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Brownie Brittle had a well-reviewed, SEO-enhanced 14-ounce chocolate chip product on Amazon, but the ASIN was averaging only about 2,100 impressions and 30 units per week. SalesDuo kept the same monthly spending on the marketing campaign but optimized the advertising approach based on Sponsored Products, search term research, negative targeting, controlled bid rules, and Amazon bids by placement. The optimization used research data on placement performance, namely Top of Search, with a 55% better result than the product detail page. After SalesDuo increased the Top-of-Search adjustment and shifted available budget toward the winning ASIN, impressions jumped to over 14,000 per week and average weekly units increased by 90% from 30 to 57.
| Metric | Current / result value |
|---|---|
| Brand | Brownie Brittle |
| Product and account context | Low-calorie chocolate chip cookie 14-ounce bag; Seller Central / Fulfilled by Amazon |
| Campaign type | Sponsored Products |
| Core tactic | Top-of-Search bid adjustment based on placement-level conversion data |
| Impressions | About 2,100 to 14,000+ in one week; reported 566% increase |
| Average weekly units | 30 to 57; 90% average increase |
| Highest weekly units | 98; 227% above the 30-unit baseline |
| Reported item sales jump | 245% |
| Average sales growth | $12.5K in reported sales growth across three weeks |
| Marketing contribution | About 9.5% to 33%; reported high point of 77% |
Brownie Brittle is a snack and cookie brand. The original case study described varieties including Salted Caramel, Chocolate Chip, and Pumpkin Spice. Brownie Brittle partnered with SalesDuo in July 2020 to grow its Amazon business.
After supporting the brand’s Vendor Central activity, SalesDuo expanded the work through Seller Central and Fulfilled by Amazon. This case study focuses on a low-calorie chocolate chip cookie 14-ounce bag selected for the placement test.
By April 2022, the focus ASIN had good reviews and an SEO-enhanced listing, yet it was not gaining enough traction in Seller Central. It averaged about 2,100 impressions and 30 units sold per week.
SalesDuo needed to increase visibility and sales without increasing the brand’s monthly marketing budget. That constraint made campaign efficiency, query filtering, placement analysis, and budget reallocation more important than simply bidding higher everywhere.
Amazon bids by placement are campaign-level bid increases that apply when a Sponsored Products ad competes for selected locations. For Top of Search, Rest of Search, and Product Pages, advertisers can set a positive adjustment from 0% to 900%; the control increases an eligible bid and does not function as a negative modifier. The standard Sponsored Products are Top of Search, Rest of Search, and Product Pages. Amazon’s Sponsored Products placement report lets advertisers compare performance by placement before changing those adjustments.
Placement adjustments work alongside the campaign’s bidding strategy so that the effective bid can rise quickly. For example, a $1.00 base bid with a 50% Top-of-Search adjustment becomes $1.50 before any further dynamic-bidding change. Use this formula before combining controls: Final maximum bid = Base bid × (1 + placement adjustment) × dynamic-bid multiplier.
Amazon’s current bid-adjustment guidance explains this interaction. For a broader strategy comparison, see SalesDuo’s Amazon PPC bidding strategies guide.
The sequence involved moving from discovery to placement-led scale: identifying queries with a commercial purpose, screening out wasteful traffic, setting up a proper intent-based bid, protecting the return target, defining the most effective placement, scaling it up, and shifting the remaining ad budget to the winning ASIN.
The item's history indicated that its performance was driven more by keyword-led demand than by natural placement on the detail page, and sponsored products matched the objective of targeting those who searched for similar products.
SalesDuo implemented an automatic campaign to reach a larger audience and define useful and unproductive queries and targets. It acted as a discovery channel and was not intended as a permanent fixture.
SalesDuo used negative targeting to refine traffic by eliminating wasteful queries and placements, optimizing the fixed monthly ad budget, and protecting it from being spent on irrelevant terms and placements, while prioritizing those that drove sales.
SalesDuo applied “rainfall” bidding to implement a laddering system in which close matches had a higher priority than broader matches, the second-level match types were allocated a smaller-than-usual budget, and the procedure was not a built-in Amazon function but rather a house bidding strategy.
The campaign initially used Dynamic Bids - Down Only. SalesDuo then added rule-based controls tied to a reported 4.55x return target. Rule-based bidding is supporting context here; the placement decision remains the core tactic. SalesDuo’s rule-based bidding case study covers that topic in greater depth.
Top-of-search placements converted 55 percent better than placements on product detail pages for this ASIN. This finding allowed the team to shift the focus from general bid maintenance to optimization at a particular placement level.
The SalesDuo team raised the Top-of-Find budget using Amazon’s adjustment feature for bidding by placements and applied it to the most performing ASIN to increase its volume. The purpose was to secure the best conversion level and not necessarily to win every single auction.
The item-level budget that was inaccessible because of the disqualifying reason was redirected towards the most astonishing ASIN. This achieved maximum impact without exceeding the monthly budget.
The sequence increased both the reach and the unit velocity. The clearest comparison is the average number of units per week; the other measures of sales and marketing contribution have less useful detail.
| Performance measure | Before | After / reported result |
|---|---|---|
| Weekly impressions | About 2,100 | 14,000+ in one week; 566% increase in visibility |
| Average weekly units | 30 | 57; 90% average increase |
| Highest weekly units | 30-unit baseline | 98; 227% above the earlier baseline |
| Item sales | Not stated | Reported 245% jump |
| Average sales growth | Not stated | $12.5K over three weeks |
| Marketing contribution | About 9.5% | 33%; high point 77% |
Impressions rose from about 2,100 to more than 14,000 in one week. Average weekly units increased from 30 to 57, and the strongest week reached 98.
The original case study also reported a 245% increase in item sales and $12.5K in average sales over three weeks. As the article only mentions the three-week increase, not specifying the period, this rewrite does not treat the 245% figure as equivalent to a 90% average weekly rise.
The percentage of the marketing contribution to overall sales rose from approximately 9.5% to 33% with a peak at 77%. As the page does not specify the calculation method, the figures should be regarded as particular to the case rather than an industry standard.
The result came from combining placement evidence with campaign controls; no single setting explains it.
Most importantly, the Top-of-Search increase followed a measured 55% conversion advantage. The team used placement data to decide where to compete more aggressively instead of applying the same multiplier across every campaign. Since placement modifiers are campaign-level settings, every target in a campaign shares the same placement adjustment. If branded and non-branded targets perform differently by placement, separate them into distinct campaigns before applying a large modifier.
Increase a placement bid only when the report shows a significant improvement, and the campaign can sustain the higher effective bid. It is a scale control mechanism, not a replacement for precise targeting, a compelling offer in the listing, or a profitable base bid.
| Observed signal | Practical next step |
|---|---|
| Top of Search converts better, and ACoS or ROAS is within target | Test a measured Top-of-Search increase and monitor CPC, orders, and efficiency. |
| Product Pages outperform search placements | Test product-page adjustments for complementary or competitor targeting. |
| Placement data are sparse or unstable | Hold the modifier and collect more data before scaling. |
| Campaign is budget-limited but has clear winners | Reallocate budget from weaker or temporarily ineligible items before raising total spend. |
| Dynamic bidding and placement adjustments are both aggressive | Calculate the compounded effective bid and reduce one lever if the exposure is too high. |
Top of Search is not automatically best. Product Pages may suit complementary or competitor targeting, while Rest of Search may deliver efficient reach. Let the report decide the test.
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