Scaled a proven winner
A top-performing hero Sponsored Products campaign was underfunded. Daily budget increased from $10/day to $25/day so Amazon had more room to spend where performance was already proven.
Case study · Ecommerce operations
Amazon Growth + Inventory Operating System for an Automotive Parts Seller
S7 Automations connected Amazon Ads, SKU performance, inventory planning, and fulfillment routing into one operating layer — helping PartsDoctor decide where to scale ads, where to cut waste, and which SKUs should move through AWD, FBA, or split shipments.
Amazon AdsCampaigns, search terms, targets, spend, ACOS, and winning SKUs.
S7 Ops EngineRanks growth opportunities against inventory, margin, and fulfillment limits.
Inventory RoutingChooses FBA, AWD, split shipment, hold, or de-risk actions.
Owner DashboardHuman-readable actions with API verification after changes go live.
The challenge
Data everywhere, decisions still manual.
PartsDoctor had the classic ecommerce scaling problem. Amazon Ads showed campaign performance. Seller Central showed inventory and fulfillment constraints. SKU profitability lived in separate exports. Search terms showed demand — but not whether the business should actually push inventory behind that demand.
The account had grown into a large operating surface: thousands of campaign, keyword, product-target, and search-term records; hundreds of SKU and inventory records; legacy campaigns accumulated over time; campaigns enabled but not serving; campaigns spending without sales; and SKU winners that deserved more budget but were tied to fulfillment realities nobody had stitched together.
The questions a real operating system needs to answer
- Which SKUs deserve more ad budget?
- Which campaigns should be paused or de-risked?
- Which products are strong enough to send deeper into FBA or AWD?
- Which SKUs should be held back because ads are creating demand faster than inventory can support?
- When should Amazon shipments be split across FBA, AWD, or staged replenishment?
- What changed, why, and how do we verify it?
What S7 built
One API-led operating loop, not three disconnected jobs.
S7 Automations built an API-led ecommerce operations workflow around the PartsDoctor account, connecting Amazon Ads campaigns, Sponsored Products budgets and bids, search-term performance, SKU-level sales and demand signals, inventory status, FBA replenishment logic, AWD vs FBA routing, human-readable audit reports, and verified API readbacks after every change.
Instead of treating ads, inventory, and fulfillment as separate jobs, S7 treated them as one operating loop.
Audit
Where spend was helping growth — and where it was creating noise.
- Campaign status, budgets, spend, and ACOS / ROAS / CPC / CTR
- Search-term quality and product-targeting performance
- Enabled campaigns with no meaningful traffic
- Enabled campaigns spending without sales
- High-ACOS targets and duplicate or legacy structures
- Existing exact and phrase keyword assets
- SKU demand signals that could inform inventory movement
This produced a clearer map of the account: which campaigns were scale candidates, which needed to be paused, which bids needed to come down, which search terms deserved their own controlled test, and which legacy assets should be consolidated before adding more complexity.
Optimization applied
A controlled batch, executed through the Amazon Ads API.
Paused wasteful active campaigns
Several campaigns were enabled but either spending without sales or adding low-quality noise. These were paused to reduce waste and clean the active account view.
Cleared zero-impression clutter
Multiple campaigns were technically enabled but not serving. Rather than blindly raising bids and creating accidental spend, dead clutter was paused and the account stayed focused.
Reduced bid pressure on high-ACOS targets
For a campaign operating at inefficient ACOS levels, product-targeting bids were reduced by ~30% and the ad-group default bid was lowered — keeping the campaign available while reducing overspend risk.
Verified every change live
The work did not stop at "API request sent." Updated campaign, ad group, and target states were read back from Amazon’s API to confirm the live account matched the intended changes.
Inventory layer
Ad performance and inventory strategy, decided together.
If a campaign is profitable but inventory is thin, blindly scaling ad spend creates stockout risk. If a SKU has strong velocity and margin, it may deserve deeper FBA or AWD coverage. If a product is slow-moving or high-risk, sending too much inventory into Amazon ties up cash and creates storage drag. The inventory layer classifies SKUs into clear action buckets:
Send deeper to AWD
Strong velocity, healthy margin, stable demand, low stockout tolerance, predictable replenishment.
AWD acts as a buffer layer so inventory sits closer to Amazon’s network without forcing every unit directly into FBA at once.
Send directly to FBA
Immediate sales demand, fast turn, profitable ad support, low overstock risk, near-term replenishment need.
These SKUs get prioritized for FBA availability because they convert quickly.
Split shipment
Demand is real but not yet fully predictable.
A portion into FBA for immediate availability, a portion into AWD as buffer, and a portion held back if margin or storage risk is high.
Hold or de-prioritize
Weak ads, low velocity, high ACOS, thin margin, high stock risk, unclear demand.
Hold inventory, reduce bids, pause ads, or wait for stronger demand signals before sending more stock into Amazon.
The operating logic
Do not scale ads without inventory context. Do not send inventory without demand context.
- Pull Amazon Ads and SKU data
- Rank SKUs by sales, margin, velocity, ROAS, and stock risk
- Identify ad winners and waste
- Decide which campaigns to scale, pause, or de-bid
- Decide which SKUs should go AWD, FBA, split, or hold
- Generate a human-readable action report
- Apply approved API changes
- Verify changes back from Amazon
- Repeat after performance data updates
Business impact
Operating the business from reports, not just looking at them.
Instead of manually checking ads, inventory, reports, and shipment options one by one, the operator gets a decision layer that says things like:
“Scale this campaign because the SKU is profitable and inventory can support it.”“Do not scale this campaign because stock is constrained.”“Send this SKU to AWD because demand is stable but FBA does not need all units immediately.”“Split this shipment because the SKU is promising but still needs controlled exposure.”“Pause this campaign because it is spending without sales.”“Lower this bid because ACOS is too high.”
Scope and capacity
What the operating layer covers.
1 operating layer across ads, SKUs, inventory, and fulfillment decisions
1,000s ad entities reviewed across campaigns, keywords, targets, and search terms
100s SKU and inventory records structured for decision-making
API-verified changes after every optimization action
4 routing decisions: FBA, AWD, split shipment, or hold
48–72h review loop for post-change monitoring
Outcome
A cleaner Amazon Ads account, and the foundation for a broader operating system.
- More budget allocated to a proven winner
- Wasteful enabled campaigns paused
- Dead zero-impression campaigns removed from the active view
- High-ACOS product-targeting bids reduced
- Live Amazon API verification completed
- A reusable optimization workflow documented for future runs
The broader system can now expand into SKU-level inventory decisions, AWD routing, FBA shipment planning, and split-shipment recommendations.
Apply this to your operation
If your ecommerce business has reports everywhere but decisions still happen manually.
S7 can build the operating layer between your tools — so ads, stock, fulfillment, and dashboards work from the same logic.
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