Four recommendations for growing sports apparel and footwear brands

By: Sam Bachra, Acquisition Manager

In a 2019 study of more than 650 sports brands, we found there are four key areas top-performing sports apparel advertisers used to help drive glance views and accelerate sales growth.

Story highlights:

Whether striving for a home run, a grand slam, or a personal best with their advertising campaigns, sports brands are always seeking to optimise their performance.

To help them achieve that, Amazon Ads studied more than 650 US sports apparel and footwear brands that sold products in Amazon’s store. Our analysis found that the top-performing advertisers achieved 2.2X higher new-to-brand (NTB) customers and 2.5X higher ad-attributed glance views (the number of times the product detail page is viewed) growth than other sports apparel advertisers.

What accounted for that performance? We identified four advertising strategies that the top-performing advertisers employed – that other advertisers did not – tied to new-to-brand customers and glance views:

  • Top-performing sports brands run Sponsored Brands campaigns throughout the year.
  • They maintain a 2:1 budget ratio between Sponsored Brands and Sponsored Products.
  • They use negative keywords and negative ASINs more often.
  • They follow best practices for customer reviews.

Read on to learn more about those tactics, plus how to incorporate them into a marketing strategy.

Index New-To-Brand and Glance Views Growth Rate performance (baseline = cluster 4)

Cluster 1

Cluster 1

Cluster 2

Cluster 2

Cluster 3

Cluster 3

Cluster 4

Cluster 4

Year-Over-Year New-To-Brand growth

Year-Over-Year New-To-Brand growth. Cluster 1: 2.2; Cluster 2: 2.0; Cluster 3: 1.8; Cluster 4: 1.0

Year-Over-Year Glance Views growth

Year-Over-Year Glance Views growth. Cluster 1: 2.5; Cluster 2: 1.7; Cluster 3: 1.6; Cluster 4: 1.0

1. Top-performing sports apparel advertisers run Sponsored Brands ad campaigns all year

Insights

Top-performing sports apparel advertisers ran always-on Sponsored Brands campaigns for 52 weeks in 2019, compared to other sports apparel advertisers who ran always-on Sponsored Brands campaigns for just two weeks. Running always-on Sponsored Brands campaigns can help keep brands top-of-mind for customers, helping to create more NTB customers.

Recommendations

Always-on campaigns are most effective when advertisers run awareness, consideration and conversion tactics together. Awareness and consideration tactics, such as display through Amazon DSP, help encourage new customers to view product detail pages and can lead to more conversions.

2. Top-performing sports apparel advertisers maintain a 2:1 budget ratio between Sponsored Brands and Sponsored Products

Insights

Our analysis showed that top-performing sports apparel advertisers maintained roughly a 2:1 impression ratio for Sponsored Brands to Sponsored Products. In the same time period, other sports apparel advertisers maintained a 20:1 impression ratio. This is significant because a 2:1 ratio helps increase product discoverability.

Recommendations

Maintain a 2:1 ratio (or a similar balance) between Sponsored Brands and Sponsored Products. This ratio is important for two reasons:

  • A healthy spend ratio between Sponsored Brands and Sponsored Products can help maximise campaign visibility.
  • Secondly, it can help increase the awareness of a product’s family, which can help increase glance views and NTB customers.

3. Top-performing sports apparel advertisers use negative keywords and negative ASINs

Insights

Our analysis shows that there is a benefit of using negative keywords (words or phrases that prevent an ad from appearing on shopping results) and negative ASINs. On average, 15% of top-performing sports apparel advertising campaigns contained negative keywords or negative ASINs. Conversely, only 4% of campaigns from other sports apparel advertising campaigns used negative keywords or serial numbers tactics.

In other words, top-performing sports apparel advertisers were 11% more likely to use negative keywords or negative ASINs than other sports apparel advertisers.

Recommendations

Use negative keywords and negative ASINs. One benefit of using negative keywords is ensuring ads don’t appear on shopping results pages for shopping queries that you know are less likely to convert. A secondary benefit of using negative keywords or negative ASINs is that they can help ensure that ads are reaching the right audience.

To improve the accuracy of negative keywords and ASINs, advertisers can use Amazon’s built-in metrics. For example, lower click-through rates (CTRs) and lower conversion rates are good indicators of keywords that are underperforming and are, therefore, good candidates to exclude.

4. Top-performing sports apparel advertisers follow best practices for customer reviews

Insights

Our analysis shows that top-performing sports apparel advertisers, on average, have 4X the number of customer reviews per unique ASIN serial numbers than the least-performant advertisers.

Recommendations

Customer reviews are an important metric for customers looking to decide to purchase a product. To help improve glance views and conversions, advertisers can use the following tools:

Vendors: Use the Amazon Vine programme. The programme was created to provide customers with more information including honest and unbiased feedback from some of Amazon's most trusted reviewers.

Sellers: Register with Amazon Brand Registry and use the Early Reviewer programme. Enrolling in Amazon Brand Registry unlocks a suite of tools designed to help you build and protect your brand, creating a better experience for customers.

Conclusion

We believe it is important to provide advertisers with research-driven insights that help improve performance. In this article, we looked at how top-performing sports apparel advertisers achieved higher numbers of new-to-brand customers and glance views. First, these advertisers ran Sponsored Brands campaigns throughout the year. Second, they maintained a 2:1 budget ratio between Sponsored Brands and Sponsored Products. Third, they use negative keywords and ASINs to increase the relevance of their ads based on the shopping query. Finally, they have 4X the number of customer reviews for each unique product advertised.

When combined together, our analysis shows that these four tactics helped the top-performing advertisers achieve 2.2X higher new-to-brand customers and 2.5X higher glance views.

Methodology

We analysed over 650 brands in the sports apparel and footwear categories in US in 2019. This study classified advertisers into clusters using advanced machine learning algorithms, and then looked into their advertising and retail attributes to extract insights-driven recommendations to help advertisers improve the performance of new-to-brand and glance view growth.

How are advertisers distributed across the clusters?
We used machine learning algorithms to automatically classify advertisers into clusters based on their advertising and retail attributes.

7%

Cluster 1

Highest growth of glance views and new-to-brand customers

60%

Cluster 2

23%

Cluster 3

10%

Cluster 4

Lowest growth of glance views and new-to-brand customers

How does the clustering work?
We created a binary composite score using a combination of return on ad spend (ROAS), year-over-year growth of retail sales and year-over-year growth 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, etc.
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 5 is the least successful.