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Technology Series: The Challenges and Opportunities With Advertising on Amazon

Feedvisor’s CTO and co-founder Eyal Lanxner provides insight into the critical role data plays — and the complexities involved — in finding the optimal combination of ad campaign structure and performance. By Eyal Lanxner December 9, 2019
Technology Series: The Challenges and Opportunities With Advertising on Amazon

The technology series focuses on Feedvisor’s AI-powered platform, data-driven intelligence, and machine-learning algorithms. The series will illustrate various ways in which our technology assists our customers in their e-commerce activities and challenges, with Amazon at the center.

Amazon is on pace to earn nearly $10 billion in net U.S. digital ad revenue in 2019, which represents a 7.6% market share. Further, nearly one in 10 U.S. digital ad dollars will go to Amazon by 2021. 

While the e-commerce platform is still a much smaller advertising player than the leading duopoly of Google and Facebook, the platform is playing a growing role in brands’ and retailers’ advertising strategies, given its vast audience, proven ability to drive performance and ROI, and bottom-of-the-funnel ad capabilities, considering how consumers frequent the platform when they are ready to convert.

Advertising, however, is only one piece of the puzzle. Brands and retailers on Amazon need to adopt a strategic approach to understanding the countless variables that influence success on Amazon, from operational intelligence and inventory insights to price optimization and brand management. To attain advertising success — and marketplace excellence as a whole — you need to be heavily reliant on data. Getting the right data, processing it, and analyzing it is at the core of the advertising problem complexity.

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About the Author

Eyal Lanxner is CTO and co-founder at Feedvisor. He served as head of VeriSign’s research and analytics and as CTO at Zoomix Data Mastering. Eyal holds a M.Sc. in computer science from Bar-Ilan University.

Applying the Correct Campaign Structure and Keywords

Campaign structure is one of the most essential components of effective advertising on Amazon. There is a multitude of proper ways to structure your campaigns, depending on the complexity and size of your catalog, and finding the right structure will enable you to efficiently and accurately analyze the data and attain the desired insights. If you do not structure your campaign the right way, the data it will generate will be unclear, ambiguous, and inaccurate.

Determining the right campaign structure can be challenging, given certain limitations such as running a maximum number of 10,000 campaigns on Amazon. Ask yourself if all of the product ads contained in the ad group are related to each other and if the keywords and ASINs appended to the ad group can apply to those products. This will make sense to implement if similar behaviors and conversion levels are seen amongst all ASINs in the family.

Ads and targeting fall into the same level under the campaign umbrella and therefore relate to each other. The more granular your ad groups are, the better your campaigns will perform. However, getting too granular can decrease efficiency. On top of identifying the proper structure and relationships between campaigns and ad groups as well as keywords and products, you need to identify which advertising option you are going to pursue: Sponsored Products, Sponsored Brands, and Product Display Ads — of which the differences and benefits are explained here.

Then, for each keyword involved in the campaign, you need to specify a broad, phrase, or exact keyword match type, which can allow you to optimize which customer search queries your ads are eligible to show against. Each match type has its own use case and the timing of when you implement each kind will depend on whether you are utilizing manual or automatic targeting for your ads.

The Role of Data in Your Ad Strategy

As evidenced by the information above, there is a seemingly endless number of advertising variations you can employ on Amazon. Identifying the optimal configuration for a given product or family of ASINs at a given point in time requires sophisticated trial and error techniques, retrospect analysis, and predictive machine-learning models, where one of the objectives is to reach an intelligent conclusion, before exhausting all possible configurations — and capital.

Conversely, in some instances, the challenge is the sparse amount of data and some keywords and products do not have sufficient data to inform actionable decisions. Accumulating this data requires time, resources, and money. Alternatively, Feedvisor suggests a strategic approach that utilizes generalized AI-driven models. These models help power Feedvisor’s advertising optimization and intelligence platform, for example, and are leveraged to identify patterns and correlations.

Overall, though, it is of the utmost importance that you cater your campaign strategies to align with your business objectives, driving relevance and value on an ongoing basis. To do so, you must leverage product specifics and unique campaigns and have a deep understanding of each product’s competition at a given point in time. 

While you may want to quickly liquidate a product’s inventory, another brand or retailer may want to maximize profits, a product’s exposure, or sales velocity. At Feedvisor, we believe that your business goals should drive the strategies behind your algorithmic bidding and ad campaign optimization. By clearly establishing and presenting your goals, you will be better equipped to decide which products to include in your campaigns, properly structure them, and measure results to inform future campaigns and your greater ad strategy.

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