Genesys Cloud - Main

HI James

-

Great question - and honestly, it's good to see a TAM asking this from a real-world angle, because Predictive Routing in Genesys often looks straightforward on paper but behaves very differently in production.

-

From what I've seen with customers using Predictive Routing in Genesys:

 - Most begin with sales queues (clear KPIs like conversion/revenue..etc)

 - Service queues come later (CSAT is harder to measure and prove impact)

-

Common objectives

• Sales: improve conversion / match high-value interactions to top agents
• Service: improve CSAT / reduce transfers or repeats

-

Real challenges

 - Data quality (wrap-ups and outcomes are often inconsistent)

 - Low volume (models struggle without enough interactions)

 - Change management (teams don't always trust AI-driven routing)

 - Routing conflicts with existing skill-based or priority logic

-

What works well

 - Start small (1–2 high-volume queues)

 - Clean up outcome data first

 - Set expectations: it's probabilistic, not deterministic

 - Measure with trends or A/B, not individual interactions

-

Biggest takeaway: Predictive Routing in Genesys Cloud works well when there's good data + enough volume + clear KPIs - otherwise results can be underwhelming.

-
In my opinion: 

Predictive: High call volume and large agent teams.

Progressive: Medium volume, and when you want more control

Power: good lists (leads good ones) and smaller or more "premium" operations

-

In general, power mode almost always is better. << again, this is in my opinion haha

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Kaio Oliveira
GCP - GCQM - GCS - GCA - GCD - GCO - GPE & GPR - GCWM

PS.: I apologize if there are any mistakes in my English; my primary language is Portuguese-Br.
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@Kaio Oliveira - Unless there are some GPR settings I'm unaware of (which there very well could be, haha), are your last 5-6 sentences referring to [Outbound] Dialer? If so, I don't believe GPR is directly related. I do wholeheartedly agree with your point prior to those though regarding clients wanting GPR, but not understanding the agent/volume requirements. That's where the Benefit Assessment can reiterate that point.

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Brian T. Jones | Ascension | Senior Specialist - Technology | Colossians 3:23-24
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Original Message:
Sent: 05-01-2026 10:26
From: Kaio Oliveira
Subject: Genesys Predictive rouitng Real-world challenges

HI James

-

Great question - and honestly, it's good to see a TAM asking this from a real-world angle, because Predictive Routing in Genesys often looks straightforward on paper but behaves very differently in production.

-

From what I've seen with customers using Predictive Routing in Genesys:

 - Most begin with sales queues (clear KPIs like conversion/revenue..etc)

 - Service queues come later (CSAT is harder to measure and prove impact)

-

Common objectives

• Sales: improve conversion / match high-value interactions to top agents
• Service: improve CSAT / reduce transfers or repeats

-

Real challenges

 - Data quality (wrap-ups and outcomes are often inconsistent)

 - Low volume (models struggle without enough interactions)

 - Change management (teams don't always trust AI-driven routing)

 - Routing conflicts with existing skill-based or priority logic

-

What works well

 - Start small (1–2 high-volume queues)

 - Clean up outcome data first

 - Set expectations: it's probabilistic, not deterministic

 - Measure with trends or A/B, not individual interactions

-

Biggest takeaway: Predictive Routing in Genesys Cloud works well when there's good data + enough volume + clear KPIs - otherwise results can be underwhelming.

-
In my opinion: 

Predictive: High call volume and large agent teams.

Progressive: Medium volume, and when you want more control

Power: good lists (leads good ones) and smaller or more "premium" operations

-

In general, power mode almost always is better. << again, this is in my opinion haha

------------------------------
Kaio Oliveira
GCP - GCQM - GCS - GCA - GCD - GCO - GPE & GPR - GCWM

PS.: I apologize if there are any mistakes in my English; my primary language is Portuguese-Br.

HI James

-

Great question - and honestly, it's good to see a TAM asking this from a real-world angle, because Predictive Routing in Genesys often looks straightforward on paper but behaves very differently in production.

-

From what I've seen with customers using Predictive Routing in Genesys:

 - Most begin with sales queues (clear KPIs like conversion/revenue..etc)

 - Service queues come later (CSAT is harder to measure and prove impact)

-

Common objectives

• Sales: improve conversion / match high-value interactions to top agents
• Service: improve CSAT / reduce transfers or repeats

-

Real challenges

 - Data quality (wrap-ups and outcomes are often inconsistent)

 - Low volume (models struggle without enough interactions)

 - Change management (teams don't always trust AI-driven routing)

 - Routing conflicts with existing skill-based or priority logic

-

What works well

 - Start small (1–2 high-volume queues)

 - Clean up outcome data first

 - Set expectations: it's probabilistic, not deterministic

 - Measure with trends or A/B, not individual interactions

-

Biggest takeaway: Predictive Routing in Genesys Cloud works well when there's good data + enough volume + clear KPIs - otherwise results can be underwhelming.

-
In my opinion: 

Predictive: High call volume and large agent teams.

Progressive: Medium volume, and when you want more control

Power: good lists (leads good ones) and smaller or more "premium" operations

-

In general, power mode almost always is better. << again, this is in my opinion haha

@James Brannigan - The biggest drawback to Genesys Predictive Routing (GPR) we've seen is agents sitting idle because the system is favoring the higher scored agents (as it should, per se). This tends to escalate agent burnout or resentment that the lower performing agents aren't carrying their weight.

Because of this, we've:

• Enabled the Utilization setting within the Workload Balancing feature in an effort to level the playing field without sacrificing too much of the significant AHT & ASA gains we're seeing.
• Voted for an Idea to add "Idle Time" as a Workload Balancing setting in an effort to prevent lower scored idle agents from sitting more than XX minutes before getting a call.
• Used it as an opportunity to partner with the business to explain that the data GPR provides (which we love) should be taken to heart with respect to coaching the lower performing agents up. Ideally all agents are performing at an optimal level to the point where GPR may not be needed anymore for said queue, thus allowing us to reallocate that token spend elsewhere within our Genesys portfolio of tools.

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Brian T. Jones | Ascension | Senior Specialist - Technology | Colossians 3:23-24
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