AI Use Cases in E-Commerce
E-Commerce is eating the world… or at least it seems poised to. In the last 7 years, the share of total retail sales by e-commerce players has doubled from 5.6% to 11.2%. Further, it appears poised to grow much faster still in the post-COVID era of social distancing.
What is the secret behind such rapid growth in a sector many considered to be “mature”? Much of the answer lies in the heavy use of Artificial Intelligence (AI) and Machine Learning (ML) to recommend products, serve the right advertisements to the right audiences, predict delivery timelines, and more.
This article explores six critical AI use cases that are fueling the success of e-commerce today and in the future.
Search & Recommendation Engine
The original AI use case in e-commerce is to provide buyers with customized search results and product recommendations, resulting in enhanced customer experience, higher sales, and greater customer loyalty.
Predictive variables can include a wide variety of factors such as current and historical searches, demographics, past purchasing behavior, cookie trails from other web sites, and many others. Traditional analytics is limited by the number of factors and significant human effort to crunch large volumes of data. This results in slower query results and far less customize product recommendations.
But AI-powered neural engines can make speedy recommendations based on thousands of such data points without the need for human intervention whatsoever. The best part is, the predictive power of such recommendations continues to improve over time. Each time a customer interacts with the e-commerce platform, the AI algorithms continue to learn and evolve even further…
Customer behavior has changed significantly in recent years. As they become increasingly conscious about their buying decisions and base their decisions on a wider variety of factors apart from just price, efficient product search and recommendation engines will play an ever-increasing role in drive e-commerce sales.
Sales & Customer Services
Successful e-commerce relies on efficient response to customer queries, from addressing pre-purchasing inquiries to tracking packages to taking care of product returns. Furthermore, customers demand quick replies, with more than 63% expecting an instant response. Fully 65% of shoppers indicate that they are likely to switch brands in the event of disappointing customer service at any point from buying to receiving their orders.
Yet dealing with such a large number of customer inquiries can be tedious, and customers are rarely willing to pay a premium for expedited customer service. AI can be used to automate labor-intensive parts of the customer service journey through the use of chatbots and robotic process automation.
Integrating AI chatbots on the site can help organizations respond to the simplest ~60% of inquiries immediately, route another ~20% of discussions to sales/customer service representatives, and refer the most complex inquiries to specialists. By developing and training a custom neural network, the chatbot comprehends explicit client requests to quickly take care of most issues. Similarly, call bots can be used for handling inbound customer calls.
Product Pricing
In today’s competitive world, customers are bound to check prices against competitors both online and sometimes even offline. AI models help e-commerce sellers determine the best prices to maximize profits and market share by analyzing factors such as inventory levels, competitor’s pricing, customer price sensitivity, and seasonality.
Buyer Segmentation
Traditionally, customer attributes are labeled manually which can be an immense task. With the help of AI, this process can be automated. Also, many labels can be broken down further to provide more accurate tagging. AI helps by creating attribute clusters and assigning them to the right customers in a way that might not be possible for humans to achieve.
Voice Interface
Alexa and other AI-powered voice assistants are increasingly being used as an interface for placing orders online and powering a more seamless user interface for customers.
Demand Forecasting
Accurate demand forecasting is essential for a cost-effective supply chain and inventory management. Accurate forecasts improve the efficiency of the manufacturing, operations, and the overall supply chain of the organization, leading to higher availability and lower unit costs on goods.
In Summary
Technological advancements have allowed retailers to acquire large amounts of data and leverage AI-based solutions to provide a customized experience to their customers. AI has become an integral part of the entire supply chain and is fueling the product and service cycle across the e-retail sector. In other words, AI has the potential to influence the purchasing decisions of buyers and help sellers predict future purchases as well!
In today’s world, AI is becoming an inseparable part of many of the leading industries globally. This is why there’s an ever-increasing demand for professionals skilled in this field. The HarvardX Data Science program helps you acquire the required knowledge base and skills to tackle data analysis challenges and excel in the field of AI and Data Science.
Bibliography
- https://www.forbes.com/sites/cognitiveworld/2019/08/29/software-ate-the-world-now-ai-is-eating-software/#bc32e5e58107
- https://www.statista.com/statistics/187439/share-of-e-commerce-sales-in-total-us-retail-sales-in-2010/
- https://www.salesforce.com/research/customer-expectations/
- https://www.drift.com/learn/chatbot/why-are-chatbots-important/
- https://www.pymnts.com/news/retail/2018/omichannel-ecommerce-consumer-habits/