Harnessing AI and In-Store Cameras in Retail
In an era where AI technologies are making waves across various industries, researchers from the Queensland University of Technology (QUT) propose an innovative approach for retailers to leverage Artificial Intelligence (AI) in conjunction with in-store cameras. The objective? To enhance the consumer experience, optimise store layouts and drive sales. Collaborating with an international team of researchers, the QUT team has uncovered promising avenues that could revolutionise the way retailers approach in-store marketing and store layout design.
Redefining Store Layouts with AI
Store layouts play a pivotal role in the shopping experience. An efficient layout can captivate customers, promoting extended browsing time, highlighting unexpected items of interest, and grouping related or alternative products conveniently. Such meticulous design can dramatically improve customer satisfaction and sales. It’s a potent in-store marketing strategy, directly swaying customer decisions and bolstering profitability.
In an investigation published in the Artificial Intelligence Review, the researchers propose a fresh framework, blending the prowess of AI and computer vision, particularly deep learning, to better understand and respond to physical shopping behaviours. The research team includes Dr Kien Nguyen, Professor Clinton Fookes from QUT’s School of Electrical Engineering & Robotics and Professor Brett Martin from QUT Business School, alongside Dr Minh Le from the University of Economics, Ho Chi Minh city, Vietnam, and Professor Ibrahim Cil from Sakarya University, Serdivan, Turkey.
Turning In-Store Surveillance into Sales Insights
With AI and in-store CCTV cameras at the heart of the framework, researchers aim to glean deeper insights into how customers interact within the store. Traditional security footage offers basic data on customer movement patterns and hotspots. However, the team proposes to take this further, tapping into facial expression recognition techniques to interpret customer emotions in real-time.
Emotion recognition algorithms, working through AI chatbots and computer vision techniques, can pinpoint key facial landmarks to determine customer emotions as they peruse the store. Understanding these emotional cues can grant managers and marketers a potent tool to gauge customer reactions to products and store layouts. Additionally, identifying typical customer behaviours, such as product handling or lengthy product examination, offers crucial insights into customer interests.
The Sense-Think-Act-Learn (STAL) Framework
The team introduces the Sense-Think-Act-Learn (STAL) framework for retailers to operationalise the benefits of their proposed system. ‘Sense’ encapsulates data collection through CCTV cameras across the store. ‘Think’ involves processing this data through sophisticated AI, data analytics, and deep machine learning techniques, much like our brains decipher incoming information. ‘Act’ utilises these insights to improve and optimise store layouts. The entire process functions as a continuous learning cycle, allowing retailers to evaluate predictions on customer traffic flow and product popularity.
While retailers like Woolworths and Coles routinely utilise AI algorithms for personalised recommendations and customer servicing, the STAL framework presents a fresh approach for data-driven store layouts and design.
5. Prioritising Data Privacy and Quality
Ensuring privacy and quality in data handling is a key concern. According to Dr Nguyen, collected data can be cleaned and transformed into a structural form, maintaining customer anonymity by examining behavioural patterns at an aggregate level. By utilising a cloud-based system, retailers can efficiently process and store the extensive data generated by CCTV cameras.
In this innovative landscape, store layout managers can consider various store design variables, employee management, and customer behaviour factors to optimise store layouts. With AI powering retail innovation, the future of shopping may be closer than we think.
