Project Details

In this project, I successfully integrated call tracking data from a third-party vendor into a Customer Relationship Management (CRM) system. Utilizing Mage.ai, I orchestrated a seamless data pipeline that involved fetching, processing, and visualizing the data for actionable insights.

Tools & Technologies

Mage.ai: Employed for extracting data from the vendor’s API.

Google BigQuery: Used for storing and managing large datasets efficiently.

Power BI: Utilized for creating interactive dashboards to visualize the data.

Project Workflow

1. Data Extraction: Leveraging Mage.ai, I connected to the API of the call tracking vendor. This involved setting up authentication and querying the necessary endpoints to gather call data relevant to our CRM needs.

2. Data Transformation: Once the data was extracted, I applied transformation rules to ensure the data was clean, structured, and ready for analysis. This included filtering out unnecessary fields, handling missing values, and formatting the data to align with our CRM data structure.

3. Data Storage: The processed data was then loaded into Google BigQuery. This cloud-based data warehouse allowed for efficient querying and analysis, ensuring scalability as our data needs grow.

4. Data Visualization: Using Power BI, I created a series of interactive reports and dashboards that provided insights into call metrics, trends, and CRM performance. This visualization made it easier for stakeholders to interpret the data and make informed decisions.

Outcomes

This project enhanced our CRM capabilities by providing real-time insights into call data, which improved decision-making processes and contributed to more effective sales strategies. The integration streamlined workflows and reduced manual data entry, ultimately leading to improved productivity and accuracy.