This project aims to improve the operatonal efficiency, strategic planning, and overall enhance the financial health of an online retail store by leveraging sophistcated analytical techniques. By integrating customer segmentation, anomaly detection, RFM analysis and forecasting, the idea is to eliminate bad utilization of resources, solve customer churn, improve inventory management and identify potential areas of growth in both domestic and international markets.
- Is there a typical behavioural pattern in customers' orders? Can we segregate customers and classify them according to their ordering pattern?
- Can we identify anomalies in purchases to understand atypical purchasing behaviours?
- Performing the Market Basket Analysis
- Recommendation Engine
- How can forecasting model enhance decision making in inventory, finances & markeng?
- Organization needs to introduce campaigns with high impact immediate offers/rewards & launch a Loyalty program to give special focus to customers.
- Using Association rules, sellers can create promotions to drive their sales.
- Anomaly detection help sellers to prepare themselves for handling unusual high demands and help cater customers well.
- Personalized recommendation engine will help sellers to run the user specific promotion and offers to drive sales and revenues.
- Forecast for local market is observed to show slight downward trends over next 12 weeks. The retail store should focus its marketing strategy to improve its local market sales.
- Sales in both local and international markets are not going to fluctuate a lot. Therefore, a stable inventory can be maintained by the retail store.