The Impact of Emerging Technologies on Consumer Returns€¦ · The Impact of Emerging Technologies...

Copyright @ 2018 Michael Galbreth and Guangzhi ShangPlease do not distribute without permission from authors.

The Impact of Emerging Technologies on

Consumer Returns:Insights from Academia

September 20, 2018

Michael Galbreth Professor and Pilot Corporation ChairHead, Department of Business AnalyticsHaslam College of BusinessUniversity of Tennessee


Recent Academic Inquiries

Returns Forecasting

Drivers of Returns

The Returns Experience

Impact of Retail Innovations



Returns Forecasting

Drivers of Returns




Return Forecasting

What we need:

Forecasted returns next

month

What we have:

POS purchase and return

transactions

Shang, McKie, Ferguson & Galbreth (2017)

ID Purchase Date Return Date

xxx Dec-01-2016 N/A

xxx Dec-01-2016 Dec-09-2016

. . . . . . . . .

Thousands oftransactionsper month !

358 returns will come back next month

A single number!


Return Forecasting

What we need:

Forecasted returns next

month

What we have:

POS purchase and return

transactions

Shang, McKie, Ferguson & Galbreth (2017)

Transaction-level granularity

Period-level granularity

Aggregate

Aggregate

Predict

Predict

Time-series ORregressionmethods

Classification-styleAND survival methods


Implementation Test 1➜ Brick & Mortar channel➜ Consumer electronics➜ Average return rate =

12%

Results➜ Average improvement =

12% reduction in forecast error (RMSE)

Returns Forecasting Shang, McKie, Ferguson & Galbreth (2017)

Audio -30%Auto Parts -9%Cable -12%Computer -19%Imaging -13%Mobile Phone -6%Phone -5%TV -1%TV Box -15%Simple Average -12%

Forecast Error Reduction


Implementation Test 2➜ Online channel➜ Jewelry and accessories➜ Average return rate =

8%

Results➜ Average improvement =

18% reduction in forecast error (RMSE)

Returns Forecasting Shang, McKie, Ferguson & Galbreth (2017)

Bracelets -25%Earrings -1%Necklaces -22%Rings -42%Other Accessories -1%Simple Average -18%

Forecast Error Reduction


TakeawayPredict-first, aggregate-second

makes return forecasts much more accurate

Why does it increase accuracy?Because it preserves and leverages the

granularity of POS data!



Returns Forecasting

Drivers of Returns




Data: large Brick & Mortar electronics retailer

Return probability decreases with product maturity➜ Longer time on shelf, more WOM and

review info for consumers

What about product variety?➜ less differentiation across products might

increase my regret…however:

➜ more options might help me make a more precise choice…

Drivers of Returns Shang, Ferguson & Galbreth (2018)

Product Maturity Return Probability

Brand New (1st day on shelf) 12.6%

6 month later 11.4%

1 year later 11.3%

Product Variety Return Probability

5 items in same assortment 13.4%

15 items 12.0%

25 items 11.3%

All else equal, both

maturityand varietydecrease the return probability


Data: mail-order catalog retailer selling women’s fashion clothing

➜ Color varietyOne more color => 0.7% decrease in return rate

➜ Size varietyOne more size => 0.5% increase in return rate

Drivers of Returns Anderson, Hansen, Simester & Wang (2006)

Copyright @ 2018 Michael Galbreth and Guangzhi ShangPlease do not distribute without permission from authors. Minnema, Bijmolt,

Gensler, & Wiesel (2016)

ValenceAverage rating

VarianceStandard deviation of

rating

VolumeNumber of reviews Purchase

Important emerging influence on retail:

user reviews

Copyright @ 2018 Michael Galbreth and Guangzhi ShangPlease do not distribute without permission from authors. Minnema, Bijmolt,

Gensler, & Wiesel (2016)

ValenceAverage rating

VarianceStandard deviation of

rating

VolumeNumber of reviews PurchaseReturn

A major online European retailer selling electronics and furniture

if rating inflated initially

No Effect

No Effect

if


TakeawayProduct maturity and variety are key drivers of returns (and bracketing is an emerging issue in

some categories)

Positive user reviews = fewer returns(but beware of artificially inflated reviews)

A wide variety of user reviews can reduce returns, if consumers find the reviews helpful



Returns Forecasting

Drivers of Returns




THE QUESTION:

“Does customer experience with the product return process affect purchasing patterns following the

return, and if so, how?”*

* Griffis et al. 2012


Return Experience Future Transactions

Evidence from Online Retail Data

I love it!no refund needed here!

I’m not happy with this product…

I’ll take a full refund, please!

# of ordersavg. order $basket size

# of ordersavg. order $basket size

Griffis et al.(2012)

less more

less more

PLUS: the faster the refund is processed, the larger all three of these gains become


Implication:optimize returns, don’t minimize them!

cost center mentality focuses only here

research supports an effect of returns on this term as well!

Petersen & Kumar (2010)


Return Experience Future Transactions

But what if a full refund is not justified?

I’m not happy…I’ll take a full refund,

please!

futurespending

Bower and Maxham(2012)

less more

I’m not happy…I’ll take a full refund,

please!

The product works perfectly, so there

will be a fee

no problem!

futurespending

current

morecurrentless


What do shoppers dislike the most?

Source: Narvar Report, June 2017 (consumer survey)


immediate retailer benefit

Are exchanges better than returns?Ertekin(2018)

How ‘bout an exchange?

HOWEVER…

Empirical Finding #1:➜ In terms of customer lifetime

value, future purchase behaviormatters more than immediate exchanges

sales pressure during returns process

lower returns satisfaction

fewer future purchases

Don’t lose focus on this!

Empirical Finding #2

plus, sales pressure does not necessarily increase exchange probability!


Are exchanges better than returns?Ertekin(2018)

How ‘bout an exchange?

So What is the Solution?

highly competent returns desk staff

higher returns satisfaction

more future purchases

Empirical Finding #3

plus, exchange probability increases!


Takeawayfull refunds, while costly, can

drive future revenues(the optimal return rate is not zero!)

restocking fees can deter abuse, but they can also lead to feelings of unfairness, regret, and

lost future business

Investments in highly competent returns desk staff might be justified



Returns Forecasting

Drivers of Returns




A CHALLENGE FOR ONLINE RETAILERS:

How do we overcome the lack of an ability for customers to truly assess the “look and feel” of a

product before purchase?


POTENTIAL SOLUTION:IMPROVED ONLINE VISUAL TOOLS

3 common approaches:

De et al.(2013)

1. color swatches

2. zoom functionality

3. alternative photos (models, angles)

no evidence of a relationship

between viewing different colors and

return rates

Some (weak) evidence: using zoom leads to

LOWER return rates

(factual information)

Strongest empirical result:

alternative photos lead to HIGHER return

rates

“impression-based” information

unrealistic expectations?


POTENTIAL SOLUTION:VIRTUAL FITTING ROOMS

Customer creates a “virtual model” by providing body measurements, skin tone, and hairstyle.

Does it work? Based on a randomized field experiment:

conversion rate

order size

Gallino and Moreno(2017)

$

$$$

return rate

10-16% increase!

1-2% increase!

>5% decrease!

Net Result: 13.5% profit increase from the virtual fitting tech


POTENTIAL SOLUTION:VIRTUAL FITTING – emerging tech


TakeawayZoom technology and other sources of factual

info seem to reduce returns. But beware of impression-based info!

There is evidence that virtual fitting rooms are very effective at reducing returns (along with

other benefits)


Thanks!Any questions?

Michael Galbreth University of Tennessee

[email protected]


Anderson, Hansen, Simester, Wang (2006) How are demand and returns related? Theory and empirical evidence. Working paper.

Bower, Maxham (2012) Return shipping policies of online retailers: normative assumptions and the long-term consequences of fee and free returns. Journal of Marketing, 76(5).

De, Hu, Raman (2013) Product-Oriented Web Technologies andProduct Returns: An Exploratory Study. Information Systems Research, 24(4).

Ertekin, N. (2018) Immediate and Long‐Term Benefits of In‐Store Return Experience. Production & Operations Management, 27(1).

Gallino, Moreno (2017) The value of fit information in online retail: evidence from a randomized field experiment. Manufacturing & Service Operations Management, 20(4).

Griffis, Rao, Goldsby, Niranjan (2012) The customer consequences of returns in online retailing: An empirical analysis. Journal of Operations Management, 30(4).

Cited Academic Studies


Minnema, A., Bijmolt, T. H., Gensler, S., & Wiesel, T. (2016) To keep or not to keep: Effects of online customer reviews on product returns. Journal of Retailing, 92(3).

Petersen & Kumar (2010) Can product returns make you money? MIT Sloan Management Review, 51(3).

Shang, McKie, Ferguson, Galbreth (2017) Using transactions data to improve consumer returns forecasting. Working paper.

Shang, Ferguson, Galbreth (2018) Where should I focus my return reduction efforts? Data-driven guidance for retailers. Decision Sciences, in press.

Cited Studies

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