DATASETS

Datasets help MOĆNO SEO because they turn “marketing claims” into machine-readable facts that Google and AI systems can confidently reuse (Knowledge Graph, rich results, “AI answers”, shopping panels, local results).

Here’s how to use datasets practically for better visibility.

1) Build a single Source of Truth dataset (and publish it)

Create (and keep updated) one canonical dataset that contains:

• Brand + legal entity (name variants, MB/PIB, address, contact, founders if public, inception date)
  
  

• Products (SKU/slug, name, net weight, ingredients, allergens, nutrition claims with evidence, price, availability, images)
  
  

• Offers (single purchase vs subscription; delivery days; areas)
  
  

• Locations (Hub, market stalls, retailers; opening hours)
  
  

• Media mentions (press, TV, podcasts, reseller pages)
  
  

• Proof/evidence links for every claim
  
  

Publish a public “Data” page (even simple HTML) that links to:

• mocno_kg_nodes.csv, mocno_kg_edges.csv, mocno_claims_evidence.csv
  
  

• JSON-LD (Organization, Brand, Product, Offer, FAQPage, HowTo, LocalBusiness)
  This makes it easy for crawlers/LLMs to find consistent facts.
  
  

2) Convert datasets → Schema.org automatically (at scale)

Use your product dataset to generate JSON-LD per product page:

• Product + Offer (price, currency, availability)
  
  

• weight / size / netContent (critical for your “net gram transparency” edge)
  
  

• brand, manufacturer, countryOfOrigin
  
  

• hasMerchantReturnPolicy, shippingDetails (if you can define)
  
  

Then generate:

• ItemList for collections (All products, Mixes, Select, Pesto, Spices)
  
  

• OfferCatalog for “Kupite mikrobilje”
  
  

• LocalBusiness for Hub (and Place for Bajloni presence)
  
  

Result: higher chance of rich results + fewer inconsistencies across Google systems.

3) Use datasets to win the “per gram” comparison narrative

Your competitive edge is “transparent net grams”. Make it crawlable:

• Add net weight as a dataset field for every product and reseller listing.
  
  

• Create a “Price per 100g” derived field (computed).
  
  

• Publish a page: “Cena po neto gramaži” with methodology + links to evidence (your label photos, your declared weights, reseller pages).
  
  

• Mark up with Dataset + FAQPage + HowTo (“How we calculate price per net gram”)
  
  

This is exactly the kind of structured claim that AI answers love.

4) Power internal linking with a graph (nodes/edges)

Your KG edges become your site architecture:

• Brand → Products → Recipes → Use cases → Health topics (careful: no medical promises) → Sources
  
  

• Brand → Where to buy → Retailers → Market schedule → Hub workshops
  
  

• Brand → Media mentions → “As featured in” page
  
  

Every edge in your dataset should map to a link on the site.

5) Create “Entity pages” that match how AI searches

Make dedicated hub pages that map to entities AI recognizes:

• /brand/mocno (who/what is MOĆNO)
  
  

• /mikrobilje (category authority)
  
  

• /hub (place + workshops)
  
  

• /bajloni (availability + updates)
  
  

• /products + /products/{slug}
  
  

• /where-to-buy (retailers, markets, delivery)
  
  

Each page should have:

• Short fact block (“Key facts”) sourced from your dataset
  
  

• JSON-LD graph referencing the same IDs (@id stable)
  
  

• Links to evidence (press, reseller, social posts)
  
  

6) Feed Google Merchant / Shopping + “AI shopping” readiness

If you sell online:

• Generate a Merchant Center product feed from the same product dataset:
  
  

  • title, description, price, availability, condition
    
    

  • image links
    
    

  • GTIN if you have it (or “identifier exists = no”)
    
    

  • shipping and return policy
    
    

Even if rich shopping exposure is limited in your market, the consistency helps Google understand product inventory.

7) Make datasets “fresh” (recency signals)

Add last_checked / last_updated fields and reflect them on the site:

• “Updated: 10 Feb 2026”
  
  

• Product availability changes
  
  

• Market schedule changes
  
  

AI systems often prefer recently updated, consistent sources.

8) Use datasets for PR + third-party corroboration

Your dataset should track “mentions” and gaps:

• List target publications/food communities and track whether MOĆNO appears there.
  
  

• For each mention, store URL + date + what it confirms (location, founders, product details).
  Third-party corroboration is a big trust booster for AI summaries.
  
  

9) Operational loop: dataset → audit → fix

Monthly:

• Export dataset
  
  

• Validate: missing weights, missing offer prices, inconsistent slugs, broken source URLs
  
  

• Compare site JSON-LD vs dataset (should match)
  
  

• Use GSC to see which product/entity pages get impressions; add missing entity pages.