Get in touch
Case Studies

Price Optimizer

Price optimization to maximize revenue with algorithmic intelligence.

Context

In this project, we used algorithms to determine the optimal price for each product among endless possibilities, aiming to maximize the company’s revenue.

Challenge

The company needed a price adjustment optimizer that, according to business rules, would deliver an optimized pricing scenario (share or revenue), mapping out price elasticity curves at SKU/State and SKU/Region levels.
The company often sets price variation targets (PINC – Price Increase) for products, which may vary by region, segment, or other granularities. The goal was to distribute the PINC in a way that optimized gains and achieved better results for the company.
Elasticity Modeling
The challenge was to calculate, through price adjustments in a specific product category and adhering to established constraints, which price variation pattern across products would maximize the company’s market share or revenue.
Approach

Our strategy

Our service is designed to provide a team of dedicated specialists to turn your challenges into opportunities.

1

We developed a model that uses price elasticity to determine how a price variation for one product impacts all others, resulting in a model that provides cross-price elasticities, allowing us to simulate the market with price variations for each product and calculate the company’s variation.

2

The project optimization uses the Monte Carlo method, generating multiple price variation iterations and using the elasticity model to calculate market share and revenue gains for each simulation, determining the iteration that maximizes these gains.
Results

Measurable Impacts and Results

Each product received a price variation and prediction based on that variation, enabling separate calculations of the price change’s impact on each product’s variables.
With the price optimizer, we suggested ideal price increases, considering the maximization of market share or revenue in a relevant way;
The resulting pricing strategy is dynamic and adaptable, continuously re-evaluated based on new data and market variable changes.
This iterative process allowed for constant price optimization, ensuring an agile response to market fluctuations.
Cases

Conheça outras histórias de sucesso

Turn your data into a competitive advantage.

Contact Oper and discover how our solutions can revolutionize your business.
Talk to Oper