SentiSight.ai enables individual users and companies from around the world to build models quickly and efficiently that can then be implemented within the retail industry.
SentiSight.ai’s retail image recognition solution is popular for numerous reasons. Here are some of the features you can expect by using our platform or asking our experienced team to build models tailored for improving different aspects of a retail business:
The rise of image recognition has exploded in recent times with it being incorporated into most industry sectors. Its prevalence is higher in some areas compared to others, with retail image recognition in particular experiencing a big boom.
Why is this so?
The use cases and integration of image recognition in retail are extensive. With retail playing such a large role in global economies, the competitive nature and demand of this industry places great emphasis on improving the quality of service. With such a large transition over the past couple decades into online retail, many of the large improvements have been down to the integration of artificial intelligence, namely retail image recognition.
If you are unsure as to the specific examples of where retail image recognition has made a big impact then take a look at the following examples (all of which are available as custom projects):
A retail planogram is a diagram that illustrates how and where retail products should be specifically positioned and placed on retail shelves or displays in order to increase customer purchases. They are considered an important tool in visual merchandising to help maximize sales by providing the opportunity to centralize the sales strategy by dictating product layouts.
A retail planogram ensures that the arrangement of goods on the shelf matches the planogram. Object detection algorithms scan a stall, detect the products present, and classify them by a manufacturer, brand etc. This is then compared to a reference planogram to flag up any mismatches that need amending.
The world of online retail is not the only sector to benefit from image recognition, self-checkout systems allow for customers to place their goods in front of the camera and immediately proceed with the payment process. Multiple studies have shown customer satisfaction to be higher when using self-checkout options for a faster and more convenient shop.
Cashierless stores are also increasing in popularity. Within these stores, image recognition systems process data gathered from CCTV or cameras built into a shopping cart to recognize the purchases and then charge the customer. The payment tends to be automatically distributed and handled via a mobile app, self-service kiosk, or even taking a biometric scan of the individual at the store entrance.
Online customers often have a rough idea of what they are looking for. This could be anything from t-shirts which are predominantly white, black shoes, blue jeans and so on… with online retail these days the options are endless!
After selecting an item and looking at the details and specifications, well-run online retailers will then recommend numerous other items which are similar to the one the user is viewing.
This is made possible by retail image recognition, specifically image similarity search models, which are able to accurately analyze the product attributes in images and then return items with the same or similar attributes to the retailer’s inventory.
Product tagging or attribute tagging allows users to utilize the search bar and category boxes to filter out any items that do not match with the tags applied, leaving them with a product list that matches their request.
In the past, retailers would spend hours manually applying multiple product tags to photos in their product catalogues. Advanced AI image recognition has helped automate this process, producing accurate attribute tagging results. Multiple tags can be applied successfully to one image courtesy of the deep learning model. This can occur once a model has been sufficiently trained via image annotation.
Retailers can then make use of their staff’s time elsewhere helping to save time and money, and most importantly their customers can easily navigate their website for the items that are of interest to them.
5. Empty Shelf Detection
According to a study from NielsenIQ researching on-shelf availability, empty shelves cost U.S. retailers $82 billion in missed sales throughout 2021. Part of this will be down to the pandemic but it highlights the importance of shelves being stocked-up for customers.
To prevent retailers from losing money from products that have not been replenished on the shelves, image recognition software notifies the staff when an SKU is missing on the shelf.
6. Visual Search
Visual search enables potential customers to search for similar products using a reference image from their photo gallery or downloaded from the internet.
Powered by image recognition in retail, users will be able to apply attribute tags to the supplied image and then return results of items in stock that have the same or similar attribute tags.
The integration of the visual search feature enables eCommerce businesses to implement this functionality into their software applications, maximizing the search potential of visual data.
The use cases of retail image recognition outlaid above showcase the vast potential within the industry. However, the applications we have mentioned above do not stop there and due to the broad nature of online retail there are many, many more. As retailers develop their business online operations, the demand for unique AI integration increases.
If your retail company has a required application that demands artificial intelligence which you would like to be tailored specifically to your business then reach out to us and a member of our team will get back to you to discuss your project and how SentiSight.ai can be of use.Contact Us