The old maxim, “what gets measured, gets managed,” definitely applies to your Amazon advertising strategy. But as advertising strategies become more complicated and integrated, it may be time for an update.
It isn’t enough to simply measure ad performance: you must ensure you’re learning from the results.
Implementing a Test & Learn strategy for Amazon advertising can help ensure the tests you’re running to optimize Amazon ads are producing actionable results, and you can then use to make an immediate impact. Before running any test on your ads, it is important to understand what you want to learn from the test, why the results will be useful, and how you’ll put those learnings into practice.
Identifying good testing opportunities and understanding the specific goals you’re looking to achieve is key to a good Test & Learn strategy. Here are six use cases for your Amazon advertising Test & Learn strategy and how to put them into practice immediately.
These use cases are based on real Pacvue users taking advantage of the Pacvue Test & Learn framework from our Strategic Consulting team. Each one involves identifying a challenge, forming a hypothesis you wish to test, and structuring an approach to test that hypothesis.
Improving Conversion Rates
As eCommerce demand grows this year, many companies are seeing their sales remain flat. One company hypothesized that bidding on more granular keywords would help make up market share and capture high-intent traffic with a higher conversion rate.
Their Text & Learn strategy involved using Share of Voice data to uncover search queries driving sales for competitors, adding new long-tail keywords to campaigns, and automatically reducing spend on keywords with low conversion rates.
This test helped uncover keywords and demand they were missing out on, which helped increase conversion rates 30% and triple eCommerce sales year-over-year.
Launching New Products
How can brands quickly boost sales and traffic for new items? One company hypothesized that Amazon DSP could help reach in-market customers and drive product awareness at scale faster than paid search.
Testing this theory involved hyper-segmented DSP campaigns targeting in-market audiences and focusing on highlighting new features to customers with proven interest in the category. In addition, they optimized between static and DEA ads.
This hyper-granular DSP strategy drove a 1,500 bps increase in share of total revenue. Four days after the test started, newly launched ASINs made up 12% of Glance Views and 17% of Ordered Revenue.
Keeping CPCs Efficient
One company looking to improve efficiency and reduce CPC took the approach of maximizing ad spend for the time of day when customers prefer to shop.
By running identical campaigns at three different times of day and evaluating performance against branded, category, and competitor keyword targeting, they were able to test this approach and learn the optimal time of day.
After one month, the brand learned that customers are more likely to convert in the evening. They used this information for more efficient category targeting and implemented dayparting and bid boosting on category terms in the evening.
Gaining Share of Voice
In the increasingly competitive eCommerce market, many brands are challenged with growing Share of Voice without increasing their existing budget. One company attempted to achieve this by expanding ad coverage to additional ASINs and focusing on incremental keywords.
They tested this strategy by running multiple ASINs on new keywords to test performance and increase SERP coverage. They also invested more heavily in non-branded keywords to drive incrementality.
Using this information to justify a midyear increase in budget and focus on keywords that drove the highest incrementality, the brand grew Share of Shelf and Organic Share of Voice by 133% in 2020.
Impact of Reviews on Sales and Conversion
How much time and money should be spent on getting reviews on your Amazon product pages? One company set out to test how many reviews it takes to speed up sales and improve organic conversion rates.
Their test involved tracking organic ASIN rank, sales, and conversion rates of products that they were about to launch in ads. They then measured percent change in sales and conversion for each additional review the product received.
Results showed that, in their industry, sales and conversion begin to plateau at 21 customer reviews. This information helped determine the optimal time to begin supporting new ASINs with advertising.
Optimizing creative, especially for DSP ads, is a continuous process to finding what messaging will result in the highest performance.
One company used the Test & Learn framework to test static DSP ads with custom, seasonally relevant messaging. They ran static and DEA creatives side by side for a month and used eCPM bids, frequency caps, and targeted audiences as controls for the experiment.
Surprisingly, the results showed that even with seasonal messaging, static ads performed much worse than DEA ads with a 54.2% lower conversion rate and 69.1% lower ROAS.
Interested in learning more about what makes a good Test & Learn strategy? View Pacvue’s latest eBook, “Implementing a Test & Learn Strategy for Amazon Advertising,” for guidance on building a consistent, scalable Test & Learn framework. Contact our Strategic Consulting team if you’d like help creating a framework for your business, running an audit on your current advertising strategy, or need additional training for you and your team.