Will AI-Based Search Kill Amazon PPC?
On a recent episode of The MMK Show Max Sinclair, founder of Ecomtent and former Amazon leader, predicted the imminent demise of Amazon’s Pay Per Click (PPC) advertising model due to a shift to Large Language Model (LLM) or AI-based search on Amazon.
This got us thinking, and we asked a few PPC experts what they thought about this trend. Read on to find out what’s really happening, and how you should prepare for upcoming changes.
Amazon’s move to LLM-based search
Drawing from his experience working closely with Amazon’s A9 teams and insights from industry mentors, Max believes there will be a complete shift towards an LLM-based search paradigm.
This transition is driven by Amazon’s commitment to enhance the customer experience and aims to replace traditional search with a more intuitive and personalized search approach.
By leveraging AI technology, Amazon seeks to curate search results based on individual preferences, streamlining the browsing process and prioritizing relevance over paid ads.
Max emphasized the inevitability of this shift, pointing out Amazon’s customer-centric ethos and its willingness to sacrifice revenue streams for the sake of enhancing user experience.
As the landscape changes, sellers must adapt their approaches, focusing on product quality and customer satisfaction rather than relying solely on paid advertising.
Amazon is currently in the experimental phase with its AI Shopping Assistant, a cutting-edge feature designed to revolutionize the customer shopping experience. Powered by a large language model, this assistant operates within the confines of Amazon’s mobile app, where it adeptly fields inquiries about various products.
By tapping into a vast repository of product details and reviews, the AI Shopping Assistant offers tailored responses to questions regarding product features, such as material composition, suitability for specific needs, and sizing details. Its primary objective is to streamline the information-gathering process, providing users with precise and relevant answers.
However, it’s worth noting that the assistant’s functionality is currently limited to handling single-product queries, lacking the ability to compare multiple items or execute actions like adding products to the cart.
Furthermore, it does not furnish information such as price history, thus representing a partial solution within Amazon’s evolving AI ecosystem. Despite these constraints, the AI Shopping Assistant demonstrates Amazon’s commitment to exploring advanced AI capabilities in e-commerce, paving the way for future developments aimed at enhancing contextual understanding and user engagement.
Implementing LLM-based search on Amazon could significantly enhance the customer experience by enabling shoppers to efficiently gather information about product characteristics, advantages, and disadvantages. This technology would streamline the process of navigating through reviews, Q&A sections, and listing details, saving shoppers time and effort. Ultimately, any advancement in data computation, such as leveraging LLMs, have the potential to enhance the overall customer experience on Amazon by facilitating quicker and more informed decision-making.
Walmart’s LLM integration
Walmart recently announced LLM-based search on their marketplace.
“From what I have seen over the years Amazon is usually ahead of everyone in terms of technology. And I believe Amazon will be doing this sooner or later. But I imagine that it’s a monumental task that takes time,” says Sean Smith a PPC expert who heads PPC AMS Accelerator.
Smart marketplaces are increasingly recognizing the significance of adopting LLMs to remain competitive in the digital landscape. LLMs empower computers to process and glean insights from vast swathes of text and content, offering unparalleled capabilities in understanding and contextualizing user queries.
By embracing LLMs, marketplaces can enhance the personalization and relevance of search results, thereby elevating the overall user experience. This heightened user experience creates prolonged engagement, as users are more likely to linger on the site and return frequently, driving sustained traffic and bolstering marketplace prominence.
The integration of LLM-based search by Walmart is a positive step, but its effectiveness ultimately depends on implementation.
Impact on PPC
Some experts believe LLM-based search has the potential to enhance the effectiveness of PPC by providing a more tailored and user-friendly shopping experience, ultimately benefiting both consumers and advertisers.
PPC expert Ritu Java who heads PPC Ninja says, “Given how important advertising is for Amazon, it is unlikely that they will make any drastic moves the way ads currently work that could disturb this highly lucrative revenue source.”
If LLM-based search systems provide a more personalized and efficient shopping experience, this could lead to higher conversion rates for advertisers. Advertisers who optimize their listings to align with the capabilities of LLMs may see improved performance, as their products are more likely to be surfaced to relevant users.
Additionally, the ability of LLMs to quickly parse through vast amounts of data could potentially lead to more targeted ad placements, ensuring that ads are displayed to users who are most likely to convert.
Sean agrees, “If we are making the assumption that LLM-based search systems provide a better user experience then this should also make advertising even more effective. If the user experience is more personalized then this could possibly translate into better conversion rates as long as a listing’s content is optimized for the LLM.”
What should sellers do
To prepare for the potential integration of LLM-based search, Amazon sellers should take proactive steps to optimize their product listings and stay informed about industry developments.
Here are some recommendations from our experts:
- Optimize product content: Sellers should familiarize themselves with best practices for content optimization on Amazon, ensuring that their product listings are well-structured and contain relevant keywords. This includes optimizing titles, bullet points, descriptions, and backend keywords to improve visibility in search results.
- Stay informed: Sellers should actively monitor news and updates related to Amazon’s technology advancements and any potential changes to search algorithms. This includes staying up-to-date with announcements from Amazon and industry publications to understand how LLM-based search systems may impact their business.
- Engage with AI: Sellers can gain insights into how their products are perceived by AI systems by engaging with features like the Q&A section on their product detail pages. By asking questions about their own products and observing the responses generated by AI, sellers can gain valuable insights into how their listings may be interpreted and optimized further.
- Adaptation: While sellers may not have direct control over how AI systems interpret their listings, they can adapt their strategies based on the feedback and insights gained from engaging with AI-driven features. This may involve refining product content, adjusting keyword strategies, or exploring new marketing tactics to ensure maximum visibility and relevance in LLM-based search results.
By taking these proactive steps, sellers can better position themselves to adapt to changes driven by LLM-based search systems and maintain competitiveness in the evolving landscape of e-commerce.
More ways to leverage AI for ecommerce
We asked, Max, Sean and Ritu for suggestions on how else sellers can use AI to enhance their ecommerce business. Here are their tips:
- Listing optimisation: Use AI tools to identify SEO-rich elements and improve product listings for better visibility and conversion rates on platforms like Amazon.
- Review management: Employ AI-driven solutions to extract and manage reviews, ensuring compliance with platform terms of service and enhancing overall reputation management.
- Creative content generation: Utilize AI for creating compelling visuals, A+ content, and sponsored brand advertisements to engage customers and drive sales.
- Automated tools: Leverage AI to develop customized micro-tools using platforms like Google Sheets, enabling automation of various tasks such as data manipulation and analysis.
- Product design: Explore AI tools like MidJourney to generate high-quality images, brand stories, and custom visuals for listings and advertisements, enhancing brand presentation and customer engagement.
- Content creation: Utilize AI-powered language models like ChatGPT to generate listing content, headlines for ads, and other marketing copy, saving time and resources while ensuring consistency and quality.
Summary
AI is posted to change the way ecommerce businesses function. These are just a few examples of how AI can be used to improve efficiency, enhance customer experience, and drive sales.
As AI technology continues to advance, there will be even more opportunities for sellers to innovate and optimize their operations. It is critical for sellers to stay on top of changes and new developments.
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