Chatbot BI & Data Pipelines
Data Analysis and Reporting that Drive Optimization Decisions
The Challenge
A chatbot had been built for customer support, but the team lacked visibility into how it was performing. Historical conversations lived in GCP BigQuery with no structured reporting. The business needed BI reports and data pipelines to understand returning customers, response times, costs, conversation length, and feedback — and to use that analysis to decide next steps and optimize the solution.
Our Approach
We designed and built data pipelines to extract, transform, and structure historical conversation data from GCP BigQuery so it was ready for reporting. This gave a single source of truth for all chatbot interactions.
Tableau dashboards were built to analyze core operational and business metrics: share of returning customers, response time distributions, cost per conversation, conversation length patterns, and feedback analysis. Stakeholders could slice by time, segment, or channel to spot trends and bottlenecks.
The dashboards and underlying data became the basis for regular reviews. Decisions about next steps — model tuning, routing changes, cost control, or UX improvements — were made from the analysis rather than intuition, leading to targeted optimization of the chatbot and support operations.
The Results
The team moved from having a live chatbot with little visibility to a clear picture of who was coming back, how fast and how long conversations were, what they cost, and how users felt. That analysis directly informed which optimizations to prioritize and how to improve both efficiency and customer experience.
GCP BigQuery Data source
Tableau dashboards Reporting
Data-driven optimization Outcome
Technologies Used
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