is a machine-learning framework that helps you analyze and segment your business, optimize your product portfolio and improve pricing.
|PriceOptimizer, which supports many data science techniques, performs data profiling and generates segment-specific optimized pricing and price guidance and delivers it to price lists, CPQ, Digital Commerce and ERP systems.||
What can you do with PriceOptimizer?
- Model, import cleanse and filter the data you need for segmentation and optimization.
- Create machine learning models to segment your business and optimize pricing.
- Calculate the willingness to pay and price elasticity for segments/categories.
- Identify and present cross sell and up-sell opportunities.
- Use external data and rules to constrain the segmentation and optimization models.
- Optimize prices and price guidance, margins, promotions, volumes accounting for costs, inventory, assortment, etc.
- Present optimized pricing and promotions in CPQ, ERP and Digital Commerce Platforms.
- Test pricing hypothesis and (auto) adjust models as necessary.
- Simulate, project and analyze optimization results.
For whom is it?
PriceOptimizer is the right tool for:
- Marketing, category and pricing management
- Price, finance and business analysts
- Data / Pricing Scientists
Machine learning and statistical framework which passes on segmentation and optimized pricing to other Price f(x) modules or other systems. The ETL process (Extract, Transform and Load), data cleansing and enrichment, big data capability, reports, charts and dashboards, hosted SaaS including all hardware, maintenance and 3rd level support, scalability.
How does it work?
Quick definition of data structures and data import (see PriceAnalyzer). Data is profiled and the machine-learning or statistical models are created and run to generate the segmentation and optimization.
Segment- and channel-specific optimized pricing is fed into CPQ, Digital Commerce, and ERP systems.
Customers and sales staff are presented with the optimal price and price guidance for a particular quote or transaction. Results can be viewed, projected, overridden or constrained by external factors. Actuals are compared to projects and changes can be made or suggested dynamically. Take a look at the following example of PriceOptimizer usage:
Customers with similar buying behavior are grouped together in a self-organizing map. Due to its dimension reduction capability, this approach is very suitable for large data sets.
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