Amazon PPC Case Study: How a Private Label Supplement Brand Lowered ACOS With Negative Keyword Mining and Exact-Match Campaigns

Amazon PPC Case Study Overview

This Amazon PPC case study breaks down how a private label supplement brand reduced wasted ad spend and improved profitability by combining disciplined negative keyword mining with a tighter exact-match campaign structure. The brand sold in a competitive wellness category where clicks were expensive, conversion rates varied sharply by search term, and broad targeting had slowly inflated ACoS without producing efficient growth.

At the start of the engagement, the account was generating sales, but too much of the budget was leaking into loosely relevant search queries. Campaigns were bundled together, high-intent keywords were mixed with discovery traffic, and the team had limited visibility into which search terms were driving profitable orders versus curiosity clicks. The goal was not simply to cut spend. It was to keep revenue moving while pushing spend toward search terms with clear purchase intent.

Over an eight-week optimization cycle, the brand restructured traffic flow, mined search term reports aggressively, and separated proven converting queries into exact-match campaigns with dedicated budgets and clearer bid control. The result was a meaningful drop in ACoS, stronger conversion efficiency, and a more stable campaign foundation for future scaling.

The Starting Problem

The brand had several core SKUs, including daily-use supplements in a category crowded with generic, branded, and symptom-based searches. Their campaigns had originally been built for coverage, not control. Automatic and broad-match campaigns were generating a large volume of search terms, but only a small percentage of those terms were truly aligned with the product positioning, price point, and customer intent.

That created three problems. First, budget was being consumed by irrelevant or low-converting traffic, including research-heavy searches, competitor brand terms, and vague health queries with weak buying intent. Second, profitable search terms were not isolated, so strong performers competed for budget with inefficient traffic. Third, bid decisions were being made at the keyword level without enough visibility into the actual search term data driving performance.

In a supplement category, that matters. Margins can be healthy, but CPCs rise quickly when multiple sellers chase the same top-of-funnel terms. If search term hygiene is weak, ACoS rises fast.

Baseline Performance Before Restructuring

The Strategy Used

1. Search Term Segmentation

The first step was pulling recent search term data and sorting queries into three buckets: profitable terms, promising but unproven terms, and waste. Profitable terms were those with repeat conversions and acceptable ACoS. Promising terms had some traction but needed tighter bid control. Waste included irrelevant searches, competitor terms that never converted, and informational phrases that attracted clicks without sales.

2. Negative Keyword Mining

Negative keyword mining was the fastest lever for reducing wasted spend. The team reviewed search term reports weekly and applied negatives at both the ad group and campaign levels. This prevented broad and automatic campaigns from repeatedly matching to expensive terms with poor downstream performance.

• Irrelevant ingredient combinations were negated.
• Low-intent research phrases were blocked after repeated spend without orders.
• Non-converting competitor brand terms were removed where they inflated cost.
• Duplicate traffic paths were cleaned up so discovery campaigns did not cannibalize exact-match campaigns.

This process alone improved query quality. Instead of letting Amazon continue exploring the same unproductive search pockets, the account began forcing spend toward better-fit traffic.

3. Exact-Match Campaign Buildout

Once winning search terms were identified, they were moved into dedicated exact-match campaigns. This gave the brand tighter control over bids, placement adjustments, and daily budgets. Rather than leaving top search terms buried inside broad or auto campaigns, the account promoted them into campaigns designed specifically to capture proven demand.

4. Budget and Bid Reallocation

• Reduce bids on waste-heavy broad targets.
• Cap spend on automatic campaigns once search term volume was sufficient.
• Increase bids on exact terms with strong conversion rates and acceptable CPCs.
• Review placement performance and push more spend into top-of-search placements only where conversion justified it.

That reallocation mattered because many of the best supplement keywords were too valuable to leave inside mixed-intent campaigns. Once isolated, the brand could compete more aggressively for the searches that actually converted.

What Changed in Performance

Within the first two weeks, wasted spend started to fall because negative keywords cut off repeated leakage. By weeks three through five, exact-match campaigns began taking a larger share of total spend and produced a stronger return. By week eight, the account had a healthier balance between discovery and conversion capture.

The most important shifts were not just numerical. The account became easier to manage. Search term intent was clearer. Budget decisions were cleaner. Performance swings were less erratic because proven queries had dedicated campaign homes instead of competing with noisy traffic.

For the brand, that meant ACoS improved from 41.8% to 26.3%, while attributed sales increased from $44,000 to $64,400. Conversion rate improved because the account was buying better clicks, not just cheaper clicks. CPC also declined slightly because bids were no longer spread across as much low-quality traffic.

Why This Approach Worked

Negative keyword mining and exact-match campaigns work especially well together on Amazon because they solve opposite sides of the same problem. Negative keywords remove waste from the funnel. Exact match protects and prioritizes the terms already proving they can generate profitable sales.

In this case, the supplement brand did not need more traffic in general. It needed more of the right traffic. Once irrelevant searches were filtered out and winning queries were isolated, the account stopped treating every click opportunity as equally valuable. That shift is often what lowers ACoS in mature Amazon PPC accounts.

Key Takeaways for Other Supplement Brands

• Do not let automatic and broad campaigns run unchecked for too long.
• Mine search term reports weekly, not occasionally.
• Promote proven search terms into exact-match campaigns with dedicated budgets.
• Use discovery campaigns to learn, not to carry the full revenue target.
• Measure success by query quality and conversion efficiency, not click volume alone.

For private label supplement sellers, lowering ACoS usually comes from tighter search term control rather than one dramatic bid change. This case study shows that disciplined keyword hygiene and campaign structure can unlock better efficiency without stalling growth.

FAQ

What is a good ACoS for a private label supplement brand on Amazon?

A good ACoS depends on margin, repeat purchase rate, and launch stage, but many established private label supplement brands aim for an ACoS that stays below contribution margin. If margins are tight, even 30% may be too high. If lifetime value is strong, a higher ACoS can still be acceptable.

How often should Amazon advertisers do negative keyword mining?

For active accounts, weekly reviews are usually the right baseline. High-spend campaigns may need review multiple times per week, especially during launches, seasonal pushes, or aggressive testing periods.

When should a search term move into an exact-match campaign?

A search term usually deserves its own exact-match placement once it has demonstrated consistent conversions, acceptable ACoS, and clear buyer intent. The goal is to isolate proven demand so you can control budget and bids more precisely.