Machine Learning enables systems to learn from experiences and results of past performances. With the emergence of powerful analytics tools backed by Artificial Intelligence, it is possible to deliver business intelligence, customer profiling, and sales conversion figures. Machine Learning continuously applies homogenous amounts of data to drive innovative solutions with the power of problem-solving and continuous self-learning. These go a long way in boosting the overall capabilities of AI and Machine Learning in eCommerce.

AI and Machine Learning are poised to build useful insights from a pool of customer data. Customers generate data at each phase of the purchase cycle – covering all the touch-points – from where data is intelligently extracted and stored. With new progress, digital virtual shopping assistants now recommend new products to new-age customers.

Following are the key areas in which Machine Learning boosts the overall potential of e-commerce, thereby bringing transformation and cutting-edge innovation:

Buyer Segmentation

In new-age virtual shopping, Machine Learning in eCommerce can differentiate between casual visitors and potential buyers. It helps in tracking the vital parameters such as social status and customer class. Machine Learning can identify customers that have a particular preference for a price, or say, the design and make of the products. It helps implement price filters to help potential buyers with the right option in real-time during their visit to the e-commerce website.

AI Chatbots

Chatbots are intelligent agents that are playing a vital role in interacting with the customers. Natural Language Processing helps the agents drive precise yet complex voice-based interactions with self-learning capabilities over time. Based on the conversation, it can suggest precise personalized offers to customers. Last but not the least; Chatbots can deliberate deeper insights with the customers when it comes to Machine Learning in eCommerce.

Inventory Management

The emergence of AI and Machine Learning in eCommerce will help businesses in maintaining optimum levels of stock at points of collection as well as fulfillment centers. Unlike traditional inventory control that relied on minimal parameters, Machine Learning in e-commerce can learn from data of seasonal buying trends, the timeline for stock replenishment, as well the rise and fall in demand vs. supply variables in quantitative terms – in addition to constraints with modern inventory management.

Personalization

With the need for a greater and closer understanding of customer preferences, Machine Learning codes promotional messages in terms of its own training data, and uses it to tweak feeds sent to potential buyers. It enables a ‘recommended for you’ feed for individual leads and potential buyers. Machine learning in ecommerce boosts personalization and brings one of the most satisfactory shopping experiences for customers.

Fraud Detection

Machine Learning can be used to secure access and usage of cards in e-commerce transactions. It can learn and store user credentials and authenticate the same based on patterns which Machine Learning systems could identify, and prevent hackers from attempting fraud. Online businesses would leverage Machine Learning in ecommerce to build rapport with the customers and prevent frauds and account thefts or the stealing of cards of users.

Smart Search

In a typical scenario, a visitor may land on a page using generic search terms from his own perspective. The prospect might not be aware of the specific product category under the catalog or site directory followed by the business listing. In such a scenario, Machine Learning in eCommerce will play a vital role in collating keywords typed by the user with product data and redirect to the meaningful or relevant product listing on the site or specific landing page.