# Bulk Outbound Call Best Practices (/docs/bulk-calls/best-practices)

> Complete guide for optimizing bulk call campaigns, from agent configuration to post-call analysis.

























Optimization playbook for bulk outbound call campaigns. Covers agent
configuration, conversation tuning, scheduling, retries, scaling, and
analytics.

<img alt="Bulk outbound call best practices" src="__img0" />

## Agent configuration [#agent-configuration]

Essential settings for optimal agent performance in bulk outbound campaigns.

### Welcome message [#welcome-message]

* Keep it short and concise. e.g., "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

<img alt="Welcome message configuration" src="__img1" />

### Prompting best practices [#prompting-best-practices]

* Start with simple prompts → test gradually → add scenarios and 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?"

### TTS-friendly response generation [#tts-friendly-response-generation]

* 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, Good: "CRM (see-are-em)", "API (ay-pee-eye)", "SQL (sequel)"

### Prompting guide for KB integration [#prompting-guide-for-kb-integration]

* Define specific triggers when the Knowledge Base should be consulted

<img alt="Knowledge base integration" src="__img2" />

## Configurations [#configurations]

Configuration settings that impact call quality and user experience.

### Silence timeout [#silence-timeout]

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

### Interruption sensitivity [#interruption-sensitivity]

* Controls how quickly the assistant stops speaking when the user starts 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

<img alt="Silence timeout settings" src="__img3" />

### Noise handling [#noise-handling]

* 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

### Language model settings [#language-model-settings]

* 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

<img alt="Language model settings" src="__img4" />

### Voice selection [#voice-selection]

* 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

<img alt="Voice selection" src="__img5" />

### Background noise simulation [#background-noise-simulation]

* 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 (e.g., 0.30)
* Test impact on call quality and user perception

<img alt="Background noise simulation" src="__img6" />

## Post-call handling [#post-call-handling]

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

<img alt="Post-call handling" src="__img7" />

## Bulk call guidelines [#bulk-call-guidelines]

Strategic approach for successful bulk campaign execution.

### Campaign management [#campaign-management]

* 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

### Call rescheduling and retry [#call-rescheduling-and-retry]

Optimizing follow-up strategies for maximum coverage and compliance.

#### Rescheduling configuration [#rescheduling-configuration]

* Update timezone handling for accurate scheduling across regions
* Add specific prompts for handling rescheduling requests naturally
* Example: if customer requests rescheduling, ask for the new date and time to call back

<img alt="Timezone configuration" src="__img8" />

#### Retry strategy [#retry-strategy]

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

<img alt="Auto-retry configuration" src="__img9" />

## How to go live with bulk calls [#how-to-go-live-with-bulk-calls]

Strategic approach for successful bulk campaign execution.

<Steps>
  <Step>
    ### Pilot internal testing [#pilot-internal-testing]

    * 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
  </Step>

  <Step>
    ### Small batch rollout [#small-batch-rollout]

    * 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
  </Step>

  <Step>
    ### Scaling approach [#scaling-approach]

    * Scale gradually: 200 → 500 → 1000 → larger volumes
    * Wait for performance stabilization before each scaling step
    * Monitor system performance and call quality at each scale
  </Step>
</Steps>

## Analysis and optimization [#analysis-and-optimization]

Data-driven approach to continuous campaign improvement.

<img alt="Analysis and optimization" src="__img10" />

### Measure key metrics [#measure-key-metrics]

* **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

### Diagnose issues [#diagnose-issues]

Common scenarios and their specific optimization strategies.

#### Scenario A: Low pickup rate (\< 20%) [#scenario-a-low-pickup-rate--20]

* 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

#### Scenario B: Good pickup (> 30%) but low interactions (\< 3 exchanges) [#scenario-b-good-pickup--30-but-low-interactions--3-exchanges]

* 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

#### Scenario C: High pickup and interactions but low conversion (\< 10%) [#scenario-c-high-pickup-and-interactions-but-low-conversion--10]

* 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

### Optimize conversation design [#optimize-conversation-design]

* 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

### Iterate and scale [#iterate-and-scale]

* 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