- Expansions in product, customer base, regions served and more make it difficult to serve the optimal prices
- PriceOptimizer can cluster by buying behavior so that pricing professionals can give the right price to the right segment
- PriceOptimizer uses machine learning to optimize prices across several segments
Imagine you have a global sporting goods store that sells every sporting product that you could possibly dream of. Your store in England sells soccer gear including English team jerseys and other similar gear at the same price that you’d sell them in the US. However, the people in the England would be willing to pay double or triple the prices that you set in America. The result is millions lost in potential revenue from not being able to optimize your prices by region or country.
As organizations grow, their product portfolios, types of customers, and regions that they cover change and grow with them. That’s why it is important to have tools that can help you to optimize your pricing across multiple segments so that businesses like the fictional sporting goods store can maximize profits. What I described above is just one of the scenarios that we’ve seen customers face in optimizing their prices.
What PriceOptimizer Can Do to Help Customers
Fortunately, PriceOptimizer is a tool that’s designed to help you overcome segmentation and optimization issues as well as:
- model, import, cleanse and filter the data you need for segmentation and optimization
- present upsell or cross-sell opportunities
- test your hypotheses (like people in the UK are willing to pay a premium for soccer gear)
- present optimized pricing and promotions in CPQ, ERP and digital commerce platforms
- create machine learning models to segment your business
- optimize prices and produce price guidance, margins, promotions, volume accounting for costs, inventory, assortment, etc.
- calculate the willingness-to-pay (WTP) and price elasticity for segments or categories
How Does PriceOptimizer Work
PriceOptimizer uses machine learning or statistical models from clean data to identify your segments. After you define your segments, you can then use PriceOptimizer to identify optimization opportunities. In practice, this works by providing your customers and staff with optimal prices or price guidance for a specific quote or transaction. Going back to our fictional sports good store, you might find out that in England you can charge more for soccer goods because soccer fans there are more passionate about their sport and teams. But that’s not all, because we’ve segmented and started to identify segmentation opportunities, you realize that there is a growing subculture of superfans of Japanese American football fans who will pay premium prices for US NFL jerseys.
The best thing is that no matter how large your growth, it will always be possible to cluster buyer behavior into a self-organizing map even if you have large data sets.