79% of buyers use AI for purchase decisions - how do we get our brand recommended?

You’re right - this IS a fundamental shift. Here’s what the data shows:

How consumers use AI for purchases:

The critical insight:

When AI influences >80% of a decision, conversion rates reach 85.9%. When AI plays minimal role (<20%), conversion rates drop to 32.6%.

This means: If your brand isn’t in the AI consideration set, you’re losing customers before they even know you exist.

The battle for customers now happens inside AI platforms.

Reviews are CRITICAL for AI purchase recommendations. Here’s why:

AI analyzes reviews for:

• Real use cases (“I use this for…”)
• Specific benefits (“Reduced my processing time by 50%”)
• Who it’s ideal for (“Perfect for small teams”)
• Honest trade-offs (“Battery could be better, but…”)

66% of shoppers hesitate with <5 reviews. AI reflects this hesitation.

Review optimization strategy:

1. Generate review volume

   • Post-purchase email sequences
   • In-app review prompts
   • Incentivized reviews (within guidelines)

2. Encourage specific feedback

   • Ask about specific use cases
   • Request comparisons to alternatives
   • Prompt for quantifiable results

3. Respond to all reviews

   • Shows active engagement
   • Adds context AI can use
   • Builds trust signals

4. Distribute reviews across platforms

   • G2, Capterra, TrustPilot
   • Amazon (if applicable)
   • Google Business Profile

Authentic, detailed reviews > high volume of generic reviews.

The review point is interesting - we have good reviews on G2 but very few on other platforms.

Question: Beyond reviews, what content specifically helps AI recommend us?

Content that influences AI purchase recommendations:

1. Comparison pages

• “[Your Product] vs [Competitor]”
• Honest, detailed comparisons
• When to choose each option

2. Use case pages

• “Best for [specific industry]”
• “How [user type] uses [product]”
• Specific outcomes and benefits

3. Feature deep-dives

• Detailed specification pages
• Technical documentation
• Integration guides

4. Case studies with numbers

• “[Customer] achieved X with [Product]”
• Quantifiable results
• Specific implementation details

5. FAQ content

• “Is [Product] right for me?”
• “What does [Product] cost?”
• “How does [Product] compare to…”

6. Buyer’s guides

• “How to choose a [category]”
• Decision frameworks
• Feature comparison tables

This content directly answers the questions users ask AI.

Important: Different AI platforms have different recommendation patterns.

ChatGPT:

• Relies heavily on training data
• Values Wikipedia, authoritative sources
• Citation frequency increasing (~28% of responses)

Perplexity:

• Real-time web search
• Cites sources directly
• Values recent, detailed content

Google AI Overviews:

• Tied to Google search rankings
• Uses structured data heavily
• Values E-E-A-T signals

Recommendation:

Track your visibility across ALL platforms. You might be invisible in ChatGPT but visible in Perplexity.

Use Am I Cited to monitor across platforms and identify gaps.

Critical but overlooked: The handoff from AI to purchase.

Research shows:

• 78.2% go to traditional channels after AI recommendation
• 24.2% go to Google
• 20.3% go to Amazon
• 18.6% go to brand websites
• 70% ultimately complete purchase

This means:

1. Your website must match AI recommendations

   • If AI says “great for video editing,” your landing page should say this
   • Misalignment creates friction

2. Be findable when they search

   • After AI recommendation, users often Google your brand
   • Ensure branded search is optimized

3. Amazon presence matters

   • 20% go straight to Amazon
   • Your Amazon listing must be consistent with AI messaging

4. Remove friction at every step

   • Clear pricing
   • Easy checkout
   • Trust signals

The AI recommendation is just the first step. Close the loop.

Here’s what I’m taking away:

Immediate actions:

1. Audit our AI visibility across ChatGPT, Perplexity, Google AI
2. Create comparison pages vs top 3 competitors
3. Launch review generation campaign on G2 and TrustPilot
4. Update product pages with specific use cases

Content to create:

1. “[Product] vs [Competitor]” pages
2. “Best for [industry]” landing pages
3. Case studies with quantifiable results
4. Comprehensive FAQ addressing common AI queries

Tracking:

• Set up Am I Cited monitoring
• Weekly AI query testing for key purchase queries
• Track recommendation position and context

This is now a core part of our marketing strategy.

Answer Engine Optimization (AEO) is the new discipline here:

Traditional SEO: Rank for keywords AEO: Be cited as authoritative source when AI answers

Key AEO tactics for purchase queries:

1. Question-based content

   • Match how users ask AI
   • “What’s the best [category] for [use case]?”

2. Direct answers first

   • Lead with recommendation
   • Follow with supporting details

3. Structured, extractable format

   • Tables for comparisons
   • Bullet points for features
   • Clear specifications

4. Authority signals

   • Author expertise
   • Citations and sources
   • Third-party validation

Your content should be designed to be extracted and cited, not just read.

How to measure impact on purchase decisions:

Direct tracking:

• Monitor AI recommendations for purchase queries
• Track brand mention frequency in AI responses
• Compare positioning vs competitors

Indirect signals:

• Branded search volume (increases after AI recommendation)
• Direct traffic quality (AI-referred visitors convert higher)
• “How did you hear about us?” survey responses

Sales team intelligence:

• Track AI mentions in sales calls
• Note competitor AI recommendations
• Document prospect AI research behavior

Connect to revenue:

• AI visibility score correlation with pipeline
• Conversion rates from AI-aware prospects
• Win rate against AI-recommended competitors

AI visibility is now a leading indicator of revenue.

Structured data specifically for purchase recommendations:

Product schema essentials:

{
  "@type": "Product",
  "name": "Your Product",
  "description": "Specific, use-case focused",
  "brand": {...},
  "offers": {
    "price": "X",
    "priceCurrency": "USD"
  },
  "aggregateRating": {...},
  "review": [...],
  "additionalProperty": [
    {"name": "Best For", "value": "..."},
    {"name": "Ideal Customer", "value": "..."}
  ]
}

SoftwareApplication schema for SaaS:

• Add applicationCategory
• Include feature list
• Document integrations

FAQ schema on product pages:

• “Who should use this?”
• “How does it compare to alternatives?”
• “What results can I expect?”

Structured data makes your product attributes extractable for AI recommendations.