Advertising spend kept rising. The profitable search terms were being missed.
An established Home & Kitchen seller was receiving steady orders, but rising advertising costs had made the account difficult to scale. The seller wanted every bid reduced. A deeper account-wide review showed that the more expensive mistake would be cutting the campaigns that were already bringing high-intent buyers.
Scenario-based analysis · Modelled data, not a verified client case
What was happening inside the account?
The catalogue consisted of 24 advertised child ASINs across FBA and merchant fulfilment. Eleven Sponsored Products campaigns had accumulated over time, with Auto, Broad and Exact targeting often aimed at similar customer searches.
Campaign-level dashboards appeared inconsistent: some campaigns had respectable ACoS but stopped delivering by afternoon, while others spent throughout the day without attributable orders. Comparing only campaign ACoS obscured the wider relationship between ad investment, total orders and after-return sales.
Before any bids changed, the seller also needed a verified mapping of child ASINs to all corresponding Seller SKUs. A wrong mapping could create a technically valid bulk upload that advertised the wrong inventory or omitted a fulfilment offer.
What the diagnostic review uncovered
Unproductive search intent
Thirteen search terms accounted for approximately ₹19,400 of spend without attributed orders during the illustrative baseline. The team would separate weak purchase intent from search terms that simply lacked enough clicks to judge fairly.
Budget-limited winners
Eight Exact-match keywords had demonstrated repeat conversions yet sat in campaigns repeatedly constrained by daily budgets. Their observed CPC and conversion rate warranted closer analysis before any bid reduction.
Overlap between discovery and high-intent traffic
Auto, Broad and Exact campaigns were competing for similar queries. Without campaign-level intent and negative-keyword discipline, discovery spend diluted accountability.
Incomplete product-ad coverage
Six advertising records needed their MFN/FBA mappings checked against the live catalogue. This validation came before bulk operations, not after upload errors.
How the response would be executed
- Days 1–4
Build the account map
Reconcile campaign, targeting, search-term, placement and advertised-product reports against the Business Report. Link each child ASIN with its true MFN/FBA Seller SKUs. Preserve proven converting keywords and record existing negative targeting.
- Days 5–9
Correct genuine waste
Evaluate each costly non-converting query for relevance, sample size, CPC and conversion opportunity. Use Negative Exact or Negative Phrase only where supported; audit existing negatives before adding new ones. Avoid excluding related searches merely because the recent period was quiet.
- Days 10–15
Recover missed demand
Harvest validated converting queries into 21 focused Exact targets and 11 controlled Phrase tests in the model. Check overlap and cannibalisation, and separate discovery traffic from proven purchase-intent traffic.
- Days 16–30
Reallocate and scale selectively
Move budget toward campaigns with evidenced demand, not simply the lowest ACoS. Compare actual CPC with bids and allowable acquisition costs, then adjust bids, budgets and placements only where data justifies the move.
- Following 45 days
Measure the account, not just the ads
Track post-change orders, attributed sales, total sales after returns, ACoS, TACoS, conversion behaviour and exceptions. Log each change and revisit decisions after sufficient observation, accounting for seasonality, stock and listing changes.
What the modelled comparison shows
Representative modelled comparison — equal 45-day periods. These are fictional representative figures, not actual customer performance.
| Performance metric | Before | After |
|---|---|---|
| Advertising spend | ₹78,600 | ₹85,900 |
| Ad-attributed sales | ₹2,78,400 | ₹4,19,800 |
| ACoS | 28.2% | 20.5% |
| Total sales after returns | ₹5,10,000 | ₹6,77,000 |
| TACoS (after-return sales) | 15.4% | 12.7% |
In the model, advertising spend rose by about 9% while attributed sales increased by about 51%. ACoS improved by 7.7 percentage points, and TACoS also fell. This illustrates how selective traffic correction and budget allocation could support both efficiency and growth. It does not prove those actions alone caused the result.
What a seller would receive
- Seller SKU–child-ASIN product map
- Campaign-level keep / change / pause decision register
- Search-term and negative-keyword action log
- Validated incremental Sponsored Products bulk file
- Weekly performance and post-change exception review
Cutting every bid can be as costly as overspending. First distinguish profitable demand from weak demand, then decide where to protect, correct or invest.
All metrics, events and outcomes in this example are modelled. Actual performance depends on product margins, stock, offer quality, competition, seasonality, marketplace decisions and implementation.