Automate Routine Support Queries
Deploying LLMs to autonomously handle common inquiries was a game-changer for the agent, who finally could give more attention to the most sensitive issues and bring quick resolutions.
Client Overview
Industry: Retail
Scope: Berin Germany
Specialization: Customer Support Improvements
Project Background
Evolving in the competitive retail sector, a prominent global German brand identified the need to upgrade its customer support offering to retain its diverse and expanding customer base. The team aspired to revamp and consolidated its customer service operations by integrating large language models (LLMs) to provide faster, more personalized support.
Technical Challenges
The team struggled with managing a high volume of customer requests, leading to extended response times.
These delays resulted in inconsistent support quality, impacting the overall customer experience.
Over the past year, there was a noticeable decline in customer satisfaction, posing a risk to customer loyalty.
This decrease in satisfaction has also negatively affected the brand's reputation.
Objectives
Personalize Interactions
LLMs enabled the team to easily access and analyze customer data and deliver real-time personalized support.
Reduce Response Times
Our team found a solution to reduce the average response time to enhance the customer experience.
Boost Customer Satisfaction
This significant improvement in customer support drastically increases satisfaction rates by almost 42%.
Solution
Our approach involved creating a bespoke LLM solution they could easily integrate into their communication channels:
LLM Deployment
Implement LLMs to manage frequent and simple queries across multiple communication platforms autonomously.
Personalization Engine
Developed an LLM-driven system that leverages customer data to tailor interactions, ensuring relevant and personalized support.
Efficient Escalation Process
Set up protocols for LLMs to recognize and escalate complex issues to human agents, maintaining a seamless support experience.
CRM Integration
Seamlessly connected LLMs with the retailer's CRM system to utilize existing customer insights.
Security Measures
Applied stringent security protocols to protect sensitive customer data.
Infrastructure
Built on a robust cloud platform to ensure scalability and reliability.
Tech Stack
Artificial Intelligence
LLM frameworks like TensorFlow for natural language processing.
Cloud Services
AWS for dependable, scalable cloud services.
Frontend Development
React JS is used to create a responsive customer interface.
Team Composition
Project Managers
Ensured the project aligned with strategic objectives.
AI Specialists
Developed and optimized the LLMs for customer interactions.
Data Analysts
Extracted insights from customer data to train the LLMs.
Customer Experience Designers
Designed interfaces that are intuitive and user-friendly.
Quality Assurance Team
Guaranteed the platform's reliability and performance.
Challenges
Balancing Automation with Empathy
Crafting LLM responses that are helpful yet empathetic to maintain the human touch.
Data Privacy
Adhering to strict data privacy standards while leveraging customer data for personalization.
Integration Complexity
Integrating advanced AI technology with existing systems without disrupting service.
Learnings
Continuous Improvement
Regular updates and training for LLMs based on new customer data are crucial for maintaining effectiveness.
Customer Feedback
Actively seeking and incorporating customer feedback is vital for refining the support experience.
Key Innovation
Coditude's integration of large language models (LLMs) into the retailer's customer support framework represents a pivotal advancement in service automation. These models analyze customer inquiries in real-time, generating personalized responses that mimic human interaction. This enhances the efficiency of the response process and ensures a high level of personalization and accuracy, markedly improving the overall customer experience. By utilizing cutting-edge AI, Coditude has elevated the standard for customer support in the retail industry, making significant strides in both response times and satisfaction rates.
Project Results
Enhanced Response Efficiency
We achieved a 40% reduction in average response time, significantly improving the immediacy of customer support.
Elevated Customer Satisfaction
Customer satisfaction scores rose by 25% due to more accurate and timely responses.
Operational Excellence
Enhanced the general customer quality service by enabling the team to save time and focus on the most complex queries.
Conclusion
Our collaboration enabled our client to streamline customer support operations effectively. This improvement in their day-to-day process contributed to boosting their brand's reputation for innovative, customer-first service and establishing a new benchmark in retail customer satisfaction.
Impact Stories: From Vision to Product
Case Study
Pioneering Progress
The client, a market leader in immigration and visa services with a vast network spanning 60 offices in 27 countries, sought to revamp their outdated digital infrastructure.
View Case StudyCase Study
