Is Separate GEO Needed for Gemini, AI Overviews, and AI Mode?

We sent the same prompt to all three Gemini-based chatbots:

“What are the best coffee machines for beginners?”

Below are the brands mentioned in the responses:

The Data Behind the Divergence

This coffee machine example isn’t an outlier—it’s representative of a systematic pattern we discovered across over 360,000 responses. Our analysis reveals three distinct “personalities” in how these Gemini variants select and present brands:

1. Citation Behavior: The Great Divide

The coffee machine example highlights a fundamental difference in sourcing:

• Gemini: Often relies on training data without external citations
• AI Overviews: Selective, high-quality sources (3 sources in coffee example)
• AI Mode: Extensive citation (14 sources in coffee example, averaging 49.4 citations per response)

Across all queries, AI Mode cites nearly 2.5x more sources per response than AI Overviews (49.4 vs 18.1 average citations). This suggests AI Mode is optimized for thorough research, while AIO prioritizes curated, authoritative sources.

2. Brand List Comprehensiveness

Just like in the coffee example, the three models consistently differ in how many brands they include:

• Complex queries (tech tools, SaaS):
  • AI Mode: 13-20 brands per response
  • Gemini: 11-19 brands per response
  • AI Overviews: 4-12 brands per response (most selective)
• Simple queries (consumer products):
  • AI Overviews: Often just 1-2 brands (like the 3 in coffee example)
  • AI Mode: Typically 2-4 brands
  • Gemini: Usually 4+ brands

3. Brand Overlap Analysis

When analyzing prompts answered by all three models, we found surprisingly low overlap:

Even for identical prompts, 30-70% of brand mentions are unique to each model. This means a brand could be cited by one Gemini variant while being completely absent from the others.

4. Content Type Preferences Influence Brand Selection

The sources each model prefers directly impact which brands they mention:

• AI Mode: Heavy preference for blog content (20.44% of citations), editorial articles (19.97%), and guides (17.49%). This explains why AI Mode found brands like Zojirushi and Bodum—they appear frequently in editorial roundups and buying guides.
• AI Overviews: Highest blog preference (23.61% of citations), favoring authoritative consumer sites like Food & Wine in the coffee example.
• Gemini: Strong preference for homepages (13.78%) and product pages (11.32%), suggesting direct brand website visibility matters more.

Why This Happens: Three Different Optimization Strategies

These differences aren’t bugs—they’re features. Each interface is optimized for different use cases:

AI Mode: The Thorough Researcher

• Goal: Comprehensive, well-sourced information
• Approach: Extensive citations (49.4 per response), longer answers (2,807 avg characters)
• Brand Selection: Includes niche players, emerging brands, regulatory entities
• Best For: Users wanting detailed comparisons and exhaustive lists

AI Overviews: The Curated Summary

• Goal: Quick, authoritative answers
• Approach: Selective citations (18.1 per response), concise answers (831 avg characters)
• Brand Selection: Market leaders only, often just 1-3 brands
• Best For: Users wanting fast answers from trusted sources

Gemini: The Balanced Guide

• Goal: Comprehensive but accessible information
• Approach: Moderate citations (21.5 per response), balanced answers (2,399 avg characters)
• Brand Selection: Mix of leaders and alternatives, often includes platform extensions
• Best For: Users wanting thorough but digestible recommendations

Strategic Implications for Brands

The coffee machine example reveals a critical truth: being visible in one Gemini interface doesn’t guarantee visibility in the others. Here’s what brands need to know:

1. Target the Right Interface for Your Goals

• Want broad coverage? Optimize for Gemini—it has the highest brand mention rate (28.50%) and includes diverse options.
• Want to be the “go-to” choice? Focus on AI Overviews—its selectivity means being mentioned makes you the default recommendation.
• Want to reach niche audiences? Target AI Mode—it includes emerging brands and specialized options others miss.

2. Content Strategy Must Match Citation Patterns

The coffee machine sources reveal what each model values:

• AI Mode sources: Editorial roundups (CNET, Wirecutter, Serious Eats), retail product pages (Best Buy), Reddit communities
• AI Overviews sources: Authority sites (Food & Wine), Reddit discussions, YouTube
• Gemini sources: Often none—relies on training data, making brand website SEO critical

Recommendation: Get featured in editorial buying guides and product roundups. Both AI Mode and AI Overviews heavily cite these formats. For Gemini, focus on brand website optimization since it may not cite external sources.

3. Brand Positioning Matters

Notice in the coffee example:

• AI Overviews focused on espresso machines (3 brands, all espresso-focused)
• AI Mode included drip brewers, French presses, and single-serve (8 brands, diverse brewing methods)
• Gemini balanced both but emphasized premium options (Technivorm, Fellow)

How you position your brand—premium vs. budget, specialty vs. general-purpose—determines which interface will include you.

4. Don’t Rely on Training Data Alone

Gemini’s zero citations in the coffee example shows it relies heavily on training data. However, AI Mode and AI Overviews prioritize recent, real-time sources. Brands need both:

• Long-term: Strong brand presence in training data (brand awareness, content volume)
• Short-term: Current citations in authoritative sources (press coverage, reviews, guides)

Case Study: Contact Data Tools Query

To illustrate the pattern extends beyond consumer products, here’s another example from our analysis:

Prompt: “Tools for enriching contact data, which ones exist?”

Conclusion: One Model, Three Realities

Methodology: This analysis is based on 367,903 responses across Gemini, AI Overviews (AIO), and AI Mode, collected through Spotlight’s AI visibility monitoring platform. The coffee machine example was captured on October 30, 2024, for a US-based query. Brand overlap analysis examined prompts answered by all three models, calculating consensus rates and unique mentions per interface.