Real-Time Store Monitoring helps to know which customer is essential to any company. Increasing customer convenience has remained the topmost priority for retail stores. In a highly competitive sector such as retail, knowing customer preferences, and buying patterns to keep focused is becoming increasingly important. Both consumers are found identical in conventional in-store retailing, which contrasts the online retail space's customized shopping experiences.A significant way to build a much healthier retail in-store sale experience is retail in-store control. It helps retailers to find meaningful insights based on the study of consumer desires data, buying patterns, demographic attributes, which are gathered through different technologies. These stores are now taking advantage of technologies such as Machine learning and deep learning to give a customer experience that was impossible in the near past. This experience is a Real-Time Store Monitoring. This allows the shoppers to skip the checkout lines and walk out with their items, with no hassle of billing and standing in queues, at the same time giving the store officials the power of Real-Time analytics and less dependency on the human workforce.
The most frustrating thing for the retail stores' shoppers is standing in the checkout queues waiting for their turn. According to A Garbango report, adults spend 32.89 days of their lives waiting in grocery store queues. It will be a wonderful experience for the customers if they have the services like to pick up their bags and leave the shop in a few moments after buying their items. They don't have to wait in long queues. At the same time, the store owners will also get a chance to Real-Time Store Monitoring, and they will get insights about exhausting stocks the popular items among the customers on a given day. This will make a positive impact on the economy, as discussed below.
What are the Benefits of Real-Time Store Monitoring in Retail?
Listed below are the economic benefits of Real-Time Store Monitoring in Retail:
Real-Time Store Monitoring will eliminate the store's workforce's need, for example, cashiers and inventory makers.
Optimized Business decisions.
Receive many data from the Real-Time Store Monitoring platform, using it to make intelligent business decisions. For instance, in real-time, which section's items are getting exhausting in the given period if we recommend something, for example, the commodity like by the shoppers based on AI algorithms applied in the backend.
Labor savings helps us know the number of labor required to complete tasks.
The proposed architecture for Real-Time Store Monitoring will use Machine Learning, as explained below in the figure. Analyze the customers using live streaming cameras fed to Deep Learning algorithms to make customer purchases and inventory updates in Real-Time Store Monitoring.The key KPI's are as follows:
Customer Traffic Analysis.
Customers Behaviour in the store for future recommendations.
Fig 1. Flow for real-time Monitoring.
The solutions needed to the problems in Real-Time Store Monitoring:
Person Detection: Detects the shoppers based on their shopping cart in the backend.
Object Recognition: We need to recognize the items taken by shoppers.
Pose Estimation: Detect the shoppers' movements whether they are putting their items in their bag or just taking to inspect the item.
Motion Sensors and Camera Feed collaboration: To make decisions, we need to collaborate with the camera and motion sensors.
How can Computer Vision help?
The points mentioned above are computer vision problems. These can be solved using Deep Learning, Computer Vision algorithms like Convolutional Neural Network (CNN). These algorithms' training and scoring will depend upon the store's requirements under consideration. The requirements will depend on the store's size and the number of customers visiting it.Explore more about Performance Monitoring Tools and Management
Why is Real-Time Store Monitoring in Retail Important?
1. Streaming Services: High-quality cameras will be required for live streaming of the store's activities and forward feed to the Computer Vision algorithm.2.The Motion Sensors: Motion Sensors will be required to trigger the activities when unusual behavior is observed that may need a response.3. Access to GPUs for computing: This is the most important requirement to train any Machine Learning algorithm, and in this case, as the process will be in real-time, high capacity GPU's will be required to perform the model training and job scoring.4. Cloud Access for storing the data: The process requires the cloud service's access for the storage of model artifacts and the data. Given below is the flow of Computer Vision:Fig 2. Computer vision algorithm flow
Real-Time Store Monitoring Use-Case:
Nowadays, businesses are looking for innovative ideas to transform their business for better growth and development digitally. Here is the Use-Case of Amazon Go, how they transformed them :
Amazon Go does the most amazing and successful Real-Time Store Monitoring implementation by developing amazing go retail stores in which the customers has to install the app for entry purpose, after that he can inspect any item in the store and pick up the items required by the customer in his bag directly the billing will be done on a Real-Time basis, once the customer is done with shopping he can leave the store and the billing information will be mailed to the customer within 30-40 minutes.
How does Amazon Go increase the shopping experience?
Based on the lower number of steps, the Amazon Go store can increase the shopping convenience by about 33%. There are no checkout lines and repacking procedures.
Quick access to groceries and convenience goods
Turn-style entry: Consumer enters via Amazon app on the smartphone.
The consumer goes around the store, picks up items, adds to bag, shops like normal.
The Real-Time Store Monitoring solution developed using Deep Learning, and Machine Learning technology can be useful in retail sectors. It helps retailers find meaningful insights based on consumer preferences, buying patterns, and demographic attributes gathered through different technologies. In the future, more development can be possible for this sector to make it more flexible and can increase the shopping convenience of customers. We recommend you to talk to our expert.