By Purva Gupta, Co-Founder and CEO of Lily AI
Key Takeaways:
- Thematic search presents both challenges and opportunities for retailers, impacting organic product discovery.
- Google’s patented fan-out technique demands richer product data, not keywords, and favors natural language.
- Investing in Product Content Optimization ensures visibility, relevance, and accuracy across SEO as well as GEO and AEO.
As is often the case with market disruption, the introduction of Google’s AI Mode, as well as insights gleaned from its thematic search patent filing, represents both a challenge and an opportunity for retailers and brands.
While these existing innovations and ongoing inventions threaten traditional SEO and keyword-based strategies, they also create a pivotal moment for companies to embrace more structured and more relevant consumer-centric product data that will be critical in the new world of Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO).
Let’s take a look at a few of the possible challenges resulting from today’s AI-driven search landscape.
Fan-Out and Thematic Search Challenges
The challenges from fan-out techniques for thematic search include:
- Query fan-out and semantic search dominance: Google’s AI Mode query fan-out technique enables AI to expand a single consumer query into dozens of semantically related searches (e.g., consumer prompt “I’m looking for a breathable floral midi dress under $50” becomes “daytime summer dress” “floral and botanical print dresses” “budget-friendly summer dresses” “dresses suitable for hot weather” “how long is a midi dress?”). This shift diminishes the value of rigid keyword targeting, as AI now anticipates and serves nuanced and complex consumer intents in real time.
- AI Mode’s conversational interface: With AI Mode, Google prioritizes natural-language answers over traditional links, blending product recommendations, reviews, and comparisons into a single response. Leveraging advanced natural language understanding (NLU), Google is capable of understanding complex, multi-part questions, even those phrased naturally, similar to how you’d speak to another person. Retailers and brands who rely on basic product titles and generic descriptions risk invisibility in this fluid, contextually-aware environment.
- Multimodal search hurdles: AI Mode’s integration of image-based queries (e.g., uploading a photo to find similar items) and virtual try-ons demands richer visual and contextual product metadata, which is a gap many retailers struggle to fill. Data silos, inconsistent data, varying formats, lack of standardization, and limited/low quality images and video assets are some of the many hurdles facing retailers when trying to optimize their products for the reality of multimodal search.
So, how can brands and retailer’s future-proof product discovery in this new world?
Opportunities to Shine with Fan-Out and Thematic Search
To address these challenges, it’s essential to start with the fundamentals and capitalize early on the opportunities that AI-friendly “machine speak” data strategies can unlock. Now is the time to get your product data house in order with Product Content Optimization (PCO).
- Transform Product Data into AI-Ready Answers: Lily AI’s PCO platform enriches product catalogs with hundreds of hyper-granular product attributes, from objective micro-attributes (tight vs. loose sleeve fit) to more subjective customer-centric attributes (summer, coastal, beachy) and synonyms (sneaker vs. trainer vs. tennis shoe), aligning with Google’s semantic and multimodal demands.For example:Basic Data: “Blue linen dress”
Lily AI-enhanced: “Breathable linen midi dress, coastal grandmother aesthetic, beach wedding guest outfit, summer capsule wardrobe staple”This depth ensures products surface across Google’s expanded query variations and image-based searches. - Neutralize Hallucination Risks: While models like Gemini and ChatGPT occasionally invent details (“hallucinate”), Lily AI grounds content recommendations in human-verified product attributes, phrases, and other content types, reducing mismatches between AI-generated responses and actual merchandise.
- Bridge the “Consumer-Retailer Language Gap” in AI Search: Google’s AI Mode prioritizes natural language (e.g., “office outfits that don’t look boring”), not industry-specific terminology or merchant and marketer jargon. Lily AI maps retailer operational taxonomies to its expansive and dynamic proprietary library of micro-attributes and consumer intent terms and phrases, ensuring products align with how shoppers actually discover, search, and shop.
- Capitalize on Vertical Search Fragmentation: As Gemini and Perplexity carve niches in research and multimodal tasks, Lily AI’s agnostic retail data model ensures products perform consistently across all platforms, whether on Google, Bing, Meta, or emerging AI tools.
Staying One Step Ahead with Product Content Optimization
While Product Content Optimization (PCO) may not be the Shiny Object D’Jour, its proven approach works, helping retailers and brands alike to preempt disruption. By enriching product detail pages with terms like “quiet luxury workwear” and “CEO office aesthetic,” both customer-facing copy and on the back-end, they dominated long-tail queries generated by query fan-out, outperforming competitors relying on generic “women’s blazer” product descriptors.
It’s both an uncertain yet an exciting time for retail, where agility and experimentation will be key in this age of AI search. Google’s newest capabilities aren’t a death knell for retailers and brands; they’re a wake-up call.
The winners will be those who:
- Recognize their product data is a strategic asset when properly harnessed.
- Treat product content as a dynamic contextual taxonomy, not a static database.
- Leverage AI-powered product content optimization to maximize search and discovery everywhere, from website PDPs to product feeds and advertising platforms.
At Lily AI, we’re dedicated to helping brands not only adapt but also lead in this new era. By transforming product data into your distinct advantage, we remain committed, no matter how search evolves, to help your products be discovered because, after all, if a product can’t be found, it can’t be bought. The time is now to rewrite the playbook of product discovery, and it starts with PCO.
In Summary:
Google’s introduction of AI Mode and thematic search, with patented techniques like query fan-out, poses challenges to traditional SEO and demands even richer product data from retailers. These AI advancements prioritize natural language and multimodal queries, risking invisibility for brands with generic or sparse product details. With PCO, brands and retailers can automatically enrich product details with hyper-granular, consumer-centric attributes. This strategy helps retailers bridge the language gap with shoppers, mitigate AI inaccuracies, and ensure their products are discoverable across various search platforms.