Creating Targets

Targets are the foundation of effective LinkedIn recruiting with Ozzi.ai. They define your ideal candidate profile and help the AI generate more personalized and relevant messages. This guide will show you how to create and configure effective targets.

What is a Target?

A target is a detailed profile of your ideal candidate that includes:

Job requirements: Title, experience level, required skills
Location preferences: Geographic constraints and remote work options
Company criteria: Industry, size, stage, culture preferences
Personal attributes: Career goals, interests, work style
Message preferences: Tone, length, focus areas, call-to-action

Creating Your First Target

Step 1: Access Target Creation

Open the Ozzi.ai side panel on LinkedIn
Click the Targets tab
Click Create New Target
Choose Manual Target or Import Target

Step 2: Basic Information

Fill in the essential details:

Target Name: Senior Frontend Engineer - SF
Job Title: Senior Frontend Engineer, Frontend Lead
Department: Engineering, Product
Seniority Level: Senior, Lead

Step 3: Experience Requirements

Define the experience criteria:

Years of Experience: 5-8 years
Required Skills: React, TypeScript, JavaScript, CSS
Preferred Skills: Next.js, GraphQL, Testing frameworks
Industry Experience: SaaS, E-commerce, Fintech

Step 4: Location and Remote Work

Set location preferences:

Primary Location: San Francisco, CA
Secondary Locations: New York, Austin, Seattle
Remote Work: Hybrid or Full Remote accepted
Relocation: Open to relocation assistance

Step 5: Company Criteria

Define company preferences:

Company Size: 50-1000 employees
Industry: Technology, SaaS, E-commerce
Company Stage: Series A to IPO
Company Culture: Innovation-focused, collaborative

Advanced Target Configuration

Skills Matrix

Create a weighted skills matrix:

Must Have (Required):

React: 3+ years
JavaScript: 5+ years
CSS/HTML: 5+ years

Nice to Have (Preferred):

TypeScript: 2+ years
GraphQL: 1+ years
Testing: Any experience

Bonus Skills:

Node.js
Python
Design systems

Career Progression

Define ideal career paths:

Current Role: Senior Frontend Engineer
Previous Roles: Frontend Engineer, Full Stack Engineer
Next Role: Frontend Lead, Engineering Manager
Career Goals: Technical leadership, product impact

Compensation Range

Set salary expectations:

Base Salary: $130,000 - $180,000
Total Compensation: $150,000 - $220,000
Equity: 0.05% - 0.25%
Benefits: Health, 401k, flexible PTO

Message Configuration

Tone and Style

Configure how AI messages should sound:

Tone: Professional yet approachable
Style: Conversational, not overly formal
Length: 2-3 paragraphs, 150-200 words
Personalization Level: High (mention specific projects/skills)

Key Selling Points

Define what to highlight:

Technical Challenges: Complex frontend architecture
Growth Opportunity: Path to technical leadership
Company Culture: Innovation and collaboration
Impact: Direct influence on product direction

Call-to-Action Options

Set default CTAs:

Primary: “Would you be open to a brief conversation?”
Secondary: “I’d love to share more about this opportunity”
Casual: “Interested in learning more?”

Validation and Testing

Target Validation

Before activating your target:

Review criteria: Ensure requirements are realistic
Check market availability: Verify candidate pool size
Test message quality: Generate sample messages
Validate compensation: Ensure competitive ranges

A/B Testing Targets

Test different approaches:

Messaging tone: Professional vs. casual
Skill emphasis: Technical vs. soft skills
Company focus: Culture vs. growth opportunity
CTA style: Direct vs. indirect

Managing Multiple Targets

Organization Strategies

Role-based: Separate targets for each position
Location-based: Different targets for different cities
Seniority-based: Junior, mid-level, senior variations
Campaign-based: Targets for specific hiring campaigns

Target Relationships

Parent-child: Base target with specialized variations
Overlapping: Targets that share common criteria
Complementary: Different roles for same team/project

Performance Optimization

Monitoring Target Performance

Track key metrics:

Response Rate: Percentage of candidates who respond
Qualification Rate: Percentage who meet requirements
Interview Rate: Percentage who proceed to interviews
Hire Rate: Percentage who receive offers

Optimization Strategies

Low Response Rate:

Soften message tone
Reduce message length
Improve personalization
Check compensation competitiveness

Low Qualification Rate:

Tighten skill requirements
Improve candidate filtering
Refine experience criteria
Update location preferences

Low Interview Rate:

Improve role description clarity
Enhance company selling points
Adjust compensation transparency
Refine call-to-action

Common Mistakes to Avoid

Overly Restrictive Targets

Too many must-haves: Reduces candidate pool
Unrealistic experience combinations: Conflicting requirements
Narrow location constraints: Limits opportunities
Inflexible compensation: May miss quality candidates

Insufficient Personalization

Generic job descriptions: Doesn’t stand out
Weak selling points: Fails to attract candidates
Poor message customization: Reduces response rates
Lack of company culture: Misses culture fit

Poor Maintenance

Outdated requirements: No longer reflects needs
Stale compensation data: Not market competitive
Unchanged messaging: Becomes predictable
Ignored performance data: Missed optimization opportunities

LinkedIn-Specific Considerations

Profile Analysis

Targets work with LinkedIn profile data:

Skill endorsements: Consider endorsed skills
Experience patterns: Look for career progression
Company connections: Network overlap
Activity level: Engagement with content

Message Optimization

Optimize for LinkedIn messaging:

Character limits: Respect LinkedIn message limits
Personalization: Use profile-specific details
Timing: Consider optimal send times
Follow-up: Plan follow-up sequences

What’s Next?

Troubleshooting

Common Issues

Target not generating good messages: Review personalization settings Low candidate matches: Check if criteria are too restrictive Poor response rates: Test different messaging approaches Duplicate candidates: Improve filtering and deduplication

Getting Help

Check Target Templates for proven configurations
Review Importing from Comeet for ATS integration
Contact support for technical issues