Topic
Game analytics
Subtopics:a/b testingbayesianbiascalibrationcausal inferenceclusteringcohort analysiscohortscpicrmdata engineeringdbtdecaydemand estimationdose responseeconomyelasticityerror propagationexperimentationforecastingguardrailsinterferencelive opslive serviceltvmatchmakingmodel selectionmonetizationmonitoringprediction intervalspvpreplicationretentionrevenuesimulated datastatisticssutvasynthetic datauncertaintyuser acquisitionvalidation
Cohort LTV Forecasting
Spend to CPI to installs to actives to spenders to revenue per user to LTV. Seven links, each with its own estimator, and a chain whose weakest point is not where anyone expects it.
A/B Testing in Games
A decade of running experiments on live games, reduced to seventeen rules anyone can apply, a worked case study for each way an experiment lies to you, and the module I built so the pipeline refuses to make the same mistakes twice.
Savepoint Analytics
I believe in the 80/20 rule — right up until the last 20% is the answer. What Savepoint is, why it exists, and the one idea it is organized around.