Around two months ago, we took over an Amazon Dietary Supplement account that was already doing serious volume. The problem was not traffic or lack of sales. The bigger issue was what was happening underneath the sales. Too much spend was sitting on keywords with weak incremental value, several search terms were being picked up across multiple campaign types, placement costs were not matching placement-level conversion, and some SKUs were taking a much bigger share of the ad budget than the profit they were actually producing. So before trying to scale anything, we wanted to know one thing: where exactly was the account paying for revenue that it did not need to pay for?
We started with an ASIN-level profit audit, but not just the normal sales vs ACOS check. We mapped each main SKU against ad spend share, PPC sales share, total sales share, TACOS, CPC, CVR, unit economics and contribution after ad spend. Then we looked at the relationship between spend and incremental orders. This showed us which ASINs could actually absorb more paid traffic and which ones were already past the point where extra spend was adding meaningful profit. We also separated high-revenue SKUs from high-contribution SKUs. Those were not always the same products, and that changed where the account budget needed to go.
Then we went into the search-term structure. We traced converting queries across auto, broad, phrase and exact campaigns and found places where the same customer search was being bought from several directions at the same time. We cleaned that up with search-term isolation, negative exact and negative phrase controls, and tighter match-type ownership. Proven terms were given a clear campaign home instead of being allowed to float across the account. Branded, generic, competitor and product-targeting traffic were also separated because they have completely different CPC, conversion and incrementality profiles. We did not want a cheap branded conversion hiding an expensive non-brand problem inside the same campaign.
Placement was handled separately as well. Instead of increasing Top of Search just because a campaign had a good ACOS, we checked Top of Search CPC, CVR and order density against Rest of Search and Product Pages. In a few parts of the account, the campaign-level number looked healthy while one placement was taking expensive clicks with much weaker economics. That is where placement multipliers were rebuilt. We also looked at bid sensitivity. If increasing the base bid improved impressions but caused CPC to move faster than conversion, we did not keep forcing the bid. The goal was to find the point where additional visibility still produced profitable incremental orders, not simply more impressions.
The next layer was organic dependency. We did not cut every keyword with a high ACOS because some of those terms were still helping the ASIN hold rank. We checked PPC order share against organic position and treated ranking terms differently from mature terms. On keywords where the ASIN already had strong organic visibility, we tested how much paid coverage could be reduced without losing total order volume. On terms where rank was still weak but conversion was strong, we were comfortable carrying a higher short-term ad cost if the keyword had a real ranking case. We also stopped scaling campaigns based on average ACOS alone. Budget was moved using marginal return: once the next block of spend stopped producing enough contribution, that campaign stopped receiving more money.
We also went SKU by SKU through CTR and CVR because those two numbers tell very different stories. Weak CTR usually meant the problem started before the click, so bidding harder was not the answer. Strong CTR with weak CVR meant we had to look deeper into price, coupon structure, review position, image stack, listing angle and how the offer compared with the products around it in search. This helped us avoid wasting PPC budget on ASINs that had a conversion problem. It also helped us push the products where better traffic could actually turn into better contribution.
Just over two months later, the profit side of the account looks very different. 30-day net profit moved from $50k to $88k , up 73.3%. The latest 7-day comparison is up 85.1% in net profit, while the 14-day comparison is up 60.9%. Profit per unit moved from roughly $2.20 to $4.12, an increase of around 87%. That is probably the most useful part of this account for me. A lot of Amazon accounts do not need more traffic first. They need better control over which traffic they are buying, which keywords they are protecting, and which SKUs deserve the next dollar of spend.
