Every SKU Has a Job: Scale, Profit, Protect, Recovery, Gateway
The SKU Jobs Framework - JudeLuxe's BOI® (Bid On Intent) methodology - assigns every product in your catalogue one of five commercial jobs: Scale, Profit, Protect, Recovery, or Gateway. Bids are set against the SKU's current job, not against blended account targets.
The single biggest reason most ecommerce Google Ads accounts under-perform isn't poor targeting or weak creative. It's that every SKU in the catalogue is bid against the same blended target. A 60% margin hero gets the same treatment as a 10% margin clearance line. A product about to stock out gets the same bid as one with six months of inventory. A customer-acquisition Gateway product gets penalised for "low ROAS" when the lifetime customer it brings in is worth ten times the order. The fix is treating every SKU as carrying one specific commercial job - and bidding against that job.
Why one ROAS target breaks multi-SKU accounts
For an account with three SKUs and identical margins, a single ROAS target works fine. For an account with 300 SKUs at margins ranging from 8% to 70%, it's a disaster. The bidder hits its blended target by scaling the SKUs with the highest revenue per click - almost always the lowest-margin lines. Revenue grows. Contribution margin collapses. The account looks healthier on the surface and worse underneath.
This is the structural failure of blended-ROAS bidding. Reporting on POAS instead of ROAS surfaces the problem, but doesn't fix it - the bidder still doesn't know which SKU to scale and which to protect. The fix is to assign every SKU a commercial job, then bid against the job.
The five commercial jobs
JudeLuxe's BOI® (Bid On Intent) framework assigns every SKU one of five jobs. One at a time. Never two. The job reflects what the business needs from the product right now.
1. Scale
The SKU has healthy margin, full stock, and there's auction headroom to capture. The bidder is told to push aggressively - broad audience signals, scaling budget, maximum impression share. Used when commercial intent is growth and unit economics support it.
Typical signals: contribution margin above 35%, stock cover of 60+ days, market share below the realistic ceiling, no major auction pressure from new entrants.
2. Profit
Default state for healthy SKUs in steady-state operation. The bidder optimises for the highest contribution margin per pound spent, not for volume. CPC ceilings constrained, intent filtered to high-quality queries, the SKU earns its share of budget on the strength of its per-unit economics. Most of any catalogue sits here.
Typical signals: contribution margin 20–40%, stable demand, stock cover 30–90 days, no competitive disruption.
3. Protect
The SKU is profitable but operating in a contested auction - competitors pushing, CPCs rising, or margin under pressure from supplier costs. The bidder defends position without chasing growth. Hold impression share at a defined floor. Hard CPC ceiling. Intent-restricted audience signals.
Typical signals: rising CPCs from new competitive entrants, share-of-voice declining, margin compression from cost increases not yet passed to retail price.
4. Recovery
Something has changed and the SKU needs careful handling. Stock running low. Margin compressed by a supplier price hike. Returns climbing past tolerance. Performance dipped after a feed change. The bidder pulls back, tightens intent, protects cash while the underlying issue is resolved. The SKU's job will be reassigned once the recovery condition clears.
Typical signals: stock cover under 14 days, return rate spike, margin drop of 5+ percentage points, feed validation errors.
5. Gateway
A customer-acquisition SKU. The bidder accepts lower per-unit margin in exchange for new-customer acquisition that the wider customer-lifetime economics justify. Bidding decisions here are made on blended CAC and LTV inputs, not on the SKU's own contribution margin alone.
Typical signals: hero product status, strong cross-sell or upsell follow-on, high repeat purchase rate from first-time buyers of this SKU, used as the entry point in TikTok or social-driven discovery.
How jobs change
A commercial job is not a one-time setup. It changes as the business changes. Three signals trigger reassignment in the BOI framework:
Stock position. When stock drops below a viable threshold - typically defined as lead time on replenishment plus a buffer - the SKU is reassigned to Recovery. There is no point bidding aggressively for sales you cannot ship.
Cash impact. When a SKU starts tying up cash - slow inventory turn, supplier price increases compressing margin, returns rising - the job is reassigned to reflect the new economics. A product that was Scale last quarter can be Profit this quarter and Recovery next quarter without anything visible changing on the Google Ads side. The bidder adjusts because the underlying contribution profile changed.
Demand and competitive signals. Auction pressure increases, seasonality shifts, a competitor changes strategy - these external signals can move a SKU between Scale, Profit, and Protect within a single week.
Stock signals are pulled daily. Margin and cash signals are pulled weekly. Reassignment is reviewed and executed weekly. No one waits for a monthly review meeting to find out the bidder is still pushing budget at a product that's been out of stock for ten days.
How this translates into Google Ads structure
The five-job framework needs to live in the platform, not just on a spreadsheet. In practice, this means:
- • Custom labels in the product feed - each SKU tagged with its current job (custom_label_0 = "scale", "profit", "protect", "recovery", or "gateway").
- • Asset group segmentation - separate asset groups by job, so the bidder treats each job's economics differently. See our Performance Max approach.
- • Value rules - applied to translate the SKU's commercial intent into the bidder's conversion value. A Scale SKU's conversion value reflects volume priority. A Profit SKU's reflects margin priority. A Gateway SKU's reflects LTV.
- • Bid strategy alignment - Target ROAS for Profit jobs, Maximise Conversion Value for Scale, Target CPA with a margin floor for Recovery.
- • Brand exclusions - applied separately by job to prevent cannibalisation between jobs running against the same query intent. Critical for Google Shopping structure.
This is the operational discipline that makes the math work. Without it, the framework is just a slide. With it, every bid in the account is making a commercially intelligent decision.
A worked example
A Birmingham-based DTC homeware brand managed by JudeLuxe had 312 SKUs running in two Performance Max campaigns and one Shopping campaign. Blended account POAS was 1.1× - effectively break-even.
Initial BOI job assignment based on the audit: 12% Scale, 51% Profit, 23% Protect, 11% Recovery (mostly stock issues), 3% Gateway.
After 90 days of running on the framework, with weekly job reassignment as stock and margin shifted:
- • The 38% of spend that had been going to SKUs below 18% contribution margin dropped to 9%
- • Most of that 29% reallocation went to SKUs sitting in Scale and Profit jobs
- • POAS moved from 1.1× to 2.1× - same total budget, roughly £18,000 a month of additional contribution margin
The account is still running. The job mix shifts every week as stock and margin shift. More similar accounts on our case study page.
Why this isn't just better reporting
The common pushback: "Couldn't we report POAS in Google Ads using a custom column?" Or: "Couldn't we just layer value rules?"
Both are useful and we use both where relevant. Neither solves the underlying problem on its own. The problem is structural: Google's bidder is built to optimise against the conversion value you give it. If the conversion value is gross revenue, the bidder optimises against gross revenue. If the conversion value is dynamic contribution margin segmented by SKU job, the bidder optimises against commercial intent - but only if the data feeding it is accurate, up-to-date, and segmented by SKU.
That last clause is where most setups fall apart. Cost data is wrong, missing fulfilment costs, ignoring returns. PMax asset group structure obscures which SKUs are getting which budget. There's no stock-awareness. There's no shared language to explain why one SKU is being defended and another scaled.
The BOI framework is the operational discipline that makes the math work. It's the cost data hygiene, the SKU-level segmentation, the stock and cash signals, the campaign structure, the five-job taxonomy, and the weekly reassignment logic - running as one connected system.
It's not a dashboard. It's how the account is run.
Where to go from here
If you're an ecommerce brand running £15k+/month on Google Ads and your account doesn't yet have SKU-level job assignment, book a free Profit Audit. We'll map contribution margin to a sample of your SKUs and show you how the job mix would shift if you adopted the framework. Whether you work with us afterwards or not, you keep the report.
Put This Framework Into Practice
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