capturing the “first moment of truth”: understanding point

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.

Marketing Science Institute Working Paper Series 1

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.


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


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


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


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


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


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


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


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.


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.


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


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).


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.


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.


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.


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.


(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.


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


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.


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.


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


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.


( )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 .


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


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,


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.


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


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.


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Figure 1

A CONCEPTUAL FRAMEWORK OF THE CURRENT STUDY


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.


Figure 3

AN EXAMPLE SHOPPING PATH

Note: The “cross” mark corresponds to the snapshot in Figure 4.


Figure 4

A SNAPSHOT FROM THE SHOPPER VIDEOS CORRESPONDING TO SHOPPER AT THE

“CROSS” MARK IN FIGURE 3


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.


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


Table 2

POSTERIOR ESTIMATES OF MODEL PARAMETERS FOR THE MODEL OF

UNPLANNED CONSIDERATION

Posterior mean Posterior s.d. 95% posterior interval


(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).


Table 3

POSTERIOR ESTIMATES OF MODEL PARAMETERS FOR THE MODEL OF

UNPLANNED PURCHASE CONVERSION

Posterior

mean

Posterior s.d. 95% posterior interval


(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)


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).


capturing the “first moment of truth”: understanding point

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