AI Demand Forecasting Case Study: Tokyo Retailer

AI demand forecasting for a Tokyo character-goods brand

Xcrino built a machine-learning demand-forecasting model for a character-goods brand based in Tokyo, Japan. The model predicts which products will sell, so the brand can plan stock and product launches. Built in Python with scikit-learn and XGBoost over 3–6 months in 2024–2025, it helped lift the brand's online sales revenue by 20–35% year on year.

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.

Questions about this project

What data did the forecasting model use?

Past sales history, product attributes such as character, category and price, seasonality and events such as holidays and launches, and web traffic and wishlist activity.

How long did the project take?

Three to six months, across 2024 and 2025. The model was built in Python with scikit-learn and XGBoost.

What result did the brand see?

Online sales revenue rose by 20–35% year on year after the model went into use, compared with the same period of the previous year.

Can Xcrino build a forecasting model for our business?

Yes, if you have enough sales history. We usually start with a fixed-price AI proof of concept from $3,000 (about ₹2.7 lakh) on your own data, so you see the accuracy before committing to a full build.