buying behavior, and at the same time help

to manage that customer’s product-return behavior.

On the other side of the coin, if a customer is re-

turning only a small percentage of products — say,

5% — his or her potential profits to the company

are not optimal. In response, a manager may offer

an incentive to purchase products from categories

that the customer has yet to shop. For example, if

the customer has been purchasing products only in

the men’s clothing department, the manager may

send the customer a coupon to purchase in the out-

door or luggage department. Note, however, that

market research is an important prerequisite: To

PURCHASE CHARACTERISTICS AND THEIR EFFECT ON PRODUCT RETURNS Each purchase characteristic, or “driver,” tends to shift a customer’s rate of product returns in one unique direction. Managers can exploit these properties to enhance a company’s long-term profits.

PURCHASE CHARACTERISTIC

DECREASE IN PRODUCT RETURNS

INCREASE IN PRODUCT RETURNS

Gifts for Family and Friends X

Holiday Season Shopping (Nov./Dec.) X

New Product Category (Same Distribution Channel)

X

New Distribution Channel (Same Product Category)

X

New Product Categories and New Distribution Channels

X

Items on Sale X

88 MIT SLOAN MANAGEMENT REVIEW SPRING 2010 SLOANREVIEW.MIT.EDU

C U S T O M E R S E R V I C E

find the appropriate new category to introduce to

the customer, it is best to know which sets of cate-

gories tend to be purchased by similar customers.

3 Quantifying the Costs and Benefits of Product

Returns. Until a manager can determine the

percentage of product returns that will maxi-

mize profits, that manager cannot formulate

appropriate marketing-resource allocation strategies

on a customer-by-customer basis. While the average

number of products returned by customers across

Company 1 is about 16%, not all of them return at that

rate. In fact, the percentage varies quite significantly

across customers, from close to 30% of customers

never returning any products at all to about 10% of

customers returning over 40% of products they

purchase. This diversity gives managers a great oppor-

tunity to reallocate the current marketing resources

allocated to each customer and devote more to those

customers with the greatest potential to increase profits.

A Field Experiment With Company 2 The final question we need to answer is whether

companies are better off in the long run with a strict

product-return policy or a lenient product-return

policy. We ran a field experiment with a second cat-

alog retailer, Company 2, which sells footwear,

apparel and other accessories through the Internet

and mail-order catalogs. The goal of this experi-

ment was to answer the following questions:

1. Can we quantify how changing the leniency

of the product-return policy affects customer

behavior and company profits?

2. Does changing the method of valuing customers

and allocating resources to customers, using our

findings from Company 1, affect customer be-

havior and company profits?

Using data from two samples of customers at two

different periods — in the year before the product-re-

turn policy change and in the year after — we analyzed

the purchase, product-return and referral behavior of

those customers. In the first time period, the compa-

ny’s product-return policy was strict, allowing returns

only for defective products or incorrect product ship-

ments. In the second time period, the policy was

lenient, allowing customers to return any product at

any time for any reason. For each of the two periods,

customers were allocated marketing resources (sent

catalogs) based on two different strategies: the “com-

pany strategy” and an “optimal resource-allocation

strategy.” The company strategy was based on the

RFM score, commonly used by direct marketing com-

panies, which rewards customer-purchase behavior

that is recent, frequent and of high monetary value. In

addition, the company reduced resource allocations

to customers who returned products. The optimal

resource-allocation strategy was based on predict-

ing each customer’s lifetime value and accounting

for the relationship between customer purchases,

company-initiated marketing

communications and customer

product-return behavior uncov-

ered by the earlier study of

Company 1.

During both time periods,

catalogs were mailed every three

weeks, based on the respective

resource-allocation algorithms

(company strategy or optimal

strategy), and purchases and re-

turns were observed. Managers

of Company 2 had stated that ap-

proximately 87% of purchases

after a catalog is mailed occur

w ithin eight weeks and 95%

occur within 12 weeks. That sug-

gested that if we waited three

COMPANY STRATEGY OPTIMAL ALLOCATION STRATEGY

Lenient

Product-Return

Policy

Avg. Purchase ($): $1,234.20 Avg. Purchase ($): $1,376.13

Avg. Product Return ($): $67.90 Avg. Product Return ($): $41.50

Avg. Profit ($): $302.36 Avg. Profit ($): $371.34

Avg. # of Referrals: 1.6 Avg. # of Referrals: 2.4

RESULTS OF THE FIELD EXPERIMENT WITH COMPANY 2 The best results come from a lenient product-return policy under an optimal allocation strategy; the worst combination is a strict policy and a “company” (non-optimal) strategy.

Note: Numbers represent averages of purchases, product returns, profit and referrals per customer per year based on data six months before and six months after the product-return policy change.

Strict

Product-Return

Policy

Avg. Purchase ($): $893.60 Avg. Purchase ($): $907.20

Avg. Product Return ($): $20.50 Avg. Product Return ($): $17.60

Avg. Profit ($): $247.58 Avg. Profit ($): $254.56

Avg. # of Referrals: 0.8 Avg. # of Referrals: 1.2

SLOANREVIEW.MIT.EDU SPRING 2010 MIT SLOAN MANAGEMENT REVIEW 89

months after the last potential catalog was mailed to

each customer, we would see about 95% of the re-

sulting purchase and product-return behavior.

The results of the field experiment reveal two

key findings. (See “Results of the Field Experiment

With Company 2.”) First, there is a significant dif-

ference between the behavior of customers when

the product-return policy is strict and when it is le-

nient. Under both st r ateg ies (company and

optimal) there are increases in average yearly pur-

chases, in average yearly customer profit and in the

average number of referrals each customer makes

per year. There is also an increase in the average

dollar value of products returned each year, which

is expected, given that the return policy is lenient.

However, this increase in product returns is more

than offset by customer purchase and referral be-

hav ior, which leads to g reater profits and a

faster-growing customer base.

Second, we see the results of a catalog-mailing

strategy that takes into account the expected future

profits from each customer and the relationship be-

tween purchases and product-return behavior — i.e.,

an optimal allocation strategy. There is an increase in

average yearly purchases, a decrease in the average

yearly dollar amount of product returns, an increase

in average yearly profit and an increase in the average

number of referrals per year. By extrapolating to the

entire base of approximately 200,000 customers, we

estimate that the introduction of the lenient return

policy gives an incremental gain in profit of more

than $10 million and that the optimal resource-allo-

cation strategy gives an additional increase in profit

of $12.5 million for a total of $22.5 million.

Summary It is crucial not to ignore product returns or treat

them as just a bitter pill the company is forced to

swallow in the company-customer relationship. In-

stead, a satisfactory product-return experience can

lead to increases in customers’ future purchases and

referrals and in the profit they yield for the com-

pany. It is possible for companies to ascertain the

role that product returns play in a customer’s deci-

sion to purchase and to quantify that customer’s

long-term value to the company. By understanding

the drivers and consequences of product returns,

managers can determine the relationship between

the costs and benefits of product returns to their

company, which allows them to allocate resources

more effectively so as to maximize company profits.

J. Andrew Petersen is an assistant professor of marketing at the Kenan-Flagler Business School, University of North Carolina at Chapel Hill. V. Kumar is the Richard and Susan Lenny Distinguished Chair in Marketing, executive director of the Center for Ex- cellence in Brand and Customer Management and director of the Ph.D. program in marketing at the J. Mack Robinson School of Business, Georgia State University, Atlanta. Comment on this article or con- tact the authors at [email protected].

ACKNOWLEDGMENTS

The authors would like to thank Company 1 and Company 2 — two major catalog retailers — for providing data and running a field experiment for this study.

REFERENCES

1. D. Blanchard, “Supply Chains Also Work in Reverse,” IndustryWeek, May 1, 2007.

2. N.N. Bechwati and W.S. Siegal, “The Impact of the Prechoice Process on Product Returns,” Journal of Marketing Research 42, no. 3 (August 2005): 358-367.

3. A.B. Bower and J.G. Maxham, “Customer Responses to Product Return Experiences,” working paper, McIntire School of Commerce, University of Virginia, 2006.

4. J.A. Petersen and V. Kumar, “Are Product Returns a Necessary Evil? Antecedents and Consequences,” Journal of Marketing 73, no. 3 (May 2009): 35-51.

Reprint 51316.

Copyright © Massachusetts Institute of Technology, 2010.

All rights reserved.

OPTIMAL AMOUNT OF PRODUCT RETURNS TO MAXIMIZE PROFITS The best rate of customers’ product returns is not zero returns. Company 1, for example, would achieve maximum profits at a return rate of 13%, or 3% less than its current rate.

-15%

$100

$20

$0

-$20

-$40 -10% 0%

Profit ($ in thousands)

-5%

$80

$60

$40

15% Percent Change in Returns

Maximum at -3%

5% 10%

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