Problem
➢
Product returns is a widely known phenomena and a real challenge to many retailers
➢
$550 billion in the U.S. alone by 2020 (Statista, 2019)
➢
A total of 13% of all online ordered products returned in the Netherlands → higher
compared to other European countries (Hoeijmans, 2020)
➢
Especially fashion segment has high return ratio’s (44%) (Hoeijmans, 2020)
➢
Important to get insights into the drivers of product return behavior
➢
Field of research contains many topics related to product returns – yet a lot of topics
remain unexplored
➢
Aim of this research: “How does basket composition and price promotions influence the
Product Returns
Many different drivers:
➢
Customer reviews, shipping fee schedules, retailers reputation,
subjective norms, product compatibility, perceived risks, costs
and complexity, social group influences, desires for uniqueness,
materialism and patronage intentions (Minnema et al. 2016)
(Lepthien and Clement, 2019) (Walsh et al. 2016)
➢
Models for predicting future product returns (Cui et al 2019)
Basket Compositions and Analyses
➢
Often referred to as Market Basket Analysis (MBA) – find pairs of
products that are jointly observed in large sample baskets →
product pairs leads to increased returning probabilities?
Cross Category Influences
➢
Gap in the literature of examining product returns, basket
compositions, cross category influences and price promotions
all in one
➢
Most studies related to cross-category models and responses
to marketing mix activities (e.g. prices and promotions)
(Ainsli and Rossi, 1998)
Price Promotions
- A lot of previous research has been done
- Raju (1992) Examined the effects of price promotions on the
variability in category sales → higher magnitude of discounts
leads to greater variability in category sales and higher
Data Information
- Data: collected from a large online retailer which operates
within the Netherlands – business-to-consumer market
- Circa 120.000 observations, 62 variables
- 17 main product categories
- 87 subcategories
- 605 product variations
- Enriched with data from KNMI
Data Transformation
- Rows for every purchase → aggregated at a product specific
level
- Cleaning was done beforehand
Complete Model for Price Promotions and Basket
Compositions on Product Returns
Product_return1 ~ Α +
𝛽
1(Promotion1*TotalBasket) +
𝛽
2(Discount1*TotalBasket) +
𝛽
3(Promotion1*CategoryDiff) +
Main Variables
Variables
Model 4
Intercept
5.1250
Category_level1_code
0.9623***
CategoryDiff
0.9998***
TotalBasket
1.0628***
Purchase_amount
1.0424
Promotion1
0.4345***
Promotion_value_eur
0.9963
Discount1
0.8703 ***
Discount_value_eur
0.9997
Voucher_value_eur
0.9765*
Garden
0.9521***
Beauty
1.0459***
Toys
0.4512***
Beachwear
1.2502***
KidsFashion
1.1074***
Other
0.0001
MensFashion
1.0212***
Health
0.5804***
LadiesFashion
1.4593***
Nightwear
1.3901***
Home
0.9071***
Sports
1.1523***
Accessoires
1.2489***
Shoes
1.4387***
Electronics
0.5098***
Baby
0.7554***
Male
0.7903***
Promotion1*TotalBasket
1.003**
Discount1*TotalBasket
1.0006
Promotion1*CategoryDiff
1.0403**
Discount1*CategoryDiff
1.0068**
➢
TotalBasket and category_level1_code
significantly influences the return
probability by +6,28% and -3,77%.
➢
Promotion1 and Discount1 are
significant and decrease the return
probabilities by 46,55% and 17,97%.
➢
Interaction effects between
Promotion1 and TotalBasket,
Control Variables
Variables
Model 4
Avg_temperature
0.9991
SeasonW
0.9937
SeasonS
1.1024***
SeasonL
0.9934
SeasonH
0.8768 ***
Male
0.7903***
➢
Summer Season and Autumn Season
influence return probabilities
significantly by +10,24% and -12,32%
➢
Purchases from male customers
➢
Whenever a basket contains multiple product
categories → return probabilities slightly decrease
➢
Exact cross-category and within-category effects not
examined due to time and resource limitations →
future research
➢
Price promotions leads to decrease in amount of
products purchased → customers buy more but
smaller baskets → exact increase in total baskets for
future research
➢
Price promotions going on at the time a customer
purchases products → return probaiblities decrease
Hypotheses
Supported?
H1: Product purchases from one product category does have an influence on
the possibility of returning products across categories
Partially,
FR
H2: Product purchases from one product category have a negative influence
on the possibility of returning product within the same category
Partially,
FR
H3: Price promotions increase the amount of product purchased
No,
opposite
H4: Price promotions lead to more different product categories within one
basket
Partially
H5: Price promotions at the time that a consumer purchased a product leads
to lower return rates
Yes
H6: Price promotions at the time that a consumer purchased a product leads
to higher return rates
Managerial Implications
➢
Basket containing multiple product categories →
decrease in return probabilities → managers should
try using cross-category selling efforts for increase in
product varieties within one basket
Limitations and Future Research
➢
Results may be different for other retailers,
industries and countries
➢
One year of data used → customer behavior could
be different for the present
➢
Future research → examine the actual effects of
different product categories within one basket on
product return probabilities
➢
Examine the effects on a even more detailed
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