FITTING ROOMS

By offering virtual tryon alternatives, personalised recommendations, and interactive features, AI technology is revolutionising the fitting room experience.

Artificial intelligence-powered virtual fitting rooms provide an immersive, personalised try-on experience (Brown, 2023), (Sharma, 2021). Smart mirrors, like those described in (maadaa.ai, 2022) and (Poornima Apte, 2017), use augmented reality (AR) technology to enable virtual try-on experiences. AI technology can also collect client feedback and allow them to post it on social media in real time (Sharma, 2021). The technology has the potential to minimise the frequency of returns due to ill-fitting clothing while also increasing customer happiness (murf. ai, 2018). Retailers should ensure that the technology interface is simple to use smooth, and trustworthy in order to incorporate technology while keeping a seamless and user-friendly experience (maadaa.ai, 2022). Retailers may provide a good and engaging consumer experience in fitting rooms by adopting AI technology (Kostic, 2021).

Intelligent Inventory Management:

Utilize AI algorithms to optimize inventory management in fitting rooms. Develop a system that uses real-time data to track clothing items brought into fitting rooms, analyse customer preferences, and suggest replenishments or alternatives based on

Data Collection: Collect customer data, including body measurements, fit preferences, and previous purchase history, to build a comprehensive dataset (Brown, 2023). This data will serve as the foundation for the AI algorithms to analyse and generate personalized

AI Algorithms: Utilize machine learning algorithms to analyse the collected data and generate accurate recommendations (Okhrem, 2023). These algorithms can identify patterns and correlations among customer preferences and inventory availability, enabling the system to suggest suitable clothing items.

Real-Time Tracking: Implement real-time tracking of clothing items brought into fitting rooms (anton, 2022). This can be done through RFID tags or other tracking technologies. By monitoring the inventory movement, the system can keep track of the availability of specific items and suggest replenishments or alternatives based on real-time data.

Availability Analysis: Analyse inventory availability throughout warehouses and channels using AI (Okhrem, 2023). This analysis can help forecast demand patterns and optimize warehouse replenishment plans. By understanding inventory levels and demand, retailers can ensure that popular items are always available for customers to try on.

Integration with Point of Sale: Integrate the virtual try-on system with the point of sale (POS) system to ensure seamless inventory management (anton, 2022). This integration allows for accurate tracking of inventory movement and enables the system to update availability in real-time.

Personalized Recommendations: Utilize AI algorithms to provide personalized outfit recommendations based on customer data and inventory availability (www.pixyle. ai, 2022). By considering individual body measurements, style preferences, and previous purchase history, the system can suggest clothing items that are likely to meet the customer's needs and preferences.

AI-Enabled Queue Management:

Implement AI algorithms to optimize the queuing system in fitting rooms. Develop a system that uses real-time data on customer arrivals, fitting room availability, and duration of usage to predict and manage waiting times, ensuring efficient customer flow and minimizing queues.

Real-Time Data Collection: Collect real-time data on customer arrivals, fitting room availability, and duration of usage (Okhrem, 2023). This data can be obtained through sensors or monitoring systems that track customer movement and occupancy in the fitting rooms.

AI Prediction Models: Utilize AI algorithms to analyse the collected data and predict waiting times (Okhrem, 2023). Machine learning techniques can be employed to identify patterns and correlations between customer arrivals, fitting room availability, and usage duration. This enables the system to make accurate predictions and manage customer flow efficiently.

Implement a queue management system that dynamically assigns customers to available fitting rooms based on predicted waiting times (Experience, 2023). The system can prioritize customers based on factors such as the number of items to try on or the urgency of their needs. This ensures a fair and efficient allocation of resources.

Provide real-time updates to customers regarding their estimated waiting times and fitting room availability (Experience, 2023). This can be done through digital signage or mobile notifications, keeping customers informed and reducing perceived wait times.

Optimization Strategies: Utilize optimization strategies to minimize queues and maximize fitting room utilization (murf.ai, 2018). This can involve dynamically adjusting the number of fitting rooms open, optimizing the allocation of staff resources, and identifying bottlenecks in the process.