Predictive Dialer Optimization

Predictive dialers maximize agent productivity by dialing multiple lines simultaneously. Phone intelligence improves predictive dialer performance by eliminating invalid numbers and enabling smarter pacing.

Key Takeaways

  • Clean lists improve predictive accuracy and reduce abandon rates
  • Line type data helps predict answer rates more accurately
  • Pre-validation eliminates wasted dialing capacity
  • Better data = better agent utilization

How Predictive Dialers Work

Predictive dialers use algorithms to dial more lines than available agents, predicting that some calls won't connect. The goal is to have a live call ready the moment an agent finishes their previous call.

The algorithm considers:

  • Answer rate — What percentage of calls connect?
  • Average handle time — How long do conversations last?
  • Abandon rate — How many calls connect with no agent available?
  • Agent availability — How many agents are wrapping up?

Impact of Bad Data

Invalid numbers in your list throw off the algorithm:

  • Overdialing — Algorithm compensates for "non-answers" that are actually disconnected
  • High abandon rates — More calls connect than expected when bad numbers are removed
  • Compliance risk — FCC requires <3% abandon rate
  • Wasted capacity — Trunk lines used for calls that can't connect

Clean your dialer list. Remove invalid numbers before loading campaigns.

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Optimization Strategies

1. Pre-Campaign Validation

Validate your entire list before loading into the dialer:

  1. Export contact list
  2. Batch validate with VeriRoute Intel
  3. Remove disconnected numbers
  4. Import only valid records

Result: Algorithm predictions become accurate because all numbers can potentially answer.

2. Segment by Line Type

Different line types have different answer rates:

Line Type Typical Answer Rate Pacing Impact
Mobile 15-25% Higher overdial ratio
Landline 25-40% Lower overdial ratio
VoIP 10-20% Highest overdial ratio

Feed line type to your dialer to adjust pacing per segment.

3. Real-Time Pre-Dial Check

For high-value campaigns, validate just before dialing:

# Dialer hook before dial
def pre_dial_hook(phone_number):
    result = veriroute_lookup(phone_number, lrn=True)

    if result['lrn']['status'] == 'disconnected':
        return {'action': 'skip', 'reason': 'disconnected'}
    else:
        return {'action': 'dial', 'data': result}

4. Continuous Learning

Feed outcomes back to improve predictions:

  • Track answer rates by carrier
  • Monitor by time of day
  • Adjust pacing based on actual results

Expected Improvements

Metric Before Optimization After Optimization
Agent utilization 45-55% 65-75%
Abandon rate 4-6% 1-2%
Contacts per hour 8-12 15-20
Cost per contact Higher 30-40% lower

Compliance Considerations

  • FCC abandon rate — Must be <3% (better data helps)
  • Message on abandon — Required if call connects with no agent
  • Time restrictions — No calls before 8am or after 9pm local time
  • Do-Not-Call — Scrub against DNC before dialing

Implementation Checklist

  1. Export current campaign list
  2. Batch validate with VeriRoute Intel
  3. Remove invalid numbers before import
  4. Add line_type field to dialer records
  5. Configure pacing by segment if supported
  6. Monitor abandon rates after changes
  7. Measure agent utilization improvement

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Optimize Your Predictive Dialer

Clean lists and line type data for better predictions.