Have you ever been browsing an apparel eCommerce site and wondered how they produce seemingly perfect product images? A photoshoot? Well, maybe not. Instead, it may be a form of artificial intelligence called Generative Adversarial Networks (GANs) that generates high-quality product images. GANs are an AI technology often used to create artificial images that look like photos taken with a traditional camera. It is becoming increasingly popular in eCommerce, as it can help create product images quickly and easily. ECommerce sites can use these generated images to create product images quickly and cheaply without investing time or money in expensive photoshoots or 3D modeling services.
That pic of the model in the sweater you’ve been coveting? It could be 100% computer generated through GAN technology. Amazon uses GANs to generate product images to increase the speed at which new items can be added to its catalog. Wal-Mart, the largest retailer in the United States, has been using GANs for its online store to provide customers with more accurate visuals of its products without having to invest heavily in photographing each item manually.
GANs are not the only AI technology retailers are leveraging to operate smart, increase revenue or gain a competitive advantage. Let’s explore a few others that are creating an impact today.
Understanding customers’ needs and preferences have always been a challenge for retailers. Collecting customer feedback is labor-intensive (e.g. focus groups, mock shops, etc). Thanks to sentiment analysis, retailers can gain valuable insights into their customer base with unprecedented accuracy and speed. Sentiment analysis uses natural language processing (NLP) and machine learning algorithms to determine the emotion behind text data. It can be used to identify positive, negative, or neutral sentiment in customer reviews, surveys, social media posts, or any other type of text-based data. This allows retailers to better understand their customers’ feelings towards their products or services without having to read through hundreds or even thousands of individual reviews manually.
While there are many benefits of sentiment analysis for retailers, a critically high-value benefit is its ability to serve as an early warning system. Sentiment analysis can alert retailers when there is potential trouble brewing so they can address problems before they become full-blown crises – something that would be impossible without the help of AI-enhanced analytics. Sentiment analysis can also be used to identify trends in customer behavior over time which can help inform decisions around product development and marketing strategies moving forward. Retailers can also use sentiment analysis to monitor competitors’ performance. By tracking conversations about other brands in the same space, retailers can gain valuable insights into what customers like and don’t like about those brands and adjust their strategies accordingly.
For years, retailers have relied on manual processes to manage inventory flow through their stores. But with AI, retailers can now automate and optimize these processes using advanced computer algorithms for real-time inventory management. One of the primary ways that AI is changing retail inventory management is by automating manual processes. This can include tracking stock levels in stores and warehouses to predicting demand for certain products based on past customer behavior. Another use case is demand forecasting. By analyzing past sales trends, stores can predict future demand for certain products or services more accurately than ever. This allows retailers to adjust their purchasing accordingly so that no item goes out of stock or sits on store shelves unsold for too long. Finally, many retailers are now using AI for price optimization. With this technology, stores can set prices dynamically based on factors like seasonality or competitor pricing models. Using machine learning algorithms that detect changes in supply and demand levels in real time, stores can quickly adjust prices as needed—allowing them to maximize their profits while still offering competitive prices to customers.
Retailers can use text categorization to automatically classify products into different categories and subcategories based on their features, such as color, size, and material. The categorization can help retailers to improve their product search functionality and make it easier for customers to find the products they are looking for, particularly in this digitally driven age. For example, a clothing retailer can use text categorization to classify their products by gender, size, color, brand, and other attributes, which helps customers find their desired product more easily.
Computer vision uses algorithms to interpret digital images or videos and extract valuable data from them. With computer vision, retailers can monitor customer behavior and make decisions based on the results. Facial recognition technology is one of the most exciting applications of computer vision in retail. Through facial recognition, retailers can identify customers quickly and accurately, allowing them to offer personalized experiences tailored just to that person. Computer vision can also be used to detect age, gender, and facial expressions—allowing retailers to understand how their customers are feeling when they’re shopping. Additionally, facial recognition can be used for security purposes, such as preventing shoplifting or identifying potential shoplifters before they enter stores. Computer vision can also be used for in-store analytics, such as foot traffic analysis or heat mapping. Heat mapping is a process that uses image processing algorithms to identify areas where customers spend most of their time while shopping—information that is invaluable for understanding customer behavior and optimizing store layouts accordingly. This data can then be used to make decisions about store design or product placement that will improve customer experience overall.
AI presents many opportunities for retail businesses – helping them reduce costs while improving the overall customer experience. It’s easy to see why many retailers are quickly adopting this technology as it offers several tangible benefits, such as improved decision-making capabilities and enhanced customer engagement strategies. As the technology continues advancing in sophistication over time, it will no doubt become an even greater asset that enables retailers to capitalize on potential growth opportunities throughout the industry.