Four tactics used by top Amazon advertisers in the Home aisle

By: Max Ming, Analytics and Media Manager

Many advertisers are curious about which key tactics are more adopted by top-performing advertisers and seek to use insights related to these tactics to inform their own marketing strategies.

Story highlights:

In a recent study of more than 7,000 China-based companies who use Amazon to advertise their Home products, we observed four marketing tactics that were more used by top-performing advertisers. (Note: The Home category includes products such as bath and laundry, window treatments, home environment, bedding, home decoration, artwork, home storage, foot care and arts and crafts.)

Learn more about advertiser selection methodology.

Top-performing advertisers have on average 40% higher year-on-year sales growth, 50% higher year-on-year growth among customers viewing their product pages on Amazon and a 30% higher return on their ad spend (ROAS). These advertisers represented 6% of the 7,000 advertisers included in the study.

1. Top-performing advertisers are more likely to use Sponsored Brands

We observed that top-performing advertisers were 60 times more likely to adopt Sponsored Brands placements to help increase brand awareness. Specifically, 61% of top-performing advertisers used Sponsored Brands placements.

Percentage of advertisers who adopted Sponsored Brands


Top performers


Lower performers

Sponsored Brands advertising placements provide advertisers with the ability to display their products in multiple prominent placements on the top and bottom of shopping results pages.

In addition to utilizing these highly visible placements more often, top-performing advertisers also utilized them differently. On average, top-performing advertisers used Sponsored Brands placements 159 days earlier when launching new products. On average, top-performing advertisers launched their first Sponsored Brands campaign within nine months (266 days) after new product launch, while other advertisers used them much later (425 days after launch), or not at all.

Learn more about Sponsored Brands ad placements or contact an Amazon Ads account executive.

Average days to launch first Sponsored Brands campaign

(within advertisers who adopted Sponsored Brands)

266 days

Top performers

425 days

Lower performers

2. Top-performing advertisers are more likely to have higher-quality product pages

Top-performing advertisers were 1.36 times more likely to have higher-quality product pages on Amazon. Specifically, 76% of top-performing advertisers had high-quality product pages.

High quality product pages


Top performers


Lower performers

Product page quality is a composite score comprised of various factors such as optimal title length (25-100 characters), more than four images with zoom-able images, product feature bullet points, description of the benefit shoppers experience with the product, more than five positive reviews with 3+ ratings, as well as offer-related features such as Featured Offer win rate (rate at which brand is the Featured Offer), Prime shipping and in-stock levels.

In addition to this composite product page quality score, top-performing advertisers had seven times more reviews. Specifically, on average, top -performing advertisers had 14 reviews, while others had only two.
Learn more about improving your product detail pages to provide an outstanding customer experience.

Average customer review counts per products

14 reviews per product

Top performers

2 reviews per products

Lower performers

3. Top-performing advertisers focus marketing budget on the most relevant audiences.

To help improve advertising efficiency, top-performing advertisers focus marketing budget on the most relevant audiences and exclude audiences from their marketing campaigns who may not be interested in their products. They do this by excluding audiences based on less-related shopping queries by using negative targeting in their Sponsored Products campaigns. Audiences querying using those keywords are then excluded from seeing the advertisement.

This directs more of the advertiser’s marketing budget toward more relevant audiences. Eighteen per cent of top-performing advertisers used this tactic.

Learn more about how to implement negative keywords and product targeting.

Percentage of negative keywords used for Sponsored Products


Top performers


Lower performers

4. Top-performing advertisers are less likely to run out of their sponsored advertising media budget.

Top-performing advertisers are 30% less likely to run out of sponsored advertising media budgets prior to their campaigns officially ending. Running out of budget causes the campaigns to pause, which reduces the continuity of brand presence with audiences.

On average, 17% of top performers run out media budget before their Sponsored Products campaign ended.
Learn more about efficiently managing budgets and avoiding out-of-budget situations, or learn about efficient dynamic bidding.

Percentage of Sponsored Products campaigns that ran out of budget


Top performers


Lower performers

Research methodology

Selecting advertisers for the study: We analyzed over 7,000 China-based advertisers in the home category selling in all Amazon regions worldwide from 1/1/2019 to 31/12/2019. To support the analysis, we performed a homogeneity audit using several retail attributes to ensure brands were comparable in product assortment size, product prices and retail sales. Specifically, we studied advertisers with 10 to 680 products for sale on Amazon, average product selling prices from $9 to $60, and retail sales from $13.6K to $1.4M.

In addition, we selected advertisers with a baseline level of advertising activity, i.e. those spending at least $1,000 on sponsored keyword based advertising (i.e. Amazon Sponsored Products or Sponsored Brands ad placements), and who had been actively advertising on Amazon (i.e. at least 40 weeks per year in both 2018 and 2019). We also excluded outliers and several highly correlated features.

Clustering advertisers by performance: We then classified advertisers into four distinct clusters. Cluster 1 advertisers (referenced as “top performers”) had the highest return on ad spend, year-over-year growth of sales and year-over-year growth of product page views (and made up 6% of all advertisers studied). Conversely, Cluster 4 advertisers had the lowest performances in these success metrics (and made up 36% of all advertisers studied).

We then compared the greatest differences in the behaviours between the most and least successful advertiser clusters to surface behaviours that differentiate them which could have contributed to their success. Using Cluster 4 as a baseline, we compared performances in three dimensions: retail sales growth year-over-year, page views year over year and return on ad spend. On average, Cluster 1 had 40% higher growth in sales, 50% higher growth in page views, and 30% higher in ROAS than Cluster 4.

How does the clustering work?
We created a binary composite score using a combination of ROAS, year-over-year growth of retail sales and year-over-year of product page views. We labelled advertisers that ranked in the top 50% across all three components as “one” and otherwise as “zero”. We then applied an XGBoost classifier to identify which features and with which weights best predict these labels. In doing so, we considered advertising or retail actions as features such as ad product usage intensity and mix, timing of advertising support, tactics of targeting, creatives and placements, customer review counts and ratings, percentage of products with quality product pages and the types of products promoted in ads.


Cluster 1


Cluster 2


Cluster 3


Cluster 4

Using the identified features and weights above, we then applied a k-medoid clustering algorithm to classify advertisers into clusters. Note that we classified advertisers by their actions rather than by the components of their composite score. Next, we ranked the final clusters by their composite scores from high to low. Cluster 1 is the most successful cluster with the highest composite score, and Cluster 4 is the least successful.