Project Overview

The goal was to enhance sales forecasting accuracy by integrating external market signals, such as economic shifts, social trends, and competitor actions into predictive models. Traditional forecasting methods relied solely on historical sales data, missing real-time contextual factors influencing customer demand.

Problem Statement

Sales performance often fluctuated in response to market events (e.g., product launches, policy changes, or global disruptions), but these effects weren’t quantified. Manual analysis was inconsistent and reactive, making it difficult to anticipate demand surges or drops driven by external forces.

Solution

We designed an AI-powered forecasting pipeline that combined natural language data extraction and market impact estimation models.
The system continuously gathered information from online news, financial reports, and social media to detect relevant market events.
A trained regression-based AI model, fine-tuned with historical event-sales correlations, then estimated the quantitative impact of each event on product demand.

Core features included:

• Automated web scraping and event detection through NLP-based entity recognition.

• Correlation modeling between events and sales KPIs.

Result

• 20–30% improvement in short-term forecast accuracy.

• Automated early warning system for event-driven demand changes.

• Actionable insights for marketing and supply chain teams to adjust strategies in real time.

• Scalable AI framework adaptable for new data sources or product lines.