Empowering Customer Service Teams with Machine Learning
Data Smiles leveraged machine learning and data integration to transform a tech company's customer service, enabling efficient issue resolution, proactive problem identification, and data-driven product enhancements.
Client Challenge:
A rapidly growing tech company struggled to manage the influx of customer service data from various sources, including Zendesk tickets, product reviews (G2, iOS App Store, Google Play Store), and NPS surveys (Qualtrics). The lack of integration and actionable insights hindered their ability to prioritize issues, identify trends, and proactively address customer concerns.
Data Smiles Solution:
Data Smiles partnered with the tech company to develop a comprehensive machine learning solution that transformed their customer service experience. The following steps were taken:
Zendesk Ticket Summarization: Data Smiles leveraged HuggingFace's summarization models to condense lengthy ticket texts into concise summaries. This enabled customer service representatives to quickly grasp the essence of each ticket, improving response times and efficiency. The summaries also facilitated pattern recognition, helping identify recurring issues or potential product outages.
Product Review Analysis: Data Smiles deployed an LLM embedding model from HuggingFace to analyze product reviews from various platforms. The resulting embedding vectors were used to create a user-friendly interface allowing product managers and engineers to search for specific topics, such as "pricing" or "performance." This semantic search approach went beyond keyword matching, surfacing reviews relevant to the context and theme of the inquiry, significantly enhancing efficiency.
Bug Prioritization: Data Smiles integrated data from the company's ticketing system (Jira) with customer revenue data from their billing system. Machine learning models were developed to match related issues in tickets and score their priority based on the number of affected customers, total revenue impact, and the duration of the reported issue. The priority scores were then automatically updated in Jira, enabling internal teams to address critical issues first, reducing customer churn and improving satisfaction.
Client Results:
Improved Response Times: Zendesk ticket summarization enabled faster ticket processing, reducing customer wait times and improving overall satisfaction.
Proactive Issue Resolution: Pattern recognition in summarized tickets allowed the company to identify and address potential product outages proactively, minimizing customer disruption.
Efficient Product Development: Semantic search in product reviews empowered product teams to quickly gather insights on specific topics, informing product development decisions and prioritizing improvements.
Enhanced Bug Prioritization: The machine learning-powered bug prioritization system ensured that critical issues were addressed promptly, minimizing customer impact and reducing churn.
Data Security: By deploying models locally, the company retained control over sensitive customer data, ensuring compliance with privacy regulations and building trust with customers.
Conclusion:
Data Smiles' machine learning-driven solution revolutionized the tech company's customer service experience, enabling them to proactively address customer concerns, prioritize critical issues, and make data-driven product improvements. The solution's focus on data security and compliance ensured that customer data remained protected while delivering tangible business value.