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.
Try Bulk UploadOptimization Strategies
1. Pre-Campaign Validation
Validate your entire list before loading into the dialer:
- Export contact list
- Batch validate with VeriRoute Intel
- Remove disconnected numbers
- 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
- Export current campaign list
- Batch validate with VeriRoute Intel
- Remove invalid numbers before import
- Add line_type field to dialer records
- Configure pacing by segment if supported
- Monitor abandon rates after changes
- Measure agent utilization improvement