Revenue Control Tower
SQL models + dashboard that quantify every workflow's pipeline impact, plus a weekly AI-written exec narrative and an ICP feedback loop back into the outbound engine.
Revenue Control Tower — attribution
Which automation actually made money? Follow the dollars, then interrogate the mix.
Watch pipeline flow into the pool, then switch views to slice win rate and ICP.
Inbound drove the most pipeline; cold is the clear drag — reallocate that capacity.
Problem
Teams ship workflows and assume they work. Nobody can say which sequence booked which meeting, which workflow's deals close, or which 'ICP' segments are quietly losing money. Without that, GTM spend is allocated on vibes.
Who it's for
The GTM Eng / RevOps owner and the CRO. The artifact that turns 'I built automations' into 'I drove $X and reallocated effort to lift win rate.'
How it works
- Every other project stamps its source_workflow into the warehouse.
- SQL models compute funnel + attribution, stage velocity / stuck deals, and segment conversion.
- A free Metabase/Looker dashboard reads the warehouse live.
- An LLM writes the weekly revenue narrative to Slack.
- Segment win-rates feed refined ICP weights back into P1 and P3 — the closed loop.
Outcome
Attribution shows inbound drove the most pipeline ($941k) while signal-based outbound converted best (36.7%).
The decisive insight: cold outbound is ~20% of deals worked but only ~7% of won pipeline — reallocating that capacity into P4 champion plays (3x win rate) is the highest-leverage move.
Segment conversion validates the ICP by revenue, not opinion: tier A 36.2% vs C 7.1%; Software/Martech/Dev Tools convert ~40–46% vs Fintech/Healthtech ~10%.
How it scales with paid data
- Point the warehouse at a CRM mirror (Fivetran/Airbyte free tier or a nightly n8n sync).
- Add multi-touch attribution once touch timestamps are tracked.
- Productionize segment weights as a config P1/P3 read on each run.