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9 Use Cases of Conversational AI in Retail in 2024

The rapid growth of online retail presents both opportunities and challenges for online retailers seeking business development. The competition intensifies, and customers expect seamless and personalized experiences. 

Conversational AI steps in as a versatile solution, addressing the complications by enabling tailored interactions, enhancing customer satisfaction, and automating various aspects of online retail operations with revenue growth. According to IBM, almost all businesses that use AI-based virtual agent technology (VAT) report a 12% boost in customer satisfaction (Figure 1).

Figure 1. Leaders’ average reported impact of using VAT

Source: IBM Institute for Business Value

In this article, we explain the top 9 use cases of conversational AI in retail.

1- Customer support

Conversational AI can be effectively used for customer support in the retail industry. By incorporating AI-powered chatbots and virtual assistants, retailers can streamline their customer service processes and provide instant, accurate information to retail customers. 

Also, automating various customer support tasks, like answering customer requests, help customer service agents to focus on more high-value and creative tasks.

A conversational AI driven customer support system can assist customers in many ways, such as:

  • Answering FAQs
  • Customer authentication
  • Multilingual support
  • Troubleshooting


Salesforce is the CRM market leader and Salesforce Contact Genie enables multi-channel live chat supported by AI-driven assistants. Salesforce Contact Center enables workflow automation for many branches of the CRM and especially for the customer service operations by leveraging conversational AI technologies.

2- Customer feedback analysis

Conversational AI can be used for customer feedback analysis in the retail sector. AI-driven tools can process and analyze large volumes of customer feedback from various sources, such as reviews, social media, surveys, and customer support interactions. 

This analysis helps retail businesses understand customer needs, preferences, and pain points, which can be used to improve products, services, and overall customer experiences.

For those interested, here is our article on the benefits of sentiment analysis in the retail industry.

3- Dynamic customer segmentation

By analyzing customer data and interactions, AI-driven conversational tools can identify patterns and group customers based on their behaviors, preferences, and needs. This enables retailers to:

Conversational AI can continuously update customer segments based on real-time data and interactions, ensuring that marketing strategies and product offerings remain relevant and effective.

4- Online in-store experience

Conversational AI can be used to enable an online store for customers, representing some of the in-store functions in online shopping. By incorporating AI-driven retail chatbots, virtual assistants, and other interactive technologies, retailers can recreate aspects of the in-store experience in a digital environment.

For example, the beauty brand Sephora uses conversational chatbots that enable customers to:

  • Reserve products and pick them up in-store
  • Search product availability
  • Ask questions on store timings and returns
  • Identify products from celebrity photos
  • Book an appointment in the store

Figure 2. Sephora Virtual Assistant 

Source: Cosmetics Business

5- Order tracking

By integrating AI-powered chatbots and virtual assistants with existing order management systems, retailers can provide customers with real-time updates on their order status, delivery times, and other relevant information. This helps improve customer experience and builds trust in the retailer’s services.

Figure 3. A conversational order tracking 


6- Personalized shopping assistance

Conversational AI can be used for personalizing the shopping experience of customers. AI-powered chatbots and virtual assistants can help customers based on their preferences, purchase history, and other available data. This personalized approach can improve the shopping experience, boost customer satisfaction, and increase sales.

Some ways conversational AI can be used for personalized shopping assistance in retail include:

  • Product recommendations
  • Outfit suggestions
  • Complementary product suggestions
  • Size and fit assistance
  • Gift recommendations

For example, the retail giant eBay leveraged its own AI powered shopping bot for enabling personalized experience for its customers.

7- Personalized promotions

Conversational AI can be used for personalizing promotions and discounts tailored to each customer. By analyzing customer data, such as purchase history, preferences, and browsing behavior, conversational AI tools can provide targeted offers and incentives tailored to each customer’s needs and interests. This personalization can lead to increased customer engagement, higher conversion rates, and enhanced customer loyalty.

8- Product details

Conversational AI can be used for providing timely product details to customers. AI-powered chatbots and virtual assistants can access product information from retail stores, databases or product catalogs and present it to customers in a conversational manner. This helps customers make informed decisions and enhances their shopping experience.

Figure 4. Illustration showing IBM Watson Assistant interaction with person asking for a specific color and size shirt

Source: IBM Watson Assistant

9- Payments and refunds

Conversational AI can be used to assist and facilitate payments and refunds in the retail industry, although it is essential to ensure the proper security measures and integrations with payment processing systems are in place. It can provide customers with a convenient way to manage transactions while maintaining a conversational and user-friendly experience.

Some ways conversational AI can be used for payments and refunds in retail include:

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Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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