Forecasting
Methods and validation
Why a Favorite Still Loses Most of the Time: A Bracket Forecast Under UncertaintyA single-elimination bracket does not ask a team to be good once. It asks a team to be good repeatedly, against opponents decided by other matches it does not control.
Methods and validation
When a Forecasted-Control MMM Can (and Can't) Answer a Budget QuestionA forecasted-control MMM can answer a budget question about known dynamics under expected future conditions, but it cannot tell a team whether an action its historical…
Methods and validation
When a Fixed Forecasting Rule Should Become a DistributionA forecasting pipeline that hard-codes its shrinkage constant either over-regresses every subject or under-regresses every subject, since a single mis-set constant pushes all of…
Industries
Where Should the Next Wegmans Open? 7 Checks for a Bayesian Site-Selection ForecastA new-store forecast should separate observed evidence from assumptions before a retail real estate team commits build-out and lease capital or accepts the risk that a new…
Industries
Bayesian Computation in Finance: Modeling Risk as a Distribution, Not a GuessA single number for expected return, volatility, or option value hides how much a model actually knows: a decision-maker cannot tell whether that figure is a confident estimate or…
Implementation
When One Metric Secretly Drives Another: A Buyer's Guide to Vector AutoregressionA data science or analytics leader choosing a forecasting approach for two or more business metrics has one decision to make first: do these metrics only respond to their own…
Industries
Why a Single Forecast Number Hides the Risk You're Actually TakingA single forecast number hides risk because it reports a point estimate without the range of error around it, so the same number can mask a wide swing between a safe bet and a…
Methods and validation
Why a 20% Retention Rate Means Different Things for a 10-User Cohort and a Million-User CohortA marketing analytics lead looks at a dashboard and sees a young cohort retaining at 20%. The number reads the same whether it came from 10 users or 1 million.
Methods and validation
Are My New Product's Sales Incremental or Cannibalistic?A new product launch into a saturated category can grow share, or it can just move units the company already had.
Methods and validation
A Donor Value Model Told This NGO Who Would Give. It Didn't Say What to ChangeA data science team inside a global children's rights NGO spent months building a Bayesian model to forecast donor value. It worked.
Methods and validation
What a World Cup Forecasting Model Teaches About Trusting a Model at AllA model can hit its headline accuracy target and still be wrong on the exact numbers a decision depends on.
Implementation
One Oil Forecast, Five Independent Models: A Case Study in Trusting a NumberA procurement team whose costs track crude oil faces a binary choice: lock in supply now at an elevated price, or wait for the market to normalize.
Methods and validation
From Uncertainty to Insight: What Bayesian Reasoning Means for a Business DecisionA marketing or analytics leader deciding how much stock to buy, how to price a product, or when to worry about churn usually has a forecast in hand.
Methods and validation
Quantify the Uncertainty Before You Pick a Risk PolicyA planner who commits capacity, budget, or inventory against a single-number forecast is committing to a guess about the future. The forecast is usually close.
Methods and validation
Why a Single-Number Forecast Hides the Decision You're Actually MakingA demand forecast that returns one number is answering a question nobody asked.