Every engagement runs through the same three stages, applied across the four specialist domains where consumer commercial tends to leak value: marketplaces, partnerships, owned digital, and customer lifetime. Each stage hands back a concrete deliverable; the three together build a commercial system that runs at scale once embedded, without further intervention from the practice.
Where the people, process, and tools currently sit; what they do well; where they are under-built. Mapping is exploratory and works whether you are at the start of an engagement or mid-stream, in steady state or in flux.
A documented read of the current state across people, process and tools — where the operation is robust, where it leaks, where the next-level decisions will run into it.
Unit economics across channels, marketplaces, franchises and agency partners. What every counterparty actually contributes; where the conflicts sit.
Cohort LTV with confidence bands. Forecast accuracy assessed against an honest baseline. Underpinned by the practice’s proprietary forecasting library.
A documented account of your current commercial operation — what the team, process and tools do well, where they leak, and where the next-level decisions will run into them. Aligned with the team on where the pain points and the opportunities sit. Worth doing on its own.
The architecture of your next-generation commercial system, designed for your business across each specialist domain. AI, machine learning and structured decision systems are built into the design where they materially lift performance — not as showcase, but as the working engine of the calls the system has to make every day.
Fee-elasticity modelling, take-rate engineering, seller-behaviour prediction. Marketplace P&L as a managed asset, not a quarterly review.
Joint-forecasting models with confidence bands. Deal-NPV simulation under uncertainty. Partner unit-economics architecture across franchise, agency and marketplace.
Funnel-decomposition modelling, basket-economics design, channel-mix optimisation. Retention loops engineered with machine learning, not assumed.
Cohort LTV modelling with churn prediction. Loyalty programme economics. Retention engineering treated as a managed asset, not a downstream metric.
A clear set of changes across each workstream — pricing rules, partnership economics, funnel design, cohort treatment — built around AI, machine learning and structured decision systems that lift performance materially. Documented, defensible, and ready to operationalise.
The build phase. Models go into production. Automated decision rules run continuously. Bespoke tools and dashboards stand up. The team is designed and trained around the new system — not as its operator, but as the small group steering the 1% of calls only humans should make. Once embedded, the system runs without further intervention from the practice.
Production-grade decision rules running continuously — pricing, promo, range, replenishment. Models in deployment, re-fitting on a cadence, not gathering dust in a PowerPoint.
Bespoke forecasting models, LTV tools, partnership-value frameworks — built on the proprietary forecasting library so they’re reusable across the business and across future engagements.
New roles around the new system. Decision rights, accountability, and the capability build so the team operates the system at speed.
Pre-committed thresholds, review cadence, the named meeting where the rules get re-tested as conditions change.
A production-grade commercial system that runs the bulk of trading decisions automatically — with the operating team designed and trained around it, the models and tools built to be reused as the business and the market change, and the governance to keep the system honest. Once embedded, it runs without further intervention from the practice.