Algorithmic price management is on the way
Algorithmic pricing has been used for many years in the airline, hotel, and car rental industries, but is now being increasingly adopted in the retail and consumer packaged goods arenas, leading to more dynamic and personalized pricing decisions. For instance, the average duration for retail regular prices has fallen from 6.7 months in 2008–2010 to 3.6 months in 2014–2017. Algorithmic price management solutions help to seize emerging opportunities in these new settings, and build a competitive advantage through adoption of data science and AI methods.
We help our clients build fundamental capabilities needed for algorithmic price management and solve end-to-end pricing use cases. These include:
- Demand and profit modeling
- Regular price optimization
- Price models for replenishable and seasonal products
- Promotion calendar optimization
- Sales events planning
- Dynamic pricing and flash sales optimization
- Price segmentation and personalized pricing
Demand and profit modeling
The ability to predict demand and profit as a function of price and other factors is a cornerstone of price optimization. We use state-of-the-art machine learning methods such as deep neural networks to build highly accurate demand and profit prediction models.
We combine machine learning with specialized economic models to optimize various elements of the price waterfall for different categories of products, including replenishable or seasonal goods. Our models also account for a range of complex effects such as demand cannibalization, price perception and inventory constraints.
External and internal signals
Accurate price modeling is only possible with a wide range of internal and external signals and datasets. Our price management solutions provide powerful data management capabilities for first-party transactional and catalog data, as well as integrations with external data sources for competitive pricing data, public events and weather.