Three tactics grocery advertisers are using to grow on Amazon.jp

By: Kazuya Murayama, Media and Analytics Manager, and Ashton Brown, Technical Writer

In this 2020 study of 400 brands in the Grocery category on amazon.jp (Japan), we compare advertising strategies of top- and lower-performing advertisers. We then use this comparison to derive actionable insights advertisers can use to improve year-over-year growth of new-to-brand customers.

Story highlights:

In this study, we analysed over 400 brands in the Grocery category on amazon.jp (Japan) between January and December 2020. To perform our analysis, we grouped Grocery brands into four clusters, with Cluster One being the most successful and Cluster Four being the least successful in terms of the year-over-year growth rate of new-to-brand customers (NTBGR).

Our analysis finds that top-performing Grocery advertisers (Cluster One) had a 1.3X higher NTBGR than lower-performing advertisers (Cluster Four).

Top-performers

1.3X

Higher new-to-brand growth rate YoY

To provide advertisers with actionable insights, we used machine learning to analyse 40+ advertising and media attributes that contribute more or less to NTBGR. We then identified which attributes have the largest positive impact on NTBGR.

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

Top performers utilise both Amazon DSP and sponsored ads

When it comes to NTBGR, product and brand discovery can help. One method for increasing brand discovery is the incorporation of Amazon DSP and sponsored ads into campaigns. Our analysis finds that top performers utilised both Amazon DSP and sponsored ads more than lower performers.

In fact, 71% of top-performing Grocery campaigns on amazon.jp combined both Amazon DSP and sponsored ads, compared to 52% of lower-performing campaigns.

Percentage of campaigns that used both Amazon DSP and sponsored ads

71%

Top-performers

52%

Lower performers

Things to consider when using Amazon DSP and sponsored ads

  • First, advertisers can consider using both Amazon DSP and sponsored ads in at least 71% of campaigns
  • Second, advertisers should consider keeping always-on DSP more than 11 weeks, and campaigns live for more than 110 days

Top performers have 4.6X more customer reviews per unique ASIN or serial number

We have found that the impact of customer reviews is often overlooked by advertisers. In our analysis of advertisers in the Grocery category on amazon.jp, we found that top performers had 23 reviews per unique ASIN or serial number, while lower performers had only 5 reviews.

Number of customer reviews per ASIN or serial number

23

Top-performers

5

Lower performers

Things to consider when looking to improve or increase customer reviews

One consideration is that advertisers may be able to increase and improve customer reviews to increase customer trust, we recommend that advertisers strive for a minimum of 23 customer reviews per unique ASIN or serial number. For more on increasing and improving customer reviews, Grocery advertisers can consider the following:

    • If you’re a vendor: Use the Amazon Vine programme. Amazon Vine invites the most trusted reviewers on Amazon to post opinions about new and pre-release items to help their fellow customers make informed purchase decisions.
    • If you’re a seller: Use Amazon Brand Registry. 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.

Top performers were 1.8X more likely to use negative targeting on keywords or ASINs

Our analysis finds that relevant shopping results can lead to higher engagement, which means that advertisers that refine their ads through negative keywords, may also achieve a higher NTBGR. In comparison, top-performing Grocery advertisers used negative targeting on keywords or ASINs in 67% of campaigns, while lower performers only used negative targeting on keywords or ASINs in 37% of campaigns.

Percentage of campaigns that used negative keyword or ASIN tactics

67%

Top-performers

37%

Lower performers

Things to consider when using keywords

  • Check your existing campaigns’ reports to find terms that need to be used with negative keyword targeting. Lower click-through-rates (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 excluded keywords frequently to learn and optimise your campaigns with the ones that work best for your brand.
  • Check for exceptions. For example, generic keywords used to increase awareness of new products launched (e.g. drink, tea, water) could have poor performance, but should not be used as excluding keywords because they are in fact the correct audience for products in the Grocery category.

Conclusion

As seen in our analysis, in combination with our supervised machine learning model, we identified three key tactics advertisers can use to grow their new-to-brand customer growth rate year over year: (1) combine both Amazon DSP and sponsored ads into campaigns; (2) maintain a minimum of 23 customer reviews per unique ASIN or serial number; (3) consider using negative keyword or ASIN tactics when possible.

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. Specifically, we followed a five-step process to create a suite of success metrics including: new-to-brand customer year-over-year growth rate (NTBGR), and then identified the top advertising and retail strategies to help increase the success metrics with machine learning algorithms.

  • Select brands: 400 brands in the Grocery category between January 2020 and September 2020.
  • Create success metric: Calculated based on year-over-year growth of new-to-brand customers.
  • Identify effective ad or retail actions: Identified top actions to help increase composite score (actions that lead to higher year-over-year growth of NTBGR). Actions include customer reviews, ad products (Sponsored Products, Sponsored Brands, Fire TV, etc.), ad strategies (negative keywords, always-on, audience segments, etc.) and more.
  • Group brands: Brands grouped by composite score (NTBGR) into four clusters ranked from highest- to lowest-performing.
  • Compare brand groups: Identified what strategies top-performing brands (Cluster One) use to increase year-over-year growth of NTBGR, compared to strategies that lower-performing brands (Cluster Four) are or are not using.