CASE STUDY · Robotic Process Automation
Our data triage focus is on leveraging insights derived from customer feedback to identify bugs on the client’s applications and perform analytics on the diagnostic data, therefore pinpointing the source of frustration experienced by our client’s customers.
THE STORY
Our data triage focus is on leveraging insights derived from customer feedback to identify bugs on the client’s applications and perform analytics on the diagnostic data, therefore pinpointing the source of frustration experienced by our client’s customers. We sift through millions of data, leveraging the power of data analytics, machine learning & LLM (Large Language Models) to understand content and context within the text submitted as feedback.
Our data triage focus is on leveraging insights derived from customer feedback to identify bugs on the client’s applications and perform analytics on the diagnostic data, therefore pinpointing the source of frustration experienced by our client’s customers. We sift through millions of data, leveraging the power of data analytics, machine learning & LLM (Large Language Models) to understand content and context within the text submitted as feedback.
Our data triage focus is on leveraging insights derived from customer feedback to identify bugs on the client’s applications and perform analytics on the diagnostic data, therefore pinpointing the source of frustration experienced by our client’s customers. We sift through millions of data, leveraging the power of data analytics, machine learning & LLM (Large Language Models) to understand content and context within the text submitted as feedback.
01 · CHALLENGE
Amigo Hub’s client, an American multinational technology corporation, faced a significant backlog of over 4 million untriaged feedback items accumulated over three years. These feedback items, collected through various systems like surveys and the Feedback Hub App, required thorough processing to identify underlying problems, suggestions, and to link these insights to the appropriate engineering teams for solution development. Additionally, feedback moderation was necessary to ensure compliance with the client’s Code of Conduct.
02 · SOLUTION
Amigo Hub leveraged data triage, moderation, and analysis techniques, including semantic analysis, diagnostic review, and machine learning models, to process and understand the feedback. They improved the Contextual Normalization model's accuracy by 22% using reinforcement learning, processed the backlog efficiently, and ensured that all feedback met the client's standards.
03 · OUTCOME
The project increased the accuracy of the Contextual Normalization model by 22% through reinforcement learning since January 2023. Amigo Hub processed a backlog of over 4 million feedback items since 2018 and regulated more than 1.2 million feedback items in two years, ensuring compliance with the client’s Code of Conduct. This resulted in enhanced understanding of customer feedback, improved bug identification, and increased customer satisfaction. Additionally, the use of machine learning models significantly improved processing speed and efficiency.