The startup offers a sales and marketing analytics platform that utilizes artificial intelligence and machine learning to integrate internal and external data for businesses. This platform provides actionable insights such as sales forecasts and churn predictions, enabling companies to enhance revenue and improve customer relationships.
Funding
$440K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Many companies struggle to accurately forecast sales, optimize pricing, and manage trade promotions due to the complexity of integrating internal data with external market factors. This often leads to suboptimal pricing strategies, inefficient promotional campaigns, and inaccurate demand predictions, resulting in lost revenue and reduced profitability.
Solution
PriceMaker offers a data science as a service (DSaaS) platform that leverages AI and machine learning to optimize pricing and trade promotions. The platform integrates internal data, such as sales history and product costs, with external data sources, including competitor pricing, economic indicators, and even weather patterns. By applying proprietary deep learning algorithms, PriceMaker identifies key variables and generates accurate predictions for price sensitivity, promotional effectiveness, and demand forecasting. The platform then delivers actionable insights directly into existing ERP, CRM, or BI tools, enabling businesses to make data-driven decisions without adopting new software.
Target Audience
PriceMaker primarily targets companies in the consumer packaged goods (CPG) industry and other sectors that rely on effective pricing and promotional strategies to drive revenue and maintain profitability.
Features
- AI-powered pricing analytics to estimate price sensitivity and optimize pricing strategies based on business goals
- Promotional analytics engine that evaluates past promotions and simulates future promotions to optimize trade promotion investments
- Collaborative demand forecasting model that integrates pricing, planning, and sales data for precise sell-in demand estimation
- Integration with external data sources, including competitor prices, economic indicators, weather data, and social network activity
- Deep learning algorithms that analyze data and identify relevant variables for accurate predictions
- Direct integration with ERP, CRM, and BI tools such as Tableau, QlikView, and Power BI
- Cross-price elasticity analysis to understand the impact of price changes on related products
- Cannibalization analysis to assess the impact of new products on existing product sales