See how Yieldigo solution works
Yieldigo uses Big Data analytics and it combines factors driving customers’ preferences while meeting requirements of grocers to identify optimal prices.
Grocers and Consumers have different views on price
Price elasticity & Cannibalisation
The Price optimization process is done by Machine Learning
After initial setup, the optimization of prices is automated, requiring minimal maintenance
Pricing optimization process is continuously completed by Machine Learning
After initial setup, the pricing optimization is automated, requiring minimal maintenance
Once the algorithm is trained on retailer’s transactional data it becomes the chain specific price optimization software
Now Yieldigo completely understands what, when, where, why, and at which price the customers want to buy
Final prices are provided under grocer’s constrains & settings in the csv format
Given the setup, now it’s the right time to reprice the assortment
User interface is intuitive and easy to use
The initial setup takes only a couple of hours, with 5 main modules needed to configure. Then the administration is highly automated.
Set the optimization objective and boundaries
Set the optimization towards profit or revenue, then set boundaries to keep the prices at desired levels.
Boundaries can be applied at multiple levels
They can be applied to entire categories or only to some article classes, stores, or brands.
Notifications will alert you in case of irregularities
Inconsistencies in data or conflicting boundaries trigger an automated alert.
All prices can be reviewed at multiple levels before export
Price report provides summary of data about current prices at category, subcategory and article level.