Forecasting is not enough for retail. It must be nowcasting.
Examples of Predictive Analysis;
- Churn analysis,
- Fraud analysis,
- Scores / score cards
- Customer behavior analysis
- Advanced Sales Forecasts and Correlations
- Customer and store segmentation
There are 3 modules of Detailer Prediction:
- SHELFCHECK - Momentary OutofStock Prediction
- LOSS PREVENTION
- POINT BASED INTELLIGENCE
SHELFCHECK - Momentary OutofStock Prediction
- Shelfcheck gives prediciton regarding stock management issues, it makes sales prediction based on location and it lets store manager to monitor and predict the changes and shelf availability in real time.
- It helps to avoid from out of stock cases by predicting and letting store managers fulfill the shelf with new products .
Our solution let retailers understand fraudelent cases regarding stock loss without being late and find out the root causes of fraud by using POS and sales data. Our solutions decreases the stock loss and saves captial for retailers.
Manage density of cashes, queues, cashiers breaks, money or carry bags to improve performance of cashiers.
- Fraud is the most of the important element of stock loss
- We use BI and data mining technics in order to identify fraudelant cases
- Employees and customers are the two top domains of the fraud
- Our solution use information sources such as POC, information kiosk, self check out
- Customers are in interaction. Those data is being collected and transformed into a Anaytical data set staying in Obase Retail DW.
- The solution allows to use and store 3rd party data within data mining and analysis process
POINT BASED INTELLIGENCE
It makes prediction and scoring in order to give insight regarding which points to open new stores. It helps to make better decisions in order to increase efficiency depending on the geographical data.