DP2022-10

Mobile Targeting: Exploring theRole of Area Familiarity, Store
Knowledge, and Promotional
Incentives
Ryo KATO
Takahiro HOSHINO
Daisuke MORIWAKI
Shintaro OKAZAKI

March 25, 2022

Mobile Targeting: Exploring the Role of Area Familiarity,
Store Knowledge, and Promotional Incentives

Ryo Kato
Associate Professor of Marketing
Research Institute for Economics and Business Administration,
Kobe University,
2-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan
Tel: +81 (0)78 803 7270
E-mail: kato_ryo@rieb.kobe-u.ac.jp

Takahiro Hoshino
Professor of Economics
Department of Economics
Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan/ RIKEN Center for Advanced Intelligence Project, 1-4-1
Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
Tel: +81 (0)3 5427 1289
Email: bayesian@jasmine.ocn.ne.jp

Daisuke Moriwaki
Research Scientist
AILab, CyberAgent, Inc.
Shibuya Scramble Sq., 2-24-12 Shibuya, Shibuya-ku, Tokyo 150-6121, Japan
Tel: +81 (0)3 5459 0202
Email: moriwaki_daisuke@cyberagent.co.jp

Shintaro Okazaki
Professor of MarketingKing’s Business SchoolKing’s College London
Bush House, 30 Aldwych
London WC2B 4BG, UK
Tel: +44 (0)20 7848 4122
Email: shintaro.okazaki@kcl.ac.uk

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Abstract
Building on psychological reactance and uncertainty reduction theory, thisresearch addresses how area familiarity, store knowledge, and promotional incentives
impact the success of mobile targeting ads. To test our predictions, we conduct
randomized field experiments in Japan. In Study 1, customers with different levels of areafamiliarity and store knowledge receive mobile ads with coupons. In Study 2, we replicate
the same experiment in which consumers receive mobile ads without any coupons. The
results indicate that whereas lower area familiarity contribute to geo-targeting ads
effectiveness only when coupons are affixed, ads of higher store knowledge tend toincrease the number of visitors regardless of the coupon attachment. We discuss our
mixing results based on the dual-system theory, leading to some managerial implications.

Keyword: area familiarity, context-based marketing, mobile targeting,psychological reactance, promotional incentives, store knowledge, uncertainty reduction

1. Introduction
Geo-targeting mobile ads are growing at a rapid pace. Geo-targeting refers to thepersonalization of ad content based on target users' geographical location (Lian et al.
2019). In the US, their sales are expected to rise from $18.2 billion in 2019 to $38.7 billion
in 2022 (eMarketer 2018). However, a recent survey of nearly 500 million digital
impressions across the UK and the US found a surprising fact—almost 29% of these ads
are being wasted due to mis-targeted location (Location Sciences 2019).
One of the limitations in geo-targeting is a lack of accurate location data based ontarget consumers' behavioral patterns; without it, markers are unable to predict andinfluence future actions. At the same time, geo-targeting seems beyond the scope of
traditional segmentation-targeting-positioning models as the mobile market is aconstantly moving target. Not surprisingly, a significant stream of empirical research has
wrestled with this issue in terms of temporal and geographical targeting (Luo et al. 2014),
competitive locational targeting (Fong et al. 2015), hyper-contextual targeting withphysical crowdedness (Andrews et al. 2016), dynamic segmentation based on past2

marketing responses and location histories (Subcsek et al. 2017), trajectory-basedtargeting (Ghose et al. 2019), and personalized targeting against low-engagement
consumers (Zhang et al. 2020), among others.
However, while these predecessors significantly advanced our knowledge on
mobile ads, there appears to have left two prominent contextual effects unexplored:
consumers' store knowledge and area familiarity. Both factors can impact consumers’
response to mobile coupons, thus introducing a new perspective to temporal-behavioral
targeting. Specifically, the goal of this research is to identify the effects of geo-targeting
mobile ads in terms of the traffic generation in the implementation of promotional
coupons. Based on an integrated view of the psychological reactance theory and theuncertainty reduction theory, we attempt to test our predictions as to whether the store
traffic can increase when mobile ads consider area familiarity, and store knowledge incombination.
This article reports two randomized field experiments in the greater Tokyo andOsaka area, Japan, in collaboration with a large interactive marketing agency. We obtainour data from real-life promotional settings, which significantly increase thegeneralizability of our findings. We estimate shoppers’ area familiarity in a given
geographical area and store knowledge based on behavioral data. Unlike Luo et al. (2014)
and Fong et al. (2015), who investigated the relationship between geographical distanceand the mobile coupon, we used location data covering smartphone user’s 24 hours
information. Therefore, we can calculate area familiarity, store knowledge, and distanceto the store from their home, which have not been considered in the context of the geotargeting ads.
The intended contributions of the present study are two-fold. Our first focal
construct is consumers' area familiarity. Unlike Fong et al. (2015), who investigated theimportance of the location in relation to the competing stores, we focus on the areas
consumers are both familiar and unfamiliar with, conceptualizing the level of areafamiliarity as the buffer of uncertainty reduction. In so doing, we try to seek reasons why
most of geo-targeting mobile ads are mis-targeted, and therefore wasted, positing thatmobile marketers and advertisers may not have taken into account the level of information
appreciation by the targeted consumers.
Our second focal construct is consumers' store knowledge. Consumers are likely3to assess the coupon offering from the mobile ad against their preferred choice. If thelevel of conflict between the preferred choice and the offering is high, psychologicalreactance is likely to happen. However, this is not the case when the coupon offering is
an only available choice or when the consumers do not possess any prior store knowledge
before receiving the offering.
We systematically examine the intertwined relationship between store knowledge
and area familiarity in terms of the uncertainty and preference-offering conflict. Ourdependent variable is the maximization of the store traffic in the areas mobile ads aretargeting. The traffic generation has been studied previously mainly in terms of pricing
and promotional discount (e.g., Gauri et al. 2017; Fox et al. 2009), In a recent exploration,
Wang et al. (2019) examined the effect of geofencing mobile ads in the retail storefront
traffic conversion because "mobile advertising has become a popular and powerful tool
for retailers to convert traffic into sales" (p. 84). Yet, research focusing on the relationshipbetween store traffic and geo-targeting mobile ads has been almost nonexistent.
In what follows, we first provide an overview of the related literature and build atheoretical framework, based on which we posit a series of predictions that are to be tested
by a field experiment. Next, after providing an explanation of the method, we describe
the results in detail. On this basis, we draw theoretical and managerial implications while
recognizing limitations and suggesting future research directions.

2. Related Literature, Theoretical Framework and Hypothesis
2.1 Literature on geo-targeting ad response
The literature on mobile targeting ads seems mainly built on two streams ofresearch. First, prior research addressed temporal and spatial boundary conditions that
may influence consumers' mobile coupon redemption or conversion. For example, Luo
et al. (2014) investigated the combined effect of promotion lead-time (i.e. time between
the mobile coupon's delivery and expiration) and geographical location (i.e. distance
between the user location and the promoted store location) on consumer purchase,finding that, when targeting proximal mobile users, the shorter lead time increased thelikelihood of consumer purchases as a result of the mobile promotions. Similarly,
Danaher et al. (2015) reported that the coupon redemption rates increased as face value
increases, expiration length is shorter, and the distance to the store offering the coupons4

is shorter. While confirming the aforementioned findings, Fong et al. (2015)
additionally revealed that competitive locational targeting or geo-conquesting increased
purchasing response. In a recent exploration, Molitor et al. (2020) addressed the type ofinterface design in geo-targeting ad, demonstrating the effectiveness of distance-basedranking interface. Taking one step further in the quest for the spatial constraints,
Andrews et al. (2016) found that consumers in physically crowded transit environments
better responded to mobile promotion.
Second, the literature has taken into account the effectiveness of incorporating
past marketing responses and location histories in targeting. For example, Subcsek et al.
(2017) found that the prediction accuracy of the mobile coupon conversion rates has
been improved by the inclusion of consumers’ location information. In the same token,
Ghose et al. (2019) found that mobile geo-targeting based on consumers' trajectory—the
physical and behavioral trace of an individual’s offline movement—leads to higher
redemption probability, faster redemption behavior, and higher transaction amounts in alarge shopping mall. Using the forward-looking hidden Markov model (FHMM), Zhang
et al. (2020) studied personalized targeting against low-engagement consumers based on
individual mobile users’ continuous reading records on a mobile reading app. They
found that personalized optimal dynamic engagement-based targeting based on the
FHMM can generate more revenue for both price promotion and free-content promotion
on a mobile e-book reading app. Matsui and Moriwaki (2021) study the effect ofadvertising on users’ revisit behavior. They found that the ad-induced visitors are more
likely to revisit the advertised stores than organic visitors.
The related literature is summarized in Table 1.

[Table 1]

2.2 Key Variables
In this subsection, we firstly introduce two key contextual variables that have notbeen investigated and hence our main focus; area familiarity and store knowledge.
2.2.1. Area Familiarity

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Previous studies considered many factors affecting redemption of geo-targetingads such as distance and time. However, more individual-specific elements, i.e.
consumers context, have received scant attention. Familiarity is thought to be an
important context because it may moderate the effect of existing non-contextual factors(Han and Yamana 2016). The concept of familiarity has been examined in consumer
research on product category (Jeng 2017), and information acquisition (Otterbring et al.
2017), reactions to advertising (Rhee et al. 2019), online communication (Pauwels et al.
2017; Bonnnin 2020), among others.
In the marketing literature, familiarity is thought to be one element of consumer
knowledge construct (Cordell, 1997), and the product category is the main research field
on familiarity (Carneiro and Crompton. 2010). Consumers do not search for the
information about experienced goods (Moore and Lehmann 1980; Putrevu and Lord
(2001). Wood and Neal (2009) indicated that past usage of a product lead to habit cuing,leading to make it less attractive to search about competing products.
This relation is also found in the research area on consumer’s travel. Assael(2004) demonstrated that consumers who once visited a place tend not to rely on
external information source but their experience. This study is related to our research
since they considered the case of consumer’s information search along with a trip. The
relation is also supported in the studies on other research areas. Information-processing
theory of a consumer proposed by Bettman (1979), whose scheme theorized knowledge
and experience is negatively related to search cost, is consistent with the results
obtained from marketing literature. To sum up, these works indicate that if consumers
are more experienced and has sufficient information-processing capability, they will notchange their mind.
The familiarity of a geographical area, however, has rarely been examined sofar. Among the few, Han and Yamana (2016) suggest that people tend to visit
unfamiliar area after work on weekdays to enjoy arts and entertainment. In such a case,they are more likely to engage unplanned behavior. The mobile coupon can beperceived as useful or beneficial since they can reduce their search cost due to a lack of
information. On the other hand, when people are familiar with a specific location, they
have sufficient information-processing capability and may not change their state ofmind, even though they are exposed to the mobile coupons.
62.2.2. Store knowledge
Prior research examined the effect of the distance between the current
consumer’s location and the focal store on the mobile coupon redemption (Spiekermann
et al. 2011; Luo et al 2014 ). However, consumers' knowledge of the focal store seems
unexplored in mobile targeting ad research.
People in unfamiliar areas do not have strong preferences about the store since
the primary goal to visit unfamiliar areas is very different from the venues distributing
mobile coupons (Han and Yamada 2016). Instead, they would like to spend their timeand resources on the primary objective (e.g. arts or entertainment). If they are exposed
to mobile coupons from the store to which they have higher familiarity, they can avoid
the risks of visiting unfamiliar stores and hence follow the coupon recommendation.
2.3. Theoretical framework and hypothesis
Prior research suggests that people are less likely to accept a persuasive message
due to the psychological reactance (Brehm and Brehm 1981). Psychological reactance is"a motivational state directed toward reattaining the restricted freedom" (Fitzsimons andLehmann 2004, p. 83). When people's freedom is restricted, they may experience
psychological reactance by ignoring or refuting product recommendations that
contradict initial impressions of choice options. When shoppers receive unsolicited geotargeting mobile ads, they first assess the fit between their 'ideal' selection and theoffering from the ads. Psychological reactance is likely to occur when there is a conflict
between the customers' preferred choice and the recommended product (Fitzsimons andLehmann 2004). The level of preference-offering conflict is high when the offering is
recommended against their preferred choice or when other alternative choices are
eliminated from consideration. The level of preference-offering conflict is low when theoffering is perceived as a clearly unattractive option or when there is no dominantoption before receiving the coupon.
On the other hand, albeit unsolicited, geo-targeting mobile ads can be viewed asa form of intelligent recommendation tool that can reduce the uncertainty surrounding adecision as well as the effort required in making a choice. In our study context, such
uncertainty includes area familiarity and store knowledge. For example, consumers withhigher uncertainty (e.g., low in both area and store knowledge) tend to feel lessconfident in their ability to make a wise purchase choice, thus relying on choice7

heuristics, such as product bundles (Harris and Blair 2006). In close analogy, geotargeting mobile ads can be received by consumers high in uncertainty.
The level of uncertainty may vary according to the combined effects of areafamiliarity and store knowledge. Whilst area familiarity affects the uncertainty at a moregeneral level, store knowledge is individual-specific, as it acts as a base for the
preference formation based on past usage experience.
Our theoretical framework is summarized in Figure 1. Whereas people in higher
uncertainty (low in area or store knowledge) will “follow” or “ignore” the targeted
advertisings, people in lower uncertainty (high in area or store knowledge) will“consider” or “reject” them. Simultaneously, whereas they will “follow” or “consider”
the ads when the conflict by the offering against their preference is low (high in store
knowledge), they will “ignore” or “reject” them when the conflict is high (low in store
knowledge).

[Figure 1]

Given the discussions above, we constitute following hypotheses. A consumer at
unfamiliar has higher uncertainty for the surrounding decision and absorb higher searchcost, and geo-targeting ads relieve these burdens. Such consumer would tend to accept
them. Therefore, lower area familiarity will contribute to the increase of the store traffic
due to the geo-targeting ads exposure.

HYPOTHESIS 1 (H1). An exposure to geo-targeting ads to mobile users located atlower area familiarity will have higher effect on the store traffic than those who locatedat higher area familiarity.

When a customer has higher knowledge on the featured store, he may have lesspreference offering conflicts and do not feel psychological reactance. Such consumer
would tend to accept the offer. On the other hand, he is exposed to the store ads that hehas less experienced, the psychological reactance tends to occur. Therefore, higher store
knowledge will contribute to the increase of the store traffic due to the geo-targeting ads
exposure.
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HYPOTHESIS 2 (H2). An exposure to geo-targeting ads of higher store knowledge tomobile users will have higher effect on the store traffic than those of lower store
knowledge.

3. Study 1
3.1. Method
To explore the effectiveness of geo-targeting strategies of mobile ads, a large
interactive marketing agency, CyberAgent, collaborated in this research. CyberAgenttransmits mobile ads and coupons to mobile users in Japan. A food chain company
which has 7 stores in a focal area issued mobile coupons with the expiration date forone month.
The mobile coupons are sent to customers based on the CyberAgent’s systems.
They conducted a randomized field experiment where the treated group subjects are
exposed to mobile coupons during the campaign period. Corresponding control groupare selected in order that “distance to the store from their home” and “frequency to visit
the store before campaign (for a month)” are balanced. In detail, they stratified wholesubjects into 4 groups based on the value of these two covariates, and divided them into
treated and control group so that about 5 % of each subgroup being the control sample.
As a result, 982,754 subjects are assigned to the treated, whereas 41,885 subjects are
assigned to the control. Out of 982,754 subjects, 34,085 users recorded positive
impressions on the mobile ads.
They capture the number of visitors to the store using location data from mobile
phones, hence enabling us to observe its conversion. We calculate the number of people
visiting the focal store and let it be the outcome of our interest. All the location data iscollected with mobile user’s consent and completely anonymized.
Our main interest lies in whether area familiarity and store knowledge havesignificant effect on the number of customers visiting the store after an impression on
mobile coupons. We employ zero-inflated Poisson regression model since somesubjects have never visited the store during the campaign period even though they are
exposed to the ads. The model is described as follows.
9 pi + (1 − pi ) exp ( − i ) for j = 0Pr ( N _ VISITi = j ) =  i exp ( − i )(1 − pi ) N _ VISIT ! for j  0
i


where
 i =  0 + 1STORE _ KNWLi +  2 DIST _ HOMEi +  3 DEST _ FAM i + β 4 PLACE _ FEi , (1)
and
 p 
log  i  =  0 +  1 AREA _ FAM i +  2 STORE _ KNWLi +  3 AREA _ FAM i  STORE _ KNWLi . (2) 1 − pi + 4 DIST _ IMPi +  5 DIST _ HOMEi +  6 DEST _ FAM i +  7 PLACE _ FEi

The definition of variables are provided in Table 2. Zero-inflated Poisson models canaccount for not only whether the customer just visited the store once but also whether
they repeatedly visited there.

[Table 2]

The interested variables are AREA_FAM representing the degree of areafamiliarity of the area where they are exposed to the coupons, and STORE_KNWL as aproxy for the degree of knowledge on the focal store. AREA_FAM is only included in
the logistic regression predicting whether or not they visit the store but not in Poissonmodel since they are measured at the moment of the first impression and it is natural toassume that these should work only for the very first visiting immediately after the
impression. We also included the interaction term of AREA_FAM and STORE_KNWL.
To finely identify the effects of the area familiarity and the store knowledge on
mobile ads effectiveness, we included several controlling variables in the model. Firstly,
we consider the distance to the store from the location where they are exposed to the
coupon (DIST_IMP). Consumers tend to accept promotional offers when they are close
to the promoting store (Banerjee and Dholakia, 2008). Luo et al. (2014) and Danaher etal. (2015) show that near-distance mobile coupons are more likely to be redeemed.
Therefore, it is expected that the larger value of the distance to the store from thelocation will decrease the number of people visiting the store. Due to the same reason
with AREA_FAM we do not include DIST_IMP in the Poisson regression.

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Second, to control for ease of visiting the store, distance between the store andtheir home is considered in the models. In our case, mobile coupons are not always
redeemed immediately after exposition since it has an expiration period of about one
month. Therefore, the distance from the place where they usually are, DIST_HOME isincluded. It is expected that they tend to visit the store more frequently if their home isclose by.
Third, DEST_FAM is also considered in the model to account for the ease ofreaching the store. If they have an image of the area a focal store locates (such as theroute to the store), they have less reluctance to visit the area (Baloglu 2001). Hence, we
posit that higher DEST_FAM results in more frequent store visiting.
One concern is that the effect of AREA_FAM and STORE_KNWL are only
observed for the subsamples which have positive impression, hence the analysis basedon these subsamples might result in biased, even though this study is designed to be arandomized field experiment. Therefore, we conduct propensity score analysis(Rosenbaum and Rubin 1983), especially the inverse probability weighting (IPW)
procedure (Williamson et al. 2013) in order to correct this selection bias. IPW isfrequently employed in many empirical fields where randomized controlled trial isinfeasible. Robins et al. (1994) developed a class of IPW estimator for regression
model, and we adopt this procedure. Firstly, we prepare a probit regression model
which predicts each customer reacting to the mobile ad and records positive
impressions. The outcome (equals to 1 if subject i has positive impression, and 0
otherwise) is explained by factors affecting the propensity to react to the ads, which is;qi = Pr ( IMP  0 )=  ( 0 + 1OSi +  2 STORE _ KNWLi +  3 DIST _ HOMEi +  4 DEST _ FAM i + δ5 PLACE _ FEi )(3)

where Φ is a cumulative distribution function of normal distribution. OS is believed to
be a good predictor since the users of iOS are known to view mobile ads more thanAndroid uses. In fact, revenues obtained from mobile app ads on iOS are about 2.5
times larger than that of Android, although Android gains a larger market share (Smaato2020). Then, each treated observation is weighted by the inverse of the estimatedpropensity score qi , and 1 qi is treated as sampling weight when estimating zeroinflated Poisson regression model (equation 1).
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3.2. Results
The descriptive statistics appear in Table 3. The table shows that covariates
STORE_KNWL, DIST_HOME, and DEST_FAM are balanced well between treatedgroup and non-treated group. The table also tells us that treated samples with positive
impression and without impression differ in variables. In detail, the positive impression
group has visited the store more nearly twice than the no-impression group, and theformer group has more store knowledge, live in a closer area to the store than the lattergroup. As expected, iOS users tend to view mobile ads more than Android users. These
differences require us to adjust the selection bias.

[Table 3]
Table 4 describes the Pearson’s and Spearman’s correlation matrix forcovariates. As can be seen from the table, STORE_KNWL and DEST_FAM, and
DIST_HOME and DIST_IMP has positive correlations. When a customer visited thestore, it contributes the increase of both STORE_KNWL and DEST_FAM, then they are
positively correlated. Also, some customers would view the ads when they are at home,hence DIST_HOME and DIST_IMP positively correlates each other. In any case, there
seems to exist no multicollinearity.

[Table 4]

The main results are provided in Table 5. Before conducting regression analyses,
all the variables in the empirical models are transformed to be standardized. This
process makes it possible to easily compare the magnitudes of the effects on the coupon
effectiveness for each variable, and to easily interpret the meanings of the obtained
results of the interaction term coefficient.

[Table 5]

The upper block shows the estimated coefficients, standard errors, and p-values
for the Poisson regression model defined in equation (1), and the lower block shows the12results for logistic regression model defined in equation (2). For readability, thecoefficients of logistic regression  is shown in the form of -  , meaning a positivecoefficient indicates increase in the corresponding variable increases the probability of acustomer visiting the store.
MODEL 1 column of the table describes the result without propensity scoreadjustment, and MODEL 2 column describes the result with propensity scoreadjustment. Here, before discussing the results of zero-inflated Poisson with andwithout the propensity score adjustment, we briefly confirm the results of probit
regression in equation (3), which estimates the tendency of a customer viewing mobile
targeting ads. Table 6 shows the results of equation (3), indicating customers tend toview mobile targeting ads when they use iOS (p<.0001), have higher store knowledge(p<.0001), and live in a closer area to the store (p<.0001). As expected, iOS users tend
to be exposed to the ads. The customers with higher store knowledge are more likely toview the ads. If they live far from the store, they tend not to tap the ads because theinformation of the store with a larger travel and time cost might be less precious forthem. Using the estimated coefficients, we calculated the propensity score for subject i.

[Table 6]

Next, we move on to the zero-inflated Poisson results. The estimated coefficientof AREA_FAM in MODEL 1 from logistic regression shows that if the customer isunfamiliar with the current area, coupon has a positive impact on visiting (p<.0001).
Thus, H1 is supported. This is the case if the model is adjusted (MODEL 2).
As expected, the coefficients of STORE_KNWL are positive for the Poissonmodel and logistic model (p<.0001), both indicating that store knowledge promotes
visiting and re-visiting the store when they are exposed to mobile ads (MODEL 1). Thisis the case if the model is adjusted (MODEL 2). Both results support H2.
The results of other covariates can be interpreted straightforwardly as expected.
The coefficient of DIST_IMP in the logistic regression is negatively significant(p<.0001), meaning that if the subject locates far from the store area when exposed toads, they tend not to visit the store during the campaign. This is consistent with the13findings from Luo et al. (2014) and Danaher et al. (2015). The results on DIST_HOME
indicates the customers exposed to the ads who live in distant areas less frequently visit
the store (p<.0001; for both Poisson and logistic regression) as expected. The
coefficients of DEST_FAM imply that the customers with larger experience at store area
visit the store more frequently if they view the coupons (p<.0001; for both Poisson andlogistic regression).
As all the variables in the regression model are standardized, we can compare
the magnitude of the variables’ effect on the number of people visiting the store.
MODEL1 and MODEL2 in Table 5 tell us that area familiarity, store knowledge, anddistance to the store from home are more important triggers to visit the focal store after
their coupon impression than destination familiarity and distance to the store. Luo et al.
(2014) and Danaher et al. (2015) state the significance of the distance to the focal store,
but in this study, the other contextual factors are more prominent in terms of the geotargeting ads effectiveness.
3.3. Discussion
We discuss our results from the perspective of the marginal effect of geotargeting ads on visiting the focal store via store knowledge and area familiarity.
Because our model contains the interactions of area familiarity and store knowledge inlogistic regression model and also these variables are transformed to be standardized, it
is difficult to straightforwardly interpret the obtained results when considering the
coupons effectiveness.
Thus, we discuss the obtained results using estimated marginal mean effects.
Note that marginal mean effect considered in this section is calculated based on thepropensity score adjusted results. Figure 2 describes the marginal mean effects of areafamiliarity and store knowledge on visiting the focal store at least once after the coupon
issue, given other variables are fixed to be their sample mean. The effects are shownwith the scale of percentile for each variable since the raw value of the area familiarity
is not straightforwardly interpreterble. Since these variables are transformed and x-axis
is described in percentile, the effect is not S-shaped unlike the logistic curve. We cansee from Figure 2 that area familiarity and store knowledge interestingly have almost
equivalent impact on coupon effectiveness.
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[Figure 2]

On average, the customers with bottom 25th percentile of area familiarity (those
who are less familiar with the area where they are exposed to the coupon) have 7.6%
possibility of visiting the store, although those with top 25th percentile of areafamiliarity (those who are more familiar with the area where they are exposed to the
coupon) only have 4.4% possibility. Similarly, the consumers with first quartile of store
knowledge (those who had experienced the store better) visit the store with 7.6% afteran exposure to the geo-targeting ad, whereas the consumers with third quartile of store
knowledge (those who had less experienced the store) have only 4.3% chance ofvisiting the store after the exposure. These results indicate that when a manager issues
geo-targeting ads, they should consider area familiarity and store knowledge of thetarget customers.
Our main focus is on customer targeting with a combination of area familiarity
and store knowledge factors. Figure 3 shows the estimated marginal mean effects of the
combination of area familiarity and store knowledge on visiting the store. In the figure,
we divide area familiarity and store knowledge into three levels, bottom 25th percentile(low), 50th percentile (mid), and top 25th percentile (high), respectively, and consider
3x3 combinations of marginal effects. The figure shows that targeting the customers
with high store knowledge and low area familiarity will produce 10.1% visiting rate
through the coupon. On the other hand, if the manager mistakenly focuses on those whohave low store knowledge and high area familiarity, the likelihood shrinks to only 3.3%.
Thus, the results indicate that targeting customers with the first or the third quartile ofarea familiarity and store knowledge generates more than 3 times the difference of geotargeting ads effectiveness.

[Figure 3]

4. Study 2
4.1. Method

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Study 1 considered the case where a geo targeting ads contains coupons.
However, in some cases, the ads do not offer any discounts but just appeal or remind
their products or stores. In the second study, we deal with such cases.
A retail store offers a space where several restaurants are located on the same
floor. The store issued mobile ads without coupons. The ads only explain the location of
the restaurants, therefore the consumers do not enjoy the discounts nor any benefits
from them. CyberAgent, likewise Study 1, is the advertising agency of the coupon.
They conducted a randomized field experiment where the treated group subjects are
exposed to mobile ads during the campaign period. Corresponding control group are
selected in the same order as study 1. In this study, 515,738 subjects are assigned to thetreated, whereas 9,989 subjects are assigned to the control. Out of 515,738 subjects,
9,853 users recorded positive impressions on the mobile ads.
Again, we record the number of visits to the store for each subject using location
data from mobile phones. We calculate the number of people visiting the focal store andlet it be the outcome of our interest. Note that because the GPS is used to count thevisiting, we cannot identify whether the customers visited the retail store space, therestaurants space, or both of them. All the location data is collected with mobile user’s
consent and completely anonymized.
Our main interest lies in whether the area familiarity and the store knowledge
have positive effects on the number of people visiting the store after an impression on
mobile ads. We employ the same zero-inflated Poisson regression model described inequation (1). The definition of variables are provided in Table 2. Other control variables(DEST_FAM, DIST_HOME, DIST_IMP) are also included in the model like Study 1.
We also conducted propensity score adjustment to remove the selection bias.
4.2. Results
The descriptive statistics in this study are described in Table 7. The table showsthat covariates seem to be balanced between two groups. The table also tells us that the
coupon impression increases the number of people visiting the store almost twice. The
positive impression group has more store knowledge, lives in a more proximal area tothe store than the no impression group, has higher familiarity on the area store located.
iOS users tend to view mobile ads more than Android users. These tendencies are very
16

similar to those of Study 1. Hence, we adjust selection bias using propensity scoreweighting.

[Table 7]
Table 8 describes the Pearson’s and Spearman’s correlation matrix forcovariates. As can be seen from the table, STORE_KNWL and DEST_FAM have smaller
correlation coefficient than that of Study 1. Since the focal store of this study is locatedin an urban area and there exist a lot of places to visit, store knowledge measured bynumber of past visits to the focal store and destination area familiarity measured bynumber of past visits to the focal area would be more weakly correlated than Study 1.
That is, the visits to the focal area rarely imply the visits to the focal area. In any case,there seems to exist no multicollinearity.

[Table 8]

The results from zero-inflated Poisson regressions are provided in Table 9.
Before the analyses, all the variables in the empirical models are transformed to be
standardized. The upper block shows the estimated coefficients, standard errors, and pvalues for the Poisson regression part defined in equation (1), and the lower blockshows the results for the logistic regression part defined in equation (2).

[Table 9]

The probit regression result for estimating propensity score, which corresponds
to the probability estimates the tendency of a customer to view mobile targeting ads, isshown in Table 10. The table indicates customers tend to view mobile targeting ads
when they use iOS (p<.0001), have higher store knowledge (p<.0001), live in a
proximal area to the store (p<.0001), and have higher familiarity to the area where thestore locates. Using the estimated coefficients, we predict the propensity score forsubject i.
17

[Table 10]

The zero-inflated Poisson results show that although the coefficients of
STORE_KNWL in the logistic regression models are both positively significant in
MODEL 1 and MODEL 2 (p<.0001), those of AREA_FAM are neither statisticallysignificant. This is a different result to Study 1, where coefficient of AREA_FAM are
negatively and statistically significant in the logistic regression part. Thus, H1 is notsupported in Study 2.
The coefficients of STORE_KNWL are also positively and statisticallysignificant in the Poisson regression part (p<.0001), indicating that, given the coupon
impression and the positive number of visiting, customers who had experienced thestore more tend to revisit the store more. Thus, H2 is again supported.
Therefore, in the setting of Study 2, customer’s those who have a larger numberof visiting the store before the coupon launch for a month tend to visit the focal store by
its impression, but area familiarity of customers seems to have no effect on coupon
efficiency. We will discuss this point in the next section.
The results on DIST_HOME indicates the customers exposed to the ads who live
in distant area less frequently visit the store (p<.0001; for both Poisson and logistic
regression) as expected. The coefficients of DEST_FAM imply that the customers withlarger experience at store area visit the store more frequently if they view the coupons(p<.0001; for logistic regression).

4.3. Discussion
We also discuss Study 2 results from the perspective of the marginal effect of
geo-targeting ads on visiting the focal store via store knowledge and area familiarity.
Discussion here is based on the propensity score adjusted results (i.e., MODEL 2
results).
Figure 3 describes the marginal mean effects of area familiarity and store
knowledge on visiting the focal store at least once after the coupon issue, given othervariables are fixed to be their sample mean. Likewise Figure 1, the effects are shownwith the scale of percentile for each variable. We can see from Figure 1 that area18

familiarity and store knowledge interestingly have almost equivalent impact on coupon
effectiveness.

[Figure 3]

On average, the consumers with first quartile of store knowledge (those who had
experienced the store better) visit the store with 7.0% after an exposure to the geotargeting ad, whereas the consumers with third quartile of store knowledge (those who
had less experienced the store) have only 4.1% chance of visiting the store after the
exposure. These are similar results to Study 1’s, indicating that when a manager issues
geo-targeting ads, they should consider area familiarity and store knowledge of thetarget customers.
However, with respect to area familiarity, the marginal mean effect is very
different. As can be seen from Figure 3, the visiting probability after an exposure to thegeo-targeting ads is unchanged if a consumer is familiar or unfamiliar to the area.
Figure 4, describing the estimated marginal mean effects of the combination ofarea familiarity and store knowledge on visiting the store, tells us the similar. In the
figure, we divide area familiarity and store knowledge into three levels, bottom 25th
percentile (low), 50th percentile (mid), and top 25th percentile (high), respectively, andconsider 3x3 combinations of marginal effects. The figure shows that although higher
store knowledge results in higher visiting rate for every scenario (6.9% - 7.2%), areafamiliarity does not affect the probability.

[Figure 4]

5. General Discussion and Conclusion
In this section, we discuss our results obtained from two studies to obtain abetter understanding of the behavioral mechanism. We firstly conduct some but simple
additional analysis. We then introduce a theory called “dual-system theory”, which
seems to be consistent with our results from main and additional analysis. We also
discuss some managerial implications lead by the results along with the theory. We also
discuss some limitations of our research.
19

5.1. Implications from two study results inconsistency
To short, two studies consistently show that, irrelevant to the coupon attendant,
those who have higher store knowledge tend to follow the geo-targeting
recommendations. On the other hand, consumers with lower area familiarity tend tofollow the geo-targeting recommendations when the coupons are attached, whereas thearea familiarity do not affect visiting probability when recommendations without
coupons (just advertising) are offered. We discuss the varying results about the areafamiliarity.
Then, we again conduct the zero-inflated Poisson regression analysis by dividing
the samples into two parts; whose store knowledge are positive (who have visited thestore at least once; STORE_KNWL>0) and zero (who have never visited the store;
STORE_KNWL=0) for study 1 and study 2 datasets, respectively. Since we split the
samples based on the store knowledge level, this analysis does not include
STORE_KNWL as an explanatory variable. The results of this analysis, along with themain analysis are summarized in Table 11.

[Table 11]

In the case where the geo-targeting recommendations with the coupons attached(study 1 case), the negative effects of area familiarity on visiting are both observed, but
the effects seem to be stronger if a customer has never visited the store. That is, thelogistic regression coefficient of AREA_FAM for STORE_KNWL=0 group is -0.586
which is statistically significant at p<.0001 level, but that of STORE_KNWL>0 group is-0.138 with p=0.107. This heterogeneous result, namely AREA_FAM coefficient differs
depending on STORE_KNWL level, is consistent with the main results that shows
negatively and statistically significant cross-term (i.e., AREA_FAM*STORE_KNWL)
coefficient in the logistic regression part.
In the case where the geo-targeting recommendations where the coupons are notattached (study 2 case), the negative effects of area familiarity on visiting are notobserved in neither group (the logistic regression coefficient of AREA_FAM for
STORE_KNWL=0 group is 0.037 with p=0.676, and that of STORE_KNWL>0 group is
0.174 with p=0.311).
20

5.2. Habitual system and goal-directed system
We introduce the theory on habitual versus goal-directed control (e.g., de Wit
and Dickinson, 2009) to explain these heterogeneous results. Recent neuroscience andbehavioral studies have found evidence that decision-making is based on dual systems,
habitual system and goal-directed system, which rely on distinct neural networks(Clithero et al., 2021). In this theory, the habitual system begets behavior which is
automatically caused by some stimuli via past learned stimulus-response associations,owing to some positive or negative reinforcements. The goal-directed system causes
behavior which is driven by goal expectancy and desire, where decisions are mediatedby the expected positive outcomes or the expected relief obtained from avoiding
negative outcomes (Balleine and O'doherty, 2010).
Only when consequences are assumed to be desirable, or when the behavioral
consequences constitute a “goal”, the goal-directed control works and activate actions
toward the goal (de Wit and Dickinson, 2009). Under the goal-directed system, people
involve careful consideration with higher cognitive resource cost for the expected
outcome. On the contrary, when they are exposed to extensive and repeatedreinforcements, the habitual control exerts dominant control on their decision-making
process. Under the habitual system, people make more simplistic and repeated action
based on the past experience and attain efficiency in terms of cognitive resources, butthey lose behavioral flexibility independent of the outcome’s value.
We adopt this dual system theory to our settings. When geo-targeted offers
without coupons nor discount are exposed to consumers, they only have a reminder
effect they tend to stay in habitual systems and follow past learned behavior. Therefore,
in the study 2 setting, people tend to visit stores with higher store knowledge.
On the contrary, when geo-targeting ads are equipped with coupon information,
the customer considers desirable “goal” and shifts to the goal-directed system. Because
people whose goal-directed control is dominant make careful consideration for the
expected benefit, they will attentively consider cost of uncertainty and searching fortheir decision making. Then, because the area familiarity, which is related to be thesearch cost and uncertainty, is the key driver to visit the store, and the customers whoare exposed to mobile coupon at unfamiliar area tend to follow the recommendations.
Especially, those who have never visited the store tend not to be reinforced to visit the21

store under the habitual system, they are more likely to retain goal-directed system.
Therefore, in the same vein, the area familiarity effect on the direct to the store is more
prominent for the customers who have never visited there than for the customers who
have experienced the store.

5.3. Practical implications
In light of these arguments supported by the theory of habitual and goal-directed
system, we can suggest some managerial implications.
People may visit unfamiliar area to enjoy unusual events (e.g., at a venue of artsand entertainment) on a weekend or after work (Han and Yamada, 2016). In this time,
people tend not to be in the habitual but the goal-directed control, and therefore areafamiliarity would have great impact on the geo-targeting ad redemption. In addition,
geo-targeting ads with coupons can make people more goal-directed. Hence, managers
should consider area familiarity and attach coupons when they distribute their ads on
weekend or after work hours.
On the other hand, people in working time on weekdays would be in the habitual
system. In this time, the value of the area familiarity is lower and it is not a key factor of
the ads effectiveness. The store knowledge is more important. Then, managers should
send geo-targeting ads which have higher store knowledge for every customer. But ifthey want to induce a customer to the store people are unfamiliar, the coupon should be
attached with the ads because it may get him more goal-directed. Some customers may
rationally consider search cost and coupon discount, then visit the focal store.
5.4. Limitation, and Future Research SuggestionsFirst, we do not consider customers demographics. Likewise prior research
investigating the geo-targeting ads, the customer information such as age, gender, orincome is hard to obtain because these data are not recorded in the location information
source (Luo et al. 2014). Therefore, we couldn’t consider the heterogenous effect of thearea familiarity or the store knowledge to the coupon redemption. Future research could
explore this issue.
Second, although we supported our result based on the theory of control andgoal-directed systems, we do not have any evidence whether they are under the goaldirected or habitual control. In neuroscience, functional magnetic resonance imaging
22

(fMRI) studies makes it possible to judge their dominant systems. However, since ourresearch rely on field experiment, such studies are difficult to execute. With
technological progress, such investigation could be possible in the future.
Lastly, we acknowledge that our findings are limited to specific stores and ads.
Although our hypotheses and the explanation for the result do not heavily rely on thestore and ads characteristics, and area familiarity and store knowledge are always
conceptualizable in geo-targeting ads study and hence commercially implementable,
future research should consider a variety of store and advertisement characteristics. 6.3
Conclusion
In this paper, we suggest two customer contextual factors affecting geo-targetingads response: area familiarity and store knowledge. We show that the targeting based on
appropriate combination of the area familiarity and the store knowledge can improve
coupon redemption rate. Moreover, we found that, in our randomized field experimentsetting, the area familiarity has more prominent impact on coupon redemption than thegeographical distance, which is often considered in geo-ad research. We hope future
research will investigate the effect of area familiarity and store knowledge on mobile
targeting advertising at various ads and store attributes.

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28

Table 1. Selected Studies on Geo-Targeting Mobile Ads
Authors (year)

Objectives

Theories

Study Method

Analytical Approach

Findings

Luo et al. (2014)

To investigate geo-targeting

Contextual marketing

Field experiments with

Logistic regression model where

The closer in time and location formobile advertisingtheory and construal

12,256 SMS users

they regress purchase likelihood

consumers, the more effective theeffectiveness with time andlevel theory

on distance, time , and these crossmobile ads.

locationterm with control variables

Danaher et al.

To examine whether the

Field experiments in a

Multivariate binomial probit

Mobile coupons are more likely to(2015)

coupon redemption rates

shopping mall of 38

regression model where they

be redeemed if the expiry length isare affected by its expiry

stores with 8,534 SMS

regress the coupon redemption on

shorter and location received islength and distance to theusers

coupon characteristics

closer to the store.

store
Fong et al.

To investigate the effect ofField experiments with

Two-sample t-test comparing

Consumers located near a(2015)

competing store location

18,000 SMS users

mean purchasing rates

competitor’s store tend to follow geo
targeting promotions.

Andrews et al.

To examine the effects of(2016)

physical crowdedness on

Mobile immersion

Field data with 10,690

Logistic regression model where

Consumers in crowded subway

SMS users

they regress purchase likelihoodtrains are respond more to a mobile

consumer responses toon crowdedness with control

ad offers than those in noncrowded

mobile ads.

variables

subway trains

Zubcsek et al.

To improve the dynamic

Dynamic location

Data based on a panel

Logistic regression model where

Colocated consumers tend to(2017)

segmentation of consumers

behavior and consumerof 15,353 observations

they regress offer redemption

respond to coupons in the sameaccording to their pastpreferences based on

on 96 offers sent to 217

likelihood on consumers network

category, indicating that consumers’

responses to marketing

standard economic

participants in a pacificvariables with control variables

location patters provide information

activities and their location

theorycountryon their preferences

histories
Grewal et al.

To examine whether mobile

Limited attentional

Eye-tracking

Two-sample t-test comparing

In-store mobile phone use increases(2018)

phone use in store increase

capacity theory

technology in fieldmean purchasing amountthe total time spend in the store, and29

the purchases

study and fieldalso it increases the total purchases

experiment

as a result

Ghose et al.

To investigate theSpatial affinity

Randomized field

Machine learning based clustering“Trajectory-based” targeting for(2019)

effectiveness of “trajectory-(homophily), temporalexperiment in a major

and recommendation system

mobile promotion can increase thebased” targeting strategy

duration, and movement

shopping mall in Asia

using features on temporal,

redemption rate of coupons

for mobile recommendation

velocity

based on 83,370 uniquespatial, semantic, and velocity

user responses for a 14-

information

day period
Qiu and Zhao

To evaluate how store(2019)

Symbiosis effectField data with 1,799

Mixed-effect linear regression

When the shopping center density isclusters affect acquisition

restaurants (11,222

model for coupon acquisition andhigher, the coupon acquisition rate is

and redemption of mobile

mobile coupons)

mixed effect logistic regression

also higher, but result in lower

model for coupon redemption

coupon redemption rate

coupons
Zhang et al.

To propose a newUser engagement andRandomized field

New structural forward-looking

By considering the time-varying(2020)

personalized-targeting

personalized targeting

experiment on ahidden Markov model (FHMM)

factor of engagement and forward-

approach to tackle theChinese mobile reading

looking behavior, FHMM canchallenge of low

app with 9,255 active

estimate their engagement stage

engagement with mobile

users in total.

which is not explicitly observed.

apps
Molitor et al.

To study location-based

Relevance-caused

Field experiments with

Hierarchical Bayesian logit model

Distance-based ranking of coupons(2020)

coupon effectiveness withranking

10,690 users

with mixed effects where theyis the most effective factors forthe provision of distance

regress coupon clicking

location-based ads, and theinformation and thelikelihood on distance and display

provision of distance information is

distance-based ranking ofrank with control variables

less important.

coupons
Present study

To explore thePsychological reactanceField experiments with

Zero-inflated Poisson regression

effectiveness of geo-

theory, regulatory focus

982,754 users (Study 1)

model with the inverse oftargeting mobile ads intheoryand 515,738 users

propensity score adjustment,

30

terms of area familiarity,(Study 2)

where we regress store traffic onstore knowledge, and typearea familiarity, store knowledge,of incentives.

and other controls

31Table 2. Definition of variables
Variable nameDefinition

N_VISITiThe number of visiting the focal store during the campaign of subject i

TREATi

AREA_FAMi

STORE_KNWLi

1 if subject i is assignt to treatment group (coupon exposure group),and 0 otherwise
Approximated ratio of time staying in the 1km mesh where subject i is
exposed to the ad before the campaign for a one month
The number of visiting the focal store before the campaign
for one month of subject i (store familiarity)

TIME12_14i

1 if subject i's first impression is from 0 p.m. to 2 p.m. and 0 otherwise

TIME15_17i

1 if subject i's first impression is from 3 p.m. to 5 p.m. and 0 otherwise

TIME18_20i

1 if subject i's first impression is from 6 p.m. to 8 p.m. and 0 otherwise

DIST_IMPi
DIST_HOMEi
DEST_FAMi

The

distance

to

the

focal

store

from

subjecti'slocationwhen exposed to the ad (km)The distance to the focal store from subject i's home (km)Approximated ratio of subject i's time staying in the 1km meshwhere the focal store locates before the campaign for a one monthOSi

1 if subject i's OS is iOS, and 0 if subject i's OS is Android.

PLACE_FEi

The 7 stores fixed effect

Table 3. Descriptive statistics (Study 1)

32

Table 4. Pearson’s correlation (lower left) matrix and Spearman’s correlation matrix(top right) (Study 1)

Table 5. Results of zero-inflated Poisson regression model (Study 1)

Independent Variable

MODEL 1

MODEL 2

(no adjustment)(propensity score adjusted)

Estimate

S.E.

p-Value

0.083

0.013

Estimate

S.E.

p-Value-0.074

0.087

0.398

14.00%

Poisson regression predicting the number of visit
Intercept

12.00%

0.207**

STORE_KNWL

0.047

0.001

<.0001 ***

0.061

0.001

<.0001 ***DIST_HOME-0.1780.021<.0001 ***-0.1960.020<.0001 ***DEST_FAM

0.023

0.004

<.0001 ***

0.021

0.004

<.0001 ***PLACE_FE

Included

Included

Logistic regression predicting the number of visit to be positive
Intercept-3.2670.120<.0001 ***-2.7780.096

AREA_FAM-0.4820.037<.0001 ***-0.4380.024 <.0001 ***<.0001 ***STORE_KNWL

0.425

0.028

<.0001 ***0.441

0.026 <.0001 ***AREA_FAM*STORE_KNWL-0.073

0.031

0.019

-0.069

0.036

0.055

DIST_IMP-0.030

0.041

0.466

-0.042

0.040

0.294

DIST_HOME-0.6240.035<.0001 ***-0.4490.027 <.0001 ***DEST_FAM

0.070

0.018

<.0001 ***0.052

0.013 <.0001 *****PLACE_FE

Included

IncludedN

34,08533,132*

Table 6. Results of Probit regression model (Study 1)

33

10.00%

Table 7.Descriptive Statistics (Study 2)

Table 8. Pearson’s correlation (lower left) matrix and Spearman’s correlation matrix(top right) (Study 2)

34

Table 9. Results of zero-inflated Poisson regression model (Study 2)

Table 10. Results of Probit regression model (Study 2)

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Table 11. Heterogeneous effect of the area familiarity on geo-targeting ads
Study 1

Study 2

Geo-targeting ads with coupon

Geo-targeting ads without coupon

Negative effect on visit

No effect on visit

Area familiarity of those
who have no store knowledge

Strong negative effect on visit

No effect on visit

Area familiarity of those
who have store knowledge

Weak negative effect on visit

No effect on visit

Area familiarity

Figure 1. Theoretical framework
Uncertainty

High

Follow

IgnoreLow

ConsiderRejectLow

High

Preference-offering conflicts

Figure 2. Effect of mobile coupons via area familiarity and store knowledge (Study 1)

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Figure 3. Effect of mobile coupons via combining area familiarity and store knowledge(Study 1)

Figure 4. Effect of mobile coupons via area familiarity and store knowledge (Study 2)

37

Figure 5. Effect of mobile coupons via combining area familiarity and store knowledge(Study 2)38