The operating system for hitting commercial targets.
The Goal Engine takes a measurable business target and turns it into a ranked queue of actions. It understands pacing, evidence, constraints, economics, and trade-offs, then recommends the next move your team can defend.
A goal is not just a number. It is a contract with constraints.
Businesses rarely want growth at any cost. They want revenue inside CAC limits, pipeline inside sales capacity, agent actions inside permission boundaries, and ecommerce growth that respects margin and stock. The Goal Engine makes those rules explicit before it recommends action.
The number the business needs to hit: revenue, CAC, ROAS, margin, pipeline, or account readiness.
The time window that changes urgency, pacing, and acceptable risk.
The campaigns, regions, products, accounts, teams, or agents the engine is allowed to affect.
Budget, stock, margin, sales capacity, compliance, permissions, and confidence thresholds.
The proof required before the system can recommend scale, pause, routing, or agent action.
The result that flows back into the engine so the next recommendation is better calibrated.
| ID | Campaign | Channel | Intent | Lift | Δ / day | Action |
|---|---|---|---|---|---|---|
| C-104 | London · Prospecting | Meta | 82 | T3 | +£1,600 | ▲ SCALE |
| C-118 | Brand · US | 74 | T2 | +£700 | ▲ SCALE | |
| C-133 | Retargeting · Cart | Meta | 58 | T2 | +£300 | ▲ SCALE |
| C-141 | New York · Wellness | TikTok | 22 | T1 | £-325 | ▼ CUT |
| C-152 | Search · Non-brand | 68 | T3 | +£1,125 | ▲ SCALE | |
| C-160 | Broad · APAC | Meta | 14 | T1 | £-300 | ▼ CUT |
▲ RECOMMENDATION · scale C-104 by +£1,600/day because London demand intent is rising, causal lift verified at Tier 3, and you are currently 14% behind your revenue target.
The engine does not wait until a target is missed. It watches the shape of the gap: whether the team is behind pace, whether the shortfall is accelerating, and which actions could realistically close it before the deadline.
From gap to ranked action queue.
The Goal Engine is opinionated. It does not produce a dashboard of everything it noticed. It narrows the field to the actions with the strongest chance of moving the goal.
Compare actual trajectory with target trajectory and identify the size, timing, and shape of the gap.
Pull live intent from campaigns, accounts, products, regions, commerce signals, and agent requests.
Downgrade attractive-looking moves when lift is weak, evidence is missing, or the result conflicts with constraints.
Estimate expected impact, risk, payback, budget requirement, margin effect, and operational load.
Prioritise the few decisions most likely to move the goal instead of flooding the team with observations.
Actioned, dismissed, and failed recommendations all feed back into the next decision cycle.
Goals it can operate against
Each goal type changes the decision logic. A revenue target may favour speed. A margin target may block an otherwise attractive scale recommendation. A pipeline target may care more about account readiness than raw lead volume.
Find the fastest path to a target number without ignoring CAC, margin, stock, or capacity.
Scale, route, bundle, retest, or promote.
Reduce acquisition cost by shifting spend toward signals with stronger incremental evidence.
Cut, reallocate, hold, or change the test plan.
Protect return on spend while avoiding false confidence from platform attribution.
Scale only where lift and economics agree.
Keep growth inside profit boundaries by connecting demand to product economics.
Hold discounts, prioritise high-margin SKUs, or cap spend.
Route accounts when intent, fit, timing, and sales capacity line up.
Prioritise outreach, assign owner, or wait.
Let AI agents act only when purpose, scope, evidence, and permission are clear.
Approve, deny, narrow scope, or request more proof.
goal_id entity_id // campaign, account, product, region, agent decision_type // scale · cut · hold · retest · route recommended_delta expected_impact confidence evidence[] constraints[] trade_offs[] risk status // pending · actioned · dismissed outcome_logged_at
Scale demand only when the expected revenue does not destroy contribution margin.
Move quickly where evidence is strong; retest when the signal is interesting but not yet trustworthy.
Permit agent actions when the scope is narrow and the graph can explain the commercial reason.
Do not create demand that sales, stock, fulfilment, or support cannot absorb.
What a recommendation looks like
The output is designed to be readable by operators and defensible to leadership: action, target, magnitude, evidence, expected impact, constraint, and risk.
SCALE - Increase C-104 by +£800/day because London demand is pacing ahead, OpenLift shows Tier 3 incremental lift, CAC remains under target, and the revenue gap is widening.
CUT - Reduce C-160 by -£420/day. Spend is rising, contribution margin is weak, and the best available evidence suggests the conversions are not incremental.
ROUTE - Send account A-442 to sales today. Pricing intent, product usage, account fit, and return frequency have crossed the readiness threshold.
HOLD - Hold discounting on Product B. Demand is rising without incentive, stock cover is low, and margin would fall below the goal constraint.
RETEST - Run a 14-day Singapore geo test. Current lift is promising but underpowered, so the engine recommends more evidence before scaling.
APPROVE - Approve the agent report request. The principal is verified, scope is read-only, and the report supports an active revenue goal.
Fewer opinions. Faster operating decisions.
Growth teams see which action is most likely to move the target today, not another retrospective report.
Finance gets the expected commercial impact, confidence, and trade-off behind each recommendation.
Sales and RevOps get account routing based on readiness, fit, evidence, and capacity.
Commerce teams can balance demand generation with margin, stock, and regional performance.
AI agents can operate against explicit goals without bypassing human-defined boundaries.