Marketing
Our deepest current coverage on the platform — measurement, attribution, customer economics, and experimentation running as one connected stack rather than four disconnected tools.
Budget allocation (MMM)
Quantify the incremental revenue driven by every channel and give finance and marketing a shared, auditable basis for moving spend.
Cross-channel attribution (MTA)
Credit the full customer journey, not just the last click — with the model-choice sensitivity made visible, not hidden.
Customer lifetime value & retention
Score acquisition on long-run value instead of first-order conversion, and flag at-risk high-value customers before they churn.
Pricing & promotion experiments
Bayesian A/B testing and price-elasticity modeling for offers and price moves, with an honest confidence read before you scale one.
Launch & geo impact (Causal / Synthetic Control)
Prove whether a launch, market entry, or campaign actually moved the number, with a real counterfactual instead of a before/after eyeball.
Demand & anomaly monitoring
Forecasting and anomaly checks on the metrics that matter, run on a schedule you set, so a real shift is flagged without waiting for someone to open a dashboard.
SaaS & Subscription
The same statistical toolkit reframed around subscription economics: retention, usage, and plan pricing. Survival analysis, hierarchical forecasting, and Bayesian testing don't know the difference between a customer and an account — only the schema changes. Early engagements are underway; this is not yet a track record.
Churn & retention
Survival modeling on account tenure — which accounts are actually at risk of cancellation, and roughly when.
Usage forecasting
Hierarchical demand forecasting applied to seat or usage-based consumption, down to accounts with thin, intermittent usage history.
Plan & pricing tests
Elasticity and Bayesian A/B testing for tier pricing and packaging changes, before they roll out to the full base.
HR & Workforce
Applying the same survival, forecasting, and anomaly models to workforce data instead of customer data. The underlying methods don't know whether the row is a customer or an employee — only the schema changes. Early engagements are underway; this is not yet a track record.
Attrition risk
Survival modeling on tenure to flag flight risk by team or segment, before it shows up in an exit interview.
Workforce demand forecasting
Hierarchical forecasting for headcount and hiring pipeline planning across team and region.
Compensation anomaly checks
Residual-based anomaly detection applied to compensation data to surface outliers worth a second look.
Insurance
Claims forecasting, policyholder retention, and causal analysis of underwriting or rate changes — the same Bayesian toolkit applied to insurance data. No engagements yet; reach out if you'd want to be a design partner.
Finance & FP&A
Cash-flow and revenue forecasting, anomaly detection for reconciliation and fraud signals, and causal testing of pricing or promotional changes. No engagements yet; reach out if you'd want to be a design partner.
Public Sector
Program impact evaluation via causal inference and synthetic control, service-demand forecasting, and anomaly detection for compliance monitoring. No engagements yet; reach out if you'd want to be a design partner.
Don't see your industry listed as live?
The packs are domain-general. If there's a real use case, we'll talk about what a design partnership looks like.
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