3 tactics top auto products advertisers use to help increase sales
By: Cecia Wang, Analytics and Media Manager
We studied over 8,600 brands in the auto products category in Amazon’s store to discover insights into how they drove year-over-year growth in sales growth rate and new-to-brand customers.
Story highlights:
In this study, we analyzed over 8,600 brands in the auto products category in the United States in 2020. The auto products category includes brands selling products such as RV parts and accessories, replacement parts, and automotive accessories products.
We created a composite score of sales year-over-year growth rate (SalesGR) and new-to-brand customer year-over-year growth rate (NTBGR), and then used machine learning to identify the top advertising and retail strategies that advertisers used to help them increase their composite score.
Auto products advertisers looking to improve their year-over-year SalesGR and NTBGR growth rates should consider:
- Running Sponsored Display and Sponsored Products ad campaigns.
- Increasing support of Sponsored Display and Sponsored Brands during tentpole events.
- Balancing investment mix.
For more information, see the Methodology section at the end of this article.
1. Top-performing auto products advertisers run Sponsored Brands and Sponsored Products ad campaigns
Insights
Sponsored Brands offer advertisers the ability to appear in multiple prominent placements on the top and bottom of shopping results pages. This analysis shows that automotive product brands with higher growth adopted Sponsored Brands in 2020. One reason for this may be the result of the high reach that can be achieved through Sponsored Display.
Recommendations
When using always-on campaigns we recommend:
- Keyword coverage: Use category keywords to help encourage new customers to move further down the funnel and use branded keywords to drive conversion.
- Sponsored Brands seasonal budgets: Shoppers’ search and purchase behaviors have peaks and dips throughout the year, and synchronizing budgets to reflect this helps maximize ROI.
- Do not change promoted ASINs too frequently: To support discovery and relevancy, allow a sufficient time period of support and do not change promoted ASINs too frequently, such as daily or weekly.
2. Top-performing auto products advertisers maximize customer reviews
Insights
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:
Recommendations
Vendors: Use the Amazon Vine program. The program 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 program. 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.
3. Top-performing auto products advertisers balance their investment mix
Insights
Standard tentpole events like Black Friday and Cyber Monday (BFCM) as well as automotive tentpole events like truck season and winterization occur at different times throughout the year. Advertising across these tentpoles may amplify your brand and help increase sales. In fact, brands with higher growth maintained a balanced spend in Sponsored Products across truck season, winterization and BFCM. Furthermore, top-performing brands also maintained a 2:1:1 ratio across truck season: winterization: BFCM, while other advertisers maintain a 10:1:1 ratio.
Recommendations
Our analysis shows that a more balanced approach can lead to higher year-over-year DPVGR and NTBGR.
Methodology
We first used a supervised model to identify a list of attributes that help improve the composite score among 40+ media and retail attributes. We then used this list of attributes and performed cluster analysis among advertisers/brands, so advertisers/brands in the same cluster are similar in ad and retail attributes, while advertisers/brands in different clusters are different in ad and retail attributes. These attributes are X1, X2, ….Xn. (Attributes shown as bubbles on the Visual).
The machine learning algorithms returned 4 clusters. We ranked these 4 clusters by the success metrics, compared the differences between the top- and bottom-performing clusters, and identified the key attributes that differentiate their performance on NTB and sales growth.
How does the clustering work?
We created a binary composite score based on DPVR, and 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.