The market for smart shopping carts is booming. Companies are recognizing the fast growth in retailers' demand in innovative technology that doesn't only make the customers' shopping experience a breeze but also increases retailers' revenue. But not every smart shopping cart fulfills its promise. Let's take a closer look at the ways in which existing solutions differ and what sets the market leaders apart from the rest.
Relying on weight alone is not the best option for a smart cart solution because it does not provide enough information to accurately determine the contents of the cart.
Weight alone cannot differentiate between different types of items, such as produce, packaged goods, or household items, which can vary significantly in weight.
The system may be trained regarding the weight, however, items also change over time and the trained system then once again is off.
Additionally, there may be items in the cart that do not have a fixed weight, such as loose produce or items that have been partially consumed. Furthermore, relying solely on weight can lead to inaccuracies when the cart is moving or if there is dirt or debris on the scale that affects the reading.
Oftentimes the biggest issue is the missing master data provided by the retailers.
Image recognition by itself is not the best option for smart cart solutions because it may not be accurate enough to identify all of the items in the cart.
Image recognition can struggle with identifying items that are partially obscured or poorly lit, as well as items that have similar appearances but different brands or types. Additionally, relying solely on image recognition can be computationally expensive and may not be practical for real-time applications where quick decisions need to be made. This could lead to delays or errors in identifying items, which could result in incorrect pricing or inventory management
OCR (Optical Character Recognition) on the other hand does not qualify as a sole smart cart component either because it may not be able to accurately identify all of the items in the cart. OCR is a technology that is used to recognize text in images, and it is often used in conjunction with barcode scanning to identify products in retail settings.
However, not all items have barcodes, and some items may have damaged or obscured barcodes that cannot be read by a barcode scanner.
In such cases, OCR may be used to try to recognize text on the packaging, such as product names or SKU numbers, but this can be unreliable and may not provide enough information to accurately identify the item.
Furthermore, relying solely on OCR can be computationally expensive and may not be practical for real-time applications where quick decisions need to be made. This could lead to delays or errors in identifying items, which could result in incorrect pricing or inventory management.
To create an ideal smart shopping cart that covers as many cases as possible, a combination of several technologies can be used. Here are some examples of technologies that can be combined in a smart shopping cart solution:
1. Weight sensors: Weight sensors can be used to detect the weight of items in the cart, which can help with inventory management and pricing.
2. Barcode scanners: Barcode scanners can be used to scan the barcodes of items in the cart, which can provide accurate and comprehensive data on the items.
3. Cameras: Cameras can be used to identify items in the cart that do not have barcodes or that have damaged or obscured barcodes and provide theft prevention that way.
By combining these technologies, a smart shopping cart solution can provide a more comprehensive view of the contents of the cart, which can help with inventory management, pricing, and personalized recommendations for customers. This can improve the overall shopping experience for customers and help retailers to optimize their operations.
