In recent years, a new generation of technologies has been reshaping the retail industry. To stay competitive in today’s market, many retailers are investing heavily in these technologies and finding that they offer several benefits both for the stores themselves and their customers.
This article will explore how computer vision systems can benefit variety stores (variety store is a retail store that sells general merchandise, such as apparel, automotive parts, dry goods, toys, hardware, home furnishings, and a selection of groceries) specifically by improving inventory management, reducing theft rates, saving on costs while increasing customer satisfaction!
- What is computer vision?
- How does computer vision work?
- Benefits of computer vision systems for variety stores
- Proven benefits of Alertiee technology
What is computer vision?
At its most fundamental, computer vision is a sub-discipline of artificial intelligence research that enables computers to pieces extract information from visual data like images and videos. This information can be used to accomplish specific tasks, such as labelling or recognising object types within images.
Computer vision algorithms are becoming increasingly popular in the retail industry for many reasons. Product proliferation and shrinking margins have created a situation where it is impractical for shopkeepers to manually track inventories, making computer vision systems exceptionally useful. Technology has also made it
How does computer vision work?
As a relatively new field, computer vision is in a state of constant evolution with regard to the technologies and processes involved. However, certain processes have become fixtures. They are:
- Feature Extraction
- Result Aggregation
Let’s go through each of these steps in order.
Acquisition is exactly what it sounds like – the act of acquiring an image to be processed by a computer vision algorithm. The acquisition can come from many different sources, including cameras embedded into mobile devices or computers themselves. With the explosion of modern smartphones, this is becoming an increasingly popular way to generate visual data sets.
Post-processing refers to any computational work carried out on raw image data after acquisition has occurred. This could involve resizing images before feeding them into an algorithm. It could also be cropping or rotating an image before feeding it into an algorithm. This is a common step for algorithms that are designed to work on images of certain sizes, so this process ensures the input is always in the right format.
Segmentation is the act of breaking down an image into several segments so they can be analysed individually. For example, let’s say you wanted to train an algorithm to detect apples in images. If you fed your data set into an algorithm without first segmenting them, the result would likely be one big list containing all instances of apples found throughout your dataset – some correct instances and many incorrect ones. By choosing to segment each instance as a single apple, each apple will have an entry in the list of instances.
Feature Extraction refers to the process of turning raw data into something that can be fed into an algorithm. For example, if you were trying to train an algorithm on images containing apples, it would be useless to feed the algorithm a simple description of what an apple looks like – it needs more information! Instead, attempts are made to extract features from the image itself so they can be tagged with descriptors associated with the kind of fruit they came from (e.g., apple = “red”, “round”, “stem”, etc.).
Classification is one of the most important steps in computer vision. Classification is when data are assigned labels based on features that have been extracted; for example, classification could entail telling apart red tomatoes from green or yellow ones. Classification is typically done using statistics in combination with machine learning. For example, if a certain feature is found to be exceptionally reliable at predicting the object type in a question (e.g., red = tomato), it will take on increased weight when making future classifications.
Result Aggregation simply means collecting and organizing the results from each classification so they can be used by the people who asked for them! One of the main benefits of computer vision systems is that they can process huge data sets much more quickly than humans; therefore, these organizations provide their final results through an API that application developers can integrate into their programs or websites. Alertiee’s range of solutions are examples of what these result aggregation dashboards look like.
As you can see, there are many different processes involved with computer vision systems like Alertiee Sense. That doesn’t mean the systems are complicated to implement or benefit from! Read on to learn a few of the main benefits of computer vision systems in retail shops.
Benefits of computer vision systems for variety stores
In this article, we’re going to focus on some examples from a typical variety store’s point of view, but many of these ideas apply universally to any type of business or organisation.
Computer vision systems can help with inventory management in variety stores by providing an accurate, efficient means of counting the products that are on store shelves. A computer vision system will take images of each shelf, detect what items are present on the shelf, and count them. The results will be returned to the business owner or manager so they can use this data to improve their inventory handling process.
Retailers can use this data to streamline the way they manage inventory and order products. When stock is low the computer vision system can automatically alert employees on the floor who can address the issue. The same alerts can be programmed to automatically alert purchasers when they need to place new product orders due to low storage stock.
External foot traffic clues retailers into patterns in the number of people that are in the vicinity of their locations at a given time. For example, a computer vision system might detect a surge in foot traffic outside a variety store 1-hour after closing on weeknights. This data allows them to make more informed decisions about when to how to set operating hours and schedule staff. Optimising a location around passers-by is a fantastic way to find missing sales in your environment and it’s made possible thanks to computer vision.
Foot traffic analysis
Internal foot traffic data is unbelievably valuable as well. Computer vision systems can be used to record the number of unique and repeat visitors inside an establishment, allowing retailers to determine how often their store is visited and by whom. This data helps them better estimate customer demand and plan future purchases and staffing needs accordingly.
In addition, this visual tracking data can be used to provide customers with targeted marketing materials that increase conversion rates and improve their experiences. Studies show that the average ROI for these targeted marketing engagement efforts is around 44% across all industries.
One major benefit for any type of business is crime prevention. Using computer vision technology, it’s possible to detect when customers leave a store with items without first checking out (i.e., shoplifting). These systems can then alert employees by dispatching a text message through their mobile device – perfect for stopping shoplifters before they’ve exited!
Similar systems can also be implemented alongside self-checkout machines to automatically scan items in a basket. This reduces the likelihood that items will be scanned incorrectly (whether on purpose or by accident). Studies estimate that variety stores like Poundland lose 33% to 147% more due to self-checkout theft than any other kind of theft. This problem could be addressed with computer vision.
Computer vision systems are excellent at collecting data on how shoppers behave in stores. Using heatmaps – maps that track where people spend most of their time when inside a store – businesses can improve many aspects of their shops, from product placement to employee training. This data provides valuable insights into how customers interact with different products and features!
As you can see, there are many benefits of using computer vision technology in retail stores! Not only are these technology platforms good for businesses, but they are also great for customers. Using computer vision technology is an excellent way to improve the shopping experience for all involved!
If you enjoyed this post: 5 Benefits of computer vision systems for variety stores, be sure to check out our recent IoT and AI pilot with Adnams here: AI and IoT for retailers how physical stores can enhance customer experience
Proven benefits of Alertiee technology
Alertiee will help you realise missing sales opportunities, stores’ performance based on conversion rate, and stores’ sales hotspots areas.
We support and guide you through how you best convert both inside and outside traffic opportunities by supplying you with options around service risks and how to serve your existing and potential new customers best.
Alertiee will help you to provide a harmonious customer experience across your omnichannel business. Our technology enables customer experience, marketing and retail teams to close the gap between customers’ digital experience and their physical experience.