Offering the right price at the right time to the right customer is a challenge. The dynamic pricing platform takes various input factors into consideration and solves this challenge while optimizing for sell-side revenue.
Client
METRO/METRONOM
Year
2018, 2019
Stack
Kubernetes, Docker containers, Java/Kotlin/Spring Boot, JavaScript/React
➞ Built for Speed
Real-time price recommendations.
➞ AI and Data
Price recommendations based on AI and massive data analysis.
➞ Open APIs
Price engine recommendation offered as a service to consuming systems.
⸻ Customer Challenge
Why a Dynamic Pricing Strategy
Pricing is an art. Companies in various industries usually opt for a cost-based pricing approach. In the past this way of static pricing worked well, however, with ever increasing software capabilities and competitors gathering pricing information in near- or real-time, companies have started implementing dynamic pricing strategies.
The goal of the dynamic pricing platform had been to develop a pricing solution which takes various factors into account, to serve the end-customer (i.e. the buyer from a wholesaler) the best price and the right time.
⸻ Solution
Moving Towards Real-Time Pricing
For achieving real-time pricing capabilities it was necessary to connect to a wide range of existing systems (from homegrown solutions to standardized off-the-shelf systems). Cleaning the gathered data and pumping the data into the pricing engine in real-time was necessary to deliver on top a real-time price recommendation to the customer.
Related project: METRO Inbound Pricing Solution
