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How a Global Financial Firm Optimized Complaint Resolution Time by 30% with Gen-AI?

Learn how Gen-AI optimized operations for a top financial firm with call center services improved complaint resolution time and staff productivity.


Generative AI - Call Center Optimization

INTRO AND CLIENT BACKGROUND


We recently partnerd with a prominent financial services company in USA, offering financial, investment, and call center services globally.


With a vast customer support center managing over 50,000 complaints monthly, the company sought to enhance efficiency and customer satisfaction through innovative solutions.


Company Size: With over 500 employees, the firm navigates the intricate world of financial, investments, and customer service.

Business Goals: The financial company aimed to empower its call center agents, streamline complaint resolution, and, most importantly, elevate customer satisfaction by reducing resolution times.



BUSINESS PROBLEMS


  1. Surge in Complaint Volume: The rapid growth in customer numbers resulted in an overwhelming surge of over 50,000 complaints and inquiries per month.

  2. Manual Workload: Traditional manual assessment processes caused significant delays in resolving complaints.

  3. Unsatisfactory Experience: Slow resolution times and complaint categorization errors led to customer frustration and negative brand reputation.



KEY CHALLENGES


To tackle these issues head-on, we needed to overcome critical challenges:


  • Complex Data Formats: Diverse unstructured data in emails, forms, and chats posed challenges in quickly parsing and understanding context.

  • Data Security & Privacy: Compliance rules around data privacy added complexity to sharing complaint details.

  • Intent Identification: Accurately categorizing complaints into various types such as fraud, billing, and charges was crucial.


OUR SOLUTION: WHAT DID WE DO?


Gym Industry - AI & Analytics

1. Text Summarization Engine:

  • We deployed an in-house private Generative AI model, leveraging Open-source Language Models (LLMs), to auto-generate summaries with core issue details.

  • The text summarization engine used natural language processing (NLP) to read and break down the customer complaints, extract the key points, and generate concise summaries in natural language.

  • The system was integrated with client's existing tools and softwares so that call center executives can easily and quickly review and act upon.



2. Complaint Categorization: 

  • Effective prompt engineering techniques were employed to accurately identify the primary intent behind each complaint summary.

  • We worked with the client to finalise a predefined set of categories to assign each summary, such as fraud, billing, charges, service quality, etc.

  • The categories were then used to prioritize the complaints and route them to the appropriate teams or departments for resolution.



3. Ongoing Support: 

  • Call center executives were trained to leverage the capabilities of our solution effectively, and continuous feedback loops were established for ongoing improvement.

  • We also provided mechanism for regular and automated updates to ensure the optimal performance and accuracy of our models.

  • Monitored the customer satisfaction scores and resolution times to measure the impact of our solution and identify areas for further enhancement.



VALUE DELIVERED: THE BUSINESS IMPACT


Happy clients

Enhanced Customer Satisfaction
  • The Gen-AI powered system achieved a remarkable 30% reduction in complaint resolution time within just 60 days of its launch, resulting in improved customer satisfaction scores.

  • It also helped the company to protect its brand reputation.


Improved Productivity
  • By automating time-consuming tasks, executives could handle inquiries more efficiently, resulting in a significant boost in overall productivity.

  • The call center agents could focus on more complex and high-value tasks, such as providing solutions and building relationships with the customers. 



ROI AND VALUE FOR MONEY

Project Duration: The Gen-AI project spanned 3 months, from inception to deployment.


The company invested $25,000 in our solution, which resulted in an quarterly benefit of $32,000, based on the reduced resolution times, improved customer satisfaction, and increased agent productivity. 


This translates to a return on investment (ROI) of 28% in just 60 days of launch. The company also gained a competitive edge in the market by adopting our cutting-edge technology and enhancing its customer service quality.



CONCLUSION


In conclusion, this case study shows how human-machine collaboration helped a leading financial services company improve its call center operations and customer satisfaction. This demonstrates the power and potential of this cutting-edge technology for delivering value for both businesses and customers.


Interested in knowing how this technology can transform your business as well? Feel free to reach out or try with our free data discovery program here: www.maticsanalytics.com/book-online.

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