Case Study

How a Premium Haircare Brand Turned Generic Search into a Growth Engine with AMC

A multi-marketplace EU brand used AMC Sponsored Ads Radar to unify SP/SB/SD performance by query, quantify NTB contribution, and diagnose funnel leakage beyond “click → order”.

Region

Europe (multi-marketplace)

Focus

Generic search, NTB, funnel leakage

The question

“We’re spending a lot on non-brand search. Is this actually growing the brand — or just burning budget?”

What was missing in console-only analysis

No unified view across Sponsored Products / Brands / Display for the same query.
NTB and halo visibility was shallow and fragmented.
No funnel view beyond “click → order” to explain performance changes.

Our objective

Turn scattered ad reporting into one coherent decision framework:

What to scale vs cut in generic search.
What role each ad product should play by query theme.
Where the conversion funnel breaks — traffic vs PDP vs checkout.

What we did (AMC modules)

We ran three core Radar modules and translated outputs into a practical action plan.

Cross Ad Type Search Term Profitability + NTB

One view per search term × match type × ad product.

Unified traffic + conversion view across SP / SB / SD.
Measured spend, sales, ROAS, NTB sales and NTB share.
Exposed which ad type should “own” a query theme.

Brand vs Generic NTB Lens

Monthly tracking framework for query buckets.

Brand vs generic flags at the search term level.
Match type bucketing + NTB metrics by bucket.
Recurring view for policy decisions (acquisition vs harvest).

Enhanced Search Term Funnel Leak Map

Impressions → Clicks → DPV → ATC → Purchases.

Mapped each query × ASIN × ad product across funnel steps.
Explained “why” behind poor ROAS (traffic vs PDP vs checkout).
Created a cross-functional roadmap (PPC + content + pricing).

Key insights and decisions

Where AMC changed decisions that console reporting couldn’t support.

Insight #1 — Generic search was the growth engine

Not all generic spend is waste — but it must be segmented.

Most rows were generic in the period (no obvious brand tokens).
A subset of high-intent generics drove both strong ROAS and meaningful NTB share.
Generic demand became an acquisition engine when isolated into the right campaigns.

Decision: carve out “NTB Generic” campaigns with clear budgets & KPIs, separate from harvest/efficiency work.

Insight #2 — Ad products had distinct roles by query

SP, SB, SD weren’t competing — they were doing different jobs.

On some themes: SB drove attention + PDP visits; SP harvested purchases more efficiently.
On others: SB was under-funded but highly incremental for new customers.
SD played a small but useful supporting role (remarketing/protection).

Decision: restructure into role-based campaigns — SB = attention/education, SP = capture/convert, SD = protect/re-engage.

Insight #3 — The real leak was DPV → ATC

PPC wasn’t broken — PDP experience was.

Many core generics had healthy CTR and DPV volume.
The biggest drop-off occurred at DPV → ATC, not Click → DPV.
This pointed to price, reviews, PDP story, images, variations, and offer structure.

Decision: build a “Funnel Leakage Radar” to prioritize PDP & offer fixes by impact (not guesswork).

From analysis to execution: Keep / Fix / Cut

By layering the NTB lens on top of the funnel map, we grouped queries into clear decision buckets and produced a prioritized action plan (not just another spreadsheet).

The buckets

Keep & Scale — strong ROAS, healthy NTB share, no major funnel leaks.
Fix, Don’t Cut — attractive NTB and demand, but PDP/offer leakage (often DPV → ATC).
Cut / Contain — weak economics, low NTB contribution, no strategic payoff.

What changed

Generic search treated as a structured acquisition strategy (not “random generic spend”).
Budgets reallocated toward high-NTB generic themes and the right ad product mix.
PDP fixes prioritized by the biggest funnel leaks and DPV volume.
Clear campaign roles created a repeatable operating model for monthly optimization.