1092 Application

# ARShopping: In-Store Shopping Decision Support
Through Augmented Reality and Immersive Visualization

Bingjie (Jenny) Xu
Northwestern University  e-mail<EMAIL_ADDRESS>Shunan Guo
Adobe Research  e-mail: {sguo, eunyee, jhoffs, rrossi<EMAIL_ADDRESS>Eunyee
Koh22footnotemark: 2
Adobe Research  Jane Hoffswell22footnotemark: 2
Adobe Research  Ryan Rossi22footnotemark: 2
Adobe Research  Fan Du22footnotemark: 2
Adobe Research

###### Abstract

Online shopping gives customers boundless options to choose from, backed by
extensive product details and customer reviews, all from the comfort of home;
yet, no amount of detailed, online information can outweigh the instant
gratification and hands-on understanding of a product that is provided by
physical stores. However, making purchasing decisions in physical stores can
be challenging due to a large number of similar alternatives and limited
accessibility of the relevant product information (e.g., features, ratings,
and reviews). In this work, we present ARShopping: a web-based prototype to
visually communicate detailed product information from an online setting on
portable smart devices (e.g., phones, tablets, glasses), within the physical
space at the point of purchase. This prototype uses augmented reality (AR) to
identify products and display detailed information to help consumers make
purchasing decisions that fulfill their needs while decreasing the decision-
making time. In particular, we use a data fusion algorithm to improve the
precision of the product detection; we then integrate AR visualizations into
the scene to facilitate comparisons across multiple products and features. We
designed our prototype based on interviews with 14 participants to better
understand the utility and ease of use of the prototype.

Human-centered computingVisualizationVisualization systems and tools; Human-
centered computingHuman computer interaction (HCI)Interaction paradigmsMixed /
augmented reality

##  1 Introduction

Purchasing decisions are often influenced by the product information provided
at the point of sale [29]. As online shopping becomes more popular, consumers
increasingly rely on the comprehensive and precise product information
available online (e.g., detailed product features, historical reviews, price
trends, etc.) to make purchasing decisions [18]. For example, consumers on a
diet usually need to review and compare the nutrition information of their
groceries to select foods that best fit their nutrition goals. When online
shopping, the shopping context and product information can be easily retrieved
from the product database and web logs; many prior systems have therefore used
this information to help online shoppers make better purchase decisions and
provide product recommendations [20, 2, 32]. The situation of in-store
purchases, however, introduces new challenges for retrieving and displaying
product information on site.

Recent advancements in augmented reality (AR) provide new opportunities to
bridge online product information with physical in-store products, and better
communicate information to support comparisons and help shoppers make faster
purchasing decisions. Existing work has explored the use of augmented reality
in product information retrieval [9, 27, 11], shopping navigation [25, 12],
and real-time product recommendation [1], which are all demonstrated to be
helpful in improving consumers’ in-store shopping experience. However, most
existing AR applications focus on detecting one product at a time, while
consumers in physical stores are often exposed to many similar product
alternatives [19] and expected to make a reasonable decision between them. In
addition, customers may need to consider multiple factors to make purchasing
decisions (e.g., nutrition, price, ratings, etc.). Thus, there is a need for
future AR systems to better support decision-making by facilitating
comparisons of multiple products across multiple relevant features.

In this work, we contribute a web-based prototype, ARShopping, for augmenting
the in-store shopping experience with online product information (e.g.,
product features, ratings, etc.) through portable smart devices (e.g., phones,
tablets, glasses). Inspired by the successful practice of applying data
visualization to facilitating multivariate data comparison [10, 6], we present
the product information in the form of visual glyphs that can represent
multiple features and enable intuitive comparisons across different products.
In particular, we developed a product detection algorithm that fuses results
from object detection and product marker detection to support better
positioning of the glyphs in the space. We confirmed the needs for in-store
decision support and product comparison via interviews with 14 participants,
and implemented the prototype based on their feedback. To evaluate the
usefulness and usability of the prototype, we conducted a second interview
study with the participants to collect feedback and investigate their
experience with using the system prototype in a virtual grocery shopping
environment.

##  2 Related Work

AR + In-Store Shopping. Mobile augmented reality contributes to smart retail
settings by improving the value for both customers and retailers alike [7].
Ganapathy et al. [9] developed a system to retrieve product information by
matching photos taken by the customer to the product image database.
Similarly, Spreer et al. [27] utilized mobile AR applications to display
product information at the point of sale. Rashid et al. [24] combined AR and
RFID to allow users to browse the shelf and search for a given product in an
AR interface. While our prototype provides a similar data intervention
experience, we allow customers to scan multiple products at the same time and
visualize the results to facilitate product comparisons.

Sayed et al. [25] provided situated and abstract information representation
with real-time analytical interaction for shopping. Different from our focus
on product comparison, this system focuses more on product searching and
navigation. Another category of AR shopping systems focuses on recommending
products to customers while shopping. For example, Junho et al. [1] developed
a mobile app to recommend and warn shoppers of grocery items in the current
isle based on their personal and family health profiles and the nutrition
information of the grocery items. The Easy Shopping app developed by Jayananda
et al. [12] demonstrated a similar usage scenario with integrated product
navigation. This system directs users to the aisles of the products, and
recommends similar items while displaying the relevant health alerts. These
systems focus on only a single product at a time, whereas our system requires
detection of multiple products at the same time that can then have the
information extracted, visualized, and compared by the user to facilitate
decision-making.

Decision Support for In-Store Shopping. Providing information to customers
while shopping can influence their purchasing behavior [17]. The advancement
of Mobile Commerce [21] and Ubiquitous Commerce [26] has enabled mobile
decision support for in-store shopping. Early implementations introduced
location-based agents (e.g., Shopper’s Eye [8], Impulse [31]) and sensor
network-based agents (e.g., MyGrocer [16]) to retrieve product information at
the point of purchase. Shoppers may also retrieve data by scanning product
barcodes or QR-codes with their phone camera [15, 30].

On top of these technologies, decision support systems (DSS) have been
developed to help consumers interact with physical products to retrieve and
compare product information [13, 5]. They utilize consumers’ requests for
specific product attributes to support decision-making. Similarly, product
recommendation agents (RA) aim to support consumers in choosing the right
product among several alternatives and distractions during in-store shopping
[3, 22]. Most of the DSSs or RAs are developed with a mobile user interface,
separate from the shopping environment. Recent advancements in eye-tracking
[23] and AR [11] for shopping have enabled new opportunities to provide more
context-aware decision support, which could have a large impact on consumers’
decision strategy [4]. While mobile shopping agents were proved to be
generally useful in prior work [22], there is a research trend highlighting
the importance of understanding how mobile services may impact consumers’
shopping experience and decision-making [14]. In this paper, we incorporate
comparative data visualization into AR for shopping decision support and build
an understanding of consumers’ potential acceptance towards an AR-based
shopping decision support system.

##  3 Formative Interviews

To understand the general needs of in-store customers and how augmented
reality can help improve the in-store shopping experience and decision-making,
we conducted preliminary interviews with 14 participants (P1–14, 6 males, 8
females, aged 23 to 52). Participants were recruited via advertisement on
social media platform with backgrounds ranging from graduate student, teacher,
software engineer, research scientist and speech-language pathologist, under
the condition that they had recent experience shopping both online and in-
store. All interviews were conducted remotely and recorded; each interview
took about 30 minutes. The interviews were semi-structured covering questions
including their shopping frequency and habits, purchasing categories, and key
product information for making purchasing decisions. Based on the feedback
from participants, we identify the following key insights and further infer
the needs for in-store shopping assistance.

Shopping Habits and Categories. We found the product categories that people
chose to purchase in-store are relatively fixed and they have a regular
frequency of visiting physical stores. For example, nearly all interviewees
(13/14) described a regular in-store cadence for grocery shopping (1–4 times
per week). In contrast, people have more diverse shopping categories and
flexible frequency online.

When deciding between in-store or online shopping, the most commonly mentioned
factors are the product categories and urgency of the need. In particular,
participants usually choose to buy fresh goods (13/14) and household
essentials (7/14 participants) in-store. Three interviewees also mentioned
that they would visit physical stores for products they found online but would
like to try out in person, such as clothes, footwear, glasses, and
instruments. Participants would also choose to buy products in urgent need,
such as medicine or other necessities through physical stores. One participant
noted that differences in price can contribute to the decision of whether to
shop in-store or online: _“Sometimes the brick-and-mortar store offers better
price to compete with online store”_ (P12). These results suggest that even
with the rapid evolution of e-commerce, people still have regular demand for
visiting brick-and-mortar stores, especially for products that are fresh and
in urgent need.

Pain Points of In-Store Shopping. When asked about the difficulties
participants experienced during in-store shopping, the most common ones
include travel time and cost, limited product variety, and the difficulty of
making purchasing decisions. In particular, three participants mentioned that
they may need to _“visit multiple stores due to the lack of supply”_ (P8, 13,
14) and two mentioned that _“it takes multiple days decide among products in
different stores”_ (P10, 11). P1 described difficulty deciding between
different products, often resulting in a less than satisfactory outcome: _“I
sometimes need to pickup stuffs on my way back home that my family need
immediately with a short notice. It’s difficult to choose the best one without
researching beforehand so I often have to pick up a random one.”_ P6 described
a similar experience: _“It’s hard to determine which brand to buy when
purchasing household goods that I do not often use.”_ P13 also recalled his
experience of searching for product information online while purchasing it in-
store: _“I had to search the product online with my mobile phone to compare
the reviews of different brand.”_ While the cost of travel are the natural
defects of in-store shopping, the hardship of making decisions when shopping
in-store raises a need for providing decision assistance support in brick-and-
mortar stores.

Shopping Decision-Making. We asked participants to recall a recent shopping
experience and describe how they decided on the product(s) they purchased. In
these conversations, we noticed several key pieces of information that they
often referred to when making decisions, including the composition,
efficiency, historical reviews, prices, and discounts across platforms. For
example, P1 recalled his recent experience purchasing medications and noted
that he referred to the chemical composition and historical reviews for
different brands. Similarly, P5 recalled her habit of reading nutrition labels
when purchasing snacks. While this information is easily accessible online,
oftentimes it is not available or is more difficult to browse when customers
are purchasing in-store. In fact, seven participants brought up searching
online for this information before visiting a physical store, and five
participants mentioned a pressing need to be able to conduct this research
while being in-store in order to decide which one to buy. For example, P9 said
that _“There were several times when I could not decide which one to buy in-
store and also felt inconvenient to research on my phone, so I took a picture
of all the available brand and returned home to look them up.”_ These results
reinforce the need for providing decision assistance in-store that can better
enable customers to access and compare relevant online information for the
products immediately available.

##  4 ARShopping Prototype

Our system, ARShopping, includes two modules (as shown in Fig. 1): a product
detection module and a user interaction module with the mobile interface. The
product detection module (Sec. 4.1) retrieves the product ID and position by
leveraging both the marker that is attached to the product price tag and the
image of the products in the scene captured from the mobile camera. The
product detection module also identifies the 3D position, rotation, and actual
size of each product for the placement of our comparative visualization. The
user interaction module (Sec. 4.2) includes the main functionality for the
user interface, including the comparative visualizations.

Figure 1: The architecture of the ARShopping prototype.

###  4.1 Product Detection

As our prototype needs to include multiple products in the scene and detect
them at the same time, there may be mutual interference between different
products when they are jammed on the shelf. To enhance the stability and
applicability of the detection, we incorporate an additional marker with each
product and combine the results of marker detection and product image
detection.

Implementation. For marker detection, we utilized the built-in marker tracking
function from the AR.js library. We print a marker with a unique ID encoded
for each product and assume the marker will be placed close to the
corresponding physical product (e.g., on the price tag) and also on the same
surface of the product shelf. In this way, we can infer the product ID based
on the encoded information and the 3D position, rotation, and actual size of
the physical product based on the detected size, transformation, and rotation
of the marker. The product object detection is implemented based on an object
detection model trained by online images of each product and transfer learning
from the TensorFlow object detection model [28]. For each object detected, the
model outputs the matched product ID, product’s center position, and the size
of the bounding box. To reduce the overlap of the immersive data
visualizations for multiple products (Sec. 4.2), it is necessary to properly
layout the visualization as users interact with the system. Therefore, we
further determine the layout of the visualization by finding the closest
product near the marker and use the area of the product to overlay the
visualization. The objects’ locations are transferred from screen coordinates
to world coordinates to calculate their distance to the marker, assuming that
the product is at the same distance as the marker from the camera.

Online Database Query. With the detected product IDs, we can retrieve the
online information of products in the camera scene from the online database.
For this initial prototype, we crawl grocery information from Target.com to
establish our database, which includes the price, ratings, in-store
deals/coupons, and specifications (e.g., nutrition, size, weight, etc.) of
each product. After detecting products through the camera, the system sends
the corresponding product IDs in the scene to the online database to query the
product information. At the same time, the system will also search the
database to see if the products are on sale or has any in-store coupons that
can be applied, and push the coupon to users in real-time.

Data Fusion Algorithm. For each product, we utilize a data fusion algorithm to
determine its existence and position. Note that products’ position refers to a
broad definition of position, including the product’s center position, size,
transformation, and rotation. There are four different cases for each product:
(1) When both methods have detected this product, its center position and size
are from the object detection results and its transformation and rotation are
from the marker detection, assuming the product’s front surface is parallel to
the marker. (2) When the product has only been detected by the marker
detection, its position is calculated only from marker’s center position,
size, transformation, and rotation with a fixed relative position between the
marker and the product (e.g., the product is on the top of the marker) and a
fixed scale between the marker’s size and the product’s size. (3) When the
product was only detected by object detection, its position only contains the
center position and size from the object detection result assuming it has no
transformation and rotation relative to the camera. (4) When both methods have
not detected this product, it is considered to not exist in the scene.

###  4.2 User Interface

Figure 2: The ARShopping Interface. (a) Filtering. (b) Multivariate data
glyphs. (c) Adding products to favorites. (d) Comparison view.

Visualizing product information. ARShopping utilizes radar charts to represent
multivariate product data. After preliminary screening and filtering (Fig.
2a), users can analyze qualified products’ information by visualizing glyphs
overlaid on the physical products (Fig. 2b). The layout of the glyph is
calculated based on the resulting product position determined by our product
detection method. For marker detection only, the glyphs may experience some
overlap and visually clutter the space due to the placement of the marker
(Fig. 3a). By leveraging the object detection, we can create a more natural
integration of the physical product and the virtual marker. In particular, we
leverage the orientation of the marker as read from the camera to tell the
orientation of the customer. The canvas of the glyphs will be distorted to
better reflect the customer’s point of focus (Fig. 3b).

Regardless of which method is employed, our glyph placement algorithm creates
a direct mapping between products and the critical online information
necessary to make an informed purchasing decision; this way, customers can
find information appropriately situated in the space and easily select the
desired product once a purchasing decision is made. Glyphs can be triggered on
and off by clicking the glyph button on the menu. When glyphs are shown, users
can decide what information to show by clicking the database button and
selecting information of interest on the dropdown list next to the button.
Each axis of the radar chart has the same scale that considers all products of
the same type in the store, which assists users in comparing products’
information by observing the relative amount of products’ specific data
intuitively.

Comparison view. The main view presents the glyphs in juxtaposition, which
gives an overview of the features across all products. However, it may hinder
customers from finding the optimal one when mutliple products have similar
features. To further help customers make final purchase decision, ARShopping
also enables users to make more detailed comparisons via the comparison view
(Fig. 2d), in which the glyphs are placed in superposition for more thorough
feature comparison. Users can add products they want to compare to the
favorite list (Fig. 2c), and the comparison view will show the overlaid radar
charts for feature comparison with subjective features such as ratings and
customer reviews.

Figure 3: A comparison of product detection and glyph placement results with
(a) marker detection only and (b) marker+object detection.

##  5 Evaluation of ARShopping

To assess the usability and usefulness of our ARShopping prototype for
assisting with decision making during in-store shopping, we conducted a second
round interview through a case study in a virtual grocery shopping setting
with the 14 participants we recruited from the formative interviews (Sec. 3).

###  5.1 Study Procedure

In each interview, we first demonstrated the prototype’s usage and
functionalities through an introduction video with a usage scenario of
purchasing protein bars in the grocery store. The introduction video is
included in the Supplemental Material. After that, we sent the participant a
picture of milk bottles on shelves in the grocery store, which was
preprocessed with a marker attached next to the price tag of each product. We
asked the participant to display the picture in full screen on a laptop. The
prototype was deployed on a remote web server, and the participants could
access it through a web link with the browser on their phone. We asked the
participants to scan the picture with the prototype as if they were purchasing
milk in the grocery stores and freely explore the prototype’s functionalities.
Participants were encouraged to think out loud, give feedback, and raise
questions about the prototype. After the prototype try-out, we further
interviewed the participants to collect their subjective feedback in terms of
the usefulness, ease of use, and directions for enhancing the current
prototype. In the end, we asked participants to answer a post-study
questionnaire with eight questions (shown in Fig. 4) on a 7-point Likert scale
designed following the Technology Acceptance Model (TAM) to measure
participants’ perceived usefulness (Q1–Q4), ease of use (Q5–Q7), and intention
of use (Q8).

###  5.2 Acceptance

As shown in Fig. 4, participants generally agreed the prototype was useful and
easy to use, and also showed their willingness to use the prototype in their
daily life. Specifically, the average perceived usefulness, ease of use, and
intention of use of all responses were 5.78, 5.45, and 6.07, respectively. For
the usefulness and ease of use, we also tested the reliability of the
responses with Cronbach’s alpha scoring 0.682 and 0.881, respectively, which
indicates that the observed acceptance towards the prototype is reliable
($>0.6$).

###  5.3  Feedback and Implications

Perceived usefulness. As reported in Sec. 5.2, participants generally found
the prototype to be useful. In particular, seven participants commented that
this prototype can _“save their time of comparing”_ (P1, 5, 7, 8, 12, 13, 14).
Three participants felt the prototype can help them _“get to know the product
better”_ and it can be particularly useful when they are _“making purchasing
decisions on products they are not familiar with”_ while _“increasing the
confidence of purchasing”_ (P2, 10, 14). For example, one participant noted
that _“This prototype provides a data-driven perspective which is more
trustworthy than other people’s opinion”_ (P10). In terms of which feature
participants felt was most useful, twelve participants voted for the
comparative visualization and six voted for the overall filtering
functionality. As P9 said: _“I really like how filtering and comparative
visualization work together. As the filtering roughly set the range of my
purchasing options, the comparative visualization can provide decision support
in much finer detail.”_

While many participants were positive about the utility of the system for
decision-making, several participants raised concerns about the usefulness of
integrating data-driven comparisons. For example, P4 noted that _“Showing data
is probably not friendly enough for novice users. I would need more text
description of the product to make decisions.”_ Other participants remained
more neutral about the usefulness of the prototype, as they think it may
depend on the _“value of the product”_ (P3), _“precision of the scanning”_
(P7, 8, 9), and the _“familiarity of the product”_ (P2, 10).

Figure 4: Post-study survey results with Technology Acceptance Model (TAM)
instruments. Responses were made on a 7-point Likert scale (1=“strongly
disagree”, 7=“strongly agree”). Q1–Q4 explore the perceived usefulness (in
red), Q5–Q7 explore the perceived ease of use (in blue), and Q8 asks about
intention of use (in orange).

Ease of use. Participants did not experience much difficulty when trying out
the prototype, which could be due to the simplicity of the prototype in the
current stage. While most participants appreciated that the prototype was a
web-based application that could eliminate the need to download and install a
separate application, two participants (P8, 14) raised their concern about the
internet connection in physical stores and wondered if the prototype can be
used offline. One participant (P7) noted that the prototype might consume a
lot of phone battery as the camera needs to be constantly turned on. P8 also
brought up that the comparative visualization may be difficult to investigate
when the number of parameters is large.

Desired features. We asked participants about the functionalities they wished
to have besides the features of the prototype. Many of them brought up product
recommendation, either by a predefined user need or the purchases of other
relevant customers. Participants also expressed a desire to have price
comparisons both online and offline across purchase channels, and to show the
historical trends of the price. P4 pointed out that the application could use
more text descriptions to make the data-driven analysis and visualization more
friendly to novice users. P14 also suggested to incorporate more text
descriptions to explain the meaning of the product specifications.

##  6 Limitations and Future Work

While our prototype is implemented as a web application and can be used on any
smart devices with a camera, we only tested its usage on smartphones, leaving
the use on tablets and smart glasses for future investigation. In addition,
since this study was conducted during the pandemic, we decided to employ the
virtual setting, which may have covered up some potential usability issues
that could arise during real-world usage. For example, there could be some
interference in a real-world environment that would affect the stability of
the product identification, such as natural light and shadows, and the size of
the markers and products in the scene. In fact, our proposed product detection
solution was partially motivated by these issues (i.e., combining marker
detection and object detection results to improve detection precision).
However, the real-world experience and performance still needs to be evaluated
in future work.

##  7 Conclusion

ARShopping provides new possibilities for a better in-store shopping
experience augmented with data visualizations and online product details.
Compared with existing in-store shopping AR applications, ARShopping aims to
help consumers make better purchasing decisions across multiple similar
alternatives. To enhance the stability and precision of the product detection,
we propose to incorporate marker detection and object detection for product
information retrieval. We also integrate data visualization to help with
displaying and comparing comprehensive product specifications. Our interview
studies with consumers point out several future research directions, including
comparing price throughout time and across different purchase channels,
improving the design of visualization to make it more understandable to novice
users, and incorporating more narratives to make the comparison results more
intuitive.

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