Despite the tremendous advances made by AI/ML solutions in e-commerce, there is still enormous potential. Companies are betting on cutting-edge AI trends and deploying solutions that improve buyer and seller experiences.
Machine learning (ML) and big data analytics play a critical role in the development of platforms that improve customers’ shopping experiences and satisfaction levels in a variety of ways while assisting sellers in driving smooth sales.
Acquiring and retaining customers, for instance, is a major challenge for an e-commerce platform or its sellers. Fortunately, machine learning techniques aid online marketplaces in remaining competitive in a variety of functions, some of which are highlighted below.
Customizing the product feed for users improves their experience and increases conversion. The product listings that a user finds in various sections of the app are chosen using ML models that predict the user’s purchase intent based on signals such as time spent viewing a product, clicks, wish-listed products, and those added to the cart, likes, and many more. The depth of feed personalization is a key differentiator in providing e-commerce players with a clear advantage in an ultra-competitive retail market.
When a user searches on an e-commerce platform, machine learning models are used to understand the intent expressed in the user’s query, retrieve relevant products for the detected intent, and rank the search results to maximize conversion. Product search engines can even interpret queries in Hindi or other local languages; for instance, a query for “laal joote” will return results for “red shoes.” Furthermore, search interfaces are no longer limited to text input. A visual search can be conducted by uploading an image as the query. Computer vision models aid in the segmentation of shoppable products from the query image and the discovery of visually similar products on the platform.
Ecommerce platforms use a variety of recommendations to increase customer engagement and shopping basket size. These recommendations include product substitutes, complementary products to the one being purchased, product bundles that are frequently purchased together, and so on. Machine learning and deep learning methods are used to generate such recommendations, which mine data from user interactions across multiple products as well as visual and text data associated with the product catalog.
Goods that are unavailable or out of stock can be a major barrier to customer retention. As a result, it is critical to restock goods in plenty of time to meet year-round demand. Maintaining supplies can be a logistical nightmare at times, especially during market disruptions. The use of ML models in demand forecasting can aid in matching goods stock levels to demand.
One of the most important aspects for any online business to maintain profitability is to ensure that the optimal price points have been accurately judged. This can only be determined by taking into account a variety of factors such as production cost analysis, market segmentation, the price flexibility of products or services, customers’ attitudes toward different price points, the pricing of competitors’ products, and so on. ML models aid in optimal pricing decisions by predicting customer reactions to different price points based on the market segment.
One of the most important aspects of any online business’s profitability is ensuring that the optimal price points are accurately judged. This can only be determined by considering a variety of factors such as production cost analysis, market segmentation, product or service price flexibility, customer attitudes toward different price points, competitor pricing, and so on. By predicting customer reactions to different price points based on market segment, ML models aid in optimal pricing decisions.
These digital tools enable both sellers and buyers to navigate an e-commerce app if they become disoriented or lost. Instead of calling customer service, they can ask the chatbot or virtual assistant questions directly through a chat interface. Aside from answering specific questions, digital assistants can also provide context-aware prompts, speeding up transactions and increasing customer satisfaction.
Despite the tremendous advances made by AI/ML solutions in e-commerce, there is still enormous potential. Companies are keeping up with cutting-edge AI trends and quickly deploying solutions that improve buyer and seller experiences.