Retail use case focuses on improving the product stocks, sales and customer insights- by gender, age, etc.. Retails stores also need data driven tools to plan, schedule and organize inventory and expenses for short term and long term profit.

Problem statements

  1. Which products get sold out frequently and how often to re-stock them?
  2. Which products can be offered at a discounted price and by how much?
  3. What is the amount of sales per city and per store? What is the average price & unit per transaction?
  4. Total no. of customers visiting and how does the customers’ age and gender play a role in the total items sold?

Key Machine Learning Models

  1. Product re-stocking index
  2. Probable discount rates
  3. Sales per store
  4. Sales per city
  5. Customer insights
  6. Expense tracking
Sample graph for sold out items

Key Business KPIs

  1. Organize inventory
  2. Probable Discount offers for maximum profit
  3. Sales and revenues
  4. Customer insights

Data Sources

  1. SAP
  2. Point Of Sales (POS)
  3. Enterprise Resource Planning (ERP)
  4. Excel/CSV Report