Ecommerce Payments Meet Customer Support AI

From Early Beginnings to Modern Applications of AI for Customer Support 

In the next 18-24 months, payment professionals are set to experience significant AI-driven impacts in areas like fraud detection, personalisation, operational efficiency, and customer experience. Rapyd’s “Future Ready: The Guide to AI and Payments” series explores how AI is shaping the payments landscape. This article examines AI in customer support for ecommmerce.

The earliest AI in customer support was basic but laid the groundwork for today’s advanced systems. Early chatbots handled frequently asked questions, providing quick responses to simple queries. Interactive Voice Response (IVR) systems guided callers through predefined paths, though their rigidity often led to frustrated cries of “talk to a human!” Rule-based ticket routing systems categorised and directed support requests but lacked flexibility

While these tools introduced automation, they were limited in handling complex or personalised support. Modern AI systems address these gaps with smarter, more responsive solutions, provided businesses invest in proper training and context.

AI and Ecommerce Customer Support: What’s Possible Now

Today, AI is engrained into customer service strategies. With technologies like generative AI and Vector Databases, businesses can create AI-accessible repositories of knowledge. This process, known as retrieval augmented generation, uses customer data and past interactions to resolve issues in natural language. Dr Reimer of BCG X notes, “Customer service can now be more automated and more tailored to customer needs.” By automating routine inquiries, businesses free skilled staff to focus on complex issues, achieving faster resolutions for most problems.

Many businesses struggle with implementing AI effectively. Dr Reimer cautions businesses may “get enamoured by the technology” and neglect foundational elements.  Successful AI implementation demands a holistic strategy that integrates AI into the organisation’s broader objectives rather than treating it as isolated projects. This starts with a clear vision and focus on use cases that align with business priorities, delivering measurable value quickly. Data readiness is critical—ensuring clean, relevant, and sufficient data to train and operate AI models.

Equally important is building a cross-functional team of technical experts, business analysts, and domain specialists to guide the process. These elements will create a foundation for AI that supports immediate goals and drives long-term growth.

Key Use Cases of AI in Customer Support for Payments

Chatbots and Virtual Assistants: AI chatbots like Neople handle tasks like payment statuses and dispute resolution using natural language processing. Ada CX functions as an AI agent replacing hard-coded chatbots to support online shoppers throughout the customer journey, including payment processes.

Personalised Customer Experience: AI analyses past interactions to deliver tailored recommendations and loyalty programs, boosting engagement and retention. Examples include: 

• Yuma.ai: Automates customer order inquiries and modifications, potentially including personalised payment recommendations based on past interactions.
• Tidio’s Lyro: Uses AI to automatically answer common questions and perform recurring tasks, which could include tailored loyalty program suggestions.

Streamlined Payment Processing: AI automates invoice processing and optimises payment routes, enhancing the customer journey. ​​Intercom’s Fin AI can handle complex tasks like explaining pricing plans and changing bookings, which could extend to simplifying payment processes.

Data-Driven Insights: Analysing transaction data helps providers identify patterns, refine services, and address emerging needs proactively. Fast Simon collects and processes customer data, enabling businesses to deliver personalised experiences and payment insights.

Challenges of Implementing AI in Customer Support

Despite its benefits, AI poses challenges:

• Integration and Customisation: Implementing AI requires significant resources for software, hardware, and fine-tuning.
• Human Oversight: Regular audits are essential to maintain service standards. Balancing AI with human agents ensures effective support for complex issues.
• Impersonal Responses: Automation can sometimes lack the human touch, frustrating customers when bots fail to address specific concerns.

Best Practices for Implementing AI in eCommerce Customer Support

To maximise AI benefits, merchants should:

• Integrate Across Channels: Deploy AI chatbots on websites, social media, and apps for consistent support.
• Focus on Personalisation: Use AI to tailor interactions based on customer data.
• Automate Routine Tasks: Assign frequent inquiries to AI, freeing human agents for complex cases.

Customer support AI offers eCommerce businesses the opportunity to streamline operations, provide tailored experiences, and maintain high service standards. As AI technology invites itself into more of our everyday lives, the potential for improved efficiency and customer satisfaction grows. With the right approach, businesses can leverage AI to enhance their support strategies and deliver best-in-class customer experiences.