Modern retail is a super-competitive field, where small and medium companies need to vie with international giants like Amazon and aBay in their race for customers. On the other hand, these giants are constantly challenged by startups and small innovators that use modern technologies to reshape and rebuild the current order.
One of the latest trends is retailers using AI to deliver better experiences and services to their customers. Also, new challengers are bringing modern features and concepts to the process of buying goods. For those less tech-savvy companies, the fear of missing out emerges.
But what exactly is changed by AI in shopping? What are some examples of AI in retail?
What is Artificial Intelligence in Retail
Artificial Intelligence is a set of techniques and technologies that enable machines to perform tasks previously performed only by humans. With modern machine learning, automated systems can recognize and process images, effectively work with natural language, and analyze gargantuan amounts of data.
A distinctive type of AI is generative AI, where a system is tasked with generating new content based on other content – for example: images based on other images , images or videos based on textual prompts, or vice-versa. These tools may enhance the retail experiences in ways unseen before.
The Rise of AI in Shopping
Currently, AI in retail can be divided into two categories – customer-facing AI, which engages in customer interactions, and Backend AI. The dividing line is obvious. A customer-facing system is one that the user has direct contact with and when the user’s decisions or actions may be affected by the AI’s decisions or actions. The latter works in a more subtle way, by controlling the processes behind the scenes, in supply chain management or cost optimization for example. So Artificial Intelligence in retail examples may include:
Customer-facing Artificial Intelligence shopping
AI-powered systems can be used to improve the customer experience in a multitude of ways. The list below shows real-life examples and applications of AI technology that enhance the user experience.
Generative AI product photography and fashion photography
One of the world’s oldest e-commerce platforms and one of the few early internet businesses that survived the dotcom bubble burst in the first years of the millennium, eBay. The company has yet to abandon its tech-trailblazer mentality and already implements AI-powered solutions in its tech stack.
One good example is the background swap tool, which enables merchants to transform their photos into professional, studio-quality photos. More details can be found on the company’s official website.
User decision process support
One of the biggest barriers in e-commerce is the lack of “touch” of the goods up for purchase. This can be a huge challenge, especially for apparel and beauty products. Also, with thousands of models to experiment with, picking the right product can be extremely challenging.
To overcome this challenge, eBay has introduced the Shop the Look feature, where the user can browse through stylizations generated by AI based on a history of their interactions with the shopping app. When the user finds a piece of garment they like, the system uses a visual search to find the most similar products from the actual offering. The company has already announced the feature on its website, making eBay one of the leading retail companies using AI.
Visual product search
Google is actively developing their Visual Product search, where users can find similar items using images as a basis. So for example, the user may use an image of a celebrity wearing a particular cap or sweater and search for the item on the internet.
Recommendation engines
AI algorithms, with their powerful abilities to process data, can find numerous applications in online retail. Offering personalized recommendations being one of the top examples. By processing large amounts of behavioral data, the system may suggest the most fitting products or services for an individual who visits a webpage.
Chatbots
Arguably the most obvious application of generative AI is to develop a chatbot that supports customer service. Marks and Spencer, the international clothing and home goods retailer, uses the Google Cloud infrastructure to power its customer-facing chatbot.
Backend AI
Backend AI is composed of systems that are not directly facing the customer, even if customers are directly influenced by its decisions and outcomes. Among these use cases one may include:
Improved inventory management and supply chain efficiency
With a distributed system of physical locations alongside logistics centers and vendors, retail companies are heavily dependent on supply chain management. This aspect is fertile ground for optimization, where even the slightest improvement can bring thousands of dollars in cost reductions.
With its ability to process and analyze vast amounts of data very quickly, artificial intelligence is the perfect tool to be applied in supply chain management.
Advanced data analytics for better decision-making
As mentioned above, there are nearly no limits regarding the amount of data that can be processed by an AI-powered system. Thus, it can be used to analyze the constant inflow of information that is seen in modern companies, as well as leverage this data for better decision making, predictive analytics, and on demand forecasting. With its ability to find and recognize patterns, the system may be used to spot new client groups or inefficiencies hidden deeply in the system.
Key Benefits of AI for E-commerce
Artificial Intelligence is a game-changer for all businesses, including e-commerce. The key is in the benefits this technology brings to a company. These include:
- Increased sales – AI systems can analyze data and deliver responses in real-time, effectively providing systems with smart insights to use in good time. This can be used in recommendation systems and smart pricing, among others.
- Reduced costs – there are multiple costs to oversee in retail, some of which are extremely hard to optimize or reduce. Using AI-powered solutions combined with ERP and WMS software may make spotting savings opportunities, like reducing shelf time or optimizing item allocation among warehouses, swifter and easier.
- Reduced waste – with more precise estimations of the goods to be sold, the company can avoid overstock. And overstocking comes with great waste, if the goods are either seasonal or easily broken or spoiled, the risk of high waste increases. Waste is also an ethical issue for companies.
According to Wastemanaged data, up to one-third of all world food is thrown away. Also, the fast fashion industry is frequently accused of producing tons of clothing items only to have them shredded shortly afterwards.
- Happier customers – more personalized offers and better adjustment to customer needs results in happier customers. Thus is loyalty increased and the overall profits also.
- Smooth operations – last but not least, a company that uses AI tools can simply hand some of the tedious and boring tasks over to the machines, which never get tired or bored no matter how dull or frustrating the task is.
Conclusion
AI technology is one of the most interesting and exciting directions for retail and e-commerce companies and the AI in retail examples above are proof. Yet the key to gaining all the benefits is in picking the right tech partner to harness all the power of the new solutions while avoiding common pitfalls.
