Founded by a data scientist who contributed to the discovery of the Higgs Boson at CERN.
Many organizations collect large amounts of data but struggle to translate it into strategic decisions.
Quatalyzer provides Fractional Chief Data Scientist services, giving companies access to senior analytics leadership without hiring a full-time executive.
Founded by Bertrand Brelier (PhD Physics, CERN), Quatalyzer applies advanced analytics to business challenges such as pricing optimization, demand forecasting, and predictive customer analytics.
For organizations that want to leverage advanced analytics but do not require a full-time data science executive.
Quatalyzer provides part-time senior leadership to help organizations:
define a data and analytics strategy
identify high-impact modeling opportunities
guide internal teams and external vendors
translate complex analytics into business decisions
This model allows companies to access senior data science expertise while maintaining flexibility and controlling costs.
Small changes in pricing strategy can significantly improve revenue and margins.
Quatalyzer develops data-driven pricing models that analyze historical sales patterns, customer behavior, and market conditions to identify optimal pricing strategies.
Typical outcomes include:
identifying price elasticity
detecting revenue opportunities across products or segments
simulating the impact of pricing changes
improving margins while maintaining competitiveness
Organizations often collect large amounts of operational and customer data but struggle to convert it into actionable insights.
Quatalyzer designs predictive models that support decisions such as:
demand forecasting
customer behavior prediction
lead conversion modeling
marketing performance analysis
These models enable organizations to anticipate trends and make more informed operational and strategic decisions.
Engagements typically begin with a short discovery phase to identify the most valuable opportunities within your data.
Business challenge
Companies often rely on static pricing rules or cost-plus pricing strategies that fail to capture changes in demand, competition, or customer behavior.
Approach
Quatalyzer analyzes historical sales data to estimate price sensitivity and simulate the impact of pricing changes across products, segments, or channels.
Outcome
Organizations gain visibility into revenue opportunities and can implement pricing strategies that improve margins while maintaining market competitiveness.
Business challenge
Marketing and sales teams often struggle to identify which customers are most likely to convert or generate long-term value.
Approach
Machine learning models analyze historical behavior and engagement data to predict customer conversion, retention, or purchasing patterns.
Outcome
Organizations can prioritize high-value opportunities and improve the efficiency of marketing and sales initiatives.
Business challenge
Inaccurate forecasts lead to inventory shortages, excess stock, and inefficient operational planning.
Approach
Predictive models are developed using historical sales patterns, seasonality, and operational signals to forecast future demand at the product or category level.
Outcome
Improved planning accuracy and better alignment between sales, inventory, and supply chain decisions. .
Business challenge
Many operational decisions involve complex trade-offs between cost, revenue, and resource constraints.
Approach
Optimization algorithms and operations research techniques are used to evaluate thousands of possible scenarios and identify optimal strategies.
Outcome
More efficient allocation of resources and improved operational performance.