Generative AI is transforming the e-commerce landscape. Explore how Generative AI helps companies improve customer experience and boost revenue.
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Generative AI is the latest form of applied artificial intelligence that enables businesses and consumers to automate, simplify, and otherwise improve various operations in their day-to-day lives. In the world of e-commerce and e-tail, vendors are implementing generative AI solutions to support employees in their daily work and give customers a new and improved buying experience.
In this guide, we’ll cover some of the most common use cases of generative AI in e-commerce today and we’ll touch on how today’s benefits may lead to further AI growth in this industry.Table of Contents: Generative AI and E-Commerce
Generative AI Use Cases in E-Commerce
Leading Generative AI Solutions in the E-Commerce Space
Bottom Line: The Benefits of Generative AI in E-Commerce Generative AI Use Cases in E-Commerce
Generative AI can be used to supplement or even supplant various components of a company’s e-commerce workflow.
Across the e-commerce sector, artificial intelligence benefits both the vendor and its employees as well as customers and prospective buyers. Learn more about generative AI’s use cases in e-commerce below:AI-Driven Analytics
E-commerce of course involves less face-to-face interaction with customers than traditional commerce, so it can be particularly challenging to gauge customer satisfaction across different parameters and data points.
Online businesses have used data analytics tools for many years now to better understand how customers engage with their brands. But these tools have been limited in the quantity and quality of data they collect as well as the speed with which data is updated.
In contrast, generative-AI-driven data analytics tools give business leaders and data scientists more contextual customer data, updated in real time and across different shopping, channel, and demographic data points.
These generative AI tools can collect data in various unstructured formats, including customer service queries, social media posts and comments, ad clicks and engagements, and other data that has historically been difficult to capture.
Additionally, these tools often go beyond predictive analytics, offering insights into what’s happening now and making prescriptive recommendations for what vendors can do to achieve better results, products, and services in the future.
Other ways users can combine generative AI and data analytics for better e-commerce outcomes include the following:
Quickly assess products, websites, and other customer-facing assets to determine if they are meeting quality requirements.
Make more accurate and timely supply chain predictions.
More effectively manage inventory and demand forecasting needs. Customer Service Chatbots, Agents, and Coaches
With generative AI chatbots and virtual agents, businesses can handle chat conversations 24 hours a day that are designed to feel like a real human is engaging with the customer.
Many brands have had 24/7 operational chatbots in the past. But without the content generation and predictive capabilities of generative AI, these previous chatbots have relied heavily on human-built workflows and prebuilt responses that don’t always meet customer needs.
