Building Blocks of a Simplified Ecommerce Experience, CIO News, ET CIO
The exponential growth of e-commerce generates massive mounds of user data. In a country as diverse as India, it is next to impossible to manually survey users across different languages, cultures, age groups, and incomes. To solve this conundrum, online marketplaces have widely deployed AI (artificial intelligence) models to understand and analyze consumer needs, facilitating seamless transaction experiences for both buyers and sellers.
In this scenario, machine learning (ML) and big data analytics play a pivotal role in developing platforms that improve the shopping experience and customer satisfaction levels in various ways while helping sellers to generate fluid sales.
For example, whether it is an e-commerce platform or its sellers, acquiring and retaining customers is a major issue. Fortunately, machine learning techniques help online marketplaces stay competitive in different functions, some of which are discussed below.
Feed personalization: Customizing the product feed for users helps improve their experience and improve their conversion. The product listings that a user finds in different sections of the app are selected using ML models, which predict the user’s purchase intent through signals such as time spent viewing a product, clicks, wishlist products, products added to cart, likes and many more. The depth of feed personalization acts as a key differentiator by providing a clear advantage to e-commerce players in an ultra-competitive retail market.
Search: When a user performs a search on an e-commerce platform, ML models are in play to understand the intent expressed in the user’s query, retrieve relevant products for the detected intent, and rank search results to maximize conversion. Product search engines are even able to interpret queries in Hindi or other local languages; for example, a query for “laal joote” will show results for “red shoes”. Moreover, today’s search interfaces are not limited to text entry. One can undertake a visual search by uploading an image as a query. Computer vision models help segment shoppable products from the query image and find visually similar products from the platform.
Product Recommendations: E-commerce platforms use a wide range of recommendations to increase their customers’ engagement and basket size. These recommendations include: substitutes for a given product, complementary products to the one purchased, bundles of products frequently purchased together, etc. These recommendations are generated using machine learning and deep learning methods that extract data from user interactions across different products as well as visual and textual data associated with the product catalog.
Inventory management: Merchandise that is unavailable or out of stock can be a major obstacle to customer retention. Therefore, it is imperative to replenish goods well in time to meet year-round demand. Maintaining supplies can sometimes be a logistical nightmare, more so during market disruptions. Using ML models in demand forecasting can help match inventory levels of goods to their demand.
Pricing: One of the most critical aspects for any online business to remain profitable is ensuring that optimal prices have been judged accurately. This can only be inferred by considering several parameters such as production cost analysis, market segmentation, price flexibility of products or services, customer attitude towards different price levels , the pricing of competitors’ products, etc. ML models facilitate optimal pricing decisions. by predicting customer responses at varying price points by market segment. In a very price-sensitive market like India, buyers are likely to ignore a garment that is priced higher than a similar garment from another seller. As a result, sellers – especially new ones – can lose business without knowing why. Price recommendations for sellers solve this problem. Based on the price of other products in the same category, e-commerce platforms may offer sellers price suggestions to stay competitive.
Virtual Assistants: These digital tools allow sellers and buyers to navigate an e-commerce application if they feel lost or confused. Instead of calling customer service, they can directly ask questions to the chatbot or virtual assistant through a chat interface. In addition to answering specific queries, digital assistants can also offer contextual prompts, speeding up transactions and improving satisfaction levels.
Despite the huge advancements made by AI/ML solutions in e-commerce, huge potential still exists. Businesses follow cutting-edge AI trends and easily deploy solutions that improve the experience of buyers and sellers.
The author is co-founder and CTO, Meesho