Predictive analytics and decision science

Planning demand, choosing which leads to pursue, deciding how to retain customers and setting prices require different estimates and different responses. The work starts by establishing the decision, the information available when it must be made and how the result will be used.

My projects span lead scoring, customer retention and retention automation, price automation, and demand forecasting across thousands of series. I work from data preparation and model development through integration with the people and systems that act on the results.

A forecast for staffing, purchasing or financial planning needs to arrive at the right level of detail and early enough to affect the decision. Large collections of related series also need a repeatable process for updating forecasts, handling missing data and identifying changes that require attention.

I develop forecasting pipelines and compare their predictions with the approach already in use. Validation follows the information and timing available in operation, while error analysis examines the consequences of over- and underestimating demand. Prediction intervals and scenario analysis help the people planning capacity assess uncertainty.

Lead scoring helps prioritize acquisition effort. Retention models identify customers at risk of leaving. Customer-value estimates connect expected behavior to future contribution. Together with lifecycle and campaign analysis, these support decisions from the first contact through renewal and expansion.

I connect those analyses to the operating response: who receives attention, which action is available, what it costs and how it is delivered. For retention, a high risk score and a strong response to an offer are different findings. Experiments and response models help determine where an intervention changes behavior, while service and communication systems put the chosen policy into practice.

Pricing work connects expected demand, margin and commercial constraints to a price that can be applied in the operating system. My work has included price automation alongside forecasting and customer analytics. The implementation must account for how often prices change, which exceptions need approval and how results feed back into the next decision.

When the question is what a changed price, offer or service action would achieve, experiments and causal analysis provide the relevant comparison. I help define the eligible population, alternatives and outcome, then connect the estimate to a decision policy that reflects capacity and costs.

Model development includes validation, interpretation and integration from the outset. I examine whether the training data represents the decision, test performance on information available at the time, and investigate errors that change the business response. Explanations should help an analyst understand a result or find a defect.

The same work applies to an existing system. The improvement may be a revised target, a repaired data pipeline, a better decision threshold or a different model. Monitoring and feedback from the people using the output show where further work is needed.

The practice draws on multi-year client engagements in healthcare, insurance and financial services, as well as advice to portfolio-company teams building their data-science capabilities. Organizations I have worked with include RxSense / SingleCare, iptiQ (Swiss Re's digital insurance business), and Loeb Enterprises and its portfolio companies.

Across that work, the recurring challenge is to connect analysis with an operating decision. The experience spans customer acquisition and retention, campaign decisions, pricing and forecasting, with responsibility for the models, data pipelines and integration that make the analysis usable.

My financial-markets work includes research infrastructure, predictive models and live trading systems. In Machine Learning for Trading, I develop the path from data and research design through model validation, portfolio decisions and implementation.

The book's worked applications and software let readers examine these methods in detail. They complement the commercial forecasting and customer-analytics work described above.

You can engage me for a specific analysis, an independent review or continuing development alongside your analysts and engineers. The starting information is the decision, the available data, the current approach and what would count as an improvement.