According to Zendesk, 42% of customers had to repeat their issues as often as three times when contacting support.
Imagine businesses setting cutting-edge technology at the forefront of their customer service process, boosting customer satisfaction and query resolutions in a cost-effective way. Thanks to Generative Artificial intelligence (AI), enhanced with Retrieval-Augmented Generation (RAG), customer support is like a breeze. RAG gained attention in Gen AI after publishing “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,” a 2020 paper by Patrick Lewis.
Odin AI is at the forefront of RAG-based AI chatbots, generating measurable results for many enterprises and being deployable in just 7 days.
Customer Service Challenges in Enterprises
Unfortunately, Forbes states that U.S. businesses lose around $62 Million annually due to poor customer support. For businesses to stay relevant in today’s digital era, customer support should meet the increasing demands for efficiency, personalization, and omnichannel experiences.
Here are some of the customer support challenges support executives or agents face due to operational inefficiencies:
Repetitive Problems: Unnecessary delays happen in the customer support process due to common, unresolved queries, leading to a disturbed support pipeline.
Training Employees: Many companies train new or experienced agents to handle issues effectively and produce consistent results. Regular knowledge transmission is necessary to smooth processes, which requires cost and effort.
Clumsy Knowledge Management: Regulating and managing a company’s extensive knowledge repository for a support system is one task. Keeping support agents informed and up-to-date with the latest product information, policies, and procedures to provide accurate support is another one.
High Volume of Requests: Companies often fail to deliver timely resolution because of the high volume of requests, which leads to a bad reputation and frustrated customers. A study by Forrester found that 73% of customers say that valuing their time is the most important thing a company can do to provide them with good service.
Cost Management: On average, Customer support operations cost approximately $1,280 to $240,000 per month to deliver a basic customer experience. Keeping up with these expenses is one of the biggest problems with any business. The average attrition rate for customer service representatives in the US has been reported to be between 30% and 45%, which is significantly higher than the average for all occupations.
Understanding RAG in Customer support
Generative AI models enhanced with RAG can boost the customer support process like never before. But beyond all the hype, questions arise: What is RAG? How does RAG work?
What is RAG? (Retrieval augmented Generation Explained)
Retrieval augmented generation is a technique that enhances the accuracy and reliability of the responses generated by AI models using data extracted from external sources. It retrieves relevant information from past tickets and feeds it to the AI model, enabling it to generate accurate and helpful responses to customer queries. This can be anything from troubleshooting steps for a common software issue to referencing warranty information for a specific product.
This technique can be a boon to customer support. Unlike Traditional Robotic Process Automation (RPA), RAG AI is adaptable to new situations and can easily understand natural language. It can help resolve complex customer queries with minimal human intervention. Let’s now dive deep into how does retrieval augmented generation work.
How does RAG work?
Retrieval-augmented generation (RAG) works as a technology to enhance the capabilities of LLMs. RAG LLMs combine the strengths of both retrieval-based and generative approaches to tackle complex tasks, especially in customer support.
So, how does retrieval augmented generation work?
Query Understanding: When a customer submits a support question or query, the system begins by understanding or interpreting the query to determine the context of the customer’s needs.
Retrieval Phase: After understanding the query, the system then searches an external database or knowledge base for information related to the query. This database could contain product manuals, FAQ sections, customer service logs, or any other relevant data. The retrieval mechanism is designed to find the most relevant pieces of information that could help answer the customer’s question.
Augmentation: The retrieved information is then fed into the LLM alongside the original query. This step is where the “augmentation” happens, as the model now has access to specific, relevant information that it wouldn’t otherwise know. This significantly enhances the model’s ability to generate a precise and accurate response.
Response Generation: The LLM generates a response with the original query’s context and the retrieved information’s specifics. This response is not just based on the model’s pre-existing knowledge (which might be outdated or too general) but is informed by the specific, relevant information retrieved in the previous step.
Delivery: The final step involves giving customers a tailored and accurate answer to their query.
The Measurable Impact of RAG AI on Customer Support
Here’s how Retrieval Augmented Generation (RAG) revolutionizes your customer support:
Faster Resolutions: RAG AI chatbots can handle routine inquiries, freeing up agents for complex issues seamlessly. This translates to shorter wait times and happier customers. A study showed that the average ticket resolution dropped from 20% to 50% using AI chatbots.
Improved Agent Productivity: With RAG AI tackling repetitive tasks like retrieving account information or product specifications, agents can focus on higher-value interactions, such as troubleshooting complex problems or providing personalized recommendations. This not only improves efficiency but also boosts agent morale and job satisfaction.
Reduced Costs: Faster resolutions and a more efficient workforce mean lower operational costs for your business. AI chatbots can save up to 30% of the operational cost in enterprises.
Case Study:
Automation Anywhere streamlines responses for support tickets with Odin
With a remarkable record of 100% automated ticket resolution and a 90% reduction in email support workload, the Odin AI revolutionizes Automation Anywhere's customer support. It optimizes response time, freeing agents for complex queries, while adeptly managing multiple conversations and adapting through user feedback.
Conclusion
Retrieval Augmented Generation is not just another technique in the arsenal; this tool can radically shift the customer support process. Enterprises must leverage the usefulness of RAG not only for a competitive edge but also to introduce timely, advanced, and cost-effective solutions to boost customer engagement. Ready to take your business customer support to the next level? Book a free demo today and understand how Odin’s tech support agent leverages the RAG tool to transform your support process with unparalleled efficiency and satisfaction.
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