43.14
%
Increase in Ad Spend
77.02
%
Increase in Ad Sales
20.28
%
Boost in CVR
19.14
%
Improvement in ACOS
The Brand
K2 Health is a leading manufacturer of high-quality health products, specializing in comfort cushions, incontinence products, and baby diapering supplies. Their products are designed to enhance comfort and well-being, utilizing advanced technology and premium materials. K2 Health features two distinct product lines: Inspire, focused on premium incontinence products, and Kolbs, specializing in ergonomic cushions and supports.
Challenges
One of the challenges faced by the brand was the limited visibility of some ASINs within multi-product ad groups, making it difficult to scale these products with the right keyword targets.
Initially, they had created a campaign structure where multiple products from the same category were grouped under a single ad group. However, over time, they realized that the impression share became concentrated on a few select products, resulting in minimal visibility for other items within the same group.
This issue was further compounded by Amazon’s Sponsored Ad reporting limitations, where advertisers have visibility into overall ASIN and keyword performance within an ad group but lack insights at the product-keyword level. This made it difficult to identify which products were performing well for specific keywords and, conversely, which products were misaligned with certain keywords, leading to wasted ad spend.
Solution: Leveraging Amazon Marketing Cloud (AMC)
To address this challenge, the brand utilized AMC to extract individual target and product-level data within ad groups. This allowed them to determine which products were gaining visibility for specific keywords and how they were performing.
Through this analysis, they identified outlier keywords that weren’t contributing to product performance within the ad groups. They then created new ad groups for these products, excluding the outlier keywords. This approach aimed to improve both overall campaign performance and product-specific results.
Insights Gathered by the Brand through AMC Analysis
AMC’s data revealed clear insights into the performance of each ASIN for individual targets within the same ad group. For example, within an ad group containing five products and five targets, the brand could determine how each ASIN performed against each target.
One notable discovery was that in a specific campaign, 50% of the total ad spend for a hero keyword was concentrated on one ASIN, driving sales at 171.24% ACOS. Meanwhile, another ASIN within the same ad group achieved sales at 77.85% ACOS for the same keyword. This indicated that the hero keyword was not optimized for the former ASIN. Based on this insight, the brand created a separate ad group for the product, excluding the outlier keyword. This change allowed them to allocate spend more efficiently.
Actions Taken by the Brand with AMC Data
Based on the insights from AMC, the brand took a systematic approach to optimizing their ad groups:
Extract Data: They pulled data from AMC on products, campaigns, ad groups, and keywords to identify which keywords weren’t driving performance for specific products.
Pause & Reassign: They paused the underperforming products within their current ad groups and created new ad groups for these products, removing the outlier keywords.
Recreate Ad Groups: For the newly created ad groups, they transferred all previous keywords, bids, and targeting parameters, excluding the outlier keyword.
Leverage Ad Tech: Over time, they used the Adbrew ad tech platform to automatically adjust bids in these new ad groups based on performance, enabling the campaigns to scale efficiently.
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Results
When looking at the aggregated performance across the six campaigns optimized using AMC data, they saw a 43.14% increase in ad spending, a 77.02% increase in sales, a 20.28% increase in conversion rate, and a 19.14% improvement in ACOS.