August 14, 2023

The Role of AI in Ecommerce: A Brief Guide 

Author imaage
Hiya Chaplot
Associate Growth Manager
AI in Ecommerce

The ecommerce industry has been an early adopter of artificial intelligence (AI) technologies, recognizing the potential for AI to transform the customer experience and optimize operations. AI-powered solutions are now commonplace in ecommerce, revolutionizing the way consumers shop online and improving overall business outcomes.

From personalized product recommendations to advanced searchandising capabilities, AI is changing the game for online retailers, offering unprecedented opportunities for growth and innovation. In this brief guide, we'll explore the role of AI in ecommerce, highlighting key benefits and use cases, and providing insights into how businesses can leverage AI to gain a competitive edge in the online marketplace. So let’s begin. 

Artificial Intelligence - Elevating ecommerce experience the smart way

With the help of machine learning and big data analytics, AI is now a game-changer in e-commerce, enabling businesses to personalize customer experiences, streamline operations, and improve their bottom line. Here are 7 powerful ways AI is transforming the ecommerce experience - 

1. AI-powered shopping assistant chatbots

AI-powered shopping assistant are designed to mimic human conversations and provide personalized shopping support to customers in real-time. These shopping assistant bots serve as virtual guides, helping customers select products through personalized conversations. By leveraging NLP algorithms, they provide real-time support and answer queries about product details, features, and more. These assistants streamline the shopping process, ensuring customers receive tailored guidance and accurate information to make informed decisions.

2. Intelligent site search 

AI-driven site search for eCommerce works by using machine learning algorithms to analyze user queries and behavior to provide accurate and relevant search results. Here's how it intelligent search engines are transforming ecommerce interactions -

3. Visual search

Visual search uses machine learning algorithms to enable users to search for products and information using images, providing a more intuitive and efficient way to search for products. Visual search also enhances product discovery, allowing users to discover products they may not have found through text-based search.

Further, it is an impactful way to gain valuable insights into user behavior, such as popular search terms and user preferences, allowing ecommerce businesses to optimize their website and marketing strategies.

4. Searchandising

Searchandising, also known as search merchandising, is the practice of strategically organizing and promoting products in on-site search results to achieve specific business objectives, such as maximizing profitability, driving sales, and enhancing customer experience. It involves optimizing search results (using AI and ML algorithms) based on predefined criteria such as relevance, popularity, margin, or stock levels, to encourage customers to explore and purchase desired products.

Some key elements of searchandising include:

5. Price optimization and/or dynamic pricing

Dynamic pricing is the practice of adjusting product prices in real-time based on various factors such as supply and demand, competitor pricing, and customer behavior.

This price optimization is powered by advanced AI algorithms and machine learning models that analyze various data points, including product demand, inventory levels, and customer behavior, to optimize prices for maximum revenue. By adjusting prices dynamically, eCommerce businesses can react to changes in market conditions and customer demand in real-time, increasing their chances of making a sale.

6. Filters and facets for enhanced product discovery 

AI-driven filters and facets can analyze user behavior and preferences, as well as product data, to provide more accurate and relevant filtering options. For example, AI algorithms can analyze user search queries and purchase history to suggest personalized product recommendations and filtering options based on individual preferences and behavior. 

Moreover, AI-powered filters and facets can provide more sophisticated and nuanced filtering options based on attributes that may not be easily captured by standard filters. By analyzing the text in product descriptions, AI algorithms can suggest filtering options based on color shades, materials, and other attributes, improving the accuracy and relevance of search results. 

7. Personalized product recommendations

AI algorithms analyze user behavior, purchase history, and other data to provide personalized product recommendations. AI can provide more sophisticated and nuanced product recommendations, taking into account factors such as seasonal trends, popular items, and complementary products. For example, if a user purchases a dress, the algorithm can suggest matching shoes, jewelry, or handbags, providing a more complete and personalized shopping experience.

AI and ecommerce: the future is here and it's time to embrace It

It's clear that AI is no longer a futuristic concept; it's already transforming the eCommerce industry, bringing unprecedented opportunities for growth and innovation. 

The numbers speak for themselves. 35% of companies have already adopted AI and 42% are considering implementing it in the future. And reasonably so. Given the fact that implementing AI can bring in 20% extra revenue in the ecommerce industry, companies that embrace AI have a significant competitive advantage.

With the rise of AI-driven product discovery and search platforms such as Zevi, ecommerce businesses can deliver intelligent search experiences, personalized product recommendations, advanced filters and facets and searchandising capabilities that drive sustained growth and unforgettable customer experiences. 

Explore AI-powered search with Zevi. Book a free trial today. 

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