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The Double-Edged Effects of E-Commerce Cart Retargeting: Does Retargeting Too Early Backfire?

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Abstract

Consumers often abandon e-commerce carts, so companies are shifting their online advertising budgets to immediate e-commerce cart retargeting (ECR). They presume that early reminder ads, relative to late ones, generate more click-throughs and web revisits. The authors develop a conceptual framework of the double-edged effects of ECR ads and empirically support it with a multistudy, multisetting design. Study 1 involves two field experiments on over 40,500 customers who are randomized to either receive an ECR ad via email and app channels (treatment) or not receive it (control) across different hourly blocks after cart abandonment. The authors find that customers who received an early ECR ad within 30 minutes to one hour after cart abandonment are less likely to make a purchase compared with the control. These findings reveal a causal negative incremental impact of immediate retargeting. In other words, delivering ECR ads too early can engender worse purchase rates than without delivering them, thus wasting online advertising budgets. By contrast, a late ECR ad received one to three days after cart abandonment has a positive incremental impact on customer purchases. In Study 2, another field experiment on 23,900 customers not only replicates the double-edged impact of ECR ads delivered by mobile short message service but also explores cart characteristics that amplify both the negative impact of early ECR ads and positive impact of late ECR ads. These findings offer novel insights into customer responses to online retargeted ads for researchers and managers alike.
Article
The Double-Edged Effects of E-Commerce
Cart Retargeting: Does Retargeting
Too Early Backfire?
Jing Li , Xueming Luo , Xianghua Lu, and Takeshi Moriguchi
Abstract
Consumers often abandon e-commerce carts, so companies are shifting their online advertising budgets to immediate
e-commerce cart retargeting (ECR). They presume that early reminder ads, relative to late ones, generate more click-throughs
and web revisits. The authors develop a conceptual framework of the double-edged effects of ECR ads and empirically support it
with a multistudy, multisetting design. Study 1 involves two field experiments on over 40,500 customers who are randomized to
either receive an ECR ad via email and app channels (treatment) or not receive it (control) across different hourly blocks after cart
abandonment. The authors find that customers who received an early ECR ad within 30 minutes to one hour after cart aban-
donment are less likely to make a purchase compared with the control. These findings reveal a causal negative incremental impact
of immediate retargeting. In other words, delivering ECR ads too early can engender worse purchase rates than without delivering
them, thus wasting online advertising budgets. By contrast, a late ECR ad received one to three days after cart abandonment has a
positive incremental impact on customer purchases. In Study 2, another field experiment on 23,900 customers not only replicates
the double-edged impact of ECR ads delivered by mobile short message service but also explores cart characteristics that amplify
both the negative impact of early ECR ads and positive impact of late ECR ads. These findings offer novel insights into customer
responses to online retargeted ads for researchers and managers alike.
Keywords
e-commerce, e-commerce cart retargeting, field experiment, online shopping cart abandonment, recency bump, timing
Online supplement: https://doi.org/10.1177/0022242920959043
Empowered by modern e-commerce technologies, many com-
panies shift their online ad budgets to immediate retargeting.
That is, companies actively engage in e-commerce cart retar-
geting (ECR), defined as a form of digital behavioral retarget-
ing wherein online reminder ads are delivered to consumers
who had carted products but left without purchasing. For
instance, Amazon sends emails to inform customers of their
carted products as a call-to-action reminder. Macy’s regularly
sends short mobile messages to remind customers who
inspected and shortlisted products but did not buy (Garcia
2018). Indeed, the rate of cart abandonment in e-commerce is
high: over 69%customers abandon carts online, and the lost
sales amounted to over $4.6 trillion in 2019.
1
These statistics
suggest a colossal opportunity for firms to deploy ECR ads. It is
no wonder that Booking.com, Taobao, and Target deliver app
notifications within minutes after customers abandon their
shopping carts (Statista 2020).
Such prevalent industry practices of immediate retargeting
are fueled by the “recency bump,” wherein early reminder ads,
relative to late ones, are premised to generate more click-
throughs and web revisits (Prioleau 2013). At first glance, this
recency bump makes sense because timing is critical. Ads may
Jing Li is Assistant Professor, School of Business, Nanjing University, China
(email: jingli@nju.edu.cn). Xueming Luo is Charles Gilliland Chair
Distinguished Professor of Marketing, Strategy, and MIS, and Founder/
Director of the Global Center for Big Data in Mobile Analytics, Fox School
of Business, Temple University, USA (email: xueming.luo@temple.edu).
Xianghua Lu (corresponding author) is Professor of Information Systems,
School of Management, Fudan University, China (email: lxhua@fudan.edu.cn).
Takeshi Moriguchi is Professor of Marketing and Director of the Research
Center of Consumer Behavior, Faculty of Commerce, Waseda University,
Japan (email: moriguchi@waseda.jp).
1
Baymard Institute reports that the highest abandonment rate is on travel sites
(81.1%) and the lowest on fashion websites (69.1%) (https://baymard.com/
lists/cart-abandonment-rate, accessed September 29, 2020).
Journal of Marketing
1-18
ªAmerican Marketing Association 2020
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/0022242920959043
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have immediate recency: immediately retargeted reminders
can be still relevant to user intent, consistent with the common
wisdom of “striking while the iron is hot” (Moore 2013).
However, this recency bump can be misleading because it
does not measure the causal impact of immediate retargeting. It
simply measures the different consumer responses between
early and late ECR ads (among the treatment group with all
retargeted ads), yet the latter is not a valid comparison baseline
for the former due to many alternative explanations (i.e., miss-
ing the control group without retargeted ads). For example,
consumers who have recently filled an e-commerce cart may
be intrinsically more likely to purchase than a consumer for
whom it is longer ago because the latter consumer’s revealed
hesitance may indicate a lack of purchase intent. If so, it is
customer self-selection or other confounds, rather than ad
recency, that causes the purchase bump. Thus, the valid com-
parison baseline of a randomized immediate control (without
early ECR ads) with similar consumers is required to scienti-
fically quantify the causal impact of immediate retargeting
(with early ECR ads).
2
Worse, immediate retargeting, relative to the control,
may annoy customers and backfire. That is, as consumers’
memory has not faded yet, an early ECR ad sent within
minutes after cart abandonment may be too pushy and may
seem like the retailer is telling them what to do so that it
can make more profits, which can trigger ad annoyance and
thus lead consumers to purchase less (e.g., Aaker and Bruz-
zone 1985; Goldstein et al. 2014; Todri et al. 2020; Yoo and
Kim 2005).
Against this backdrop, we develop a conceptual framework
of the double-edged effects of ECR and empirically support it
with a multistudy, multisetting design. Study 1 involves two
field experiments on over 40,500 customers who are rando-
mized to either receive an ECR ad via email and app channels
(treatment) or not receive it (control) across different hourly
blocks after cart abandonment. Results show that in the
absence of ECR ads, customer purchases in the control group
decayed over time, in line with the memory decay literature.
However, relative to the early control, early ECR ads sent
within 30 minutes to one hour after cart abandonment have a
significantly negative incremental impact on customer
purchases. That is, the immediate retargeting is less effective
than the randomized early control. In other words, the purchase
rate with early ECR ads is even worse than that without them.
By contrast, a late ECR ad sent 24–72 hours after cart aban-
donment has a positive incremental effect: late retargeting ads
are more effective than the randomized late control. While the
early retargeting treatment generates higher purchase rates
than the late retargeting treatment, the early control has even
higher purchase rates than the late control. Thus, the causal
incremental impact is negative for early ECR but positive for
late ECR, in support of the double-edged effects of ECR ads on
customer purchases.
Study 2 involves another field experiment on over 23,900
customers from a different company with ECR ads delivered
by mobile short message service (SMS). The results first
replicate the double-edged impact of ECR ads. Furthermore,
because customers have different reasons for cart abandon-
ment, they may have quite different purchase responses to
ECR ads. Leveraging the detailed clickstream data on shop-
ping cart characteristics such as product quantity and product
prices, we find that the double-edged effects of ECR ads are
significantly moderated by these cart features. That is, both
the negative impact of early ECR ads and the positive impact
of late ECR ads are amplified when the products in the retar-
geted carts are of a larger quantity and at higher average
prices.
Our findings contribute to the literature in three key ways.
(1) Substantively, as Table 1 shows, we are among the first to
reveal a causal adverse incremental impact of immediate retar-
geting on customer purchases. Advancing prior research on
retargeting (Bleier and Eisenbeiss 2015; Johnson, Lewis, and
Nubbemeyer 2017; Lambrecht and Tucker 2013; Sahni, Nar-
ayanan, and Kalyanam 2019), we not only conceptually differ-
entiate early ECR from late ECR but also empirically
demonstrate the double-edged effects of ECR ads and explore
the moderated effects. (2) Methodologically speaking, we
leverage a multistudy, multisetting research design with three
large-scale randomized field experiments based on a fine-
grained hourly level of retargeted ads and over 64,000 custom-
ers from different companies, which can rigorously test the
causal incremental effects of early and late ECR ads and attain
a higher generalizability of our findings. (3) Managerially,
companies should not blindly follow the recency bump and
shift their online ad budgets to immediate retargeting. Deliver-
ing the ECR ads too early can engender worse purchase rates
Table 1. Literature Gap.
Negative Effects of Ads Positive Effects of Ads
Retargeting Our article Lambrecht and Tucker (2013); Bleier and Eisenbeiss (2015); Hoban and
Bucklin (2015); Johnson, Lewis, and Nubbemeyer (2017); Sahni et al.
(2019)
Nonretargeting Aaker and Bruzzone (1985); Yoo and Kim (2005);
Goldstein et al. (2014); Jenkins et al. (2016)
Bettman (1979); Alba and Chattopadhyay (1985); Tellis (1988); Lewis and
Reiley (2014); Van Heerde et al. (2004, 2013)
2
For ease of exposition, we use “immediate retargeting” and “early ECR ads”
interchangeably.
2Journal of Marketing XX(X)
than without delivering them, thus wasting online advertising
budgets. Prudent advertisers ought to match the timing of ECR
ads with the retargeted cart features (for detailed research and
managerial implications, see the “Discussion and Implications”
section).
Literature and Hypotheses
Retargeting Literature
A recent stream of research in marketing has examined retar-
geting ads (Bleier and Eisenbeiss 2015; Johnson, Lewis, and
Nubbemeyer 2017; Lambrecht and Tucker 2013; Sahni, Nar-
ayanan, and Kalyanam 2019). As Table 2 shows, researchers
investigated with days or weeks after abandonment. That is,
retargeted ads in prior studies were sent in the first few days or
weeks after consumers left the focal website. In contrast, we
examine with hours: our immediate retargeted ads are sent
within 30 minutes to one hour after cart abandonment. Because
the retargeting technology aims to “reduce the time lag
between the consumers leaving the website and the beginning
of the campaign to almost zero” (Sahni, Narayanan, and Kalya-
nam 2019, p. 401), a finer-grained time interval with hours
(relative to days or weeks) after abandonment can more accu-
rately capture the immediacy in retargeted reminder ads.
In addition, prior studies focused on ad personalization and
compared different ad copies (Bleier and Eisenbeiss 2015;
Lambrecht and Tucker 2013), whereas we put the spotlight
on the causal effects of early and late retargeted ads. For both
early and late ECR ads, we have the randomized early and late
controls to scientifically identify the incremental effects.
Recently, Sahni, Narayanan, and Kalyanam (2019) examined
the frequency and timing of retargeted ads at daily level. They
found that the effect of frequent retargeting ads is positive and
largest for the first day within the first week. We extend their
study by examining retargeted ads at hourly level, uncovering
the potential annoyance effect of retargeted ads when delivered
too early and exploring the moderating role of cart
characteristics.
Furthermore, prior works rely on one channel—namely,
internet banners—to deliver the retargeted ads (Bleier and
Eisenbeiss 2015; Johnson, Lewis, and Nubbemeyer 2017;
Lambrecht and Tucker 2013; Sahni, Narayanan, and Kalyanam
2019). By contrast, we use multiple channels: email, app, and
mobile SMS, which enhances the generalizability of the find-
ings across different customer touchpoints. Indeed, companies
are now retargeting their customers via emails, app notifica-
tions, and SMS in an omnichannel manner (Garcia 2018; Sta-
tista 2020).
In addition, whereas most prior studies rely on website revi-
sits and click-throughs (cf. Lambrecht and Tucker 2013), we
use customer purchases to measure the outcome of retargeting.
While web clicks and visits are important, they are upper-
funnel metrics heralding sales revenues. By contrast, customer
purchases are lower-funnel outcomes directly related to sales
revenues for companies. Furthermore, advancing prior studies
on retargeting consumers who abandoning websites in general
(some of them just browse around, while others inspect product
details), we take a deeper dive into the consumer decision-
making journey by focusing on retargeting consumers who
have placed products in their carts but then left the online store.
Extending Lambrecht and Tucker (2013) and other studies that
documented the positive impact of retargeting sent in days after
abandonment, we uncover the negative impact of immediate
retargeted ads delivered within the first hour after cart
abandonment.
Hypothesis Development
Figure 1 presents our conceptual framework of the negative
incremental impact of early ECR ads and positive incremental
impact of late ECR ads on customer purchases. In our frame-
work, the timing of retargeted ads refers to the time lag (e.g.,
hours, days) between a consumer abandoning the online shop-
ping cart without buying and the start of retargeting ad cam-
paigns. Specifically, early ECR ads are delivered to customers
within the first hour after cart abandonment,
3
whereas late ECR
ads are delivered at least one day after cart abandonment.
As Figure 1 illustrates, in the absence of ECR ads, customer
purchase rates decrease over time with a downward trend in the
control group. This is because, according to the memory decay
theory (Brown 1958; Mueller et al. 2003; Thorndike 1914),
after consumers abandon the shopping carts, their memory of
the products fades over time; thus, their purchase probability of
the carted products dwindles as the time elapses after abandon-
ment. Ad reminders then can be leveraged to rekindle this
memory, as the ability of ads to remind consumers is fairly
well established (Alba and Chattopadhyay 1985; Bettman
1979).
However, the incremental effects of early and late ECRS
ads, over the early and late control, are not straightforward.
Specifically, our conceptual framework posits that early ECR
ads, relative to the early control, have a negative incremental
impact on customer purchases, whereas late ECR ads, relative
to the late control, have a positive incremental impact. This
contrasting pattern results from the two driving forces: negative
ad annoyance and positive ad reminder.
3
Our definition of early ECR ads within one hour after abandonment is in line
with industry practices, where immediate retargeting means sending ads within
one hour after consumers leave the website in retailing, fashion, travel, and
other industries (Garcia 2018; Statista 2020). We do not consider time over a
week in our late ECR ads because if a very long time has elapsed after cart
abandonment, consumer memory can be totally lost and is notoriously difficult
to restore (Kelley and Gorham 1988). Indeed, research has found that
retargeted ads sent one week after abandonment are ineffective in generating
incremental purchases (Moriguchi, Xiong, and Luo 2016). We return to this
point in the “Discussion and Implications” section. Furthermore, because
consumers rarely put cars in shopping carts online (most people would still
need to test drive the cars in the physical world offline), immediate retargeting
is more applicable to online purchases in business sectors such as retailing and
fashion.
Li et al. 3
Table 2. A Comparison of Prior Studies on Retargeted Ads.
Article Timing Interval IV Channel DV Moderators Key Findings
Lambrecht and Tucker
(2013)
Days after
abandonment
Ad content Banner Customer
purchase
Browsing review website Dynamic retargeted ads (relative to generic ads) have a
positive effect for consumers who browsed a review
website.
Bleier and Eisenbeiss (2015) Weeks after
abandonment
Ad content Banner Click-
throughs
Retargeted personalization ads (relative to
nonpersonalization ads) have a positive effect, but quickly
lose effectiveness as time (in days) passes since that last
visit.
Hoban and Bucklin (2015) Days after
abandonment
Ad content Banner Web revisit Pre-experiment stages Retargeted ads (relative to charity ads) have a positive effect
for visitors except those without creating the account.
Johnson, Lewis, and
Nubbemeyer (2017)
Days after
abandonment
Ad content Banner Website visit Retargeted ads (relative to ghost ads) have a positive effect.
Sahni, Narayanan, and
Kalyanam (2019)
Days after
abandonment
Ad frequency
and timing
Banner Web revisit Retargeted ads (relative to control) have a positive effect
and are most effective for the first day of the first week.
Our study Hours after
abandonment
Early and late
ECR ads
Email, app,
mobile SMS
Customer
purchase
Cart features such as product
quantity and prices
Retargeted ads have double-edged effects: early ECR ads
within 30 minutes to one hour (relative to the early
control) have a negative effect, while late ECR ads in one
to three days (relative to the late control) have a positive
effect.
Notes: IV ¼independent variable, DV ¼dependent variable.
4
On the one hand, ads may annoy consumers. Prior studies
have pointed out some adverse effects of ads. For example,
Yoo and Kim (2005) note that fast animation banner ads can
annoy customers and result in negative attitudes toward the
advertisers. Others find that consumers are irritated when
exposed to commercial ads that are too strident and frequent
(Aaker and Bruzzone 1985; Burke and Edell 1986; Pokrywc-
zynski and Crowley 1993). By and large, the literature suggests
that ad repetition may annoy consumers and negatively affect
the purchase funnel (Todri et al. 2020) because it interrupts
consumer goals, such as surfing the internet (Goldstein et al.
2014) and accomplishing a task online (Jenkins et al. 2016).
Extending this stream of research that frequent ads engender
consumer annoyance, we note that the one-time ECR ad may
also annoy consumers when it is delivered too early.
4
On the other hand, ads may remind consumers. Advertising
can allow brands to signal superior quality over rivals and
commendably remind consumers about their products (Bag-
well 2007; Lewis and Reiley 2014; Nelson 1974; Van Heerde,
Leeflang, and Wittink 2004). Viewing reminder ads can rekin-
dle memories
5
associated with the advertised products and thus
help consumers recall the focal brands (Alba and
Chattopadhyay 1985; Bettman 1979; Van der Lans, Pieters,
and Wedel 2008). In other words, advertising can persuade
consumers and enable advertisers to win in the marketplace
(i.e., through output interference and displacement of other ads;
see Leenheer et al. 2007; Sahni, Narayanan, and Kalyanam
2019; Van Heerde et al. 2013). These two competing forces
lead to the differential effects between early and late ECR ads,
as we elaborate next.
Negative incremental effects of early ECR ads. When retargeted
ads are deployed as soon as customers abandon their shopping
carts, their memories have not faded yet, so there is little ben-
efit from rekindling memory (i.e., low positive ad reminder
effect; Tellis 1988; Van Heerde et al. 2004, 2013). However,
consumers may feel a high level of the negative ad annoyance
effect. This is because as consumers’ memories have not wilted
yet, early ECR ads (relative to a control without early ECR ads)
sent within minutes after cart abandonment may be too pushy
and seem like the retailer is telling them what to do so that it
can make more profits. This can trigger ad annoyance and thus
negatively influence customer purchases. In other words, very
early retargeting comes across as too pushy, almost like a too-
insistent salesperson who desperately wants customers to buy
but actually annoys them and ends up with fewer sales (Babin
et al. 1995; Gillis et al. 1998; Martin 2017). By contrast, a
control group without early ECR ads has the same time elapse
after cart abandonment but no such negative ad annoyance
because it has no reminder ads served. Thus, to the extent that
early ECR ads (relative to a control without early ECR ads)
lead to a high level of negative ad annoyance but low positive
ad reminder, early ECR ads likely backfire with a negative
incremental impact on customer purchases.
H
1
:Relative to the randomized early control, early ECR
ads backfire with a negative incremental impact on cus-
tomer purchases.
Positive incremental effects of late ECR ads. In the case of late ECR
ads, consumer memory has faded extensively, and the remin-
ders help overcome this. That is, as the memory wanes, late
ECR ads (relative to a control group without late ECR ads) can
rekindle the rusty memory of the carted products, thus leading
to a high positive ad reminder effect (Alba and Chattopadhyay
1985; Bettman 1979; Leenheer et al. 2007). Furthermore,
because of the extensive memory loss, late ECR ads may not
be too pushy to consumers and thus trigger little ad annoyance.
The late control without late ECR ads also has the same time
elapse after cart abandonment but no such positive ad reminder
effect, because no ads are served. Thus, to the extent that late
ECR ads (relative to a control group without late ECR ads) lead
to a high positive ad reminder effect but low negative ad annoy-
ance, late ECR ads likely have a positive incremental impact on
customer purchases.
Early Late
− Ad annoyance (high)
+ Ad reminder (low)
Customer Purchases
Treatment group with ECR ads
Control group without ECR ads
H1
H2+
− Ad annoyance (low)
+ Ad reminder (high)
Figure 1. Conceptual framework.
Notes: Early ¼within the first hour after cart abandonment; Late ¼one to
three days after cart abandonment. This framework is empirically supported by
our multistudy, multisetting research design with three large-scale randomized
field experiments on over 64,000 customers retargeted by email, app, and SMS
ads of different companies.
4
As we discuss subsequently, the one-time exposure to an immediate ECR ad
can stimulate annoyance because very early retargeting, when consumer
memory has not faded yet, comes across as too pushy, akin to a too-insistent
salesperson. Indeed, for preliminary evidence that early ECR ads lead to ad
annoyance among consumers, which then reduces their purchase intention, see
Web Appendix A.
5
Prior psychology literature has noted that human memory decays over time
(i.e., forgetting) (Thorndike 1914). Forgetting is a function of age, perceptual
speed, and central executive functioning (Fisk and Warr 1998), and different
people have different memory decay patterns. While some still have a fresh
memory after a long time, others forget quickly; thus, unobserved
heterogeneity exists across consumers. Consequently, we conducted field
experiments to account for such unobserved heterogeneity by randomizing
consumers who have the same time elapse after cart abandonment into
treatment and control groups.
Li et al. 5
H
2
:Relative to the randomized late control, late ECR ads
have a positive incremental impact on customer
purchases.
Study 1
Data and Design
A major Japanese online fashion retailer (that wishes to remain
anonymous) cooperated with us to conduct a set of field experi-
ments. The retailer sells fashion products such as clothing,
shoes, and handbags, in addition to household items. The retai-
ler targets a wide variety of customers, ranging from children to
older adults, and its core customers are men and women aged
20–45 years. The retailer provided us data on customer demo-
graphics, such as gender, age, area of residence, and customer
tenure, in addition to purchase history, clickstream browsing,
and shopping cart data. The time window of the data collected
covers three periods: six months before the experiments, during
the experiments, and one month after the experiments. From
September 21 to October 25, 2016, the retailer conducted two
randomized field experiments.
The retailer has two major communication channels: email
and a messaging app called Line (similar to WhatsApp’s dom-
inance in the United States, Line is the most popular mobile
messaging app in Japan). Thus, in Experiment 1, the retargeting
message was sent via email to a random sample of 33,234
customers. It is worth noting that email is also the most popular
retargeting channel in the United States. In Experiment 2, the
retargeting message was sent via Line to a different random
sample of 7,314 customers. This smaller sample size reflects
the fact that the retailer has many fewer users using its mobile
app, which registers users for receiving updates from the retailer
on Line. Because customers self-select the email or mobile app
channel, customers updated through Line and those updated
through email differed in their patterns of shopping behavior.
To account for this difference, we conducted two separate
experiments to ensure the generalizability of our results.
The research design is similar in the two experiments: the
company randomly assigned its customers into 16 groups (8
hour blocks 2 retargeting conditions). After extensive con-
sultation among the research team who ensured experimental
rigor and company executives who oversaw the experimental
execution, the retargeted customers in the treatment groups
were sent reminder messages in the eight blocks: .5, 1, 3, 6,
9, 12, 24, and 72 hours after cart abandonment. The retailer also
had randomized control groups—customers who were not
retargeted and did not receive such messages—for each of the
eight blocks. Thus, each of the eight retargeting timings had a
unique pair of treatment and control groups, and each pair has
the same amount of time elapsed after cart abandonment. This
is a crucial feature of our experimental design because it
enables us to identify the causal incremental effect of the spe-
cific hour block while estimating the whole data set simulta-
neously. In other words, the randomized control conditions
empower us to reveal the causal effects of ECR while account-
ing for many alternative explanations such as the general loss
of interests in the carted product over time (e.g., customers
have bought that or a different product at another store), sea-
sonality, and competition effects in the marketplace.
The retailer’s retargeting ads include product information
(brand name, category name, and price). It sent the retargeting
messagetocustomerswhohadabandonedonlyoneproductin
their shopping cart. For these customers, the retargeted prod-
uct in the message is the same as the abandoned product; this
allows them to more precisely identify the effects of the
product-specific retargeting message.
6
Web Appendix B,
Panel A, presents some examples of the retargeting message,
which contains no new information or price incentives; they
are simply reminders about the carted product that was not
purchased prior to the experiment (Sahni, Narayanan, and
Kalyanam 2019).
As for the experimental execution, if the retailer observed
customers to have abandoned the cart and forgone purchasing
the product for half an hour, for example, these customers were
randomly assigned into either the retargeting treatment or con-
trol group. Thus, customers were randomly assigned into all
other experimental cells in both Experiments 1 and 2, allowing
us to estimate the causal effects. To avoid customer complaints
of receiving messages late in the night, the retailer has the
policy of not sending messages to customers between 10:00
P.M. and 9:00 A.M. Therefore, some messages could not be sent
to subjects in the treatment group who had abandoned the cart
late in the day. Our results are robust to additional analyses
accounting for bias from this messaging policy.
7
The analyses
included propensity score matching, which was used to balance
the subjects of the treatment and control groups for each of the
eight timings. The variables in propensity score matching were
age, gender, area of residence, customer tenure, total money
spent (in JPY) in the past six months, number of products
purchased in the past six months, and dummy variables corre-
sponding to the time at which the carts were abandoned.
The characteristics of final subjects in the treatment and
control groups are summarized in Web Appendixes C and D.
According to the data presented in these appendices, the treat-
ment and control groups did not significantly differ with
respect to demographics and past purchases for each of the
eight hour blocks. Web Appendix B, Panel B indicates that the
distribution of the product categories was highly similar across
the treatment and control groups. Therefore, the data passed the
randomization checks.
6
Among the retailer’s customers who abandoned carts, approximately 90%
abandoned just one item in their shopping carts.
7
This policy might bias our results, as it would not affect the control groups but
would affect the treatment groups across the hour blocks. Thus, it may be
informative to conduct additional analyses with subjects that abandoned
carts between 9 A.M. and 12:59 P.M. because they could be assigned to all
hour blocks except the 12 hours. We checked the robustness with these
subjects and found consistent results (results available on request).
6Journal of Marketing XX(X)
Model-Free Results
Figure 2, Panel A, illustrates the comparison of the purchase
rates between the retargeted group (marked by dark bars) and
control group (marked by light bars) for the various timings in
Experiment 1, along with their 95%confidence intervals. The
purchases are measured within one month after sending the
retargeting ads to the subjects. In other words, the purchase
window is not constrained to the hour block of the retargeting
message, but rather one month after because it can take a while
before the purchase happens after the message has been
received.
Consistent with the theory of organic decaying memory (e.g.,
Baddeley et al. 1975; Brown 1958; Thorndike 1914), the control
group exhibited a generally downward trend in purchase rates,
attributable to the fading memory of the carted products over
time when not retargeted. In other words, for the control group
without retargeting ads, the organic purchase rate decreases over
time, with the highest at .5 hours and 1 hour after cart abandon-
ment, and the lowest at 72 hours after cart abandonment.
Recency Bump or Adverse Impact of Immediate
Retargeting?
For the treatment groups, the ad for early ECR (.5 hour or 1
hour) had the highest purchase rate of 13.1%, greater than the
other hour blocks ads for late ECR. At first glance, in the
A
:Email
B: App
.00
.02
.04
.06
.08
.10
.12
.14
.16
.18
.20
.5 Hour 1 Hour 3 Hour 12 Hour 24 Hour 72 hour
6Hour 9Hour
Retargeting Control
.00
.05
.10
.15
.20
.25
.5 Hour 1 Hour 3 Hour 12 Hour 24 Hour 72 hour
6Hour 9Hour
Retargeting Control
*
**
***
***
***
**
**
***
***
Figure 2. Model-free evidence for the purchase rates of treatment and control (retargeting channels: email and app).
Notes: Error bars here represent +1 standard error.
Li et al. 7
absence of a comparison with the control group, one might
erroneously conclude that early ECR is more effective and
beneficial than late ECR, as was done in the recency bump
(Moore 2013; Prioleau 2013). However, purchase rate com-
parisons between immediate and late retargeting yield invalid
comparisons because user self-selection is not controlled
for—users receiving the later message are likely to have not
purchased the product for a longer time after abandoning the
cart. In other words, consumers who receive an early ECR ad
(relative to a late ECR ad) may have higher purchase intent
and buy more even without the ad. Luckily, we have the
randomized controls (where no retargeting was involved but
with the same amount of time elapsed after abandonment),
which account for user self-selection such as consumer mem-
ory decay over time, lost interests in the products, competi-
tion, seasonality, or any other observed or unobserved
confounds. Thus, comparisons with these randomized con-
trols can effectively parse these confounds from the retarget-
ing timing effects. That is, we can use the incremental
effectiveness, by comparing retargeted users with the rando-
mized control within each of the specific hour block groups,
to account for this self-selection bias. Such comparison
reveals that in the control group, the purchase rate was also
highest for the half-hour and one-hour blocks, even higher
than that in their respective treatment group. Thus, early ECR
within half an hour and one hour had significantly negative
effects on purchase rates relative to their respective control
groups (.5 hour: 13.1%vs. 15.2%,p¼.012; 1 hour: 13.4%
vs. 16.7%,p<.01). That is, purchase rates with the early
ECR ad are even significantly lower than those without it,
thus wasting online ad budgets. Consequently, simple abso-
lute purchases are not causal and can be misleading when
used as a measure of retargeting success. By using the rela-
tive purchases incremental to the early control, we reveal that
early ECR ads can actually backfire. That is, immediate retar-
geting after cart abandonment has a causal adverse impact on
customer purchases. Therefore, H
1
is initially supported by
such model-free evidence.
According to Figure 2, Panel A, messages sent in the hour
blocks of 3, 6, and 9 hours after cart abandonment have no
significant effects relative to the control baseline (3 hours:
12.6%vs. 14.3%,p¼.114; 6 hours: 11.2%vs. 10.4%,p¼
.523; 9 hours: 11.7%vs. 9.7%,p¼.142). Such zero incre-
mental effect of middle hour blocks makes sense because of
the trade-off between the negative ad annoyance and positive
ad reminder effect (i.e., these two forces may cancel each
other, thus leading to insignificant effects in the middle hour
blocks). However, late ECR ads at 24 hours or 72 hours had
significantly positive effects on incremental purchases (24
hours: 8.3%vs. 6.0%,p<.01; 72 hours: 4.8%vs. 1.8%,p
<.01) over the late control baseline. Thus, H
2
is initially
supported as well.
Figure 2, Panel B, presents the purchase rates across the
hour blocks in the app channel–based retargeting message.
Again, early ECR had negative effects on incremental purchase
rates (half-hour: 18.0%vs. 21.1%,p¼.098; one hour: 14.4%
vs. 18.8%,p¼.074). The treatments and controls in the 3-, 6-,
9-, and 12-hour blocks did not significantly differ (3 hours:
13.3%vs. 13.7%,p¼.842; 6 hours: 12.9%vs. 14.0%,p¼
.673; 9 hours: 12.2%vs. 8.4%,p¼.060; 12 hours: 14.4%vs.
10.2%,p¼.099). Nevertheless, late ECR at 24 and 72 hours
had significantly positive effects on incremental purchase rates
(24 hours: 8.0%vs. 4.0%,p<.01; 72 hours: 6.2%vs. 2.8%,
p¼.037), replicating the pattern observed for the email chan-
nel retargeting message. Thus, these initial model-free results
support the double-edged effects of ECR: whereas early ECR
has a negative incremental impact, late ECR has a positive
incremental impact on customer purchases.
Model and Results
We formally test H
1
and H
2
by using a moderated logit regres-
sion model as follows.
dij ¼1;if user i in hour block j makes a purchase within
the next month
0;otherwise
:
8
>
<
>
:ð1Þ
This purchasing decision between 1 and 0 is based on a
latent-utility function U. Specifically, the differences in pur-
chase decision between the retargeting (treatment) and control
groups are moderated by the various hour blocks.
Uij ¼goþX
j2J
gj
1Retargeting ij Hour ij þg2Retargeting ij
þX
j2J
gj
3Hour ij þgkWij þEij j¼1;2;...;J hour blocksðÞ;
ð2Þ
where Retargeting
ij
is the treatment variable (1 and 0 represent
the retargeting treatment and control, respectively), and Hour
ij
denotes the hour blocks (.5, 1, 3, 6, 9, 12, 24 and 72 hours, and
the middle-range block of 6 hours is the baseline). W
ij
is a
vector of covariates (including the customer’s gender, age,
membership, area of residence, tenure, past message received,
past shopping frequency, past shopping expenditure, day fixed
effects, and time-of-day fixed effects).
Table 3 reports the results. Compared with the middle hour
block, the early and late hour blocks had significantly positive
and negative effects, respectively, on purchase rates (all ps<
.01). That is, similar to previous model-free results and consis-
tent with the memory decay theory (e.g., Baddeley et al. 1975;
Brown 1958; Thorndike 1914), the purchase rate generally had
an organic downward trend over time if there were no retarget-
ing messages.
Our hypotheses pertain to the interactions between hour
block and the retargeting treatment. The results in Table 3
consistently suggest that the interaction effects between early
hour blocks (.5 and 1 hours) and retargetingonincremental
purchase rate are significantly negative in both the email
and app channels (most p<.05). As such, these results
8Journal of Marketing XX(X)
support the negative effects of early ECR in H
1
. In addition,
the interaction effects between late hour blocks (24 and
72 hours) and retargeting on incremental purchase rate are
significantly positive in both email and app channels (at
least p<.05), thus supporting the positive effects of late
ECR in H
2
.
8
Moreover, Figure 3 plots the model-based incremental
impact of retargeting (coefficients in Table 3), which visualizes
that early ECR ads (in the .5- and 1-hour blocks) have a
significantly negative incremental impact, while late ECR ads
(in the 24- and 72-hour blocks) have a significantly positive
incremental impact on customer purchases for both the email
channel in Experiment 1 and app channel in Experiment 2.
Furthermore, to more directly test the effects of early and late
ECR ads, we combine the .5- and 1-hour blocks into the “Early”
group; the 3-, 6-, 9-, and 12-hour blocks into the “Middle” group;
and the 24- and 72-hour blocks in the “Late” group. (Web
Appendix E visualizes the model-free evidence.) Then, we run
the regression models and report the results in Table 4. Again,
the interaction effects between Early and Retargeting on incre-
mental purchase rates are significantly negative in both the email
and app channels (at least p<.05), in support of H
1
. Further-
more, the interaction effects between Late and Retargeting on
Table 3. Regression Results on Incremental Retargeting Effects with Hourly Block Interactions.
Email Channel Email Channel App Channel App Channel
.5 h Retargeting (H
1
:).321** .324** .307** .315**
(.136) (.136) (.142) (.142)
1hRetargeting (H
1
:).336** .339** .232* .248*
(.132) (.132) (.147) (.148)
3hRetargeting .025 .027 .0490 .0380
(.049) (.069) (.290) (.291)
9hRetargeting .132 .125 .243 .225
(.182) (.183) (.465) (.466)
12 h Retargeting .166 .167 .482 .472
(.159) (.159) (.319) (.319)
24 h Retargeting (H
2
:þ) .451** .454** .824** .808**
(.191) (.199) (.341) (.342)
72 h Retargeting (H
2
:þ) .950*** .954*** .923** .912**
(.350) (.352) (.461) (.461)
.5 h .527*** .506*** .502*** .497***
(.0963) (.0965) (.168) (.168)
1 h .441*** .405*** .356** .369**
(.0934) (.0936) (.170) (.180)
3 h .358*** .341*** .0195 .000327
(.105) (.105) (.203) (.203)
9h .0801 .0991 .211 .203
(.133) (.133) (.275) (.276)
12 h .110 .131 .358 .348
(.116) (.116) (.233) (.233)
24 h .778*** .767*** 1.363*** 1.349***
(.137) (.137) (.262) (.263)
72 h 1.868*** 1.839*** 1.730*** 1.698***
(.241) (.241) (.369) (.369)
(Baseline: 6 h)
Retargeting .0741 .0723 .0889 .0782
(Baseline: control) (.116) (.116) (.211) (.211)
Covariates Yes Yes Yes Yes
Product category effects No Yes No Yes
Time effects No Yes No Yes
Constant 2.146*** 2.136*** 1.819*** 1.950***
(.0833) (.105) (.147) (.219)
Pseudo R
2
.0211 .0269 .0377 .0407
N 33,234 33,234 7,314 7,314
*p<.1.
**p<.05.
***p<.01.
Notes: Robust standard errors in parentheses.
8
We have also estimated the marginal effects for the logit model (where we
hold all other variables at the mean level; Norton et al. 2004) and found
consistent results. Results are available on request.
Li et al. 9
incremental purchase rates are significantly positive in both the
email and app channels (at least p<.05), in support of H
2
.
Overall, these model-free and model-based results provide
consistent empirical evidence for H
1
and H
2
and thus strongly
support the double-edged effects of ECR ads (the negative
incremental impact of early ECR and positive incremental
impact of late ECR) on customer purchases across Experiments
1 and 2.
Study 2
The aim of Study 2 is twofold. First, it aims to replicate the
double-edged effects of H
1
and H
2
with a different company to
improve the generalizability of the findings. Here we engaged a
different retailer and used a different channel of SMS to deliver
the ECR ads. Our anonymous corporate partner in Study 2 is a
category killer (focusing on maternal and baby products) in
China. Considering that our partner in Study 1 was a fashion
retailer in Japan, our research settings cover more than one
country and two different companies with multiple product
lines. Second, Study 2 empirically explores the moderated
effects for the double-edged effects of ECR. Cart abandonment
in Study 2 involves multiple products left without purchasing.
This setting enables us to effectively identify cart characteris-
tics such as the quantity and prices of products left in the
retargeted carts to explore the moderated effects, in addition
to replicating the double-edged effects of ECR.
Data and Design
Study 2 involves a retargeting message delivered via SMS, thus
complementing Study 1’s focus on the email and mobile app
channels. Compared with email and the mobile messaging app,
SMS delivery is displayed as a banner on personal devices (Lai
2004), and the probability that people receive and read the SMS
message might be higher (Luo et al. 2014). In addition, SMS
promotions are gaining popularity among companies such as
Macy’s and Target in the United States. Our retail partner in
A
: Email
B: App
−.6
−.4
−.2
.0
.2
.4
.6
.8
1.0
1.2
1.4
.5 Hour 1 Hour
3 Hour 9 Hour 12 Hour 24 Hour 72 hour
−1.0
−.5
.0
.5
1.0
1.5
.5 Hour 1 Hour
3 Hour 9 Hour 12 Hour 24 Hour 72 hour
** ***
***
***
** **
*** ***
Figure 3. Model-based evidence for the incremental purchases of treatment over control (retargeting channels: email and app).
Notes: These figures plot the coefficients and the robust standard errors in Columns 2 and 4 of Table 3. Baseline is six hours after cart abandonment and without
retargeted ads.
10 Journal of Marketing XX(X)
Study 2 is similar to Babies R Us in the United States. The
retailer sells a wide variety of maternal and infant supplies,
including diapers, infant formula, equipment, toys, baby
clothes, and household items. Its customers are primarily
young parents with children under four years old. Our retailer
partner sent targeted message through SMS to its customers
after they had abandoned their carts online (this constituted the
triggering event). The experiment involved a random sample of
23,914 customers and was conducted from March 6 to March 9,
2017. To ensure generalizability, the experimental designs of
Study 2 were similar to those in Study 1. During our experi-
ment window, if consumers left a new product in the shopping
cart, they entered our sample pool and would receive the retar-
geting message treatment in hours after cart abandonment (or
not receive any message if he or she was in the control group).
In addition to consumers’ demographics and past purchase
information, we collected the shopping cart characteristics
based on the clickstream data.
The company randomly assigned the customers into eight
experiment groups (4 hour blocks 2 retargeting conditions).
Thehourblockswere1,3,9,and24hoursafterthefirst
shopping cart abandonment during our time window. Given
time and resource limitations, other hour blocks could not be
tested. The company also determined these four hour blocks to
be the most common in the local market. As per the standard
practice in Chinese e-commerce, the retailer had the mobile
numbers of its customers. Their customers are required to pro-
vide their mobile numbers when registering as a member on the
retailer’s website, and this number is used to authenticate their
membership. As in Study 1, the experiment in Study 2 had a
between-subjects design, where consumers neither were in
multiple experimental conditions nor received more than one
SMS message. All subjects were customers who made at least
one purchase in the six-month period prior to the experiment.
Web Appendix B, Panel C, presents an example of the SMS
retargeting treatment message.
An extension in Study 2 is the execution of experiment rando-
mization. In Study 1, customers were randomly assigned toeither
the retargeting treatment or control groups within each hour block
(e.g., 1 hour or 24 hours after cart abandonment),thus allowing for
estimating causal effects within each hour block for early and late
ECR ads. However, Study1 did not randomize the hourblocks ex
ante by using an intent-to-treat (ITT) approach (Gerber and Green
2012; Johnson, Lewis, and Nubbemeyer 2017). Thus, across the
hour blocks, customers might be different due to a self-selection
bias (i.e.,customers who received ECR ads 24 hours later may be
intrinsically less likely to buy the product than those 1 hour later).
To further account for this potential bias, Study 2 also randomizes
the hour blocks, besides the random assignment of treatment or
control groups. More specifically, customers are randomly
assigned into the treatment and control across all hour blocks
ex ante by using the ITT approach (Gerber and Green 2012;
Johnson, Lewis, and Nubbemeyer 2017; Lambrecht and Tucker
2013).
9
This ITT execution ensures that all individuals are the
same ex ante, regardless whether they received the early or late
ECR in a specific hour block. In other words, such randomization
ensures the unbiasedness of the incremental effects of retargeting
(i.e., differences between the ECR and control groups) across all
Table 4. Regression Results on Incremental Retargeting Effects with Hourly Block Interactions.
Email Channel Email Channel App Channel App Channel
Early Retargeting (H
1
:).284*** .285*** .369** .370**
(.0725) (.0727) (.151) (.151)
Late Retargeting H
2
:þ.577*** .582*** .630** .624**
(.134) (.134) (.251) (.252)
Early .408*** .393*** .653*** .643***
(.0508) (.0509) (.106) (.107)
Late 1.167*** 1.134*** 1.286*** 1.276***
(.105) (.105) (.201) (.201)
Retargeting .0596 .0555 .135 .137
(.0553) (.0554) (.114) (.114)
(Baseline: Middle)
Covariates YES YES YES YES
Product Category Effects NO YES NO YES
Time Effects NO YES NO YES
Constant 2.063*** 2.073*** 2.017*** 2.146***
(.0395) (.0753) (.0824) (.180)
Pseudo R
2
.0182 .0242 .0348 .0379
N 33,234 33,218 7,314 7,313
*p<.1.
**p<.05.
***p<.01.
Notes: Robust standard errors in parentheses. Early ¼the .5- and 1-hour blocks; Middle ¼the 3-, 6-, 9-, and 12-hour blocks; Late ¼the 24- and 72-hour blocks.
9
Note that ITT design may have an issue of compliance, where treatment is
only administrated to individuals who have not dropped out (Gerber and Green
2012).
Li et al. 11
hour blocks. Thus, such ITT estimates allow for not only identify-
ing the causal effectsof the early and late ECR ads relativeto early
and late controls but also directly comparing the causal incremen-
taleffectofearlyECRwiththatoflateECR.
The data time window here was six months before the
experiment and one week after the experiment. Summary sta-
tistics of all variables and randomization check results are
reported in the Web Appendix F. The treatment and control
groups did not significantly differ with respect to demographics
and past purchase characteristics; therefore, the data set passed
randomization checks.
Model-Free Results
Figure 4, Panel A, presents the purchase rates for all hour
blocks. We use the purchase rate within a week after sending
the retargeting ads to the subjects as our outcome variable.
Similar to Study 1, for early ECR (1 hour), the treatment group
(marked by dark bars) had a significantly smaller purchase rate
than its control group counterpart (marked by gray bars).
Furthermore, Figure 4, Panel B, presents the differences in the
purchase rates between treatment and control groups for all
hour blocks. The results suggest that the difference is negative
for the early ECR. That is, relative to the early control, retar-
geting in the 1-hour block had a significantly lower purchase
rate (p<.01). Thus, the incremental effect of early ECR was
negative for this SMS channel data, too. Furthermore, the
results indicate that the difference is around zero for retargeting
in the 3- and 9-hour blocks. Thus, relative to the control,
retargeting in the 3- and 9-hour blocks had similar purchase
rates. However, the difference is positive for the late ECR. That
is, relative to the late control, retargeting in the 24-hour block
had a significantly higher purchase rate (p<.01). As such,
similar to Study 1 with email and app channels data, the
incremental effect of late ECR was positive for this study
when using SMS channel data, too.
Model-Based Results
The moderated regression models in Equation 2 were also
fitted to the data set of Study 2. The results are reported in
Table 5, and the middle range 9 hours block is the baseline.
We report effects, as measured by various metrics: purchase
A
: Purchase Rates (SMS)
B: Differences in Purchase Rates (SMS)
.00
.02
.04
.06
.08
.10
.12
.14
.16
1Hour 24Hour
3Hour 9Hour
Retargeting Control
−.06
−.05
−.04
−.03
−.02
−.01
.00
.01
.02
.03
.04
1Hour 3Hour
9 Hour 24 Hour
*
*** ***
***
***
Figure 4. Model-free evidence for the differences in purchase rates
between treatment and control (retargeting channel: SMS).
Notes: Error bars here represent +1 standard error.
Table 5. Regression Results on Incremental Retargeting Effects with
Hourly Block Interactions (SMS Channel).
Purchase
Incidence
Purchase
Amount
Logit Tobit
1hRetargeting (H
1
:
)
.545*** 11.01***
(.130) (2.921)
3hRetargeting .269 8.794
(.278) (6.047)
24 h Retargeting
(H
2
:þ)
.104** 1.237**
(.045) (.613)
1 h .351** 3.360***
(.079) (.866)
3 h .203*** 2.342***
(.073) (.762)
24 h .0037 .159
(.089) (2.045)
(Baseline: 9 h)
Treatment .0743 4.588**
(Baseline: control) (.0923) (2.159)
Covariates Yes Yes
Product category effects Yes Yes
Time effects Yes Yes
Constant 2.180*** 16.78***
(.0570) (1.310)
Pseudo R
2
/R
2
.039 .016
N 23,914 23,914
*p<.1.
**p<.05.
***p<.01.
Notes: Robust standard errors in parentheses.
12 Journal of Marketing XX(X)
incidence (in the logit model) and purchase amount (Tobit
model) one week after sending the ECR ads. The results sug-
gest that across all these effectiveness metrics, compared with
the middle hour block, the main effects of early hour blocks had
significantly positive effects on purchase rates (p<.05), thus
supporting the organic decaying memory theory (Baddeley
et al. 1975; Brown 1958) in the absence of ECR.
More importantly, the interaction between retargeting treat-
ment and the hour blocks is significantly negative for early
ECR (Treatment 1 hour) on incremental purchase incidence
(logit model) and incremental purchase amount (Tobit model)
(all p<.01), thereby revealing additional support for H
1
with
SMS channel data.
In addition, we observed a significantly positive interaction
effect for late ECR (Treatment 24 hour) on incremental
purchase incidence (logit model) and incremental purchase
amount (Tobit model) (all p<.05), thus again supporting H
2
.
Exploring the moderating effects of cart characteristics for the
double-edged effects of ECR. Because customers have different
reasons (e.g., high prices, low budget, multiple products to
inspect) for cart abandonment (Garcia 2018; Kukar-Kinney and
Close 2010; Luo et al. 2019), they may have quite different
probabilities of purchasing after viewing ECR ads. Since we
have detailed clickstream data on cart characteristics such as
product quantity and product prices, we can further extend
prior literature (Bleier and Eisenbeiss 2015; Lambrecht and
Tucker 2013; Sahni, Narayanan, and Kalyanam 2019) by
exploring whether these cart characteristics may moderate the
double-edged effects of ECR ads.
It is plausible that when the products in retargeted carts are
of a larger quantity and at higher average prices, the early ECR
may induce even more ad annoyance among shoppers because
they may feel the retailer is pushing them to buy more expen-
sive products in a larger amount to make more profits and
hence react more negatively (Goldstein et al. 2014; Yoo and
Kim 2005). Meanwhile, when the products in retargeted carts
are of a larger quantity and at higher average prices, these
customers tend to be more serious shoppers (who may buy
more with higher interest in the carted products), so their mem-
ory of the carted products is less likely to fade quickly (Leenh-
eer et al. 2007; Tellis 1988). Then, the immediate ECR ads sent
too early are more likely to engender low ad reminder effect.
Thus, by inducing even more ad annoyance but less ad remin-
der effect, the negative impact of early ECR ads may be
enlarged when the products in the retargeted carts are of a
larger quantity and at higher average prices. On the other hand,
as time elapses after cart abandonment, and consumer memory
of the more expensive and larger quantity of products fades
extensively, the serious shoppers will be more likely to appreci-
ate the ad reminders rekindling their rusty memory of those
inspected products (Raj 1982; Suri and Monroe 2003; Van
Heerde et al. 2004, 2013), with even higher ad reminder effects
and lower ad annoyance, hence likely strengthening the posi-
tive incremental impact of late ECR on customer purchases. As
such, the double-edged effects of ECR ads can be moderated by
these cart features: not only the negative impact of early ECR
ads but also the positive impact of late ECR ads are amplified
when the products in the retargeted carts are of a larger quantity
and at higher average prices.
10
To test these moderating effects of the cart characteristics of
Product Quantity (Pnum) and Product Price (Pprice), we spe-
cify the interaction model in Equation 3.
Uij2 ¼xoþx1Retargeting ij Early ij
Pnumii þx2Retargeting ij Early ij Pprice ii
þx3Retargeting ij Late ij Pnumii þx4Retargeting ij
Late ij Pprice ii þx5Retargeting ij Early ij
þx6Retargeting ij Late ij þx7Retargeting ij
Pnumij þx8Retargeting ij Pprice ij þx9Retargeting ij
þx10 Early ij þx11 Late ij þx12 Pnumii þx13 Pprice ii
þxkWij þEij2 :
ð3Þ
where Early is the 1-hour block, and Late is the 24-hour block
(baseline is Middle with the 3- and 9-hour blocks). As shown in
Table 6, the results suggest that the three-way interaction
between Early, Retargeting, and Product Quantity is signifi-
cantly negative (p<.01) for both purchase incidence and
amount. Thus, when the products in retargeted carts are of a
larger quantity, the negative effect of early ECR is stronger. In
addition, the three-way interaction between Late, Retargeting,
and Product Quantity is significantly positive (p<.05). Thus,
the positive effect of late ECR is also amplified, when the
products in retargeted carts are of a larger quantity. Further-
more, results show that the three-way interaction between
Early, Retargeting, and Product Price is significantly negative
(p<.01). As such, when the products have higher average
prices, the negative effect of early ECR is also amplified. How-
ever, the coefficient of the three-way interaction term between
Late, Retargeting, and Product Prices is insignificant. Thus,
these explorative results provide some evidence that the
double-edged effects of ECR ads are moderated by cart char-
acteristics. By and large, both the negative impact of early ECR
ads and the positive impact of late ECR ads are amplified when
the products in the retargeted carts are of a larger quantity and
at higher average prices.
Discussion and Implications
On the basis of multistudy, multisetting data from randomized
field experiments, our research reveals that ECR ads have
10
There could be other arguments for the effects. For example, more expensive
products tend to have longer purchase decision processes, so customers may
simply need more time to decide. Another aspect might be that ads retargeting a
larger basket of products could be less annoying, as consumers might more
quickly forget about the specific items in a large assembly. These arguments
can be fruitful for future research.
Li et al. 13
double-edged incremental effects on customer purchases. In
particular, an early ECR ad has a negative incremental effect,
whereas a late ECR ad has a positive incremental effect.
Explorative analyses suggest that such double-edged effects
of ECR ads are moderated. Both the negative impact of early
ECR and positive impact of late ECR can be amplified when
the products in the retargeted carts are of a larger quantity and
at higher average prices. Our findings have broad research and
managerial implications.
Research Implications
Our findings offer several research implications. We are among
the first to reveal a causal adverse incremental impact of imme-
diate retargeting on customer purchases in e-commerce. Extend-
ing prior research on retargeting (Bleier and Eisenbeiss 2015;
Lambrecht and Tucker 2013; Sahni et al. 2019), we not only
conceptually differentiate early ECR from late ECR but also
empirically show the double-edged effects of ECR ads. Our
novel insight here is that early ECR ads within the first hour
after cart abandonment may backfire with significantly negative
incremental effects on customer purchases. This insight is non-
trivial for two key reasons. First, it may rectify the wrong one-
sided view of the effectiveness of immediate retargeting. By
simply comparing purchase responses with early versus late
ECR ads in the treatment (as done in the recent bump view)
without valid early and late controls, researchers may erro-
neously conclude that immediate retargeting has a positive
impact and is more effective than late retargeting. However, with
scientific randomized controls, the opposite is true: the former
has a causal negative impact, while the latter has a causal pos-
itive impact and is more effective in reality. Thus, research that
documents only the positive impact of retargeting ads could
overestimate the effect of early ECR ads and should reckon that
immediate retargeting within minutes after cart abandonment
(ads served too early) might engender harmful impacts on con-
sumer behavior. Second, it may change our vision for the tech-
nology–consumer interface. Sahni et al. (2019, p. 401) note that
“retargeting technology aims to reduce the time lag between the
consumers leaving the website and the beginning of the cam-
paign to almost zero.” We agree and add that the modern retar-
geting technology is one thing, but consumer response is another.
Although technologies can immediately retarget customers
based on the fine-grained shopping cart data, doing so too early
may actually annoy customers and adversely impact their pur-
chases. Thus, research on the technology–consumer interface
should account for the double-edged (both positive and negative)
consumer responses to the innovative retargeting technologies.
Furthermore, we leverage a multistudy, multisetting
research design with three large-scale randomized field experi-
ments on over 64,000 customers from different companies via a
fine-grained hourly level of retargeted ads, which can rigor-
ously test the causal incremental effects of early and late ECR
with a higher generalizability in findings. Prior research exam-
ined retargeting ads at the daily level and found a generally
positive effect on clicks and web revisits (Bleier and Eisenbeiss
2015; Johnson, Lewis, and Nubbemeyer 2017; Sahni et al.
2019). In support of this line of research, we find that late ECR
ads delivered one to three days after cart abandonment lift
customer purchases. Furthermore, extending this stream of
research, we are among the first to operationalize immediate
Table 6. Explorative Results on the Moderating Effects of Cart
Characteristics.
Purchase
Incidence
Purchase
Amount
Early Retargeting
Pnum
.264*** 1.953***
(.0221) (.430)
Late Retargeting
Pnum
.0388** 6.806**
(.0196) (3.369)
Early Retargeting
Pprice
.214*** 1.010**
(.0636) (.482)
Late Retargeting
Pprice
.0195 4.970
(.0181) (3.491)
Early Retargeting .872*** 2.198***
(.249) (.616)
Late Retargeting .853*** 2.100***
(.203) (.622)
Retargeting Pnum .202 3.557
(.182) (2.709)
Retargeting Pprice .0398 .291
(.0351) (.908)
Early Pnum .142 1.120
(.0982) (2.362)
Early Pprice .129*** 2.043***
(.0371) (.568)
Late Pnum .425 7.225
(.435) (7.022)
Late Pprice .0273 1.195
(.0431) (1.071)
Early .286*** 5.526***
(.089) (1.449)
Late .0854 1.700
(.367) (1.382)
Retargeting .824 1.939
(.602) (1.108)
Pnum .0649 1.528
(.0571) (1.392)
Pprice .183*** 3.632***
(.0195) (.431)
Covariates Yes Yes
Constant .553** 75.27***
(.265) (8.121)
Pseudo R
2
.157 .086
Observations 23,914 23,914
*p<.1.
**p<.05.
***p<.01.
Notes: Robust standard errors in parentheses. Early ¼the 1-hour block; Middle
¼the 3- and 9-hour blocks; and Late ¼the 24-hour block. Pnum ¼product
quantity (in natural log); Pprice ¼average product prices (in natural log) of the
retargeted carts.
14 Journal of Marketing XX(X)
retargeting at the hourly level within the first day after cart
abandonment and uncover the negative impact on customer
purchases of immediate retargeted ads. A finer-grained time
interval with hours (vs. days) can more accurately capture the
immediacy in retargeting.
In addition, advancing prior studies that focused on retar-
geted ads’ content, placement, and frequency (Bleier and
Eisenbeiss 2015; Johnson, Lewis, and Nubbemeyer, 2017;
Lambrecht and Tucker 2013; Sahni et al. 2019), we are among
the first to put the spotlight on the timing of retargeted ads, a
crucial but underresearched factor influencing the conversions
in e-commerce. Even with perfectly crafted and placed ad con-
tent with the appropriate frequency, retargeting campaigns may
bypass the opportunity to earn higher purchase responses by
not taking into account the timing (early or late) of ECR ads.
Moreover, our explorative findings enrich the understanding
of the moderated double-edged effects of ECR ads. Extending
the literature (Bleier and Eisenbeiss 2015; Lambrecht and
Tucker 2013; Sahni et al. 2019), we reveal another new insight
that both the negative impact of early ECR ads and positive
impact of late ECR ads can be amplified when retargeting carts
with a larger quantity and higher average price of products.
These findings on the moderated double-edged effects are non-
trivial because research might over- or underestimate the
impact of early and late ECR ads if ignoring the moderating
roles of carted product features. Because customers have dif-
ferent reasons for cart abandonment, they will have different
probabilities of purchasing after viewing ECR ads (Kukar-
Kinney and Close 2010; Luo et al. 2019). This is different from
the cross-sectional consumer heterogeneity, because, over
time, even the same individual may have different reasons to
abandon the shopping cart. Thus, a comprehensive understand-
ing of consumer responses to early and late ECR ads should
consider the contextual factors such as cart characteristics.
Matching early and late ECR ads with such contextual factors
is crucial for the efficacy of retargeted ads in e-commerce.
Relatedly, our findings have implications for the advertising
literature. Prior literature has well documented a myriad of ad
effects: provide informative content, offer output interference,
and displace other ads (Sahni et al. 2019; Tellis 1988; Van
Heerde et al. 2004, 2013). We contribute to this literature by
uncovering the nuanced timing (early vs. late) effects of ECR
ads. Indeed, prior studies on ad annoyance are largely based on
the frequency (i.e., ad repetition; Aaker and Bruzzone 1985;
Goldstein et al. 2014; Todri et al. 2020). Advancing these
studies, we uncover that the one-time ECR ad may also annoy
consumers when it is delivered too early.
Furthermore, the negative effects of early ECR ads yet pos-
itive effects of late ECR ads help account for the mixed effects
of digital advertising in the literature (Aaker and Bruzzone
1985; Goldstein et al. 2014; Lambrecht and Tucker 2013; Man-
chanda et al. 2006). In this sense, we extend the literature by
suggesting the importance of implementing contextual ads in
retargeting (i.e., deliver the ads in the right time, not within the
first hour after cart abandonment, and for the right shopping
carts). This is critical because marketers might stall if they
blindly advertise to customers without accounting for when,
how many, and how expensive the carted products are.
Managerial Implications
Given the prevalence of retargeting ads in practice, our findings
provide managers with specific guidance on implementing ECR
ads to boost return on investment on retargeting campaigns.
First, companies should not heedlessly follow the recency bump
and shift all their online ad budgets to immediate retargeting.
Delivering the ECR ads too early can engender worse purchase
rates than without delivering them. That is,reminder ads sent too
soon may annoy consumers and backfire, thus not only squan-
dering ad budgets and but also likely hurting customers’ long-
term satisfaction. Prudent marketers should resist the temptation
of using the immediate retargeting, even though advanced digital
e-commerce technologies can deliver retargeting ads within min-
utes after consumers abandon carts online. Nevertheless, early
ECR with price discounts or scarcity framing may allow man-
agers to engender more purchase responses (Luo et al. 2019).
However, price discounts are not a panacea: when repeatedly
used, they may train strategic customers who purposefully cart
products and then wait for price discounts before purchasing.
Second, it is pivotal to scientifically gauge the causal impact
of ECR ads. Firms should not rely on the absolute purchases as
a measure of success but rather adopt the relative purchase,
(i.e., incremental to the control without retargeting). Naively,
if not comparing the retargeting with the control, managers
may mistakenly conclude that the early ECR is most effective:
our data indeed show that if simply observing the absolute
effect, the early ECR within one hour induces the highest abso-
lute purchases. Yet, compared with the early control, the early
ECR actually backfires with negative incremental purchase
responses. Thus, we underscore the importance of scientific
experimental methodology for managers to avoid the erroneous
conclusion on the true effects of ECR ads.
Furthermore, we find that a late ECR ad can be effective and
win back potential customers with an increase in return on
investment on advertising. Thus, firms can better deploy ECR
ad campaigns with a delay after consumers abandon carts to
minimize the negative ad annoyance, as well as maximize the
positive ad reminder effects on customer purchases. Indeed,
retargeting carts in e-commerce has enormous business poten-
tial because over 69%consumers abandon carts online, which
amounts to over $4.6 trillion (Garcia 2018; Statista 2020). An
interesting point is that the right timing of ECR does not incur
additional financial costs in retargeting but can significantly lift
customer purchases.
Finally, managerial actions call for an appropriate match
between the timing of ECR ads and retargeted products. It is
necessary to use ECR to cater to different types of cart aban-
donment; different cases would include carts with a high quan-
tity of products versus carts with only one item, or carts with an
expensive product versus those with a cheap one (Kukar-
Kinney and Close 2010; Luo et al. 2019). Thus, we reveal the
tactic e-commerce retailers can use to more accurately retarget
Li et al. 15
customers with different digital carts. Strategically, firms can
decide the time to turn on ECR, depending on its suitability for
different types of carts, to maximize conversions. For example,
managers win back more customers by implementing late ECR
ads for carts with a larger quantity of products abandoned.
Limitations and Future Research
Our research has several limitations, which serve as avenues
for future studies. First, although our findings are drawn from
two countries and different companies, they may not be gen-
eralizable to other cultures and products. Thus, more empirical
evidence from other settings can be provided in the future. For
example, an early reminder might work well for impulse pur-
chases about which customers do not have to ponder much,
while products that require a lot of deliberation before purchas-
ing (e.g., cars) might benefit from late ECR. Incorporating the
idea of the length of the purchase decision process could also
be pertinent, because early versus late in retargeted ads can be a
relative concept. Moreover, the strength of our field experiment
is about documenting the causal impact of early and late ECR
ads on customer purchases, rather than the underlying mechan-
isms. Future research could investigate the related psychologi-
cal mechanisms in the lab and explore how privacy concerns,
seasonality, and ad competition in retargeting messages regu-
late the effects of ECR. Furthermore, our results on the mod-
erating role of cart characteristics are exploratory in nature.
Future research could use survey data to pinpoint consumers’
specific reasons for cart abandonment first and then retarget
them with different ad framing and incentives to enhance the
efficacy of ECR ads.
Finally, our data are limited to the hourly level within the first
several days after cart abandonment. Thus, the effect of even
later ECR ads (weeks or months later) is not tested here. Future
research might investigate the impact of much later ECR at the
weekly or monthly level. Nevertheless, Bleier and Eisenbeiss
(2015) find that if the time since last online store visit is over
48 days, the incremental effect of retargeted ads on click-through
rates is close to zero. Likewise, Moriguchi, Xiong, and Luo
(2016) show that retargeted ads sent one week after cart aban-
donment are ineffective in generating incremental customer pur-
chases. Sahni et al. (2019) find that the effect of retargeted ads on
web revisits is positive but becomes negligible by the end of the
first week. These findings suggest thattoo late ECR ads may turn
out to be ineffective. Indeed, once too long a time has elapsed
(e.g., after several months or years) since cart abandonment,
consumer memory of carted products might be totally lost given
the large amount of information in social media people are
exposed daily. Then, the reminder function of ECR ads will not
work anymore because the memory trace is too weak for a
reminder ad to be successful: it is difficult to restore or activate
the lost memory (Kelley and Gorham 1988). However, if
designed with price incentives, too late retargeted ads might still
be effective (Luo et al. 2019). Taking a broad perspective of the
literature on ad personalization based on customers’ preferences
and demographic profiles (Lambrecht and Tucker 2013) and
browsing content (Bleier and Eisenbeiss 2015), future research
may also consider how these characteristics regulate consumer
purchase responses to too late ECR ads.
In conclusion, this study represents an initial effort in exam-
ining the double-edged effects of ECR ads on customer pur-
chases. We hope that our study stimulates future research on
ECR, an increasingly important topic in digital marketing.
Associate Editor
S. Sriram
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to
the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for
the research, authorship, and/or publication of this article: Xianghua
Lu acknowledges support from the National Natural Science Founda-
tion of China [Grants 71872050 and 91746302]. Takeshi Moriguchi
acknowledges support from the JSPS KAKENHI Grant Number
JP16H03675.
ORCID iDs
Jing Li https://orcid.org/0000-0002-2242-1987
Xueming Luo https://orcid.org/0000-0002-5009-7854
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