How should ecommerce product descriptions be written for AI? Template-based descriptions aren't getting cited

Your template problem is common. Here’s what AI needs:

Why templates fail:

AI is looking for SPECIFIC answers to SPECIFIC questions:

• “Best running shoe for marathon training”
• “Most comfortable office chair for 8-hour days”
• “Laptop for video editing under $1500”

Generic descriptions can’t answer these specifically.

What AI-citable descriptions include:

1. Specific use case

   • Not: “Perfect for active lifestyles”
   • But: “Designed for marathon runners needing cushioning for 20+ mile training runs”

2. Quantifiable differentiators

   • Not: “Exceptional comfort”
   • But: “Features 8mm foam heel drop for reduced impact on long distances”

3. Who it’s for (and NOT for)

   • “Ideal for neutral pronators. May not suit severe overpronators.”

4. Problem-solution framing

   • “If you struggle with [problem], [Product] addresses it with [specific feature].”

5. Comparison context

   • “Compared to [competitor], offers [specific difference].”

Template = generic = invisible. Specific = citable = recommended.

Specific elements to include in descriptions:

Structure template (for Tier 1/2):

1. Opening hook (1-2 sentences)

   • What problem does this solve?
   • Who is it perfect for?

2. Key differentiator (2-3 sentences)

   • What makes this specific product unique?
   • How does it compare to alternatives?

3. Feature details (bullet points)

   • Specific, quantified features
   • Not generic marketing speak

4. Use case examples (2-3 sentences)

   • Specific scenarios where this excels
   • Specific scenarios where another product might be better

5. Who should buy (1-2 sentences)

   • Clear recommendation for target user

Example:

“The Marathon Pro X is designed for serious long-distance runners logging 40+ miles weekly. Unlike lightweight racers, it prioritizes cumulative joint protection over weight savings - the 32mm stack height absorbs impact over 20+ mile training runs.

Key features:

• 32mm heel stack height (8mm higher than standard)
• 8mm drop for smooth heel-to-toe transition
• 12oz weight - heavier than racers but lighter than max cushion

Best for: Runners prioritizing joint protection during high-mileage training Consider alternatives if: You’re a competitive racer focused on speed”

This gives AI specifics to match against user queries.

The tiering approach makes sense. We can’t do 5,000 but we can do 500 well.

Question: How do I identify which products to prioritize for Tier 1 beyond sales data?

Product prioritization for AI optimization:

Scoring factors:

How to identify high AI potential:

1. Test AI queries - Which of your products SHOULD be recommended but aren’t?

2. Review competitive gap - Where are competitors cited but you’re not?

3. Customer questions - What do customers ask about most? These questions = AI queries.

4. Category opportunity - Which categories have AI answering but not mentioning you?

Quick method:

List your top 100 products by revenue. For each, ask AI: “Best [category] for [use case]?” Mark if you’re mentioned or not. Prioritize non-mentioned high-revenue products.

Mine customer reviews for description content:

Why reviews help:

Reviews contain:

• Real use cases
• Specific benefits customers experienced
• Who the product is for
• Comparisons to alternatives

Process:

1. Read reviews for Tier 1 products
2. Extract common themes:
   • “Great for [use case]”
   • “Better than [alternative] because…”
   • “Perfect for [user type]”
3. Incorporate into descriptions

Example transformation:

Original description: “High-quality running shoes for all runners.”

From reviews:

• “Finally found shoes that don’t hurt my knees on long runs”
• “Perfect for my weekly 50-mile training”
• “Better arch support than my previous Asics”

New description: “Engineered for high-mileage runners who prioritize joint protection. Runners consistently report reduced knee pain during long training runs compared to standard cushioned shoes. The enhanced arch support system addresses common complaints with traditional running shoes…”

Reviews = real specificity from real customers.

Great insights. Here’s my plan:

Phase 1 (Month 1): Prioritization

• Score products: revenue + margin + AI query potential
• Identify Tier 1 (500 products)
• Test current AI visibility for Tier 1

Phase 2 (Months 2-3): Tier 1 Rewrites

• New description template with required elements
• Review mining for each product
• Full custom descriptions (500 products)
• Add enhanced product schema

Phase 3 (Months 4-5): Tier 2 Enhancement

• Enhanced template for next 1,500
• Focus on unique elements for each
• Less intensive than Tier 1

Measurement:

• AI visibility before/after for Tier 1
• Citation rate by product
• Revenue from AI-referred traffic

This is a 6-month project but should transform our product content.

Quick wins while you work through the full project:

1. Add “Best for” to existing descriptions (1 hour per 50 products)

• Add one line: “Best for: [specific use case]”
• Quick enhancement to templates

2. Add comparison note (1 hour per 50 products)

• Add: “Compared to [alternative], this offers [specific difference]”

3. Add problem statement (1 hour per 50 products)

• Add: “If you struggle with [common problem], [product] helps with [feature]”

These quick additions make template descriptions more specific without full rewrites.

Apply to Tier 1 immediately while planning full rewrites.

Don’t forget schema alongside description improvements:

Product schema essentials:

{
  "@type": "Product",
  "name": "Marathon Pro X Running Shoes",
  "description": "[Your improved description]",
  "brand": {"@type": "Brand", "name": "Your Brand"},
  "offers": {...},
  "aggregateRating": {...},
  "additionalProperty": [
    {"name": "Best For", "value": "Marathon training, high mileage"},
    {"name": "Heel Drop", "value": "8mm"},
    {"name": "Weight", "value": "12oz"}
  ]
}

The additionalProperty field lets you add structured specs AI can extract.

Good description + good schema = maximum AI potential.