What growth tactics are driving results for Kids Fashion advertisers on Amazon?

By: Vivian Qin, Senior Analytics and Media Manager, and Ashton Brown, Technical Writer

In this 2019-2020 study of over 1200 brands in the Kids Fashion category in Amazon’s store, we compare advertising strategies of top- and lower-performing advertisers. We then use this comparison to derive actionable insights advertisers can use to improve New-to-Brand customer Growth Rate, Sales Growth Rate, and detail page view Growth Rate year-over-year.

Story highlights:

In this study, we analyzed over 1200 brands in the Kids Fashion category between January 2019 and December 2020. The Kids Fashion category includes brands selling children clothing, shoes, and backpacks (excluding baby-related items). To perform our analysis, we grouped Kids Fashion brands into five clusters, with Cluster 1 being the most successful in terms of New-to-Brand customer Growth Rate (NTBGR), Sales Growth Rate (sales), and detail page view Rate (DPVGR) year-over-year, and Cluster 5 being the least successful.

Our analysis finds that top-performing Kids Fashion advertisers (Cluster 1) had a 2.6x higher New-to-Brand Growth Rate, 2.4x higher Sales Growth Rate, and a 3.0x higher Detail Page View Growth Rate in comparison to lower-performing advertisers (Cluster 5).



Higher NTBGR


Higher Sales Growth Rate


Higher DPVGR

To provide advertisers with actionable insights, we used machine learning to analyze 40+ advertising attributes that contribute more, or, less to DPVGR/sales/NTBGR. We then identified which attributes have the largest positive impact on the year-over-year growth rate of DPVGR/sales/NTBGR. This article provides insights/best practices on the key attributes or strategies by quantifying the degree to which top-performing Kids Fashion advertisers (Cluster 1) and lower-performing Kids Fashion advertisers (Cluster 5) have adopted each key attribute or strategy.

For more on how we performed this study, see the Methodology section at the end of this article.

Top-performing Kids Fashion advertisers ran campaigns nearly 3x longer than other advertisers

During the period observed, top-performing Kids Fashion advertisers ran ad campaigns for an average of 11 of the 12 available back-to-school season weeks (from the middle of June to the middle of September, while lower-performing Kids Fashion advertisers only ran ad campaigns for an average of 4 weeks.

Average number of weeks advertised during the back-to-school season





Learnings and best practices when advertising during back-to-school season

  • Lead-up: Promote brands in advance of back-to-school season.
  • Promote: When parents are actively shopping, they typically view 6-9 product detail pages. Use a combination of ad products to help boost your product and brand awareness.
  • Lead-out: Re-engage with audiences at scale. Shoppers often continue to stay engaged after key shopping moments, so advertisers may want to consider also advertising just after the back-to-school season to remain in the minds of customers.

Top-performing Kids Fashion advertisers deliver impressions and diversify ads across a minimum of 2 ad products

Top-performing Kids Fashion advertisers, on average, reach customers through at least 2+ ad products, while lower-performing Kids Fashion advertisers rely solely on one ad product. With customer presence and shopping preferences being split across devices (channels), advertisers can drive awareness and consideration by using an omni-channel approach. Meaning, brands can advertise across multiple products and devices.

Average number of ad products used per campaign





Why use Sponsored Display?

  • Brands can create a Store that showcases their full suite of products.
  • Brands should consider using a minimum of two ad products. For example: (1) Sponsored Products and Sponsored Brands - allow brands to reach customers activity searching using keywords. (2) Sponsored Display - allows brands to reach audiences who are not actively searching but who have researched in the past.


As seen in our analysis, in combination with our supervised machine learning model, we identified two key tactics advertisers can use to grow their New-to-Brand customer Growth Rate, Sales Growth Rate, and detail page view Growth Rate year-over-year: (1) advertising during back-to-school-season; (2) diversifying ad spend across a minimum of 2 ad products.


We first used a supervised model to identify a list of attributes that help improve the composite score among 40+ media and retail advertising attributes. Specifically, we followed a five-step process to create a suite of success metrics including: Display Product Views year-over-year Growth Rate (DPVGR), Sales, and New-to-Brand Customer year-over-year Growth Rate (NTBGR), and then identified the top advertising and retail strategies to help increase the success metrics with machine learning algorithms.

  • Select brands: 1200+ brands in the Kids Fashion category between January 2019 and December 2020.
  • Create success metric: calculated based on year-over-year growth of New-to-Brand customers and Detail Page Views.
  • Identify effective ad or retail actions: identified top actions to help increase composite score (actions that lead to higher year-over-year growth of New-to-Brand customers and Detail Page Views). Actions include customer reviews, ad products (Sponsored Products, Sponsored Brands, Fire TV, etc.), ad strategies (negative keywords, always-on, audience segments, etc.), and more.
  • Group brands: brands grouped by composite score (DPVGR, Sales, and NTBGR) into five clusters ranked from highest to lowest performing.
  • Compare brand groups: identified what strategies top-performing brands (Cluster 1) use to increase year-over-year growth of New-to-Brand customers and Detail Page Views, compared to strategies that lower-performing brands (Cluster 5) are or are not using.