The challenge
A character-goods brand sells many products, and demand for each one can rise or fall quickly. Ordering too much ties up money in unsold stock. Ordering too little means popular items sell out and sales are lost.
Demand also shifts with seasons, holidays and new product launches, so last year's numbers alone were not enough to plan by. The brand wanted a reliable way to know which products would sell before committing to stock and launches.
What Xcrino built
Xcrino built a machine-learning model that predicts demand for the brand's products. It learns from four kinds of data:
- Past sales history
- Product attributes such as character, category and price
- Seasonality and events such as holidays and launches
- Web traffic and wishlist activity
The model was developed in Python with scikit-learn and XGBoost, a gradient-boosting library that works well on this kind of mixed sales and product data.
The forecasts run inside the client's own system, so the team sees them where they already plan stock and launches, with no separate tool to open.
Results
After the model went into use, the brand's online sales revenue rose by 20–35% year on year, compared with the same period of the previous year.
How Xcrino runs AI projects like this
Most AI work at Xcrino starts with a fixed-price proof of concept from $3,000 (about ₹2.7 lakh), so the client sees results on their own data before committing to a full build. Source code and models transfer to the client on full payment.