3 tactics top health and personal care advertisers use for brand growth
By: Catherine Bai, Analytics and Media Manager
We studied over 7,500 brands in the Health and Personal Care category in Amazon’s store to reveal insights into how they achieved year-over-year growth in detail page views and new-to-brand customers.
We looked at the 2019 performance of over 7,500 brands in the Health and Personal Care category in the United States. This analysis classified brands into clusters and then looked into their advertising and retail attributes to extract insights-driven recommendations to help advertisers improve their performance, specifically in terms of year-over-year growth of glance views and new-to-brand customers. To simplify the comparison, we focused on the top-performing health and personal care advertisers compared to other advertisers. Results show that top-performing health and personal care advertisers differentiate themselves from other advertisers in three areas. Advertisers looking to improve their detail page views year-over-year detail page view growth rate (DPVGR) and new-to-brand growth rate (NTBGR) should consider:
- Using audience remarketing
- Using negative keywords.
- Investing in Amazon DSP (demand-side platform) offsite ad placements.
For more information, see the Methodology section at the end of this article.
1. Top-performing health and personal care advertisers leverage audience remarketing
For top-performing health and personal care advertisers, 26% of their total impressions came from remarketing tactics.
Leverage Amazon events such as Prime Day and Cyber Monday and upper-funnel advertising products such as Streaming TV ads and Amazon DSP to increase the potential audience available to build awareness and consideration. Then follow up by remarketing to audiences that viewed product detail pages. Advertisers can also reengage audiences who viewed other brands' products for cross-selling or upselling, or shoppers browsing similar products to drive brand-level engagement.
2. Top-performing health and personal care advertisers use negative ASINs to market to relevant customers
For top-performing health and personal care advertisers, 41% of campaigns, on average, used negative keywords or negative ASINs tactics. For other advertisers, 0% of campaigns used negative keywords or negative ASINs tactics.
When using negative ASINs, consider using metrics to select negative keywords. Lower click-through rates (CTRs) and lower conversion rates are good indicators that some keywords are underperforming. Look at CTR and conversion to identify negative keyword candidates.
3. Top-performing health and personal care advertisers invest in Amazon DSP ads off-Amazon
Top-performing health and personal care advertisers delivered 18% of their total impressions from Amazon DSP offsite inventory (Amazon Publisher Services or third-party exchanges), while other advertisers delivered none of their impressions from Amazon DSP offsite ads.
When advertisers are thinking about buying from Amazon DSP offsite, we recommend they
We created a composite score of Detail Page Views year-over-year Growth Rate (DPVGR) and New-to-Brand Customer year-over-year Growth Rate (NTBGR) from 2018 to 2019. Brands that rank in the top 50% among total brands for DPVGR and that rank in the top 50% for NTBGR are considered successful, otherwise, we considered them to have been unsuccessful) We then used machine learning to identify the advertising and retail strategies they used to help increase their composite score.
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 at 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 cluster.