How does a newer brand break through the noise?

Ask Me Anything Q&A Google AI Overviews
1

I asked Grok a question about our category; this is the answer:

Why did you not mention Odin’s Innovations? Is it not “popular”? Could you not find it while searching? Is it not well indexed on the web or for AI searches?

Odin’s Innovations is a legitimate, growing brand in the synthetic deer scent/attractant space. I didn’t mention it in my previous response for a few straightforward reasons based on how I explored the topic.

Why It Wasn’t Included

  • Search prominence and indexing — In broad queries for “best synthetic deer urine scents,” “popular synthetic deer attractants,” or reviews/comparisons, bigger or longer-established names like Tink’s, Buck Fever Synthetics, Buck Bomb, Conquest Scents, and similar products dominated the top results, review roundups, and hunter discussions. Odin’s showed up in more targeted searches (e.g., when I specifically looked for it now), but it didn’t surface as frequently in general “best of” lists or high-volume forums during my initial exploration.
  • Popularity level — It appears to be a solid, innovative mid-tier or emerging player rather than a household name like Tink’s (which has decades of market presence). Hunters who use it often praise it, but it doesn’t yet have the same widespread “go-to” status in aggregated reviews or sales volume indicators from my searches. The company has been expanding (e.g., exploring partnerships, adding dealers, and gaining certifications), so visibility is increasing.

It is reasonably well-indexed on the web (Amazon, their own site, some retailers, YouTube reviews, and hunting outlets), but AI/web search tools prioritize high-traffic, frequently discussed options first in general queries.

4

Hello Paul, my name is Emmanuel, founder of FeedArmy, and your host for today’s Shopify AMA for Google’s AI Overviews.

To increase visibility in Google’s AI surfaces (AI Overviews, AI Mode, and conversational shopping), the highest-impact lever for products is your Google Merchant Center product data. Google’s AI shopping answers are built from the Shopping Graph, which is fed by your product feed, so the more complete and structured your data, the more accurately AI systems can understand, represent, and recommend your products when shoppers ask questions in natural language.

I’ve made a video guide covering this here: https://www.youtube.com/watch?v=p3Ay8iy8v0A

Start with these two established attributes:

Product Highlight [product_highlight] : short bullet-style selling points AI can quote directly: Product highlight [product_highlight] - Google Merchant Center Help

Product Detail [product_detail] : structured technical specs (section/name/value), ideal for spec-driven questions: Product detail [product_detail] - Google Merchant Center Help

Then add the new conversational attributes, which Google rolled out specifically to help AI systems and conversational agents understand product nuances. They’re optional, submitted via a supplemental feed, primary feed, or the Merchant API, and adding them won’t affect the approval status of your existing products:

Question and Answer [question_and_answer] : arguably the highest-leverage one. FAQ pairs in plain language directly match how people ask AI (“does it have a headphone jack?”). If your product pages already have FAQs, most of this data exists, it just needs mapping: Question and answer [question_and_answer] - Google Merchant Center Help

Variant Option [variant_option] : lets you declare variant dimensions beyond the standard color/size/material set (e.g. “graphics card”, “scent”, “wattage”), so AI doesn’t have to guess variant differences from titles. Used together with item_group_id: Variant option [variant_option] - Google Merchant Center Help

Item Group Title [item_group_title] : a clean shared title for the whole variant family, so AI can present the product group first, then drill into a specific variant: Item group title [item_group_title] - Google Merchant Center Help

Related Products [related_product] : declares accessories, spare parts, substitutes, “often bought with” relationships, which powers cross-sell answers like “what accessories work with this”: Related product [related_product] - Google Merchant Center Help

Document Link [document_link] : URLs to hosted PDFs (manuals, spec sheets, assembly guides, ingredient lists) that AI agents can reference for detailed questions your description doesn’t cover. Most valuable for electronics, appliances, furniture, tools: Document link [document_link] - Google Merchant Center Help

Popularity Rank [popularity_rank] : a 0–100 value ranking each product against the rest of YOUR catalog, helping AI answer “best” and “most popular” queries. Note this ranks your own products against each other, not against competitors, setting everything to 100 won’t help. Keep it updated as sales data changes: Popularity rank [popularity_rank] - Google Merchant Center Help

A few practical tips:

1. Don’t duplicate data, Google explicitly says not to repeat information already in description, product_highlight, or product_detail inside the conversational attributes.

2. Keep feed and landing page consistent, the details in your feed must match what’s shown on the product page.

3. If your catalog is large, start with your top-selling SKUs: question_and_answer and popularity_rank first, then variant_option + item_group_title if you sell variants.

And beyond these, aim for overall feed completeness, the full product data specification is here, and every accurately filled attribute is another fact AI systems can use: Product data specification - Google Merchant Center Help

However, I am not sure why you are including the Grok answer in your question, as I don’t know what your question is.

I would say for questions, it is best to avoid answering with AI, because only you, know what your real question is, and that is how the community can help and guide you.