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Playbook: Increase Recommendations

Many organizations are visible within AI-generated responses but are rarely recommended as the preferred solution for important customer questions.

What Problem Does This Solve?

Being mentioned is valuable, but recommendations have a much greater influence on user decisions.

Organizations that are consistently recommended are more likely to be considered during product discovery, vendor evaluation, and purchasing decisions.

This playbook provides a structured framework for understanding why recommendations are limited and how to improve recommendation visibility over time.

The objective is not simply to increase recommendation frequency, but to improve recommendation quality within the business contexts that matter most.

When Should You Use This Playbook?

Use this playbook when:

  • Your organization is mentioned but rarely recommended.
  • Competitors are consistently recommended instead of your brand.
  • Recommendation patterns differ significantly across AI models.
  • Important customer intents produce weak recommendation visibility.
  • You want to strengthen your position within AI-assisted buying journeys.

Step 1: Confirm That a Recommendation Gap Exists

Begin by validating that recommendation behavior is consistently weaker than expected.

Review:

  • Brand Report
  • Recommendation Intelligence
  • Share of Voice
  • Evidence Layer
  • Confidence Score

Key questions:

  • Are recommendations consistently limited?
  • Which Prompt Sets show the weakest recommendation visibility?
  • Which AI models behave differently?
  • Is the evidence sufficient to support further investigation?

Avoid optimizing based on isolated AI responses.

Step 2: Identify High-Value Recommendation Opportunities

Not every recommendation opportunity has equal business value.

Focus on the user intents most closely aligned with your business objectives.

Identify:

  • High-value Prompt Sets
  • Commercial buying journeys
  • Competitive evaluation prompts
  • Decision-stage user questions

Recommendation optimization should prioritize the prompts that influence meaningful business outcomes.

Step 3: Investigate Why Competitors Are Recommended

Recommendation behavior should always be investigated before optimization.

Review:

  • Prompt Reports
  • AI Perception
  • Entity relationships
  • Competitive Intelligence
  • Citation Intelligence
  • Change Drivers

Key questions:

  • Which competitors are consistently recommended?
  • What characteristics do recommended organizations share?
  • Which supporting evidence explains the observed recommendation patterns?
  • Are recommendation differences consistent across AI models?

Understanding why recommendations occur is more valuable than measuring recommendation frequency alone.

Step 4: Evaluate Recommendation Signals

Recommendations are often influenced by multiple analytical signals working together.

Review whether your organization demonstrates:

  • Strong AI Perception.
  • Clear entity associations.
  • Relevant topical expertise.
  • Appropriate citation visibility.
  • Competitive differentiation.
  • Consistent representation across Prompt Sets.

Organizations that perform well across these dimensions are generally more likely to be recommended.

Step 5: Prioritize Optimization Opportunities

Based on the investigation, prioritize improvements that are most likely to strengthen recommendation visibility.

Potential opportunities may include:

  • Improving topical authority.
  • Clarifying organizational positioning.
  • Strengthening entity relationships.
  • Expanding authoritative information.
  • Improving AI accessibility.
  • Increasing visibility within strategically important Prompt Sets.

Optimization should be prioritized according to business impact rather than recommendation frequency alone.

Step 6: Validate Recommendation Improvements

Generate new reports after meaningful optimization efforts.

Compare current observations with previous reporting periods.

Review:

  • Recommendation visibility
  • AI Perception
  • Share of Voice
  • Citation patterns
  • Competitive positioning

Improvement should be evaluated across multiple observation periods rather than individual AI responses.

Step 7: Continue Monitoring

Recommendation behavior evolves continuously.

Organizations should continue monitoring through:

  • Scheduled Brand Reports
  • Prompt Observatory
  • Competitive Intelligence
  • Historical comparison

Long-term monitoring helps identify emerging opportunities while validating previous optimization efforts.

Common Causes of Weak Recommendations

Organizations with limited recommendation visibility often exhibit one or more of the following patterns:

  • Weak AI Perception.
  • Limited topical authority.
  • Stronger competitive positioning.
  • Insufficient entity associations.
  • Low citation visibility.
  • Weak alignment with important user intents.
  • Recent changes in AI behavior.

These conditions should be investigated collectively rather than independently.

How to Measure Success

Recommendation improvement should be evaluated using multiple indicators.

Potential indicators include:

  • More frequent recommendations within high-value Prompt Sets.
  • Broader recommendation consistency across AI models.
  • Improved competitive positioning.
  • Stronger AI Perception.
  • Increased Share of Voice.
  • Greater recommendation stability over time.

Long-term consistency generally provides more strategic value than temporary increases in recommendation frequency.

Expected Outcomes

After completing this playbook, you should be able to:

  • Identify why AI systems recommend competitors instead of your organization.
  • Prioritize the highest-impact recommendation opportunities.
  • Strengthen recommendation visibility using evidence-based decisions.
  • Validate recommendation improvements through continuous observation.

Related Products

This playbook primarily uses intelligence from:

LLM Tracking may provide additional operational context where website accessibility or AI interaction influences recommendation opportunities.

Related Reports

Related Concepts

Next Playbook

Continue with:

Increase Citations

Increase Citations explains how organizations can improve observable source attribution within AI-generated responses and strengthen the visibility of authoritative information.