As customer definitions of what constitutes good service evolve rapidly, leveraging cutting-edge technology is crucial for exceeding their expectations. Generative AI, or GenAI, has become an important tool to empower the next generation of customer experience offering a game-changing innovation that can help to redefine how organizations interact with their clientele.
There are many benefits of utilizing AI in a modern customer success environment. While some chatbots can help with customer engagement, we believe that nothing compares to human interaction. AI can help to improve those interactions by increasing engagement, conversion quality, selling opportunities, data collection and more.
With that partnership between the human and digital (AI) workforce in mind, our team is excited to introduce our latest demo, showcasing how AI can transform your customer’s experience: a custom Generative AI application designed specifically for customer success and sales.
This state-of-the-art solution aims to transform traditional customer success models by delivering personalized, efficient, and scalable interactions to help customer service and sales agents better communicate with customers. In this article, we’ll delve into the leading-edge technology behind our tailored application and explore the benefits it offers.
System Architecture
There are many ways to design a Generative AI system for customer service. This solution can be implemented on any leading cloud platform such as Amazon Web Services (AWS), Google Cloud (GCP), or Microsoft Azure.
For this application, any of the major Large Language Models such as ChatGPT, Claude, Mixtral, etc. can be leveraged. All the conversation history on both the customer and agent sides is stored in a vector database to inform how the model interacts with the customer in all future conversations. Retrieval-Augmented generation system (RAG) is used to take advantage of the pre-trained LLMs and provide new context based on the history of chat conversations and internal databases.
This system can be integrated with any customer success platform that collects and utilizes data to feed the GenAI platform. If you use Salesforce, a direct pull from those data sources can be used to create embeddings to better inform the GenAI tool on how to engage and support your customer service team.
Key Features of Our GenAI Customer Success App
This customer success application has multiple features to demonstrate the broad range of capabilities using GenAI to enhance the customer experience. Each component is its own Generative AI tool that integrates and runs seamlessly in a user-friendly interface.
1. Sentiment Analysis: This benefits customer interactions by performing sentiment analysis on conversation history to create a real-time measure of how your customers are feeling, before and during the customer interaction.
2. Chat Summarization: To aid in distilling the lengthy and detailed conversations, the LLM chat summarization feature automatically distills each interaction into its more important details. This helps your representatives become the most informed about the customer in the shortest amount of time.
3. Product Recommendations: AI-driven personalization recommends products based on sales goals, customer interactions, and cross-selling and upselling opportunities.
4. Question and Answer: A personalized virtual customer service and sales assistant aids your representative’s contextual response accuracy and speed.
Implementation Process
There is no one size fits all solution for using GenAI to assist customer facing agents, however, there are many tools that attempt to provide a general solution that does just that. We believe these tools fall short in terms of customization and personalization for your organization’s unique needs.
This is why we created a system that is robust but extremely customizable. Prompts can be modified to change the Question-and-Answer context or add new GenAI prompts altogether for additional features. A custom graphical user interface (GUI) can be tailored for seamless integration with your current customer success tools.
While the LLM’s come pre-trained, the database of chat history, customer products, and any additional company-specific data would be integrated for fine-tuning. The more data collected, the better results you are likely to have from the system.
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We aim to remove the guesswork from the customer success and sales process using GenAI-based tools.
Every organization has their own unique problems and objectives which this tailored solution can be designed around. This innovative AI-driven solution can increase efficiency and conversions for customer service and sales representatives while also improving the quality of those interactions.
Of course, we understand that any tool is only helpful if it can demonstrate a significant ROI for your business. This is why RevGen aligns and tailors any Generative (or other) AI solution to your business objectives – to drive revenue and sales, increase productivity, save costs, and build growth capabilities.
Fine-Tuning GenAI
There are many additional features that could be added to this customer success GenAI solution. However, the most important features are those that help your business achieve your goals and improve KPIs. We can work together to identify which features would be most useful based on your unique business needs.
Conclusion
At RevGen, we understand that AI is only as useful as the benefits it provides. AI is still an emerging technology that is evolving and adapting to new economic and social challenges. We see AI as a tool to help build better relationships and increase efficiency that will have a significant return on investment.
This custom Generative AI solution can be used in any industry to help your customer success team follow up on leads, convert potential clients, and sell new services. The goal is never to replace people, but instead to help them be the best at their job.
Curious about how this tool can work for your organization? Contact us today to schedule a demo.
Derek Plemons is a Senior Consultant of Data Science and Artificial Intelligence at RevGen. He is passionate about leveraging data to solve complex business problems, including building forecasting models, optimization and decision-making algorithms, large language models and cloud computing.
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