Pricing - machine learning & algorithms
We help our clients make better pricing decisions and exploit the full potential of existing data through the use of prescriptive models and machine learning algorithms
Value for customers
For B2B companies that offer a large number of product codes or families and serve customers operating in different sectors and in different geographical areas, the use of descriptive data analysis is often not enough. We create machine learning models for our customers that make it possible to evolve, simplify and make pricing decision-making processes more effective.
The challenges
The main decisions and challenges companies need to face to improve profitability and successfully compete on the market.
Segmentation
how we can leverage existing data to develop segmentation that takes into account the propensity to pay
How can we prescriptively determine the customers with the greatest potential for improvement through pricing?
Pricing health per customer
Price risk and price power
how can we dynamically adapt pricing levels according to the price risk or the pricing power of the company by segment?
Customer health score
how can we adjust the price per customer according to the customer health score or the customer's risk of loss?
How we help our customers
Data enrichment
Behavioral feature calculation
Influence factors analysis
Decomposition tree on price index
Classifying algorithms
Deployment on machine learning software (knime, rapidminer)
Machine learning models for segmentation
Multivariate regression on target price
Cross & up selling score
Churn score and price risk
Customer profiling based on the type of corrective action (price, volume, mix)
Machine learning models for the price target
Price risk by segment
Price power by segment
Health score per client
Price opportunity score per customer
Business intelligence deployment (Microsoft power BI, Qlik)
Design and training on decision-making processes
Price scoring
(Healh)
Expected results
Predictive models for pricing
Development of predictive and prescriptive machine learning models for pricing based on enriched internal (transactions, offers, etc.) and (where available) external data
Integration with existing systems
Integration of models with existing ERP and CRM systems
Autonomy
Use of user-friendly and low-cost platforms to allow our customers to manage models and algorithms independently or with limited external support.
Why Alyant
Specialization : experience focusing on the development of prescriptive models in the field of pricing
User-friedly technologies : adoption of self-service machine learning software that does not require code development
Integration : integration of models with existing systems (ERP, CRM, business intelligence)