Bulk Outbound Call Best Practices

Essential settings for optimal agent performance in bulk outbound campaigns.

• Keep it short and concise. Eg- Hello am I speaking with Aman?
• Add personalization using variables (e.g., [name], [company])
• State purpose clearly after user acknowledges the call
• Test different variations for optimal response rates

• Start with simple prompts → test gradually → add scenarios & conditions
• Use one-shot and few-shot prompting for LLM efficiency. Example: 'You are a sales agent. When user says they're busy, respond: I understand you're busy. Would 2 minutes next Tuesday work better?'
• For multi-lingual campaigns, include language-specific prompting guidelines. Example: 'If user responds in Spanish, continue conversation in Spanish with appropriate cultural context'
• Add fallback instructions for unexpected user responses. Example: 'If you don't understand the user's response, say: I want to make sure I understand you correctly. Could you help me clarify that?'

• Write dates and numbers in spoken format.
• Example 1 - Good: 'January fifth, twenty twenty-five' | Bad: '01/05/2025'
• Example 2 - Good: 'twenty-five dollars' | Bad: '$25'
• Example 3 - Example: 'CRM (see-are-em)', 'API (ay-pee-eye)', 'SQL (sequel)'

• Define specific triggers when Knowledge Base should be consulted

Configurations settings that impact call quality and user experience.

• Time to wait after speech ends before generating a response.
• Recommended : 300ms (0.3 seconds) for optimal performance
• Adjust based on target demographic (Eg - older users need more time)
• Test different timeouts with pilot campaigns

• It controls how quickly the assistant stops speaking when user start talking.
• If speech doesn’t reach the set threshold (ms), audio is ignored.
• 150 ms (High Sensitivity) → Very sensitive, may trigger on background speech.
• 600 ms – 1s (Medium) → Balanced, best for natural conversations.
• 1000 ms – 3000 ms (Low) → Less sensitive, may miss short replies (“yes”, “no”).
• Start with 600–1000 ms for most cases.
• Test interruption handling in different scenarios to find optimal setting

• Apply noise reducer to minimize background environmental sounds (fan, traffic, etc.)
• Does not cancel out active conversations or background talking
• Test with different ambient noise scenarios

• Choose LLM based on conversation complexity and speed needs
• Start with GPT-4o-mini for balanced performance
• Enable streaming for real-time, low-latency responses
• Set temperature: 0.2–0.4 for factual, 0.5–0.7 for natural/balanced tone
• Test different models with real conversation scenarios
• Continuously monitor trade-offs between response quality and speed

• Choose voice that matches your brand personality and target audience
• Consider regional accents for local market relevance
• Test voice clarity and naturalness with sample conversations
• A/B test different voices for optimal engagement rates
• Consider gender preferences based on campaign type and audience

• Add subtle background noise for more natural feel (optional)
• Choose appropriate environment sounds (office, restaurant etc)
• Keep volume low to avoid distraction from main conversation (Eg. - 0.30)
• Test impact on call quality and user perception

Comprehensive data extraction and follow-up processes for maximum campaign value.

• Save complete transcription for quality analysis and compliance
• Generate structured call summary with key points and outcomes
• Extract predefined variables relevant to campaign objectives
• Example 1 - Lead qualification: Hot lead, warm lead, cold lead, not qualified
• Example 2 - Intent level: High interest, moderate interest, low interest, not interested
• Add google sheet post call for data analysis and reporting

Strategic approach for successful bulk campaign execution.

• Use descriptive, date-stamped campaign names (e.g., 'Q3_Product_Launch_East_Coast_2024')
• Include context columns matching agent variables (name, company, industry, etc.)
• Configure timezone-aware scheduling for optimal call timing

Optimizing follow-up strategies for maximum coverage and compliance.

• Update timezone handling for accurate scheduling across regions
• Add specific prompts for handling rescheduling requests naturally
• Example 1 - If customer requests rescheduling, ask for the new date and time to callback

• Configure maximum retry attempts per number (typically 2-3 times)
• Space retries appropriately: 24-48 hours between attempts

Strategic approach for successful bulk campaign execution.

• Start with 5-10 internal test calls using sample numbers
• Test different conversation scenarios and edge cases
• Verify agent responses to common objections and questions
• Check technical functionality: call quality, data extraction, integrations
• Document issues and optimize before real user testing

• Dispatch initial batch of ~200 calls to real prospects
• Monitor calls in real-time during initial hours
• Track key metrics: pickup rate, conversation length, completion rate, success rate etc
• Collect immediate feedback from answered calls
• Pause campaign if major issues are detected
• Analyze results before proceeding to larger volumes
• Optimize based on real-world performance data

• Scale gradually: 200 → 500 → 1000 → larger volumes
• Wait for performance stabilization before each scaling step
• Monitor system performance and call quality at each scale

Data-driven approach to continuous campaign improvement.

• Pickup Rate: Track by time of day, day of week, lead source, geography
• Conversation Duration: Average length, completion rate, early hang-ups
• Interaction Count: Back-and-forth exchanges indicating engagement level
• Conversion Rate: Percentage achieving primary campaign objective
• Lead Quality Score: Hot/warm/cold lead distribution from calls
• Agent Performance: Response accuracy, objection handling, flow adherence
• Technical Metrics: Call quality, connection success, system performance

Common scenarios and their specific optimization strategies.

• Analyze lead quality: source, age, verification status
• Optimize call timing: test different hours, days of week, seasonal patterns
• Implement local number presence for better pickup rates
• Analyze geographic and demographic pickup patterns

• Simplify opening conversation flow and reduce complexity
• Shorten bot questions and responses for better engagement
• Clarify value proposition in opening line within first 10 seconds
• Reduce cognitive load with simpler language and concepts
• Test different conversation pacing and natural pauses
• Improve interruption handling and conversation recovery
• A/B test different opening scripts and value propositions

• Analyze conversation quality issues in detail
• Objection handling: Review common objections and response effectiveness
• Interruption management: Ensure natural conversation flow recovery
• Clarification requests: Improve agent's ability to understand and respond
• Value communication: Strengthen benefit articulation and relevance
• Call-to-action clarity: Make next steps obvious and compelling
• Trust building: Enhance credibility indicators and social proof
• Closing techniques: Improve commitment and follow-through processes

• Adjust prompts based on actual conversation patterns and outcomes
• Improve objection handling with real examples from call analysis
• Enhance agent training data with successful conversation examples
• Test new conversation flows with A/B testing methodology

• Run new test batch with updated conversation flow and configuration
• Compare performance metrics against baseline from previous iterations
• Continue optimization cycles until metrics stabilize at acceptable levels