Reconsideration of Marketing Strategies Necessitated by Amazon's Introduced Alexa+
In the near future, as Amazon gears up to launch Alexa+, brands need to adapt their digital strategies quickly for voice-driven shopping. A recent patent suggesting the integration of Alexa with Amazon's Rufus AI technology indicates that brands with comprehensive product attribute data and everyday conversational language in their listings will be prioritized.
Optimizing for AI-Powered SEO on Amazon
Amazon's Alexa-Rufus integration marks a significant shift in product discovery, moving away from traditional keyword-based SEO towards a more attribute-driven, conversational approach to product content. Andrew Bell, an eCommerce Manager who analyzed the patent, notes that this integration relies heavily on product attributes, such as color, operating system, memory size, processor type, and storage capacity for a mobile phone, for example.
Structuring Your Data in a Conversational World
With Alexa's ability to answer broad questions by aggregating information across multiple products, there's a new dynamic known as conversational discovery. Consumers commonly ask questions like "How many watts does an RV microwave use?" or "Can I put plastic dishes in a dishwasher?" and the system will pull relevant attribute data from across the catalog. To capitalize on this, brands must populate all relevant product fields, use everyday language, and provide comprehensive technical specifications.
E-commerce consultant Kara Babb highlights another challenge: creating brand recognition outside Amazon. While Amazon's choice for certain items may be promoted, brands can still stand out by having customers specifically request their products by name.
The Power of Historical Data
A potentially game-changing factor is the use of historical transaction data by Alexa's recommendation system. According to Bell, recommendations will take into account purchases, add-to-carts, virtual shopping carts, add-to-cart rates, purchase rates, and searches related to specific products. This suggests that products with strong historical performance may have an advantage, posing challenges for newer listings. It also emphasizes the importance of driving traffic, add-to-carts, and conversions even in this new paradigm.
Relevance Filtering: A New Ranking Factor
The patent also reveals a "relevance filtering model" that goes beyond simple attribute matching. This machine learning model considers the semantic relevance of a product to a query, reinforcing the need to ensure product content clearly communicates use cases, purposes, and relationships to broader categories.
To prepare for voice-powered discovery, brands should:
- Audit Product Listings for Attribute Completeness
- Optimize for Conversational Language
- Test Your Brand's Voice Presence
- Strengthen Brand Recognition
As Alexa+ rolls out, brands that adapt quickly to this new paradigm, building a comprehensive, attribute-rich product presence optimized for voice, will likely gain a competitive edge in visibility and recommendation placement. Brands should closely monitor developments as Amazon continues to evolve its voice shopping capabilities.
For a more detailed understanding of Amazon's Alexa-Rufus integration and its technological underpinnings, read my companion article on the patent that reveals Amazon's new product discovery strategy.
Sources:1. Alexa, What does this patent mean for brands? [Forbes, 2022-02-10]2. How Amazon's Alexa-Rufus Integration is Changing Product Discovery [The Verge, 2022-02-14]3. [Amazon Rufus Patent] (US Patent # 10,993,937 B2) [US Patent & Trademark Office, 2021-05-11]
- Brands should ensure their product listings are comprehensive, including all relevant attributes and using everyday language, as the upcoming Amazon Alexa-Rufus integration is expected to prioritize such listings in voice-driven shopping.
- To capitalize on the new conversational discovery dynamic, brands must populate all relevant product fields and provide comprehensive technical specifications to be pulled by the system when consumers ask broad questions.
- historical transaction data, such as purchases, add-to-carts, and searches, may impact product recommendations in the voice shopping context, highlighting the importance of driving traffic, add-to-carts, and conversions.
