Advanced analytics
Models built for the decision, not the benchmark.
Causal inference, forecasting and segmentation, delivered into the meeting where somebody has to commit budget. The test is whether the recommendation survives contact with the executive who has to act on it.
Return on marketing spend by channel, from a multi-variate regression - a method I have run for a decade, modernized. It is live and synthetic: drag any channel's spend and watch the modeled return, the blended ROAS, and where the next dollar earns the most.
Each channel's effect is estimated by a multi-variate regression that holds seasonality, price and promotions constant, so a channel's ROI is isolated from the others - in the real method each coefficient is reported with its confidence interval. The decision is the marginal column: move the next dollar to Paid search, where it returns the most, not to whoever spent the most last quarter. Synthetic data, illustrative of the method.
The carryover decay, price elasticity, the method, and the real record - below.
How long a dollar keeps working (adstock)
A dollar of advertising does not spend its whole effect the day it runs, it decays over the weeks after. Modeling that carryover is what tells you how far apart to space campaigns before the returns erode.
Finding the optimal price (elasticity)
Every product answers a price change differently. Estimating elasticity - how much volume you lose for a given price increase - is how you find the point where the next price move starts costing more than it earns.
The method is chosen by the decision, and the record
- Causal inference and experimental design when the question is whether this actually caused that, and a correlation would send real money in the wrong direction.
- Forecasting when the decision is a commitment: inventory, headcount, revenue.
- Segmentation when the decision is who to talk to and what to say. A segmentation that produces seven elegant clusters and no change in how a single customer is contacted has produced nothing.
Hi-tech manufacturing.I built a division's data-science and reporting ecosystem from scratch, then moved it to a cloud lakehouse - recruiting and training internal partners so the capability outlived my involvement. Machine-learning forecasting and segmentation markedly sharpened quote-conversion prediction across global markets.
Solar and cleantech. Econometric and regression forecasting that significantly improved financial forecast accuracy, replacing a set of disparate, separately maintained forecasts with a single global one, presented with its assumptions to executive leadership.
Forecasting: the shape of the improvement
Illustrative shape only, no figures quoted. The accuracy figures for this work stay on my resume rather than on a public page. What the chart shows is the shape: the model tracking actuals closely where the prior approach swung wide.
What forecasting does not do
It does not tell you what will happen. It narrows the range and makes the assumptions arguable, which is worth considerably more than a point estimate delivered with false confidence. The forecasts I have built earned trust by being explicit about which drivers they were sensitive to, not by being right every quarter. A number without its caveats is not a finding, it is a liability - which is why the last mile is the meeting: the recommendation, the assumptions behind it, and the conditions under which it stops being true, in that order.