Business Situation
- A US-based mid-market investment bank needed a comprehensive solution to manage its unstructured deal-related data and enhance its buyer and investor outreach.
- The client wanted to assimilate large volumes of unstructured data scattered across Excel files, emails, PDFs, databases, and third-party subscriptions into a centralized, unified, standardized data warehouse that integrated easily with the customer relationship management (CRM) tool.
- The bank required incorporating cutting-edge automation technologies into the process to replace traditional, inefficient data extraction systems.
SGA Approach
- Data Extraction: Gathered data from multiple unstructured sources using Python and structured query language (SQL) to unify the extract, transform, and load (ETL) pipelines into a single data warehouse.
- Data Structuring: Standardized syntaxes and formats of names, business descriptions, and key personnel details including designations, emails, and phone numbers.
- Data Cleansing: Created multiple customized micro-automations to identify and correct anomalies in the extracted data versus the standard format, ensuring all data was consistent, usable, and integration-ready.
- Data Usability: Ensured compatibility with CRMs such as MadeMarket, DealCloud, Pipedrive, etc.
- Data Ingestion: Enabled seamless data integration into client’s CRM systems, ensuring usability and accessibility through application programming interfaces (APIs).
- AI-Led Outreach: Deployed Agentic Artificial Intelligence (Agentic AI) workflows to automate buyer and investor outreach, track responses, and refine engagement strategies.
Key Takeaways
- Efficiency Gains: Achieved an 80% reduction in time spent on data extraction and cleansing.
- Automation Impact: AI-driven workflows improved response tracking and outreach efficiency by 70%.
- Customer Acquisition: Enhanced outreach accuracy, leading to a 60% increase in investor and buyer engagement rates.
