AI and Machine Learning technology to optimize business decisions
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Pricing Analytics
PriceMaker combines powerful numerical methods to estimate the price sensitivity of products with optimization models and business rules to find a set of prices that optimizes your business goals.
Promotional Analytics
Equipped with a powerful predictive analytics engine and deep Machine Learning capabilities, our platform helps you optimize the investment made on trade promotions by automatically evaluating past promotions, learning from history and simulation the results of future promotions, allowing your team to make better decisions every day.
Demand Forecast
Through a collaborative forecasting model, which integrates pricing, planning and sales in a Machine Learning model, our platform gives you and your team a more precise sell-in demand estimation that will allow you to anticipate the needs of your clients and stakeholders.
How it works
Data Integration
PriceMaker integrates your company data with external data such as competitors’ prices, economics indicators, weather, social network activity, reputation, among others.
Deep Learning Training
Our proprietary deep learning algorithms will analyze your data and decide with variables are relevant. Our engine will train dozens of algorithms and choose the best predictions.
Get results
PriceMaker injects the results directly into your ERP, CRM or BI tool, so you don’t need to learn to use a new software. PriceMaker integrates with Tableau, QlikView, Power BI, among others.
All the tools needed to accelerate revenue and increase margins
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Sales Forecast
Price Optimization
Cross-price Elasticity Analysis
Cannibalization Analysis
Customer Micro-targeting
Churn Prediction
Recommendation Engine
Cross-selling Recommendations
Promotion Optimization
They trust us
Use Cases
Trade Promotion Optimization
PriceMaker helped our client increase their promotional ROI by 5x
Our client is a leading Chilean CPG company with more than USD 700 million in yearly revenue, with more than half of that coming from key supermarket accounts. Due an increased intensity in promotional activities over the last 5 years in that channel, and the growing complexity of those trade promotions, their pricing team was unable to analyze and evaluate each promotion to derive insights from them.
As a result, their promotional ROI had been presenting a downward trend for 3 years and was approaching 0%. PriceMaker and its predictive analytics capabilities were implemented to optimize pricing decision-making. After 12 months from its implementation, their promotional ROI went from only 2% to 10%, and their trade promotion margin increased in more that USD 1 million.
