Here's what we learned from 200k Sponsored Products campaigns

This article provides insights/best practices on the key attributes or strategies by quantifying the degree to which top-performing China-based sellers (Cluster One) and lower-performing China-based sellers (Cluster Five) 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.

Advertisers should begin with Sponsored Products campaigns. There are also three considerations for using dynamic bidding:

• Consider testing dynamic bidding strategy on a stable campaign: When testing bidding strategy, it is best to choose a campaign that is stable. Meaning, campaigns that have been running for at least two weeks and shows a positive relationship with conversions.
• Consider limiting changes while testing: We recommend that advertisers limit changes while testing strategies, so that they can attribute the difference in performance to a specific change.
• Consider comparing strategies on different campaigns: When creating new campaigns, be sure to compare the new bidding strategy against an existing, stable, strategy (when possible).

The third insight-driven recommendation is to specifically increase Click-Through-Rates by increasing the number of ASINs, or products, that offer Prime shipping. We use machine-learning models to identify products with a high likelihood of being clicked on if advertised. We’ve also found that having the Prime badge alongside advertised products helps increase the chance of a customer clicking on advertisements.

• Consider enrolling Sponsored Products ASINs into the Fulfillment by Amazon (FBA) program.
• Consider enrolling Sponsored Products ASINs into the Seller Fulfilled Prime (SFP) program.
• Consider combining both FBA and SFP programs to increase Prime delivery offerings.

Finally, in previous articles we’ve found that including negative keywords on product ASINs or serial numbers was an effective tool, in the case of Chinese apparel sellers (selling to a global audience), we have found that targeted keywords are also an effective tool for ensuring that ads are relevant to the right customers.

• Check your existing campaigns’ reports to find terms that you should use negative targeting on. Lower CTRs, higher spends, and lower conversion rates are good indicators of targeting that is underperforming and could become excluding keywords.
• Check the performance of your excluding keywords frequently to learn and optimize your campaigns with the ones that actually work for your brand.
• Check for exceptions. For example, non-branded keywords used to increase awareness of new products launched could have poor performance, but should not be used as excluding keywords because they are in fact the correct audience for products in the apparel category.

• Select brands: In this study, we analyzed 222,853 apparel’s Sponsored Products campaigns delivered in 12 worldwide locales between August 2020 and July 2021. Each campaign was launched by endemic Chinese advertisers i.e., Chinese advertisers that sell products on Amazon.com. 12 locales include United States, United Kingdom, Germany, France, Japan, Canada, Italy, Spain, India, Australia, United Arab Emirates and Mexico.
• Create success metric: Calculated based on 3 metrics, Return-on-Ad-Spend, conversion rate, and Click-Through-Rate.
• Identify effective ad or retail actions: Once the success metrics were defined, we identified the top campaign actions to help increase the success metrics with machine learning algorithms. We leveraged a gradient-boosted decision trees model to identify the most important campaign actions that contribute to the success metrics. This method helps us understand which advertising actions are the most important to drive strong success metrics.
• Group brands: Campaigns by their success metrics and assigned them into five clusters. Leveraging more than 20 advertising actions, we then analyzed their impact on the success metrics. Advertising actions include SP campaign duration in days, dynamic bids, negative keyword tactics, etc.
• Compare brand groups: identified what strategies top-performing brands (Cluster One) use to increase clicks and conversions, compared to strategies that lower-performing brands (Cluster Five) are or are not using.