Using a Virtual Store As a Research Tool to Investigate Consumer In-store Behavior
July 24th, 2017
This paper describes the use of a desktop virtual store to create virtual shopping environments to investigate in-store consumer behavior. A description of the protocol to build and run experiments, example results from an experiment concerning store layout, and important considerations when conducting virtual store experiments are presented.
The overall goal of this protocol is to provide information on when a virtual supermarket can be helpful in consumer research and how the virtual supermarket software can be used. The virtual store help answer key questions about how consumer respond to in-store marketing tactics, especially when these are difficult to share in real life, such as store layout. The main advantage of this technique is that the virtual store enable the study of in-store consumer behavior in a realistic and flexible, yet highly controlled environment.
The relevance of the virtual store in studies of consumer behavior extends beyond traditional retailing, because virtual reality is not limited to actual or current shopping environments. Generally, individuals new to these methods will struggle because the virtual store consists of many features and functions. Visual demonstration of this method is critical, because preparation and data collection steps can be difficult to learn.
Many details require expertise to ensure correct preparation and implementation. Begin by opening the virtual store builder interface by double-clicking on the VirtualShop editor icon on the desktop. Open a store template that is suitable for the study by clicking File and clicking Open.
Select the desired store template. To make modifications to the store based on the experimental conditions and research plan, use the right mouse button and move the mouse to zoom in and out. Use the left mouse button and move the mouse to change the viewpoint.
Replace existing products with products of interest by double-clicking on an existing product and subsequently pressing the Delete button on the keyboard to delete this product. Then, click on the blue arrow in the menu bar to open the product library. Click Product Category, and then select the product category of interest.
For example, fruit. Drag a selected product by holding the left mouse button, and place the product on the desired shelf. To set up for the main experiment, first select the name of store condition on the ShopConfig box, and type the name of store environment in the Environment box, such as Supermarket001.
Click on Reload shop to open the store for the main test. Then the Begin box will appear. Next, open the data management program on another computer.
Create a new observation by clicking on Observe in the top menu bar, and then clicking on Observation and New. Name the observation and click OK.Start recording by pressing the red circle button, and fill in user-defined variables such as an experimental condition, then click OK.Ensure that the program starts recording data by checking that the status data plugin and status event plugin windows show green check marks, that time is elapsing, and that the number of sample column in the status data plugin window is growing. Now that the setup is ready for the main task, seat a participant who has already completed the practice task in front of the middle LCD screen and at a short distance from the middle screen.
Adjust the chair until the participant's eye level matches the position of the screens. Next, instruct the participant to press Begin to start visiting the store. Inform the participant to use the joystick to move in the store or to change the level of eye view.
Tell them to use the mouse to select, buy, or return products, and to select their walking direction at an intersection. Then leave the participant alone to shop without interruption. Check the data management program again to ensure that the data is recording by checking the status data plugin and the status event plugin windows, showing an increasing number of samples and events.
Wait until the participant finishes shopping in the virtual store. Remind the participant to check the shopping cart by pressing F1, and to press Escape to complete the shopping task. Next, press the stop button of the data management program on the other computer to stop recording.
Escort the participant to another area and have him or her fill out a questionnaire measuring their shopping experiences, perceptions about the store, and willingness to revisit the store. Finally, return to the data management program and click on the Visualize button to check the recorded data. Ensure that the graph and data of bought products are shown.
Set up a data filter for the shopping-related behaviors by clicking Data Profile under the Analyses folder on the left menu column. Following that, select the Nest over Behaviors box under the Select Intervals heading, which will pop up the box of nested behaviors. Then, select all the behaviors of interest and click OK.Drag the nested behaviors box and drop it between the Start and Results boxes.
Then ensure that all boxes are connected with arrows and that the Results box shows the correct number of observations. Under the Analyses folder, click on Behavior Analyses, and then click New Behavior Analysis to open the table of behavior-related results. Click on Calculate on the top left of the menu bar to extract the results.
Ensure that the shopping behaviors for participant are shown in separate rows. Finally, click the Export button to export the data, and name the exported file. In this study, supermarket stores were built using four different layouts in which shelf length, short versus long shelves, and shelf orientation, paralleled versus unparalleled arrangements, were varied.
Results indicated that when shelves were placed in parallel, the length of the shelves did not affect the number of products examined, nor the number of products purchased. In contrast, when the orientation of the shelves was unparalleled, shorter shelf lengths stimulated a higher number of products examined and purchased than longer shelf lengths. In addition to product choice behaviors, the virtual store also recorded the total shopping time and the walking distance, which indicated significant differences between parallel and unparalleled shelf orientations.
Lastly, results revealed that consumers with a hedonic motivation searched for and purchased more products than consumers with a utilitarian motivation. They also spend more time in the store and walked longer distances. While attempting this procedure, it's important to remember that the practice session is vital to familiarize participant with the virtual store and minimize biases generated from different computer skills.
Moreover, clear instructions on what the participant should or should not do are extremely important the help the participant complete the experiment. Following this procedure, others tools like an eye tracker can combined in order to answer questions like what information consumers use to make choices. This technique paves the way for researchers in the field of consumer studies to examine consumer responses to in-store marketing strategies and to new products that are not yet on the market.
After watching this video, you should have a good understanding of how to use a desktop virtual supermarket to create virtual shopping environments and study in-store consumer behavior. Don't forget that the virtual store is a new computer technology in development. The searchers should be cautioned to save data as frequently as possible to avoid data loss.
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Overview
This paper discusses the use of a desktop virtual store to create virtual shopping environments for investigating consumer behavior. It outlines the protocol for building and running experiments, including example results related to store layout and considerations for conducting virtual store experiments.
Key Study Components
Area of Science
- Consumer behavior
- Virtual reality applications
- Marketing research
Background
- Virtual supermarkets can aid in consumer research.
- They allow for the study of in-store marketing tactics.
- These environments are beneficial when real-life sharing is challenging.
- New users may find the features and functions complex.
Purpose of Study
- To explore consumer responses to in-store marketing.
- To demonstrate the advantages of virtual shopping environments.
- To provide a protocol for conducting virtual store experiments.
Methods Used
- Creation of virtual shopping environments.
- Experimental design focused on store layout.
- Data collection in a controlled virtual setting.
- Visual demonstrations to aid understanding.
Main Results
- Insights into consumer behavior in virtual settings.
- Evidence of the effectiveness of store layout on consumer choices.
- Demonstration of the flexibility of virtual environments for research.
- Identification of challenges for new users of the technology.
Conclusions
- Virtual stores are valuable tools for studying consumer behavior.
- They provide a realistic yet controlled research environment.
- Proper training and visual aids are essential for effective use.
Frequently Asked Questions
Executive Industry Relevance
Understanding consumer responses to in-store marketing tactics is critical for optimizing product placement, packaging, and promotional strategies in the pharmaceutical and consumer health sectors. Virtual store environments enable realistic yet controlled investigation of shelf layout, product assortment, and navigation behaviors that directly influence purchase decisions. This approach supports predictive confidence in early-stage concept testing and reduces reliance on costly real-world pilots.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Scientific Value: Enables hypothesis testing of how environmental factors like shelf length and orientation influence consumer examination and selection behaviors.
- Operational Value: Provides a flexible, reusable platform to test product concepts without physical prototyping or market availability constraints.
Screening & Assay Development
- Scientific Value: Generates quantitative dependent variables such as shopping time, walking speed, and number of products examined to evaluate consumer engagement.
- Operational Value: Standardizes data collection through automated logging of behavioral metrics, supporting reproducible experimental conditions across studies.
Translational & Preclinical Research
- Scientific Value: Captures motivation-driven behaviors (hedonic vs. utilitarian) that correlate with deeper consumer engagement and product affinity.
- Operational Value: Facilitates translation of virtual findings to real-world retail strategies by isolating environmental variables difficult to manipulate in physical stores.
Pipeline & Workflow Integration
The virtual store method integrates into the consumer insights workflow by enabling early-stage hypothesis generation and behavioral de-risking before market testing or clinical translation of OTC products.
- Discovery Biology: Supports interrogation of how store layout influences product discovery and decision-making pathways in simulated environments.
- Screening: Delivers assay-ready behavioral outputs like product examination counts and purchase rates for comparative condition analysis.
- Analytics: Enables extraction of nested behavior data streams (e.g., time, distance, selection patterns) for statistical comparison across experimental layouts.
- Translational Research: Connects virtual observations to real-world shelf optimization by demonstrating how orientation and length affect movement and choice.
- Enterprise Reuse: Functions as a reconfigurable platform for testing multiple product categories, layouts, and consumer segments without physical reconstruction.
Operational & Enterprise Impact
- Scientific Value: Reduces mechanistic ambiguity in consumer behavior by isolating environmental influences on product interaction and choice.
- Operational Value: Ensures reproducibility through standardized data management plugins and synchronized event tracking across devices.
- Strategic Value: Improves go/no-go decisions for product concepts by predicting real-world shelf performance under controlled variables.
- Portfolio Impact: Enables risk-adjusted prioritization of packaging, placement, and formulation strategies based on observed navigation and selection patterns.
Implementation Considerations
- Requires expertise in virtual environment setup, including template modification and product library configuration.
- Dependent on dual-computer synchronization: one for virtual store rendering, another for data management and behavioral logging.
- Necessitates participant training and practice sessions to minimize bias from varying computer literacy or navigation skills.
- Requires clear instructional protocols to standardize participant actions (e.g., joystick use, product selection, cart verification).
- Limited to behaviors observable in a desktop VR setting; does not capture physiological or neurocognitive responses without external sensor integration.
