Predictive Analytics for Sales Forecasting

The Problem:

A retail chain struggled with inaccurate sales forecasts, leading to stockoutson fast-moving products and overstock on slow sellers — directly impactingrevenue.

Our Solution:
Blockgen implemented a machine learning-based predictive model trained onhistorical sales data, seasonality, regional trends, and external factors likeholidays and promotions. The model continuously improved with live data.

The Results:

• Forecasting accuracy improved by 35%Contributed to a 12% increase in overall sales revenue

• Stockouts reduced by 20%

• Inventory holding costs lowered by 18%

• Enabled more aggressive and accurate promotional planning

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