Machine Learning in Retail
Finance and Sales
- Dynamic pricing based on market situation;
- Assessment of product layout and shopping routes;
- Predict customer churn based on loyalty score and user history (social media messages, number and frequency of purchases, average spend, and etc.);
- High-precision demand forecast and sales turnover in the long-term horizon.
Logistics and Inventory Management
- Cut costs of online order storage through demand management;
- Introduce new loyalty programs and leverage ROPO;
- Customer segmentation and enhanced product recommendations;
- Reduce the risk of overstock.
Marketing
- Win new customers with help of profiling and personal preferences;
- Customer churn prediction and NPS;
- Targeting and cross-selling.
Internal and External Resources
- Reduce workload on first- and second-level support staff with chatbots;
- Measure NPS from incoming messages;
- Set and track KPIs for employees/retail outlet/region;
- Visualize productivity information and get recommendations on how the current situation can be improved.