Capturing the “First Moment of Truth”: Understanding Point ...
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Marketing Science Institute Working Paper Series 2012 Report No. 12-101 Capturing the “First Moment of Truth”: Understanding Point-of-Purchase Drivers of Unplanned Consideration and Purchase Yanliu Huang, Sam K. Hui, J. Jeffrey Inman, and Jacob A. Suher “Capturing the ‘First Moment of Truth’: Understanding Point-of-Purchase Drivers of Unplanned Consideration and Purchase” © 2012 Yanliu Huang, Sam K. Hui, J. Jeffrey Inman, and Jacob A. Suher; Report Summary © 2012 Marketing Science Institute MSI working papers are distributed for the benefit of MSI corporate and academic members and the general public. Reports are not to be reproduced or published, in any form or by any means, electronic or mechanical, without written permission.
Report Summary
In order to optimize their shopper marketing strategies, retailers and manufacturers are interested in understanding in-store drivers of unplanned spending. In particular, they are interested in understanding shopping behavior at the point of purchase, termed by Procter & Gamble as the “first moment of truth.” Here, Yanliu Huang, Sam Hui, J. Jeffrey Inman, and Jacob Suher develop a conceptual framework of the shopping trip-level drivers of unplanned considerations and the point-of-purchase behavior drivers of conversion to unplanned purchases. With few exceptions, previous academic research on unplanned purchases relies on scanner data. Typically, a shopper’s purchase, as recorded by scanner data, is compared to an entrance survey to identify whether a certain purchase is planned or unplanned. What happens during the trip, however, is not recorded. As such, previous studies are typically limited to studying the role of demographics (e.g., gender) and psychographics (e.g., impulsivity) factors on unplanned purchases. Point-of-purchase behaviors along the shopping path are rarely considered. In this research, the authors address two important questions about unplanned considerations and purchases. First, what shopping trip-level characteristics are related to a higher number of unplanned considerations? Second, for each unplanned consideration, what aspects of point-of-purchase behavior are related to a greater likelihood of a conversion to purchase? The authors conducted a field study in a medium-sized grocery store located in a northwestern U.S. city, where shoppers were asked to wear portable video cameras to observe each incidence of their point-of-purchase decision making process. The authors also collected their shopping intentions, and gathered relevant demographic and psychographic information via entrance and exit surveys. Consistent with their hypotheses, the authors find that longer in-store travel distance and lower shopping “efficiency” lead to more unplanned considerations. They further show that an unplanned consideration is more likely to develop into an actual purchase if a shopper (1) spends more time in consideration, (2) touches more products, (3) references external information (e.g., circular, coupon, smart phone), (4) stands closer to the shelf, (5) views fewer product shelf displays, and (6) interacts with the store staff. These empirical insights lead to several key shopper marketing implications. For instance, this analysis shows that “deep” considerations are more likely to result in unplanned purchase than “wide” considerations, suggesting that retailers should try to encourage “focused” considerations whenever possible, such as avoiding promoting multiple brands in a product category. As another example, retailers should encourage shoppers to reference external information during an unplanned consideration, such as distributing store circulars/coupons not only at the entrance, but also at different in-store locations. Yanliu Huang is Assistant Professor of Marketing, LeBow College of Business, Drexel University. Sam K. Hui is Assistant Professor of Marketing, Stern School of Business, New York University. J. Jeffrey Inman is the Albert Wesley Frey Professor of Marketing and Associate Dean of Research and Faculty, Katz Graduate School of Management, University of Pittsburgh. Jacob A. Suher is a doctoral student in marketing at University of Texas, Austin. The four authors contributed equally to this project and the authorship is alphabetical.
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The majority of grocery purchases are unplanned at the category level (Inman, Winer,
and Ferraro 2009; POPAI 1995). Because of the economic importance of unplanned spending,
manufacturers and retailers alike are very interested in understanding in-store drivers of
unplanned purchases in order to optimize their shopper marketing strategies (Grocery Marketing
Association 2007). They are especially interested in understanding shopping behavior at the
point of purchase, termed by Procter & Gamble as “the first moment of truth” (Inman, Winer,
and Ferraro 2009; Nelson and Ellison 2005). In particular, given the importance of product
consideration in product purchase (e.g., Hauser and Wernerfelt (1989) argue that approximately
70% of the variance in a choice decision is accounted for by consideration), retailers try to
identify trip- and point-of-purchase- level factors that lead shoppers to make more unplanned
considerations, and raise the likelihood that these considerations will turn into actual purchases.
With a few notable exceptions (Hui et al. 2011; Stilley, Inman, and Wakefield 2010b),
previous academic research on unplanned purchases often relies on scanner data (Beatty and
Ferrell 1998; Bell, Corsten, and Knox 2011; Bucklin and Lattin 1991; Inman et al. 2009; Park,
Iyer, and Smith 1989). Typically, a shopper’s purchase, as recorded by scanner data, is compared
to an entrance survey that is administrated before the shopping trip begins to identify whether a
certain purchase is planned or unplanned (Bell et al. 2011; Inman et al. 2009). Importantly, what
happens during the trip (e.g., how a shopper considers and purchases from each product
category) is not recorded. Thus, previous studies are typically limited to studying the role of
demographics (e.g., gender, age, income) and psychographics (e.g., impulsivity) on unplanned
purchases. Point-of-purchase behaviors along the shopping path are rarely considered, because
such information is unavailable from scanner data.
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In this research, we address two important questions about unplanned considerations and
unplanned purchases. First, what shopping trip-level characteristics are related to a higher
number of unplanned considerations? Second, for each unplanned consideration, what aspects of
point-of-purchase behavior are related to a greater likelihood of a conversion to purchase? We
begin by presenting a conceptual framework of unplanned considerations and conversion to
unplanned purchase that identifies key shopper trip-level and point-of-purchase-level drivers of
unplanned consideration and purchase behavior.
We then describe our methodology that we use to test the hypotheses from our conceptual
framework. We record point-of-purchase behavior using video tracking. This video tracking
device allows us to observe each incidence of shoppers’ point-of-purchase decision making
process, from the moment a shopper starts considering a product category, to the moment she
decides to (or not to) purchase the product category. In conjunction with an entrance and exit
survey that asks shoppers to state their shopping plans and other demographics and
psychographic information, the video data allow us to identify each incidence of unplanned
consideration and purchase conversion.
After controlling for other demographic and psychographic factors, we use these data to
estimate a model that tests our hypotheses and yields several key insights. Regarding unplanned
consideration, we find that shopping trips with longer in-store travel distance and lower shopping
“efficiency” are associated with more unplanned considerations. Further, we find that an
unplanned purchase conversion is more likely if a shopper (i) spends more time in consideration,
(ii) touches more products, (iii) views fewer product shelf displays, (iv) stands closer to the
shelf, (v) references external information (e.g., circular, coupon, smart phone), and (vi) interacts
more with the store staff. These insights are of interest to researchers and practitioners alike, and
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to the best of our knowledge, have never been heretofore empirically demonstrated.
The remainder of this paper is organized as follows. The next section briefly reviews the
previous literature on drivers of unplanned purchases and presents our conceptual framework.
We then describe the video dataset in detail, discuss how we operationalize our focal constructs
from the dataset, and present several key summary statistics. We subsequently describe our
statistical methodology and present our results. We close with a discussion of the theoretical and
managerial implications and directions for future research.
Hypothesis Development
Prior literature and conceptual framework
The main goal of our research is to study the influence of various in-store factors on
unplanned considerations and their conversion to unplanned purchases. Figure 1 (see Figure 1,
following References) presents our conceptual framework, clarifies our terminology, and relates
the current research to previous literature. As illustrated in Figure 1, our view of the shopping
process is as follows. Each shopper enters the store with a certain set of product categories that
she plans to purchase. Once in the store, she takes a shopping path to acquire her planned
purchases. Depending on the particular path that she takes, she may be attracted by certain in-
store stimuli to make several unplanned considerations. Each of these unplanned considerations
may or may not turn into an actual unplanned purchase. Finally, the shopper checks out with all
of her planned and unplanned purchases.
Most of the previous literature that studies unplanned purchases focuses on those
variables collected either before a shopper enters the store or after the shopper checks out (e.g.,
Bell et al. 2011; Kollat and Willett 1967; Granbois 1968; Park et al. 1989; Rook and Fisher
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1995). For instance, Bell et al. (2011) look at the role of “pre-shopping” factors such as the
abstractness of the shopping trip goal. They show that more abstract shopping goals such as
shopping weekly or less frequently increase unplanned purchases up to 60% compared with
more specific shopping goals such as shopping for immediate consumption. In another example,
Kollat and Willett (1967) find that product purchase frequency, transaction size, and the length
of the shopping party’s marriage are all positively related to unplanned buying. From a different
perspective, Park, Iyer, and Smith (1989) demonstrate that the incidence of unplanned purchase
is greater when shoppers’ store familiarity is low and when they experience no time pressure in
the store. Finally, in testing a comprehensive model of the effects of customer and product
factors on unplanned purchases, Inman et al. (2009) find that certain customer characteristics
(e.g., having a larger household size) and product category characteristics (e.g., the presence of
store displays) increase unplanned buying.
Much less empirical research, however, has studied unplanned considerations, or more
generally, consumers’ dynamic decision making process while they are in the store. Part of the
reason for this is because, until recently, in-store behavior (i.e., the box in Figure 1 that includes
unplanned consideration, unplanned purchase conversion, the shopping path, and consideration
characteristics) was very difficult and costly to measure. As discussed earlier, most of the
previous research on in-store decision making relies solely on survey and scanner data, which
limits the type of information available.
However, new technologies are creating opportunities to examine in-trip effects more
deeply. For example, Stilley et al. (2010b) equip shoppers with a handheld scanner to scan the
barcode of each product as they placed it in their carts, and Hui et al. (2009ab) track shoppers’
shopping paths using Radio Frequency Identification (RFID). Notably, none of the above
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research captures the incidence of consumers’ unplanned considerations and purchase
conversions. This is the void that we seek to fill in this research, using a novel video tracking
data collection methodology. Below, we develop specific hypotheses which are subsequently
tested using the video dataset.
Shopping path characteristics and unplanned considerations
It is well documented that a product must first be considered in order to be purchased
(Alba, Hutchinson, and Lynch 1991; Hauser and Wernerfelt 1989; Roberts and Lattin 1991).
Individuals often consider a series of alternatives in order to arrive at a choice. Product
consideration, therefore, is of both theoretical and practical importance (Hauser and Wernerfelt
1989; Howard and Sheth 1969; Priester et al. 2004).
As discussed earlier, before a shopper enters the store, she has in her mind certain
product categories she plans to purchase on this specific shopping trip. Ideally, in order to shop
efficiently, the shopper should follow the shortest shopping path connecting all of her planned
product categories. However, depending on the particular path that she takes, she may be
attracted by certain in-store stimuli to consider some unplanned product categories. Thus, both
the length of the shopper’s travel path and the extent to which she follows the most efficient path
to obtain her planned items may be associated with the number of unplanned considerations she
makes. Therefore, we study two characteristics of shopping path that can be related to the
number of unplanned considerations: (i) the in-store travel distance, and (ii) the “efficiency” of
the shopping path.
First, several researchers (e.g., Beatty and Smith 1987; Beatty and Ferrell 1998; Granbois
1968; Hui et al. 2011; Inman et al. 2009; Iyer 1989; Park, Iyer, and Smith 1989) have argued that
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shoppers should engage in more unplanned purchases if they are exposed to more products, in-
store displays, and store promotions, which may in turn trigger forgotten needs (Stilley et al.
2010a). Clearly, the longer the distance that a shopper travels in the store, the more in-store
stimuli she will pass by and get exposed to. Thus, we hypothesize that a longer in-store travel
distance is associated with more unplanned considerations.
H1: A longer in-store travel distance leads to more unplanned considerations.
Second, even if two shoppers have exactly the same shopping path, the amount of store
stimuli for unplanned categories they get exposed to can differ, depending on their shopping
plan. Consider two shoppers (A and B) who take the same in-store shopping path. Shopper A has
a large number of planned purchases, and her shopping path represents the shortest path that
connects all of her planned product categories. Shopper B, however, only plans to buy a handful
of product categories, but takes the same shopping path as shopper A.
Intuitively, even if shoppers A and B take the same shopping path, one would expect
shopper B to be exposed to more in-store stimuli for unplanned product categories and hence
engage in more unplanned considerations than shopper A. Planned purchases can be seen as a
goal-derived category that is created to achieve a shopper’s grocery shopping goal (Barsalou
1983). When shopper A takes a more “efficient” shopping path (Hui et al. 2009a), essentially
going from one planned purchase to the next, she is more likely than shopper B, who takes a
more meandering path through the store, to be in a goal-directed state and therefore less likely to
respond to goal-irrelevant in-store stimuli (Gollwizer 1993; Gollwitzer and Brandstatter 1997).
In addition, when grocery shoppers do not plan forward efficiently to take the shortest path, they
may focus on the actions that maximize immediate utility rather than ones that maximize utility
over a relatively longer time horizon (Hutchinson and Meyer 1994). This diminished regard for
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future consequences is often driven by hedonically complex feeling and associates with an urge
to buy immediately (Rook 1987). Thus, we hypothesize that controlling for in-store travel
distance, shoppers who take a less efficient shopping path through the store (defined
mathematically in the next section) are likely to engage in more unplanned considerations.
H2: Lower shopping trip efficiency leads to a greater number of unplanned
considerations.
Consideration characteristics and unplanned purchase conversion
Once a shopper is engaged in an unplanned consideration, she might take time thinking
about both pros and cons of the product, examine the specific product in more detail, refer to
external information such as in-store circulars and coupons, or interact with store staff before
making her final purchase decisions. We now discuss in detail how consideration characteristics
can be related to whether an unplanned consideration will convert into an unplanned purchase.
First, when consumers shop in a grocery store, the wide variety of sensory stimuli
presented in their decision environment might activate their important shopping goals and
therefore increase their engagement in a product purchase (Celsi and Olson 1988). This
engagement represents consumers’ degree of interest in the product and the extent to which the
product relates to the self and/or the hedonic pleasure received from the product (Bloch and
Richins 1983; Laurent and Kapferer 1985; Richins and Bloch 1986). Since this heightened
product engagement generally leads to greater purchase intentions (Bloch and Richins 1983;
Howard and Sheth 1969), we expect that the more engaged a shopper is during an unplanned
consideration, the more likely it is to result in a purchase conversion. Specifically, we
hypothesize that longer consideration duration and more product touches, both of which are
indicative of higher engagement (Celsi and Olson 1988; Peck and Childers 2003ab), are
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associated with a higher likelihood of purchase conversion. In addition, the opportunity to touch
an object may result in an increase in perceived ownership of that object and is associated with a
higher likelihood of unplanned purchasing (Peck and Childers 2006; Peck and Shu 2009).
H3a: Longer consideration leads to a higher likelihood of unplanned purchase
conversion.
H3b: More product touches leads to a higher likelihood of unplanned purchase
conversion.
Two shoppers who spend the same amount of time considering a product category may
exhibit very different types of consideration behavior. Consider shopper A, who spends two
minutes examining a certain SKU in a great amount of detail, and contrast that with shopper B
who spends two minutes looking at several different products within the category. We refer to
the first type of consideration as a “deep” consideration and the latter as a “wide” consideration.
We argue that a “deep” consideration (shopper A) may be more likely to result in a purchase
conversion than a “wide” consideration (shopper B). By focusing her attention on a small
number of products, shopper A may feel more involved/engaged with the specific product, which
makes her more likely to purchase it (Bloch and Richins 1983; Celsi and Olson 1988).
In contrast, by having more products in the field of view, shopper B could easily suffer
from “choice overload” and become less likely to make a purchase (Iyengar, Huberman, and
Jiang 2004; Iyengar and Lepper 2000; Scheibehenne, Greifeneder, and Todd 2010). The choice
overload phenomenon (also referred to as the “Paradox of Choice”, Schwartz 2004) occurs when
a person is facing a large set of options (vs. a small set) and experiences a decreased motivation
to make a purchase (Iyengar and Lepper 2000). For instance, Iyengar and Lepper (2000) find that
a large assortment of 24 jams attracted more consumers than a small set of 6 jams. However,
when it came to actual purchase, only 3% of consumers who saw the large assortment purchased
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a jam eventually, compared with 30% of those who saw the small assortment. Therefore, given
that too many options often decrease the motivation to make a choice, we hypothesize that a
deep consideration is more likely to develop into a purchase than a wide consideration.
Based on the above discussion, we consider two factors that are associated with the
likelihood of an unplanned purchase conversion. First, controlling for the duration of
consideration, the fewer shelf displays viewed by the shopper allow her to be more focused on
certain products (i.e., a “deep” type of consideration) and thus more likely to make an unplanned
purchase. Second, by physically standing closer to the product shelf, the shopper’s field of vision
will necessarily contain fewer products. Concomitantly, the product shelf display will become
more salient because the angle of vision becomes larger, and this cue salience should lead to a
higher purchase rate (Kardes et al. 1993; Stern 1962). Thus, both fewer options and increased
product salience encourage a “deep” type of consideration that increases the likelihood of a
purchase conversion. This leads to our next two hypotheses:
H4: The fewer number of shelf displays viewed by the shopper, the more likely an
unplanned consideration will turn into an actual purchase.
H5: Standing closer to the product shelf leads to a higher likelihood of unplanned
purchase conversion.
Shoppers may reference the in-store circular, coupons, or interact with store employees
while they are engaged in an unplanned consideration.1 Shoppers’ ongoing information search
during a particular decision results from different motives (Bloch, Sherrell, and Ridgway 1986;
Punj and Staelin 1983). For instance, they may try to obtain tangible consumer benefits such as
cost savings. In this case, purchase satisfaction will be derived from these concrete benefits (Punj
1 Of course, the shopper may also interact with other shoppers, but this was extremely rare in our data so we could
not examine its effect here.
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and Staelin 1983). Alternatively, they might engage in search because the process brings them
hedonic feelings or “shopping enjoyment” (Hirschman and Holbrook 1982; Venkatraman and
Maclnnis 1985). Since both enhanced shopper satisfaction and heightened hedonic feelings lead
to a higher likelihood of product purchase (Cronin, and Taylor 1992; Oliver 1980; Rook 1987),
we hypothesize that referencing external information relevant to the current product under
consideration relates to greater purchase conversion. Similarly, since interacting with store staff
is one important way to obtain product relevant information and the sales interaction was shown
to influence purchase behavior (Olshavsky 1973; Woodside and Davenport 1974), we
hypothesize that more customer-store staff interaction leads to a higher likelihood of purchase
conversion. Unfortunately, we do not observe whether shoppers are referencing information
related to the specific SKU that they are considering (which should increase the likelihood of
purchase conversion) or other SKUs (which may decrease the likelihood of purchase
conversion). Thus, we test the direction of these hypotheses empirically.
H6a: Referencing external information other than one’s shopping list leads to a higher
likelihood of unplanned purchase conversion.
H6b: More interaction with staff leads to a higher likelihood of unplanned purchase
conversion.
Finally, another type of external information that a shopper might reference is her
shopping list. Referencing one’s shopping list during an unplanned consideration should remind
the shopper of her shopping plans and make her more goal-directed (Block and Morwitz 1999;
Gollwitzer 1993). This should make an unplanned purchase less likely because the shopper may
exhibit better self-control (Inman et al. 2009).
H7: Referencing a shopping list during an unplanned consideration leads to a lower
likelihood of unplanned purchase conversion.
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Data And Measures
Data overview
Our dataset was collected at a medium-sized grocery store located in a northwestern U.S.
city, from June 2009 to September 2009. Throughout the data collection period, we recorded all
the store-wide promotions taking place on each day (e.g., end-of-the-aisle displays and weekly
store flyer). A total of 250 shoppers were intercepted at the store’s sole entrance and were asked
to complete an entrance survey. Each shopper was asked to check all the products she planned to
purchase during the current shopping trip from a list of 114 product categories on a tablet
computer. This forms the set of “planned categories” which are subsequently used to identify
unplanned considerations and purchases. Each shopper also indicated (i) whether she was using a
shopping list, (ii) her total shopping budget, (iii) whether she was shopping with anyone else, (iv)
how often she shops at the store, and (v) her familiarity with the store layout and product
placements. These demographics measures are included in our analysis as control variables.
After completing the entrance survey, participants were helped by the experimenter to
don a portable video camera (shown in Figure 2, following References) and start their shopping
trip. The portable video camera is worn like a Bluetooth headset; it tracks the field of vision of
the shopper by following her head movement and reports the location of the shopper using a
built-in RFID tag. This allows us to obtain not only the shopping path (Larson, Bradlow, and
Fader 2005; Hui et al. 2009ab, 2011), but also the change in her visual focus as the shopper
walks around the store. Thus, video tracking combines the advantages of RFID-based path
tracking (Hui et al. 2009ab) and eye tracking (Pieters and Wedel 2004; Thales, Wedel, and
Pieters 2011; Zhang, Wedel, and Pieters 2009), allowing us to explore factors related to both the
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shopping path and point-of-purchase behavior. The procedure we followed to extract various
measures from this video data is discussed in further detail in the next section.
After participants finished their shopping trip and paid for their purchases, we made a
copy of their final receipt. Participants were asked to complete an exit survey in which they were
asked several additional demographics and psychographics questions, including their gender,
age, household size, and household income. In addition, their impulsivity trait was assessed
using Rook and Fisher’s (1995) nine five-point semantic differential scales. Further, by
subtracting the expenditure of planned product purchases from her overall budget, we are able to
compute an approximate measure of “in-store slack” for each participant (Stilley et al. 2010ab).
These variables are included as control variables in our statistical analysis. Finally, each
participant was given a $5 gift card, thanked, and dismissed.2
Thirteen participants had corrupted video data due to technical problems with the video
tracking system and were excluded from the dataset, leaving 237 shoppers for our analysis.
Summary statistics of the shopper demographics information are shown in Table 1 (see Table 1,
following References). In our dataset, 64% shoppers are female, with an average age of 53 years
old. Roughly 16% of all shoppers are a single adult living in their household, 83% were shopping
alone, and 37% carried a shopping list. These figures are generally consistent with the summary
statistics reported in previous research (Bell et al. 2011; Granbois 1968; Inman et al. 2009; Park
et al. 1989).
Measures
The dataset obtained from video tracking is extremely rich, but also highly unstructured,
thus requiring a carefully-devised protocol to extract relevant information. To illustrate the video
2 The $5 incentive was given at the end of the study to avoid a windfall effect (Heilman, Nakamoto, and Rao 2002).
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data, Figure 3 shows the in-store path of a sample shopper, and Figure 4 shows a snapshot from
the corresponding video, when the shopper is located at the position marked by a cross in Figure
3 (see Figures 3 and 4, following References). The video data was manually coded by five
research assistants trained and supervised by TNS Sorensen, following the protocol discussed
next. All discrepancies were resolved through discussion between the research assistants and
TNS Sorensen staff with expertise in video coding.
Dependent measures: unplanned consideration and unplanned purchases
As discussed earlier, from the entrance survey we identify which product categories are
“unplanned” for each shopper. We defined an “unplanned consideration” as beginning when the
criteria below were met:
(i) the shopper is facing the shelf display of an unplanned product category;
(ii) the shopper has slowed to a nearly stopped or stopped pace; and
(iii) the shopper’s field of vision stabilizes upon the product category.
A consideration ends when the shopper either changes her location or shifts her gaze to
look at a different product category. An unplanned consideration ends with the shopper either
buying something from the product category (i.e., an unplanned purchase) or deciding not to buy
anything. The total number of unplanned considerations and whether each of these
considerations results in an unplanned purchase are the two key dependent measures in our
analysis. On average (see Table 1), shoppers made about 5.6 unplanned considerations (with a
median of 4.8). The number of unplanned considerations in a trip ranged from 0 to 25. Roughly
63% of these unplanned considerations turned into an unplanned purchase.
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Shopping path characteristics (h1-h2)
We measure each consumer’s in-store travel distance (H1) using the RFID tag embedded
in the portable video camera, which tracks the location of each shopper during her entire
shopping trip. As can be seen in Table 1, shoppers on average covered a distance of around 1448
feet in the store.
We define the “efficiency” of each shopping trip (H2) by comparing the length of the
actual path taken to the shortest possible path that allows a shopper to pick up all her planned
purchases, which Hui et al. (2011) term the “TSP-path”. The TSP-path is defined as the shortest
path that connects the entrance, all of a shopper’s planned product categories, and the checkout
counter. We consider shoppers who deviate less from their TSP-path as more “efficient” than
shoppers who deviate more from their TSP-path. Formally, we define the “efficiency” of a
shopping path as:
path shopping actual oflength
path TSP oflength Efficiency Trip . [1]
The longer the actual shopping path is compared to the TSP-path, the lower the shopper’s
trip efficiency. In general, we expect trip efficiency to be between 0 and 1, where a trip
efficiency close to 1 corresponds to the situation where a shopper’s path coincides with the TSP-
path.3 From Table 1, we see that the average trip efficiency is approximately 0.5, indicating that
shoppers’ average actual travel path is twice the length of their TSP-path.
Consideration characteristics (h3-h7)
3 Note that it is possible (though rare) that a shopper’s trip efficiency can exceed 1, if she “misses” one or more of
her planned purchases. This accounts for only five cases in our dataset. Results are substantively unchanged if these
five shoppers are omitted from the data.
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Corresponding to H3-H7, we extract the following measures for each incidence of
unplanned consideration. First, consideration duration (H3a) is measured by the amount of the
time between the start and end of an unplanned category consideration (defined earlier). Across
our dataset, shoppers’ consideration duration ranges from less than one second to more than six
minutes, with an average of around 34 seconds. Similarly, the number of product touches (H3b)
is defined as the number of times that the shopper physically touches a product during the
consideration. On average, shoppers touched products 1.7 times during a consideration.
Interestingly, examples of top unplanned categories that were touched but not purchased
included fresh vegetables and fruit, condiments and spices, kitchenware, and prepared meats.
We measure the total number of standard shelf displays viewed (H4) during an unplanned
consideration; each “standard shelf display” is approximately four feet wide. The average
number of shelf displays viewed in a consideration ranges from 1 to 12, with an average of 1.7
shelf displays. During an unplanned consideration, we also measure the distance the shopper
stood from the shelf display (H5). After a few training sessions, research assistants were able to
reliably estimate from the video the average distance between a shopper and her focal display
during a consideration.4 The average shopper stood about 3 feet from the shelf display during an
unplanned consideration.
Finally, we measure whether shoppers referenced external information or interacted with
store staff during the consideration. Towards that end, research assistants were instructed to
check for instances where shoppers were looking at the following objects during a consideration:
in-store circular, coupon, smartphone (H6a), or their shopping list (H7). They were also asked to
indicate whether a member of the store staff was present and was interacting with the shopper
4 The inter-rater agreement allowing 1 foot variation in either direction was approximately 95%. The disagreements
were resolved through discussion among raters.
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during an unplanned consideration and how long the interaction lasted (H6b). Table 1 shows that
in approximately 6% of unplanned considerations, shoppers referenced a
circular/coupon/smartphone during an unplanned consideration5. In 15% of all unplanned
considerations, shoppers looked at their shopping list, and the average time a shopper interacted
with the store staff was 66 seconds.
Category, location, and other controls
In order to test our predictions regarding the effect of shopping path characteristics (H1-
H2) and consideration characteristics (H3-H7) on unplanned considerations and purchases, we
need to control for other factors (in addition to the demographics factors discussed earlier) that
may also be related to unplanned consideration/purchases. Specifically, we control for (i)
category characteristics, (ii) category location, (iii) in-store slack (Stilley et al. 2010ab), and (iv)
cumulative time-in-store and “momentum” factors.
(i) Category characteristics: We include three variables to control for category
characteristics: category hedonicity and whether the product needs to be refrigerated or frozen.
The hedonicity of each category is taken from the survey results from Wakefield and Inman
(2003). Note, however, that the list of product categories reported by Wakefield and Inman
(2003) is slightly narrower than our list of categories. The categories that are not in Wakefield
and Inman (2003) were calculated as an average of other similar categories. Category hedonicity
was measured on a 1-7 scale, with an average of 3.8 (see Table 1). Approximately 32% and 6%
of the unplanned considerations are for refrigerated and frozen product categories, respectively.
5 We tried to estimate separate parameters for each type of information referencing (i.e., checking circulars,
coupons, and smartphones respectively). Unfortunately, the incidence for each activity was too low, so we instead
use a single parameter.
Marketing Science Institute Working Paper Series 17
(ii) Category location and promotional display: We control for the store location where
each unplanned consideration takes place. From discussions with TNS Sorensen and the retail
store where the experiment was conducted, the store is divided into six general areas (Aisles,
Bazaar, Endcap, Checkout, Racetrack, and Service and Other), as shown in Figure 5 (see Figure
5, following References). As can be seen in Table 1, most unplanned considerations took place in
the aisle (39%) or bazaar (40%) regions. Further, we included a binary variable (promotional
display) to indicate whether the unplanned consideration happens at a temporary promotional
display.
(iii) In-store slack: Following Stilley et al. (2010ab), we include a measure of “in-store
slack”, which is a proxy for the amount mentally set aside by shoppers for unplanned purchases
in their mental trip budgets and has been shown to have a positive effect on the amount of
unplanned purchases in the store (Hui et al. 2011; Stilley et al. 2010ab). To control for this, we
compute a measure of in-store slack by subtracting expenditures on planned purchases from the
shopper’s total trip budget (measured in the entrance survey). The average amount of in-store
slack remaining during an unplanned consideration is about $11.
(iv) Cumulative time-in-store and “momentum”: Finally, to control for potential fatigue
effects, we include cumulative in-store time (up to the point when the unplanned consideration
takes place) as a control variable. To control for a shopping momentum effect where the
proximity between two product categories influences the likelihood of the second one later in
time being purchased (Dhar, Huber, and Kahn 2007), we added the “time since last purchase”
(measured in minutes) as a control variable. Summary statistics of these variables can be found
in Table 1.
Marketing Science Institute Working Paper Series 18
Model and Findings
Statistical methodology
We test hypotheses H1-H7 using a Bayesian framework to jointly estimate the
relationship between shopping path characteristics and consideration characteristics on
unplanned consideration and purchases. Let ),,1( Iii index shoppers, and denote the number
of unplanned considerations that shopper i makes during her trip as in . Let ),,1( injj index
the incidence of each unplanned consideration for shopper i during her shopping trip, and let ijy
be a binary indicator that takes the value of 1 if the j-th unplanned consideration by shopper i
converts into an actual unplanned purchase, and 0 otherwise.
Next, similar to the assumption in Bell et al. (2011), we model the number of unplanned
considerations in using a Poisson distribution, where the rate parameter for the i-th shopper i is
driven by shopping path characteristics (i.e., in-store travel distance (H1) and path efficiency
(H2)), together with other demographics controls shown in Table 1. An additional idiosyncratic
error term i is introduced to allow for over-dispersion (McCullagh and Nelder 1989).
)(~| iii Poissonn [1]
iiiii EFFPATHLENx 21
')log( [2]
where xi denotes the vector of demographics covariates for shopper i. PATHLENi and EFFi
denotes the in-store travel distance and path efficiency of the i-th shopping trip, respectively.
Relating back to our hypotheses, H1 predicts that 01 and H2 predicts that 02 .
For each unplanned consideration, the likelihood of purchase conversion ( ijy ) is modeled
using a standard probit specification (Rossi et al. 2005). We denote the latent utility of purchase
(for the j-th consideration for the i-th shopper) as iju , and specify that a purchase conversion will
Marketing Science Institute Working Paper Series 19
occur if latent utility is larger than zero. Latent utility of purchase iju is then modeled as a
function of consideration characteristics (H3-H7), demographics control variables, category
characteristics, and other controls (listed in Table 1). That is,
]0[]1[ ijij uy [3]
ijiijbij
aijijijbijaijijiij
LISTSTAFF
EXTDISTNDISPTOUCHDURzxu
76
65433
''
[4]
where ix is the vector of demographics controls from Equation 2 and '
ijz includes category
characteristics and other controls. ijDUR denotes the consideration duration, ijTOUCH denotes
the number of touches, ijNDISP denotes the number of shelf displays viewed, ijDIST denotes the
physical distance of the shopper from the display, ijEXT , ijSTAFF , and ijLIST denote whether
the shopper references external information, the length of the shopper’s interaction with store
staff, and whether the shopper looks at her shopping list, respectively.6 Parameter i is a
shopper-level random effect, and ij are idiosyncratic error terms that are i.i.d. N(0,1).
Finally, we allow for correlations between the error terms i in Equation [2] and the
shopper-level random effects i in Equation [4] to allow for potential dependencies between the
incidence of unplanned consideration and the outcomes of purchase conversions. We have:
),0(~'
Nii [5]
To complete our model specification, all model parameters are given weakly informative,
conjugate priors (Gelman et al. 2003). Specifically, the parameters 721 ,...,,,,,, are
6 As a robustness check, we also estimated an alternative version of the model where several variables, namely
DUR, TOUCH, and DIST, NDISP, are mean-centered based on (i) shopper (ii) category, and (iii) shopper-category
to control for potential idiosyncratic consideration characteristics at the shopper or category level. The results are
very similar to those presented here.
Marketing Science Institute Working Paper Series 20
given a diffuse )100,0( 2N prior, and a ),1( IWishartInv prior is specified for the covariance
matrix . The posterior distribution of all model parameters is sampled using a standard MCMC
procedure; details are available from the authors upon request.
Results
We draw a total of 2,000 samples using MCMC, discard the first 1,000 samples as burn-
in (Gelman et al. 2003), and use the remaining 1,000 samples to summarize the posterior
distribution of model parameters. The posterior mean, standard deviation, and 95% posterior
interval of each variable is shown in Table 2 and Table 3 (see Tables 2 and 3, following
References).
Relationship between shopping path characteristics and unplanned considerations. We
begin by examining how shopping path characteristics are related to the number of unplanned
considerations (see Table 2), as hypothesized in H1 and H2. Both of our hypotheses regarding
the relationship between shopping trip characteristics and unplanned considerations are
supported. First, consistent with H1, the posterior mean of 1 is 0.463 x 10-3
, with a 95%
posterior interval of (0.331 x 10-3
, 0.602 x 10-3
). This suggests that, as hypothesized, the longer
the distance that a shopper travels in the store, the more unplanned considerations she makes. To
put this in perspective, if a shopper’s in-store distance increases by 150 feet (roughly a 10%
increase based on the average travel distance), the number of unplanned consideration is
predicted to increase by approximately 7%. This highlights the importance for retailers to
encourage people to shop the store as extensively as possible, perhaps by scattering popular
categories around the store (e.g., Granbois 1968). We return to this issue when we discuss
managerial implications.
Marketing Science Institute Working Paper Series 21
Next, controlling for the total in-store distance, we find that shoppers whose shopping
path is less efficient (as defined by Equation [1]) tend to engage in more unplanned
considerations. Consistent with H2, the posterior mean of 2 is -0.897, with a 95% posterior
interval of (-1.268, -0.541). This implies that the farther a consumer deviates from her shortest
path connecting all of the products she plans to purchase, the more likely she is to engage in
additional unplanned considerations. The large value of 2 suggests that retailers may profit by
targeting “less efficient” consumers, for example by using a location-based mobile app. We
elaborate on this in the discussion.
Finally, we turn to the role of the other demographics controls listed in Table 2. First,
consistent with previous literature on unplanned spending (Stilley et al. 2010ab), we find that the
effect of in-store slack on unplanned consideration is positive and significant
( )05.;007.010 p , suggesting that shoppers with more money mentally set aside for
unplanned purchases make more unplanned considerations. Second, the use of a shopping list is
found to reduce the number of unplanned considerations by approximately 21%
( )05.;223.05 p . This result is consistent with the view that using a shopping list is a self-
control technique that helps shoppers focus on their planned purchases and therefore leads to
fewer unplanned considerations (Kollat and Willett 1967; Inman et al. 2009). Finally, older
shoppers are also found to engage in more unplanned considerations, though the effect of age is
small ( )05.;008.03 p .
Relationship between consideration characteristics and unplanned purchase conversion.
Table 3 shows the parameter estimates for the model of unplanned purchase conversion. Most of
our hypotheses about the relationship between consideration characteristics and unplanned
purchase conversion are supported. First, supporting H3, an unplanned purchase conversion is
Marketing Science Institute Working Paper Series 22
more likely if the shopper spends a longer time in consideration ( )05.;007.03 pa and
engages in more product touches ( )05.;284.03 pb . The magnitude of a3 and b3 suggests
that each additional 10 seconds that the shopper spends in consideration increases purchase
likelihood (on average7) by approximately 2%. Further, each additional product touch increases
purchase likelihood by an average of 6.3%. Both results highlight the need for retailers to
encourage a higher level of engagement at the point of purchase through shopper marketing
strategies, an important managerial implication that we will return to in the discussion.
The estimates for 4 (H4: number of shelf displays viewed) and 5 (H5: physical distance
from product shelf) suggest that, after controlling for the amount of engagement (consideration
duration and the number of touches), the likelihood of a purchase conversion depends on the
“type” of consideration behavior. Consistent with H4, we find that the fewer product shelf
displays viewed by the shopper during a consideration, the more likely the consideration will
turn into an actual unplanned purchase ( )05.;323.04 p . Controlling for consideration
duration and number of touches, viewing an additional shelf display reduces the likelihood of
purchase conversion by 8.0%. In addition, consistent with H5, we find that physical proximity
with the product shelf during a consideration is associated with a greater likelihood of unplanned
purchase ( )05.;341.05 p . The estimate of 5 shows that by standing 1 foot closer to the
product shelf display, the likelihood of unplanned purchase (on average) increases by 7.5%. This
confirms our hypothesis that a more focused (“deep”) consideration, as opposed to a “wide”
consideration, is associated with higher purchase likelihood.
Further, we find that shoppers who reference external information – in-store circular,
coupon, smartphone ( )05.;426.06 pa – or have more interactions with store staff
7 Marginal effects of variables are computed by fixing other variables at the overall mean.
Marketing Science Institute Working Paper Series 23
( )05.;180.06 pb are more likely to make an unplanned purchase. Marginally, referencing
external information is associated with a 9.1% increase in the probability of purchase conversion.
Rather surprisingly, referencing one’s shopping list during an unplanned consideration does not
appear to be related to the likelihood of a purchase conversion ( .).;301.07 sn . This suggests
that using a shopping list is a useful self-control tool to reduce the incidence of unplanned
consideration (as discussed earlier), but once the shopper is engaged in an unplanned
consideration, looking at one’s shopping list no longer helps to limit unplanned purchase.
Turning to t other control measures, we find that most of these measures have the
expected effects on unplanned purchase conversion. Category hedonicity is positively associated
with a higher likelihood of unplanned purchase ( )05.;174.01 p , and unplanned purchase
conversion is more likely for refrigerated categories ( )05.;269.02 p . Consistent with
common belief among practitioners, purchase conversion is more likely at the endcap
( )05.;084.17 p , aisles ( )05.;302.14 p , and bazaar ( )05.;880.05 p , as compared to
the racetrack (the omitted category), service and others ( );272.08 ns , and the checkout
( );338.06 ns . This provides important product placement implications for manufacturers and
retailers. Further, we find that purchase likelihood increases with the amount of remaining in-
store slack that a shopper has at the point of consideration ( 10 = 0.005; p < .05). Note that
although remaining in-store slack is correlated with the cumulative time that a shopper has
already spent in the store, this cannot be explained by “fatigue” alone because cumulative in-
store time is controlled for and is found to be insignificant ( .).;006.011 sn . We also do not
find any “shopping momentum” effect, as the time since last purchase is not a significant driver
of unplanned purchase conversion .).;052.0( 12 sn .
Marketing Science Institute Working Paper Series 24
Finally, we turn to the role of demographics controls. Most of the demographics controls
are not found to be significant, which is expected because of the inclusion of a shopper-level
random effect parameter i . Nonetheless, consistent with the previous literature (Ramanathan
and Williams 2007; Rook and Fisher 1995), we find that higher shopper impulsivity is positively
associated with purchase conversion ( )05.;172.09 p .
Discussion
In this paper, we present a conceptual framework of the drivers of unplanned
considerations and conversion to unplanned purchases and test it in a field test applying video
tracking to observe the entire process of grocery shopping from the shopper’s perspective. To the
best of our knowledge, our research is the first one applying a portable video camera to
understand grocery shopper behavior in a field setting. Tracking consumers using video cameras
combines the advantage of RFID tracking of shopping path (Hui et al. 2009ab) and eye-tracking
techniques that follow consumers’ visual foci (Pieters and Wedel 2004; Thales et al. 2011;
Zhang et al. 2009), allowing us to capture shoppers’ in-store shopping process in a more accurate
and less intrusive fashion in comparison with previous studies. The resulting video dataset allows
us to identify each incidence of unplanned consideration, and hence develop and test hypotheses
about how shopping path characteristics and consideration characteristics are related to
unplanned considerations and purchases.
Analysis of our dataset supports most of our predictions. First, shopping path
characteristics are found to be significantly associated with the number of unplanned
considerations. Specifically, shoppers who travel a greater distance in the store are more likely to
engage in more unplanned considerations, presumably because they are exposed to more in-store
Marketing Science Institute Working Paper Series 25
stimuli. Further, controlling for in-store travel distance, shoppers who are less “efficient” in their
shopping paths tend to engage in more unplanned considerations. As we discuss subsequently,
these findings have important implications for product placement and mobile targeting.
Second, once an unplanned consideration occurs, several consideration characteristics are
found to be associated with whether the consideration results in a purchase. Specifically, longer
consideration durations and more product touches tend to result in an unplanned purchase. After
controlling for these factors, we find that considerations that are more “focused” are more likely
to result in a purchase than considerations that are “wide”. In particular, we find that the fewer
shelf displays the shopper views during the consideration, the more likely that consideration will
result in a purchase. Similarly, shoppers who stand physically closer to a product display are
more likely to make an unplanned purchase. In addition, we find that shoppers who reference
external information (circular, coupon, smartphone) or have more interactions with store staff
during the consideration have a higher likelihood of purchase conversion.
Implications for shopper marketing
The empirical insights above lead to several key shopper marketing implications. First,
given that encouraging shoppers to travel longer in the store increases the number of unplanned
considerations, retailers should position their product categories strategically, to force shoppers
to cover more of the store. Similar recommendations are given by Hui et al. (2011) and Inman et
al. (2009). For example, Inman et al. (2009) suggest that “products that are frequently purchased
(e.g., milk) should be placed in locations that will lead consumers to pass as many other
categories as possible”. However, an alternative, potentially more effective way to encourage
shoppers to travel the store more extensively is to send targeted promotions through mobile apps,
Marketing Science Institute Working Paper Series 26
a strategy recommended by Hui et al. (2011). For example, when the shopper is located in one
corner of the store, the mobile app can send her a promotion for a product category located
across the store to encourage the shopper to walk over there as well (and past many other
categories on the way).
Second, the use of mobile apps leads to another promotional strategy that can be
employed by retailers. Based on our finding that shoppers whose paths are less efficient are more
likely to engage in unplanned considerations, if retailers can systematically identify these
shoppers early in their trips, the retailer can send targeted promotions to these “inefficient”
shoppers to encourage unplanned considerations. For example, in conjunction with RFID
tracking, a mobile app that allows a shopper to enter her shopping list before her trip begins (see
Hui et al. 2011) can be used to systematically identify how efficient the shopper is so far during
her trip, and therefore help retailers target inefficient shoppers dynamically.
Third, over one third of unplanned considerations are not converted to purchases. The
incidence of unplanned considerations and the likelihood of conversion to unplanned purchases
are strongly related to controllable in-store factors. Specifically, our analysis shows that
“deep/focused” considerations are more likely to result in unplanned purchase than “wide”
considerations, suggesting that retailers should try to encourage “focused” considerations
whenever possible. Potential strategies may include, for instance, avoiding promoting multiple
brands in a product category, and focusing only on a single brand, in order not to divert
shoppers’ attention when they are considering an unplanned purchase. Another possibility is to
offer product samples or highlight certain store display features to encourage shoppers to
physically stand closer to the shelf, a factor that we find to be positively associated with purchase
conversion.
Marketing Science Institute Working Paper Series 27
Finally, retailers should encourage shoppers to reference external information during an
unplanned consideration. They can do so by distributing store circulars (and/or coupons) not only
at the entrance, but also at different in-store locations, so that shoppers are more likely to pick it
up. They can also generate a QR (Quick Response) code next to individual products that contain
product details to encourage shoppers to scan the code using their smart phones. Another
possibility is to provide additional product information (e.g., a coupon or specific
recommendation recipes for a product) through a store mobile app or QR codes that can be
scanned into a cell phone in advance to promote shoppers’ use of smart devices during an
unplanned consideration. In addition, store staff members should be available at locations where
unplanned considerations are most likely to happen as identified in our analysis (e.g., near the
bazaar) to assist shoppers with their purchases.
Limitations and directions for future research
While this is the first study that employs mobile video tracking to study in-store decision
making behavior, our research does have a few key limitations that can serve as fruitful
directions for future research. First, we focus only on grocery shopping. Future studies may
explore the generalizability of our findings by using video tracking to collect data about
shopping behavior in other settings such as department stores and shopping malls. Such studies
may also assess whether the video tracking device as used in our study is suitable for tracking
other settings, or whether certain adjustments are needed.
Second, from a modeling standpoint, our model does not fully capture the process of how
unplanned consideration occurs, but only captures the total number of unplanned considerations.
Future research may consider building an integrated model, such as using a latent variable
Marketing Science Institute Working Paper Series 28
framework such as Hui et al. (2009b), that jointly models the shopping path, each incidence of
unplanned consideration, and purchase conversion. An integrated model as such is extremely
difficult to specify and estimate, but would provide an even more comprehensive description of
in-store shopping behavior.
Finally, we rely on observational data in the current research to shed light on shoppers’
dynamic decision making. In order to generate a more comprehensive picture regarding how
people make in-store shopping decisions, it would be helpful in future studies to post-interview
some of the shoppers by having them go through their videos and asking them to explain their
own behavior and decisions. For instance, we demonstrate that a lower purchase conversion rate
is associated with more products in the field of vision. However, we did not examine the
fundamental mechanism of this phenomenon. Prior research on choice overload shows that
individuals might also experience at the same time increased negative emotions such as regret
and disappointment (Schwartz 2004) or decreased satisfaction with the chosen option (Chernev
2003; Iyengar and Lepper 2000). Future research examining post-purchase feelings is needed to
shed light on this issue.
Marketing Science Institute Working Paper Series 29
References
Alba, Joseph W., J. Wesley Hutchinson, and John G. Lynch (1991), “Memory and Decision
Making,” in Handbook of Consumer Behavior, ed. Thomas S. Robertson and Harold H.
Kassarjian, Englewood Cliffs, NJ: Prentice-Hall, 1–49.
Barsalou, Lawrence W. (1983), “Ad Hoc Categories,” Memory and Cognition, 11 (May), 211-
227.
Beatty, Sharon E., and M. Elizabeth Ferrell (1998), “Impulse Buying: Modeling it Precursors,”
Journal of Retailing, 74(2), 169-191.
Beatty, Sharon E. and Scott M. Smith (1987), “External Search Efforts: An Investigation Across
Several Product Categories,” Journal of Consumer Research, 14(June), 83-95.
Bell, David R., Daniel Corsten, and George Knox (2011), “From Point of Purchase to Path to
Purchase: How Preshopping Factors Drive Unplanned Buying,” Journal of Marketing, 75
(January), 31-45.
Bloch, Peter H. and Marsha L. Richins (1983), “A Theoretical Model for the Study of Product
Importance Perceptions,” Journal of Marketing, 47 (3), 69-81.
Bloch, Peter H., Daniel L. Sherrell and Nancy M. Ridgway (1986), “Consumer Search: An
Extended Framework,” Journal of Consumer Research, 13 (June), 119-26.
Block, Lauren G., and Vicki G. Morwitz (1999), “Shopping List as an External Memory Aid for
Grocery Shopping: Influences on List Writing and List Fulfillment,” Journal of
Consumer Psychology, 8(4), 343-375.
Bucklin, Randolph E., and James M. Lattin (1991), “A Two-State Model of Purchase Incidence
and Brand Choice,” Marketing Science, 10 (Winter), 24-39.
Celsi, Richard L., and Jerry C. Olsen (1988), “The Role of Involvement in Attention and
Comprehension Processes,” Journal of Consumer Research, 15 (September), 210-224.
Chernev, Alexnder (2003), “When More Is Less and Less Is More: The Role of Ideal Point
Availability and Assortment in Consumer Choice,” Journal of Consumer Research, 30
(2), 170–83.
Cronin, J. Joseph Jr., and Steven A. Taylor (1992), “Measuring Service Quality: A
Reexamination and Extension,” Journal of Consumer Research, 56 (July), 55-68.
Dhar, Ravi, Joel Huber, and Uzma Khan (2007), “The Shopping Momentum Effect,” Journal of
Marketing Research, 44 (August), 370-378.
Marketing Science Institute Working Paper Series 30
Gelman, Andrew, John Carlin, Hal Stern, and Donald Rubin (2003), Bayesian Data Analysis, 2nd
Edition, Chapman & Hall.
Gollwitzer, P. M. (1993), “Goal Achievement: The Role of Intentions,” European Review of
Social Psychology, 4 (January), 141-185.
Gollwitzer, Peter M., and Veronika Brandstatter (1997), “Implementation Intentions and
Effective Goal Pursuit,” Journal of Personality and Social Psychology, 73(1), 186-199.
Granbois, Donald H. (1968), “Improving the Study of Customer In-Store Behavior,” Journal of
Marketing, 32(4), 28-33.
Grocery Marketing Association (2007), “Shopper Marketing: Capturing a Shopper’s Mind,
Heart, and Wallet,” report prepared by Deloitte LLP.
Hauser, John R., and Birger Wernerfelt (1989), “The Competitive Implications of Relevant-Set
Response Analysis,” Journal of Marketing Research, 26 (November), 391-405.
Heilman, Carrie M., Kent Nakamoto, and Ambar G. Rao (2002), “Pleasant Surprises: Consumer
Response to Unexpected In-Store Coupons,” Journal of Marketing Research, 39 (May),
242-52.
Hirschman, Elizabeth C., and Morris B. Holbrook (1982), “Hedonic Consumption: Emerging
Concepts, Methods, and Propositions,” Journal of Marketing, 42 (Summer), 92-101.
Howard, John A. and Jagdish Sheth (1969), The Theory of Buyer Behavior. New York: Wiley.
Hui, Sam, J. Jeffrey Inman, Yanliu Huang, and Jacob A. Suher (2011), “Monetizing the Effect of
In-Store Travel Distance on Unplanned Purchases: The Relative Effectiveness of Mobile
Shopping Apps versus Store Layout Strategies,” Working Paper.
Hui, Sam, Peter Fader, and Eric Bradlow (2009a), “The Traveling Salesman Goes Shopping: The
Systematic Deviations of Grocery Paths from TSP-Optimality,” Marketing Science,
28(3), 566-572.
Hui, Sam, Eric Bradlow, and Peter Fader (2009b), “Testing Behavioral Hypotheses Using An
Integrated Model of Grocery Store Shopping Path and Purchase Behavior,” Journal of
Consumer Research, 36(3), 478-493.
Hutchinson, J. Wesley, and Robert Meyer (1994), “Dynamic Decision Making: Optimal Policies
and Actual Behavior in Sequential Choice Problems,” Marketing Letters, 5(4), 369-382.
Inman, J. Jeffrey, Russell S. Winer, and Rosellina Ferraro (2009), “The Interplay Between
Category Characteristics, Customer Characteristics, and Customer Activities on In-Store
Decision Making,” Journal of Marketing, 73 (September), 19-29.
Marketing Science Institute Working Paper Series 31
Iyer, Easwar S. (1989), “Unplanned Purchasing: Knowledge of Shopping Environment and Time
Pressure,” Journal of Retailing, 65(1), 40-57.
Iyengar, Sheena S., Gur Huberman, and Wei Jiang (2004), “How Much Choice Is Too Much?
Contributions to 401(k) Retirement Plans,” in Pension Design and Structure: New
Lessons from Behavioral Finance, ed. Olivia S. Mitchell and Steve Utkus, Oxford:
Oxford University Press, 83–95.
Iyengar, Sheena and Mark Lepper (2000), “When Choice Is Demotivating: Can One Desire Too
Much of a Good Thing?”Journal of Personality and Social Psychology, 79 (6), 995–
1006.
Kardes, Frank R., Gurumurthy Kalyanaram, Murali Chandrashekaran, and Ronald J. Dornoff
(1993), “Brand Retrieval, Consideration Set Composition, Consumer Choice, and the
Pioneering Advantage,” Journal of Consumer Research, 20 (June), 62-75.
Kollat, David T., and Ronald P. Willett (1967), “Customer Impulse Purchase Behavior,” Journal
of Marketing Research, 4 (February), 21-31.
Larson, J. S., E. T. Bradlow, and P. S. Fader (2005), “An Exploratory Look at Supermarket
Shopping Paths,” International Journal of Research in Marketing, 22(4), 395-414.
Laurent, Gilles and Jean-Noël Kapferer (1985), “Measuring Consumer Involvement Profiles,”
Journal of Marketing Research, 22 (Feb), 41-53.
McCullagh, Peter, and John Nelder (1989), Generalized Linear Models, 2nd
Edition, Chapman &
Hall.
Nelson, Emily and Sarah Ellison (2005), “Shelf Promotion: In a Shift, Marketers Beef Up Ad
Spending Inside Stores,” Wall Street Journal, September 21, A1.
Oliver, Richard L. (1980), "A Cognitive Model of the Antecedents and Consequences of
Satisfaction Decisions," Journal of Marketing Research, 17 (November), 460-9.
Olshavsky, Richard W. (1973), “Customer-Salesman Interaction in Appliance Retailing,”
Journal of Marketing Research, 10 (May), 208-12.
Park, C. Whan, Easwer S. Iyer, and Daniel C. Smith (1989), “The Effects of Situational Factors
on In-Store Grocery Shopping Behavior: The Role of Store Environment and Time
Available for Shopping,” Journal of Consumer Research, 15 (March), 422-433.
Peck, Joann and Terry L. Childers (2003a), “To Have and To Hold: The Influence of Haptic
Information on Product Judgments,” Journal of Marketing, 67 (April), 35–48.
——— (2003b), “Individual Differences in Haptic Information Processing: The ‘Need for
Touch’ Scale,” Journal of Consumer Research, 30 (December), 430–42.
Marketing Science Institute Working Paper Series 32
——— (2006), “If I Touch It I Have to Have It: Individual and Environmental Influences on
Impulse Purchasing,” Journal of Business Research, 59, 765–69.
Peck, Joann, and Suzanne B. Shu (2009), “The Effect of Mere Touch on Perceived Ownership,”
Journal of Consumer Research, 36 (October), 434–47.
Pieters, Rik, and Michel Wedel (2004), “Attention Capture and Transfer in Advertising: Brand,
Pictorial, and Text-Size Effects,” Journal of Marketing, 68(2), 36-50.
Point of Purchase Advertising Institute (POPAI) (1995), The 1995 POPAI Consumer Buying
Habits Study, Englewood, NJ: Point-of-Purchase Advertising Institute.
Priester, Joseph R., Dhananjay Nayakankuppam, Monique A. Fleming, and John Godek (2004),
“The A2SC
2 Model: The Influence of Attitudes and Attitude Strength on Consideration
and Choice,” Journal of Consumer Research, 30 (March), 574-87.
Punj, Girish N. and Richard Staelin (1983), “A Model of Consumer Information Search Behavior
for New Automobiles,” Journal of Consumer Research, 9 (March), 366-80.
Ramanathan, Suresh, and Patti Williams (2007), “Immediate and Delayed Emotional
Consequences of Indulgence: The Moderating Influence of Personality Type on Mixed
Emotions,” Journal of Consumer Research, 34 (August), 212-223.
Richins, Marsha L. and Peter H. Bloch (1986), “After the New Wears off: The Temporal Context
of Product Involvement,” Journal of Consumer Research, 13 (September), 280-85.
Roberts, John H. and James M. Lattin (1991), “Development and Testing of a Model of
Consideration Set Composition,” Journal of Marketing Research, 28 (November), 29–
440.
Rook, Dennis W. (1987), “The Buying Impulse,” Journal of Consumer Research, 14
(September), 189-196.
Rook, Dennis W., and Robert J. Fisher (1995), “Normative Influences on Impulsive Buying
Behavior,” Journal of Consumer Research, 22(3), 305-313.
Rossi, Peter E., Greg M. Allenby, and Robert McCulloch (2005), Bayesian Statistics and
Marketing, Wiley.
Scheibehenne, Benjamin, Rainer Greifeneder, and Peter M. Todd (2010), “Can There Ever Be
Too Many Options? A Meta‐Analytic Review of Choice Overload” Journal of Consumer
Research, 37 (October), 409-25.
Schwartz, Barry (2004), The Paradox of Choice: Why More is Less, HarperCollins, New York.
Marketing Science Institute Working Paper Series 33
Stern, Hawkins (1962), “The Significance of Impulse Buying Today,” Journal of Marketing,
26(2), 59-62.
Stilley, Karen M., J. Jeffrey Inman, and Kirk L. Wakefield (2010a), “Planning to Make
Unplanned Purchases? The Role of In-Store Slack in Budget Deviation,” Journal of
Consumer Research, 37 (August), 264-278.
Stilley, Karen M, J. Jeffrey Inman, and Kirk L. Wakefield (2010b), “Spending on the Fly:
Mental Budgets, Promotions, and Spending Behavior,” Journal of Marketing, 74 (May),
34-47.
Thales, Teixeira, Michel Wedel, and Rik Pieters (2011), “Moment-to-Moment Optimal Branding
in TV Commercials: Preventing Avoidance by Pulsing,” Marketing Science, 30(5), 550-
561.
Venkatraman, Meera P. and Deborah J. MacInnis (1985), "The Epistemic And Sensory
Exploratory Behavior Of Hedonic And Cognitive Consumers", in Advances in Consumer
Research, Volume 12, eds. Elizabeth C. Hirschman and Morris B. Holbrook, Provo, UT :
Association for Consumer Research, Pages: 102-107.
Wakefield, Kirk L., and J. Jeffrey Inman (2003), “Situational Price Sensitivity: The Role of
Consumption Occasion, Social Context, and Income,” Journal of Retailing, 79(4), 199-
212.
Woodside, Arch G. and J. William Davenport, JR. (1974), “The Effect of Salesman Similarity
and Expertise on Customer Purchasing Behavior,” Journal of Marketing Research, 74
(May), 198-202.
Zhang, Jie, Michel Wedel, and Rik Pieters (2009), “Sales Effects of Visual Attention to Feature
Ads: A Bayesian Mediation Analysis,” Journal of Marketing Research, 46, 669-681.
Marketing Science Institute Working Paper Series 34
Figure 1
A CONCEPTUAL FRAMEWORK OF THE CURRENT STUDY
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Figure 2
A PHOTO OF AN ACTUAL PORTABLE VIDEO TRACKING DEVICE USED IN OUR
FIELD STUDY
Note: The “video camera” is worn like a Bluetooth headset by the participant. The “view/record
unit” stores the resulting video.
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Figure 3
AN EXAMPLE SHOPPING PATH
Note: The “cross” mark corresponds to the snapshot in Figure 4.
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Figure 4
A SNAPSHOT FROM THE SHOPPER VIDEOS CORRESPONDING TO SHOPPER AT THE
“CROSS” MARK IN FIGURE 3
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Figure 5
THE STORE LAYOUT DIVIDED INTO SIX DIFFERENT REGIONS
Note: six areas include Aisles, Bazaar, Racetrack, Endcap, Checkout, Service and Others. The
areas that are not in one of the five boxes are “Service & Others” area.
Marketing Science Institute Working Paper Series 39
Table 1
SUMMARY STATISTICS OF THE DATASET
Mean S.D. Min Max
Demographics controls
Gender (1: male) 0.363 0.482 0.000 1.000
Age 53.000 13.000 21.000 65.000
Income (1: <$75k; 0: >$75k) 0.494 0.501 0.000 1.000
Single (1: single) 0.160 0.368 0.000 1.000
Shopping list (1: yes) 0.371 0.484 0.000 1.000
Shopping alone (1: yes) 0.827 0.379 0.000 1.000
Shopping frequency (1-7 scale) 1.544 1.284 1.000 7.000
Familiarity with store (1-5 scale) 4.257 1.084 1.000 5.000
Impulsivity (0-5 scale) 2.253 0.664 1.000 4.220
In-store slack ($) 15.274 20.967 -17.690 126.790
Shopping trip characteristics
(H1) In-store travel distance 1448.162 739.192 74.000 4209.600
(H2) Path efficiency 0.511 0.433 0.053 10.139
Consideration characteristics
(H3a) Consideration duration 34.098 42.372 0.000 406.000
(H3b) Number of touches 1.730 2.531 0.000 30.000
(H4) Number of shelf displays viewed 1.654 1.130 1.000 12.000
(H5) Distance from shelf (ft) 3.040 0.863 1.000 6.000
(H6a) Reference to external information 0.057 0.242 0.000 1.000
(H6b) Interaction with staff (log- seconds) 0.185 0.845 0.000 5.512
(H7) Reference to shopping list 0.153 0.360 0.000 1.000
Category characteristic and other controls
Category- Hedonicity 3.799 1.056 1.430 6.100
Category- Refrigerated 0.318 0.466 0.000 1.000
Category- Frozen 0.058 0.234 0.000 1.000
Location: Aisle 0.393 0.489 0.000 1.000
Location: Bazaar 0.400 0.490 0.000 1.000
Location: Checkout 0.008 0.091 0.000 1.000
Location: Endcap 0.069 0.253 0.000 1.000
Location: Service 0.064 0.245 0.000 1.000
Promotional display 0.182 0.386 0.000 1.000
Remaining in-store slack ($) 11.026 23.151 -66.590 126.790
Cumulative in-store time (minutes) 11.462 9.265 0.000 53.300
Time since last purchase (minutes) 1.573 1.239 0.000 10.400
Dependent measures
Number of unplanned considerations 5.595 4.811 0.000 25.000
Unplanned purchase conversion (1: yes) 0.632 0.483 0.000 1.000
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Table 2
POSTERIOR ESTIMATES OF MODEL PARAMETERS FOR THE MODEL OF
UNPLANNED CONSIDERATION
Posterior mean Posterior s.d. 95% posterior interval
Demographics controls
(Intercept) 0.570 0.398 (-0.190, 1.329)
1 (Gender) -0.127 0.087 (-0.308, 0.030)
2 (Age) 0.008* 0.004 (0.000, 0.015)
3 (Income) 0.002 0.082 (-0.163, 0.156)
4 (Single) 0.197 0.116 (-0.028, 0.427)
5 (Shopping list) -0.223* 0.089 (-0.391, -0.042)
6 (Shopping alone) 0.001 0.119 (-0.225, 0.226)
7 (Shopping freq.) 0.072 0.040 (-0.007, 0.149)
8 (Familiarity) -0.026 0.052 (-0.132, 0.073)
9 (Impulsivity) 0.104 0.065 (-0.029, 0.223)
10 (In-store slack) 0.007* 0.002 (0.004, 0.011)
Shopping path characteristics
1 (H1: In-store distance) x 10-3
0.463* 0.069 (0.331, 0.602)
2 (H2: Efficiency) -0.897* 0.200 (-1.268, -0.541)
*indicates that the 95% posterior interval does not cover 0 (similar to p<.05).
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Table 3
POSTERIOR ESTIMATES OF MODEL PARAMETERS FOR THE MODEL OF
UNPLANNED PURCHASE CONVERSION
Posterior
mean
Posterior s.d. 95% posterior interval
Demographics controls
(Intercept) -1.252* 0.546 (-2.290, -0.146)
1 (Gender) 0.145 0.120 (-0.080, 0.383)
2 (Age) -0.001 0.004 (-0.010, 0.008)
3 (Income) -0.091 0.104 (-0.280, 0.111)
4 (Single) -0.247 0.145 (-0.555, 0.015)
5 (Shopping list) 0.164 0.122 (-0.068, 0.405)
6 (Shopping alone) 0.051 0.129 (-0.203, 0.293)
7 (Shopping freq.) 0.065 0.050 (-0.035, 0.163)
8 (Familiarity) 0.098 0.064 (-0.022, 0.226)
9 (Impulsivity) 0.172* 0.083 (0.017, 0.340)
Category characteristics and other controls
1 (Category hedonicity) 0.174* 0.043 (0.087, 0.260)
2 (Category refrigerated) 0.269* 0.134 (0.011, 0.527)
3 (Category frozen) 0.183 0.200 (-0.229, 0.594)
4 (Location: Aisle) 1.302* 0.240 (0.833, 1.777)
5 (Location: Bazaar) 0.880* 0.216 (0.452, 1.305)
6 (Location: Checkout) 0.338 0.440 (-0.510, 1.199)
7 (Location: Endcap) 1.084* 0.222 (0.652, 1.519)
8 (Location: Service) 0.272 0.298 (-0.301, 0.874)
9 (Promotional display) -0.107 0.174 (-0.434, 0.255)
10 (Remaining in-store slack) 0.005* 0.002 (0.000, 0.009)
11 (Cumulative in-store time) -0.006 0.006 (-0.016, 0.006)
12 (Time since last purchase) -0.055 0.037 (-0.124, 0.018)
Consideration characteristics
a3 (H3a: consideration duration) 0.007* 0.002 (0.003, 0.010)
b3 (H3b: number of touches) 0.284* 0.031 (0.226, 0.346)
4 (H4: number of shelf displays
viewed)
-0.323* 0.049 (-0.423, -0.231)
5 (H5: distance from shelf) -0.341* 0.055 (-0.451, -0.239)
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a6 (H6a: external information) 0.426* 0.193 (0.055, 0.783)
b6 (H6b: interaction with staff) 0.180* 0.073 (0.043, 0.325)
7 (H7: reference to shopping list) 0.301 0.163 (-0.037, 0.628)
*indicates that the 95% posterior interval does not cover 0 (similar to p<.05).
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