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.

How it works
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Grocers and Consumers have different views on price

Grocer

  • Business KPIs
  • Price perception

  • Margin
  • Competitor’s promo

  • Price elasticity & Cannibalisation

  • Much more

Consumer

  • Price

  • Promotion

  • Substitute product

  • Quality

  • Brand

  • Much more

Yieldigo uses both grocers and consumers perspectives and translates them into optimal prices

Pricing optimization process is continuously completed by Machine Learning

After initial setup, the pricing optimization is automated, requiring minimal maintenance

image/svg+xmlAlgorithm training and calibration Optimal prices modeling Mapping Customers’ Purchase Behavior Repricing in store 1 3 4 2 Given the setup, now it’s the right time to reprice the assortment Now Yieldigo completely understands what, when, where, why, and at which price the customers want to buy Final prices are provided under retailer’s constrains & settings in the csv format { article_id , store_id , optimal_price } Once the algorithm is trained on retailer’s transactional data it becomes the chain specific price optimization software Weekly, daily or even hourly repricing frequency ERP client’s system Machine Learning Continuously evaluates customers’ reaction to prices and gets smarter with every repricing.

Pricing optimization process is continuously completed by Machine Learning

After initial setup, the pricing optimization is automated, requiring minimal maintenance

1. Algorithm training and calibration

Once the algorithm is trained on retailer’s transactional data it becomes the chain specific price optimization software

2. Mapping customer purchase behavior

Now Yieldigo completely understands what, when, where, why, and at which price the customers want to buy

3. Optimal prices modeling

Final prices are provided under grocer’s constrains & settings in the csv format

4. Repricing in store

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 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.

See what Yieldigo delivered to its clients

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