Predictive routing: Testing and benefit assessment FAQs
How can I test Predictive Routing?
Predictive Routing is part of the AI Experience Bundle, which includes a 60-day free trial. The AI Experience Bundle also provides monthly tokens for continued use. For more information, see AI Experience token metering and pricing.
What does a controlled pilot look like, and how should success be measured?
A typical controlled pilot runs for approximately six weeks across four stages.
Stage 1: Select and baseline (weeks 1 to 2)
- Run a benefit assessment to identify high potential queues per KPI. Benefit assessments are free and consume no AI Tokens.
- Shortlist one or two queues that meet the readiness criteria: at least 90 days of historical interaction data and more than four agents assigned.
- Establish the baseline. For AHT, take it from historical queue data. For NCA, note that queue level data is not available before enablement. The Channel Insights dashboard reports NCA at organization level by media type, so the benefit assessment output serves as the reference point.
- Agree on the success threshold and the guardrail metrics before enabling anything.
Stage 2: Train and activate (weeks 3 to 6)
- The model trains on the queue’s history. Once activated, the model takes a short time to become usable. Standard routing continues to distribute interactions in the meantime with no impact.
- Use comparison or value monitoring mode together with A/B testing, so that lift is measured against a live control rather than against recalled performance. No AI Tokens are consumed for interactions that do not route via predictive routing.
Stage 3: Read the results
- Allow for the measurement lag. AHT results appear immediately but become meaningful after approximately two weeks. NCA results appear after the seven day lookup period.
- Test with and without workload balancing, approximately two weeks each, to see what balancing costs in KPI lift and what it returns in distribution.
Stage 4: Tune and expand (week 6 onward)
- Adjust the timeout and the workload balancing mode, then extend to further queues once the pattern is established.
Measuring success
The pilot should be measured on the selected KPI together with a small set of guardrails, so that a KPI gain is not achieved at the cost of fairness or service level.
| Metric | What it confirms |
|---|---|
| Primary KPI lift | Lift in the selected KPI compared with the A/B control on the pilot queue |
| Workload distribution | Whether volume is concentrating on higher scored agents, visible in the Queue Performance Detail view and the Interactions view, both of which identify the predictive agent selected and the agents proposed |
| Service level and ASA | Confirmation that the timeout is not extending wait times beyond what the queue can absorb |
| Occupancy spread | If occupancy moves outside the acceptable band, switch to occupancy based workload balancing |
| Model plausibility | Whether the Predictive Model view shows the model relying on features that make operational sense |
Routing admin ownership: stages 2 and 4 belong to the routing admin, covering activation, timeout, workload balancing mode, and the A/B configuration.
Workforce manager ownership: stages 1 and 3 belong to the workforce manager, covering queue selection, the success threshold agreed in advance, and the judgment on whether the KPI lift justified the workload trade off.
Why am I not seeing results in comparison testing after a few days?
The availability of GPR results depends on the selected KPI:
- Average Handle Time (AHT): Results are available immediately but become more meaningful after approximately two weeks of data collection.
- Next Contact Avoidance (NCA) and Net Conversion Rate: Results are available after the seven-day look-up period.
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