- Augmented Analytics
- Big Data Analytics
- Business Analytics
- Data Governance & Data Fabric Architecture
- Data-Driven Strategy & Transformation
- Performance Management & Improvement
- Quiénes somos
- Webroom & Events
Machine learning is the technology behind any sophisticated dynamic pricing algorithm. These algorithms make optimal pricing decisions in real time.
At the core of the pricing algorithm is a regression model that estimates the impact on revenue for each possible price configuration.
This approach does okay, but it is slightly simple because it does not explicitly model any information about the price configuration itself.
Although new ways of collecting and processing data greatly help them with that, most specialists still use manual crawling techniques.
Price adjustments made for the whole list in just a second, in response to real-time demand, is much more effective than those set manually with all the human mistakes. It is where machine learning steps into the room, giving retailers an option to optimize not only prices, but also marketing and costs.
The very first thing a retailer needs to handle for strategic pricing is data. Before any pricing decision can be made, clean and useful data needs to be collected.
All profitable and loss pricing decisions depend on the data quality.
The next stage in handling a dynamic pricing system is data analysis that should lead retailers to correct and quick decisions based on direct price suggestions or recommendations. To get more insights out of the collected information, there’s a need to visualize it. Good visualization provides the possibility to quickly grab all deviations and react on them.
Price Index and sales time series analysis determine the Price Index elasticity of sales. Machine learning time series analysis can be applied to different periodic fluctuations of any nature by year, month, week.
When a retailer handles a qualitative visualization, he can get the best time for competitive price monitoring, analyze and improve sales performance, study customer behavior.
The number of variables that needs to be taken into account before price setting is very wide, to apply all of them to a single product pricing, the what-if approach can be helpful, but it’s nearly impossible to handle thousands of products in no time without automated rules-based pricing algorithms.
Retailers with no strategic pricing are playing in a blind zone with no estimation engine that is able to calculate, analyze, adjust, and set prices without mistakes, and which allows testing all hypotheses before applying them to the whole inventory.