Fractional CDAO & Strategic Data/AI Advisor
Turn analytics and AI into better decisions, stronger economics and measurable business outcomes.
Senior Data & AI leadership for companies that already have data, teams and technology — but are not getting enough business value from them.
20+ years in analytics · Executive leadership · Retail · E-commerce · Entertainment · Logistics · iGaming
The Outcome
Understand which data, analytics and AI initiatives actually deserve investment.
Find where acquisition, retention, pricing, operations or incentives create or destroy value.
Move from explaining yesterday to anticipating customer and business behaviour.
Build the right operating model, ownership, processes and analytical capabilities.
Replace conflicting metrics, endless dashboards and ad-hoc analysis with a coherent decision system.
The Real Problem
There are dashboards everywhere, but different teams interpret the business differently.
Most analytical capacity disappears into reporting and ad-hoc requests.
Insights are produced, presented — and rarely change what the business actually does.
Interesting technology is being built without a clear link to revenue, margin or risk.
Local metrics improve while overall business outcomes stay flat.
Companies rarely lack data.
They lack a system that turns data into better business decisions.
When It Helps
The business needs senior Data & AI leadership but does not yet need another permanent C-level role.
Analytics exists, but priorities, business ownership and decision-making are fragmented.
The organization is preparing for growth, investment, transformation or expansion and needs the Data function to mature with it.
The company wants AI initiatives that improve economics rather than simply produce interesting demonstrations.
Where I Create Value
Understand acquisition quality, retention, churn, lifetime value, segmentation and customer profitability.
Use data to improve conversion, pricing, monetisation, cross-sell and revenue opportunities.
Move from explaining what happened to anticipating customer behaviour, demand, value and risk.
Identify which channels, campaigns and customer sources create sustainable economics rather than volume.
Prioritise the analytical and AI capabilities that can materially affect business performance.
Create the operating model, ownership, metrics and analytical processes leadership can actually use.
Ways to Work Together
Ongoing senior Data & AI leadership without adding another full-time executive role.
Strategy · Priorities · Roadmap · Team · Decision Systems · Business Impact
An independent senior sparring partner for CEOs, founders, CDOs, Heads of Data and senior leadership teams.
Strategic Choices · Investment Priorities · AI Opportunities · Business Economics · Transformation
A defined engagement around a specific business problem.
Customer Economics · Predictive Analytics · Pricing · AI Strategy · Analytics Redesign · Decision Systems
The engagement model follows the business problem — not the other way around.
Selected Cases
Different industries. The same principle: analytics matters only when it changes a decision.
Retail
+5%
Sales
A predictive algorithm analysed each customer's profile and purchase history to estimate what they were likely to buy next — and when. Shortly before the predicted purchase window, an automated chatbot contacted the customer with a timely reminder. The system generated a 5% uplift in sales.
Predictive Analytics · Next Purchase · Customer Activation
Prediction → Timely Action → Additional Revenue
iGaming
3×
VIP Conversion
A predictive model analysed the first day of customer activity to identify players likely to become VIPs — before that behaviour had developed. The business placed them into proactive retention and relationship management from the start. The share who ultimately reached VIP status increased 3×.
Customer Value · Predictive Analytics · Retention
Early Signal → Early Intervention → Higher Customer Value
Entertainment / Cinema
+8–10%
Revenue
A demand forecast predicted attendance from screening time and day, stage of the film's run, genre, cast and director, weather, location and other factors. Ticket prices were adjusted to expected demand, delivering an 8–10% increase in revenue.
Demand Forecasting · Dynamic Pricing · Revenue Optimization
Demand Forecast → Pricing Decision → Revenue Growth
Where is value created? Where is it lost? What will happen next? What decision should change?
Retail, entertainment, logistics, digital businesses and iGaming use different language and operate differently. But many analytical problems share the same underlying logic: understand behaviour, predict what comes next and turn that prediction into a better business decision.
About
More than two decades building and leading analytics capabilities inside real businesses.
I have spent more than 20 years working with data and analytics across retail, e-commerce, logistics, digital businesses and iGaming.
My work has covered the full path from business economics and analytical strategy to predictive models, customer intelligence, AI systems and data organization design.
I work at the intersection of business, analytics and technology — translating each into the language of the other.
View LinkedIn Profile →Let's Talk
If you think there may be a fit, send me a message directly. No sales process, no generic discovery call — just a conversation about the business problem.
+34 690 742 449