Workforce Engagement Management

The individual planning groups are going to negate any economies of scale you are getting by having everyone multi-skilled.  Genesys CloudCX cannot figure out the weighed staffing.  We went with one planning group for that reason.  We are in the middle of converting over to Genesys and that is my understanding.  Anyone: If I was told something incorrect, please chime in.  

Hello,

That is our understanding as well. For one of our business units we have 86 planning groups (yes, you read that right) due to the different skilling/licensing of our agents. As a result, we've had to get very creative with our filtering techniques when viewing the schedule to ensure we are not looking at inflated numbers. At this point we are just waiting for Genesys Cloud to roll out some sort of enhancement where the system will identify if an agent is sitting in multiple queues (with varying skills) to ensure we have an accurate picture of their impact on call volume rather than just counting them as a full FTE. 

The individual planning groups are going to negate any economies of scale you are getting by having everyone multi-skilled.  Genesys CloudCX cannot figure out the weighed staffing.  We went with one planning group for that reason.  We are in the middle of converting over to Genesys and that is my understanding.  Anyone: If I was told something incorrect, please chime in.  

The individual planning groups are going to negate any economies of scale you are getting by having everyone multi-skilled.  Genesys CloudCX cannot figure out the weighed staffing.  We went with one planning group for that reason.  We are in the middle of converting over to Genesys and that is my understanding.  Anyone: If I was told something incorrect, please chime in.  

Hi Felicity, 

when balancing/blending requirements over multiple Planning Groups. Genesys uses a complex Contact Centre model that uses both historical demand and performance data:

-Interaction Volumes
-Average Handle Times
-Average Speed of Answer
-Abandonment rates
-Agent time
-Available time
-Shrinkage

when combining the Contact Centre model with our simulation models we can more accurately forecast demand, staffing requirements and resulting service goal predictions.

Depending on your level of historical data, we may not have access to all of the performance data required to build and refine these models. In these instances, we fall back to a more Erlang-based methodology, which would explain your current results.

As your historical data builds the models will learn and improve, more accurately reflecting what is actually happening within your environment.

Paul's post is spot on. We ran test with each modification and found that driving the email planning group down moved the agent count in line with reality. Without the adjustment we were -30+ all the time and now it is more realistic at 0 +/- 2 agents at SLA.

Hi Felicity, 

when balancing/blending requirements over multiple Planning Groups. Genesys uses a complex Contact Centre model that uses both historical demand and performance data:

-Interaction Volumes
-Average Handle Times
-Average Speed of Answer
-Abandonment rates
-Agent time
-Available time
-Shrinkage

when combining the Contact Centre model with our simulation models we can more accurately forecast demand, staffing requirements and resulting service goal predictions.

Depending on your level of historical data, we may not have access to all of the performance data required to build and refine these models. In these instances, we fall back to a more Erlang-based methodology, which would explain your current results.

As your historical data builds the models will learn and improve, more accurately reflecting what is actually happening within your environment.

Thanks for replying Greg.  Could you explain what you mean by "driving the email planning group down" please?  

Paul's post is spot on. We ran test with each modification and found that driving the email planning group down moved the agent count in line with reality. Without the adjustment we were -30+ all the time and now it is more realistic at 0 +/- 2 agents at SLA.

Thanks for replying Greg.  Could you explain what you mean by "driving the email planning group down" please?  

Paul's post is spot on. We ran test with each modification and found that driving the email planning group down moved the agent count in line with reality. Without the adjustment we were -30+ all the time and now it is more realistic at 0 +/- 2 agents at SLA.

Hi Felicity, 

when balancing/blending requirements over multiple Planning Groups. Genesys uses a complex Contact Centre model that uses both historical demand and performance data:

-Interaction Volumes
-Average Handle Times
-Average Speed of Answer
-Abandonment rates
-Agent time
-Available time
-Shrinkage

when combining the Contact Centre model with our simulation models we can more accurately forecast demand, staffing requirements and resulting service goal predictions.

Depending on your level of historical data, we may not have access to all of the performance data required to build and refine these models. In these instances, we fall back to a more Erlang-based methodology, which would explain your current results.

As your historical data builds the models will learn and improve, more accurately reflecting what is actually happening within your environment.

Thanks for explaining!  So you've had to fudge your metrics in order to replicate a realistic result.

How long have you been on Cloud CX?  Have you found over time that the results get more realistic as Paul has suggested they should?  Or have you had to stick with adjusting your AHT forecast?

Thanks for replying Greg.  Could you explain what you mean by "driving the email planning group down" please?  

Paul's post is spot on. We ran test with each modification and found that driving the email planning group down moved the agent count in line with reality. Without the adjustment we were -30+ all the time and now it is more realistic at 0 +/- 2 agents at SLA.

Hi Felicity, 

when balancing/blending requirements over multiple Planning Groups. Genesys uses a complex Contact Centre model that uses both historical demand and performance data:

-Interaction Volumes
-Average Handle Times
-Average Speed of Answer
-Abandonment rates
-Agent time
-Available time
-Shrinkage

when combining the Contact Centre model with our simulation models we can more accurately forecast demand, staffing requirements and resulting service goal predictions.

Depending on your level of historical data, we may not have access to all of the performance data required to build and refine these models. In these instances, we fall back to a more Erlang-based methodology, which would explain your current results.

As your historical data builds the models will learn and improve, more accurately reflecting what is actually happening within your environment.

Hi @Paul Wood , 

How long would we need to build the appropriate amount of data to have accurate predictions? We are in a similar boat where Genesys doesn't seem to account for cross training across planning groups - so it doesn't account for efficiencies in staffing resulting in us being overstaffed. 

I will note that we are also using the bullseye routing method, which according to the resource center is not compatible with the forecasting module. 

Thoughts?

-Melanie

------------------------------
Melanie Williams
Camis Inc
------------------------------


Original Message:
Sent: 05-25-2023 11:19
From: Paul Wood
Subject: Belonging to multiple Planning Groups does not drive efficiency

Hi Felicity, 

when balancing/blending requirements over multiple Planning Groups. Genesys uses a complex Contact Centre model that uses both historical demand and performance data:

-Interaction Volumes
-Average Handle Times
-Average Speed of Answer
-Abandonment rates
-Agent time
-Available time
-Shrinkage

when combining the Contact Centre model with our simulation models we can more accurately forecast demand, staffing requirements and resulting service goal predictions.

Depending on your level of historical data, we may not have access to all of the performance data required to build and refine these models. In these instances, we fall back to a more Erlang-based methodology, which would explain your current results.

As your historical data builds the models will learn and improve, more accurately reflecting what is actually happening within your environment.

------------------------------
Paul Wood
Product Manager for Genesys Cloud Workforce Management

Hi @Melanie Williams

we usually recommend 3 months of historical data before the models are able to tune to your environment and for you to see more reflective staffing requirements, in some environments we can see improvements within as little as 3-4 weeks. 

------------------------------
Paul Wood
Product Manager for Genesys Cloud Workforce Management
------------------------------


Original Message:
Sent: 06-06-2023 14:16
From: Melanie Williams
Subject: Belonging to multiple Planning Groups does not drive efficiency

Hi @Paul Wood , 

How long would we need to build the appropriate amount of data to have accurate predictions? We are in a similar boat where Genesys doesn't seem to account for cross training across planning groups - so it doesn't account for efficiencies in staffing resulting in us being overstaffed. 

I will note that we are also using the bullseye routing method, which according to the resource center is not compatible with the forecasting module. 

Thoughts?

-Melanie

------------------------------
Melanie Williams
Camis Inc


Original Message:
Sent: 05-25-2023 11:19
From: Paul Wood
Subject: Belonging to multiple Planning Groups does not drive efficiency

Hi Felicity, 

when balancing/blending requirements over multiple Planning Groups. Genesys uses a complex Contact Centre model that uses both historical demand and performance data:

-Interaction Volumes
-Average Handle Times
-Average Speed of Answer
-Abandonment rates
-Agent time
-Available time
-Shrinkage

when combining the Contact Centre model with our simulation models we can more accurately forecast demand, staffing requirements and resulting service goal predictions.

Depending on your level of historical data, we may not have access to all of the performance data required to build and refine these models. In these instances, we fall back to a more Erlang-based methodology, which would explain your current results.

As your historical data builds the models will learn and improve, more accurately reflecting what is actually happening within your environment.

------------------------------
Paul Wood
Product Manager for Genesys Cloud Workforce Management

Thanks @Paul Wood

We have 4.5 years worth of call data. Which historical data are you referring to? Historical data stored in the planning groups or call data?

It seems that with using the bulls eye routing - the system is projecting that we are understaffed since it can't tell that we are routing more efficiently. Therefore it shows we are understaffed with a low SL prediction (usually about 2-3FTE off). Does your team have any suggestions for work arounds?

Thanks!

-Melanie

Hi @Melanie Williams

we usually recommend 3 months of historical data before the models are able to tune to your environment and for you to see more reflective staffing requirements, in some environments we can see improvements within as little as 3-4 weeks. 

------------------------------
Paul Wood
Product Manager for Genesys Cloud Workforce Management


Original Message:
Sent: 06-06-2023 14:16
From: Melanie Williams
Subject: Belonging to multiple Planning Groups does not drive efficiency

Hi @Paul Wood , 

How long would we need to build the appropriate amount of data to have accurate predictions? We are in a similar boat where Genesys doesn't seem to account for cross training across planning groups - so it doesn't account for efficiencies in staffing resulting in us being overstaffed. 

I will note that we are also using the bullseye routing method, which according to the resource center is not compatible with the forecasting module. 

Thoughts?

-Melanie

------------------------------
Melanie Williams
Camis Inc


Original Message:
Sent: 05-25-2023 11:19
From: Paul Wood
Subject: Belonging to multiple Planning Groups does not drive efficiency

Hi Felicity, 

when balancing/blending requirements over multiple Planning Groups. Genesys uses a complex Contact Centre model that uses both historical demand and performance data:

-Interaction Volumes
-Average Handle Times
-Average Speed of Answer
-Abandonment rates
-Agent time
-Available time
-Shrinkage

when combining the Contact Centre model with our simulation models we can more accurately forecast demand, staffing requirements and resulting service goal predictions.

Depending on your level of historical data, we may not have access to all of the performance data required to build and refine these models. In these instances, we fall back to a more Erlang-based methodology, which would explain your current results.

As your historical data builds the models will learn and improve, more accurately reflecting what is actually happening within your environment.

------------------------------
Paul Wood
Product Manager for Genesys Cloud Workforce Management

we look:

• Interaction Volumes/AHTs
• Average speed of answer
• Service Levels
• Abandonment rates
• Agent time + available times...

to name a few. 

Without seeing your data specifically, it's hard to say why your results show understaffing.  I'd suggest raising a support ticket so that this can be investigated further.

Thanks @Paul Wood

We have 4.5 years worth of call data. Which historical data are you referring to? Historical data stored in the planning groups or call data?

It seems that with using the bulls eye routing - the system is projecting that we are understaffed since it can't tell that we are routing more efficiently. Therefore it shows we are understaffed with a low SL prediction (usually about 2-3FTE off). Does your team have any suggestions for work arounds?

Thanks!

-Melanie

Hi @Melanie Williams

we usually recommend 3 months of historical data before the models are able to tune to your environment and for you to see more reflective staffing requirements, in some environments we can see improvements within as little as 3-4 weeks. 

------------------------------
Paul Wood
Product Manager for Genesys Cloud Workforce Management


Original Message:
Sent: 06-06-2023 14:16
From: Melanie Williams
Subject: Belonging to multiple Planning Groups does not drive efficiency

Hi @Paul Wood , 

How long would we need to build the appropriate amount of data to have accurate predictions? We are in a similar boat where Genesys doesn't seem to account for cross training across planning groups - so it doesn't account for efficiencies in staffing resulting in us being overstaffed. 

I will note that we are also using the bullseye routing method, which according to the resource center is not compatible with the forecasting module. 

Thoughts?

-Melanie

------------------------------
Melanie Williams
Camis Inc


Original Message:
Sent: 05-25-2023 11:19
From: Paul Wood
Subject: Belonging to multiple Planning Groups does not drive efficiency

Hi Felicity, 

when balancing/blending requirements over multiple Planning Groups. Genesys uses a complex Contact Centre model that uses both historical demand and performance data:

-Interaction Volumes
-Average Handle Times
-Average Speed of Answer
-Abandonment rates
-Agent time
-Available time
-Shrinkage

when combining the Contact Centre model with our simulation models we can more accurately forecast demand, staffing requirements and resulting service goal predictions.

Depending on your level of historical data, we may not have access to all of the performance data required to build and refine these models. In these instances, we fall back to a more Erlang-based methodology, which would explain your current results.

As your historical data builds the models will learn and improve, more accurately reflecting what is actually happening within your environment.

------------------------------
Paul Wood
Product Manager for Genesys Cloud Workforce Management

Is there a way of uploading all of this information so we can get ahead of the curve?  I know we can import historical volume and AHT, but I haven't seen a way to include the other metrics you've mentioned.

we look:

• Interaction Volumes/AHTs
• Average speed of answer
• Service Levels
• Abandonment rates
• Agent time + available times...

to name a few. 

Without seeing your data specifically, it's hard to say why your results show understaffing.  I'd suggest raising a support ticket so that this can be investigated further.

Thanks @Paul Wood

We have 4.5 years worth of call data. Which historical data are you referring to? Historical data stored in the planning groups or call data?

It seems that with using the bulls eye routing - the system is projecting that we are understaffed since it can't tell that we are routing more efficiently. Therefore it shows we are understaffed with a low SL prediction (usually about 2-3FTE off). Does your team have any suggestions for work arounds?

Thanks!

-Melanie

Hi @Melanie Williams

we usually recommend 3 months of historical data before the models are able to tune to your environment and for you to see more reflective staffing requirements, in some environments we can see improvements within as little as 3-4 weeks. 

------------------------------
Paul Wood
Product Manager for Genesys Cloud Workforce Management


Original Message:
Sent: 06-06-2023 14:16
From: Melanie Williams
Subject: Belonging to multiple Planning Groups does not drive efficiency

Hi @Paul Wood , 

How long would we need to build the appropriate amount of data to have accurate predictions? We are in a similar boat where Genesys doesn't seem to account for cross training across planning groups - so it doesn't account for efficiencies in staffing resulting in us being overstaffed. 

I will note that we are also using the bullseye routing method, which according to the resource center is not compatible with the forecasting module. 

Thoughts?

-Melanie

------------------------------
Melanie Williams
Camis Inc


Original Message:
Sent: 05-25-2023 11:19
From: Paul Wood
Subject: Belonging to multiple Planning Groups does not drive efficiency

Hi Felicity, 

when balancing/blending requirements over multiple Planning Groups. Genesys uses a complex Contact Centre model that uses both historical demand and performance data:

-Interaction Volumes
-Average Handle Times
-Average Speed of Answer
-Abandonment rates
-Agent time
-Available time
-Shrinkage

when combining the Contact Centre model with our simulation models we can more accurately forecast demand, staffing requirements and resulting service goal predictions.

Depending on your level of historical data, we may not have access to all of the performance data required to build and refine these models. In these instances, we fall back to a more Erlang-based methodology, which would explain your current results.

As your historical data builds the models will learn and improve, more accurately reflecting what is actually happening within your environment.

------------------------------
Paul Wood
Product Manager for Genesys Cloud Workforce Management

Thanks @Paul Wood

We have 4.5 years worth of call data. Which historical data are you referring to? Historical data stored in the planning groups or call data?

It seems that with using the bulls eye routing - the system is projecting that we are understaffed since it can't tell that we are routing more efficiently. Therefore it shows we are understaffed with a low SL prediction (usually about 2-3FTE off). Does your team have any suggestions for work arounds?

Thanks!

-Melanie

Hi @Melanie Williams

we usually recommend 3 months of historical data before the models are able to tune to your environment and for you to see more reflective staffing requirements, in some environments we can see improvements within as little as 3-4 weeks. 

------------------------------
Paul Wood
Product Manager for Genesys Cloud Workforce Management


Original Message:
Sent: 06-06-2023 14:16
From: Melanie Williams
Subject: Belonging to multiple Planning Groups does not drive efficiency

Hi @Paul Wood , 

How long would we need to build the appropriate amount of data to have accurate predictions? We are in a similar boat where Genesys doesn't seem to account for cross training across planning groups - so it doesn't account for efficiencies in staffing resulting in us being overstaffed. 

I will note that we are also using the bullseye routing method, which according to the resource center is not compatible with the forecasting module. 

Thoughts?

-Melanie

------------------------------
Melanie Williams
Camis Inc


Original Message:
Sent: 05-25-2023 11:19
From: Paul Wood
Subject: Belonging to multiple Planning Groups does not drive efficiency

Hi Felicity, 

when balancing/blending requirements over multiple Planning Groups. Genesys uses a complex Contact Centre model that uses both historical demand and performance data:

-Interaction Volumes
-Average Handle Times
-Average Speed of Answer
-Abandonment rates
-Agent time
-Available time
-Shrinkage

when combining the Contact Centre model with our simulation models we can more accurately forecast demand, staffing requirements and resulting service goal predictions.

Depending on your level of historical data, we may not have access to all of the performance data required to build and refine these models. In these instances, we fall back to a more Erlang-based methodology, which would explain your current results.

As your historical data builds the models will learn and improve, more accurately reflecting what is actually happening within your environment.

------------------------------
Paul Wood
Product Manager for Genesys Cloud Workforce Management

New or changed route paths, which are part of the planning groups, are what needs time to have actual historical aggregated data collected by Analytics in order for WFM to more accurately forecast. When we query for historical data we are querying for the individual route paths that have been configured.

As far as bullseye routing, my recommendation is to not use it for queues you want to accurately forecast and staff for. Instead use skills-based routing. Bullseye routing just does not mix well with WFM - a big knock is that simple skills-based routing would route to an available agent whereas bullseye can potentially wait at a ring, or multiple rings, for the entire configured duration unnecessarily. The other issue with bullseye is there simply is no way to tell in aggregate analytics data what the final skill requirements were - only the original set is known.

Conditional routing would be a choice, when available, that would be more WFM friendly than bullseye that would give you some capabilities above stock skills-based routing: https://help.mypurecloud.com/articles/conditional-group-routing-overview/

Thanks @Paul Wood

We have 4.5 years worth of call data. Which historical data are you referring to? Historical data stored in the planning groups or call data?

It seems that with using the bulls eye routing - the system is projecting that we are understaffed since it can't tell that we are routing more efficiently. Therefore it shows we are understaffed with a low SL prediction (usually about 2-3FTE off). Does your team have any suggestions for work arounds?

Thanks!

-Melanie

Hi @Melanie Williams

we usually recommend 3 months of historical data before the models are able to tune to your environment and for you to see more reflective staffing requirements, in some environments we can see improvements within as little as 3-4 weeks. 

------------------------------
Paul Wood
Product Manager for Genesys Cloud Workforce Management


Original Message:
Sent: 06-06-2023 14:16
From: Melanie Williams
Subject: Belonging to multiple Planning Groups does not drive efficiency

Hi @Paul Wood , 

How long would we need to build the appropriate amount of data to have accurate predictions? We are in a similar boat where Genesys doesn't seem to account for cross training across planning groups - so it doesn't account for efficiencies in staffing resulting in us being overstaffed. 

I will note that we are also using the bullseye routing method, which according to the resource center is not compatible with the forecasting module. 

Thoughts?

-Melanie

------------------------------
Melanie Williams
Camis Inc


Original Message:
Sent: 05-25-2023 11:19
From: Paul Wood
Subject: Belonging to multiple Planning Groups does not drive efficiency

Hi Felicity, 

when balancing/blending requirements over multiple Planning Groups. Genesys uses a complex Contact Centre model that uses both historical demand and performance data:

-Interaction Volumes
-Average Handle Times
-Average Speed of Answer
-Abandonment rates
-Agent time
-Available time
-Shrinkage

when combining the Contact Centre model with our simulation models we can more accurately forecast demand, staffing requirements and resulting service goal predictions.

Depending on your level of historical data, we may not have access to all of the performance data required to build and refine these models. In these instances, we fall back to a more Erlang-based methodology, which would explain your current results.

As your historical data builds the models will learn and improve, more accurately reflecting what is actually happening within your environment.

------------------------------
Paul Wood
Product Manager for Genesys Cloud Workforce Management

New or changed route paths, which are part of the planning groups, are what needs time to have actual historical aggregated data collected by Analytics in order for WFM to more accurately forecast. When we query for historical data we are querying for the individual route paths that have been configured.

As far as bullseye routing, my recommendation is to not use it for queues you want to accurately forecast and staff for. Instead use skills-based routing. Bullseye routing just does not mix well with WFM - a big knock is that simple skills-based routing would route to an available agent whereas bullseye can potentially wait at a ring, or multiple rings, for the entire configured duration unnecessarily. The other issue with bullseye is there simply is no way to tell in aggregate analytics data what the final skill requirements were - only the original set is known.

Conditional routing would be a choice, when available, that would be more WFM friendly than bullseye that would give you some capabilities above stock skills-based routing: https://help.mypurecloud.com/articles/conditional-group-routing-overview/

Thanks @Paul Wood

We have 4.5 years worth of call data. Which historical data are you referring to? Historical data stored in the planning groups or call data?

It seems that with using the bulls eye routing - the system is projecting that we are understaffed since it can't tell that we are routing more efficiently. Therefore it shows we are understaffed with a low SL prediction (usually about 2-3FTE off). Does your team have any suggestions for work arounds?

Thanks!

-Melanie

Hi @Melanie Williams

we usually recommend 3 months of historical data before the models are able to tune to your environment and for you to see more reflective staffing requirements, in some environments we can see improvements within as little as 3-4 weeks. 

------------------------------
Paul Wood
Product Manager for Genesys Cloud Workforce Management


Original Message:
Sent: 06-06-2023 14:16
From: Melanie Williams
Subject: Belonging to multiple Planning Groups does not drive efficiency

Hi @Paul Wood , 

How long would we need to build the appropriate amount of data to have accurate predictions? We are in a similar boat where Genesys doesn't seem to account for cross training across planning groups - so it doesn't account for efficiencies in staffing resulting in us being overstaffed. 

I will note that we are also using the bullseye routing method, which according to the resource center is not compatible with the forecasting module. 

Thoughts?

-Melanie

------------------------------
Melanie Williams
Camis Inc


Original Message:
Sent: 05-25-2023 11:19
From: Paul Wood
Subject: Belonging to multiple Planning Groups does not drive efficiency

Hi Felicity, 

when balancing/blending requirements over multiple Planning Groups. Genesys uses a complex Contact Centre model that uses both historical demand and performance data:

-Interaction Volumes
-Average Handle Times
-Average Speed of Answer
-Abandonment rates
-Agent time
-Available time
-Shrinkage

when combining the Contact Centre model with our simulation models we can more accurately forecast demand, staffing requirements and resulting service goal predictions.

Depending on your level of historical data, we may not have access to all of the performance data required to build and refine these models. In these instances, we fall back to a more Erlang-based methodology, which would explain your current results.

As your historical data builds the models will learn and improve, more accurately reflecting what is actually happening within your environment.

------------------------------
Paul Wood
Product Manager for Genesys Cloud Workforce Management

Thanks @Jay Langsford . How long do we need to allow for the system to gather data in the route paths in the planning groups in order for it to calculate staffing more accurately. 

New or changed route paths, which are part of the planning groups, are what needs time to have actual historical aggregated data collected by Analytics in order for WFM to more accurately forecast. When we query for historical data we are querying for the individual route paths that have been configured.

As far as bullseye routing, my recommendation is to not use it for queues you want to accurately forecast and staff for. Instead use skills-based routing. Bullseye routing just does not mix well with WFM - a big knock is that simple skills-based routing would route to an available agent whereas bullseye can potentially wait at a ring, or multiple rings, for the entire configured duration unnecessarily. The other issue with bullseye is there simply is no way to tell in aggregate analytics data what the final skill requirements were - only the original set is known.

Conditional routing would be a choice, when available, that would be more WFM friendly than bullseye that would give you some capabilities above stock skills-based routing: https://help.mypurecloud.com/articles/conditional-group-routing-overview/

Thanks @Paul Wood

We have 4.5 years worth of call data. Which historical data are you referring to? Historical data stored in the planning groups or call data?

It seems that with using the bulls eye routing - the system is projecting that we are understaffed since it can't tell that we are routing more efficiently. Therefore it shows we are understaffed with a low SL prediction (usually about 2-3FTE off). Does your team have any suggestions for work arounds?

Thanks!

-Melanie

Hi @Melanie Williams

we usually recommend 3 months of historical data before the models are able to tune to your environment and for you to see more reflective staffing requirements, in some environments we can see improvements within as little as 3-4 weeks. 

------------------------------
Paul Wood
Product Manager for Genesys Cloud Workforce Management


Original Message:
Sent: 06-06-2023 14:16
From: Melanie Williams
Subject: Belonging to multiple Planning Groups does not drive efficiency

Hi @Paul Wood , 

How long would we need to build the appropriate amount of data to have accurate predictions? We are in a similar boat where Genesys doesn't seem to account for cross training across planning groups - so it doesn't account for efficiencies in staffing resulting in us being overstaffed. 

I will note that we are also using the bullseye routing method, which according to the resource center is not compatible with the forecasting module. 

Thoughts?

-Melanie

------------------------------
Melanie Williams
Camis Inc


Original Message:
Sent: 05-25-2023 11:19
From: Paul Wood
Subject: Belonging to multiple Planning Groups does not drive efficiency

Hi Felicity, 

when balancing/blending requirements over multiple Planning Groups. Genesys uses a complex Contact Centre model that uses both historical demand and performance data:

-Interaction Volumes
-Average Handle Times
-Average Speed of Answer
-Abandonment rates
-Agent time
-Available time
-Shrinkage

when combining the Contact Centre model with our simulation models we can more accurately forecast demand, staffing requirements and resulting service goal predictions.

Depending on your level of historical data, we may not have access to all of the performance data required to build and refine these models. In these instances, we fall back to a more Erlang-based methodology, which would explain your current results.

As your historical data builds the models will learn and improve, more accurately reflecting what is actually happening within your environment.

------------------------------
Paul Wood
Product Manager for Genesys Cloud Workforce Management

I agree with what Paul had said: "We usually recommend 3 months of historical data before the models are able to tune to your environment and for you to see more reflective staffing requirements, in some environments we can see improvements within as little as 3-4 weeks."

It could be 2-4 weeks is reasonably good. You could run different types of forecasts and compare overall forecast error (without modifications) and then make a decision on when to switch to automatic best method. We have and will continue to look at ways to help with the cold or semi-cold states (new or just recently new route paths in terms of historical data). To properly train the models there just needs to be a threshold.

If after a few weeks you still see low forecast accuracy (e.g., < 90% without modifications), I would recommend opening a support ticket so we can investigate.

For staffing requirements, forecast is a big piece; however, there are multiple things that attribute to either a higher or lower than expected staffing requirement. The following resource article I wrote covers many of the items: https://help.mypurecloud.com/faqs/why-are-staffing-requirements-higher-or-lower-than-expected/

Thanks @Jay Langsford . How long do we need to allow for the system to gather data in the route paths in the planning groups in order for it to calculate staffing more accurately. 

New or changed route paths, which are part of the planning groups, are what needs time to have actual historical aggregated data collected by Analytics in order for WFM to more accurately forecast. When we query for historical data we are querying for the individual route paths that have been configured.

As far as bullseye routing, my recommendation is to not use it for queues you want to accurately forecast and staff for. Instead use skills-based routing. Bullseye routing just does not mix well with WFM - a big knock is that simple skills-based routing would route to an available agent whereas bullseye can potentially wait at a ring, or multiple rings, for the entire configured duration unnecessarily. The other issue with bullseye is there simply is no way to tell in aggregate analytics data what the final skill requirements were - only the original set is known.

Conditional routing would be a choice, when available, that would be more WFM friendly than bullseye that would give you some capabilities above stock skills-based routing: https://help.mypurecloud.com/articles/conditional-group-routing-overview/

Thanks @Paul Wood

We have 4.5 years worth of call data. Which historical data are you referring to? Historical data stored in the planning groups or call data?

It seems that with using the bulls eye routing - the system is projecting that we are understaffed since it can't tell that we are routing more efficiently. Therefore it shows we are understaffed with a low SL prediction (usually about 2-3FTE off). Does your team have any suggestions for work arounds?

Thanks!

-Melanie

Hi @Melanie Williams

we usually recommend 3 months of historical data before the models are able to tune to your environment and for you to see more reflective staffing requirements, in some environments we can see improvements within as little as 3-4 weeks. 

------------------------------
Paul Wood
Product Manager for Genesys Cloud Workforce Management


Original Message:
Sent: 06-06-2023 14:16
From: Melanie Williams
Subject: Belonging to multiple Planning Groups does not drive efficiency

Hi @Paul Wood , 

How long would we need to build the appropriate amount of data to have accurate predictions? We are in a similar boat where Genesys doesn't seem to account for cross training across planning groups - so it doesn't account for efficiencies in staffing resulting in us being overstaffed. 

I will note that we are also using the bullseye routing method, which according to the resource center is not compatible with the forecasting module. 

Thoughts?

-Melanie

------------------------------
Melanie Williams
Camis Inc


Original Message:
Sent: 05-25-2023 11:19
From: Paul Wood
Subject: Belonging to multiple Planning Groups does not drive efficiency

Hi Felicity, 

when balancing/blending requirements over multiple Planning Groups. Genesys uses a complex Contact Centre model that uses both historical demand and performance data:

-Interaction Volumes
-Average Handle Times
-Average Speed of Answer
-Abandonment rates
-Agent time
-Available time
-Shrinkage

when combining the Contact Centre model with our simulation models we can more accurately forecast demand, staffing requirements and resulting service goal predictions.

Depending on your level of historical data, we may not have access to all of the performance data required to build and refine these models. In these instances, we fall back to a more Erlang-based methodology, which would explain your current results.

As your historical data builds the models will learn and improve, more accurately reflecting what is actually happening within your environment.

------------------------------
Paul Wood
Product Manager for Genesys Cloud Workforce Management

New or changed route paths, which are part of the planning groups, are what needs time to have actual historical aggregated data collected by Analytics in order for WFM to more accurately forecast. When we query for historical data we are querying for the individual route paths that have been configured.

As far as bullseye routing, my recommendation is to not use it for queues you want to accurately forecast and staff for. Instead use skills-based routing. Bullseye routing just does not mix well with WFM - a big knock is that simple skills-based routing would route to an available agent whereas bullseye can potentially wait at a ring, or multiple rings, for the entire configured duration unnecessarily. The other issue with bullseye is there simply is no way to tell in aggregate analytics data what the final skill requirements were - only the original set is known.

Conditional routing would be a choice, when available, that would be more WFM friendly than bullseye that would give you some capabilities above stock skills-based routing: https://help.mypurecloud.com/articles/conditional-group-routing-overview/

Thanks @Paul Wood

We have 4.5 years worth of call data. Which historical data are you referring to? Historical data stored in the planning groups or call data?

It seems that with using the bulls eye routing - the system is projecting that we are understaffed since it can't tell that we are routing more efficiently. Therefore it shows we are understaffed with a low SL prediction (usually about 2-3FTE off). Does your team have any suggestions for work arounds?

Thanks!

-Melanie

Hi @Melanie Williams

we usually recommend 3 months of historical data before the models are able to tune to your environment and for you to see more reflective staffing requirements, in some environments we can see improvements within as little as 3-4 weeks. 

------------------------------
Paul Wood
Product Manager for Genesys Cloud Workforce Management


Original Message:
Sent: 06-06-2023 14:16
From: Melanie Williams
Subject: Belonging to multiple Planning Groups does not drive efficiency

Hi @Paul Wood , 

How long would we need to build the appropriate amount of data to have accurate predictions? We are in a similar boat where Genesys doesn't seem to account for cross training across planning groups - so it doesn't account for efficiencies in staffing resulting in us being overstaffed. 

I will note that we are also using the bullseye routing method, which according to the resource center is not compatible with the forecasting module. 

Thoughts?

-Melanie

------------------------------
Melanie Williams
Camis Inc


Original Message:
Sent: 05-25-2023 11:19
From: Paul Wood
Subject: Belonging to multiple Planning Groups does not drive efficiency

Hi Felicity, 

when balancing/blending requirements over multiple Planning Groups. Genesys uses a complex Contact Centre model that uses both historical demand and performance data:

-Interaction Volumes
-Average Handle Times
-Average Speed of Answer
-Abandonment rates
-Agent time
-Available time
-Shrinkage

when combining the Contact Centre model with our simulation models we can more accurately forecast demand, staffing requirements and resulting service goal predictions.

Depending on your level of historical data, we may not have access to all of the performance data required to build and refine these models. In these instances, we fall back to a more Erlang-based methodology, which would explain your current results.

As your historical data builds the models will learn and improve, more accurately reflecting what is actually happening within your environment.

------------------------------
Paul Wood
Product Manager for Genesys Cloud Workforce Management

Thanks again @Jay Langsford . 
When will this solution be available?

New or changed route paths, which are part of the planning groups, are what needs time to have actual historical aggregated data collected by Analytics in order for WFM to more accurately forecast. When we query for historical data we are querying for the individual route paths that have been configured.

As far as bullseye routing, my recommendation is to not use it for queues you want to accurately forecast and staff for. Instead use skills-based routing. Bullseye routing just does not mix well with WFM - a big knock is that simple skills-based routing would route to an available agent whereas bullseye can potentially wait at a ring, or multiple rings, for the entire configured duration unnecessarily. The other issue with bullseye is there simply is no way to tell in aggregate analytics data what the final skill requirements were - only the original set is known.

Conditional routing would be a choice, when available, that would be more WFM friendly than bullseye that would give you some capabilities above stock skills-based routing: https://help.mypurecloud.com/articles/conditional-group-routing-overview/

Thanks @Paul Wood

We have 4.5 years worth of call data. Which historical data are you referring to? Historical data stored in the planning groups or call data?

It seems that with using the bulls eye routing - the system is projecting that we are understaffed since it can't tell that we are routing more efficiently. Therefore it shows we are understaffed with a low SL prediction (usually about 2-3FTE off). Does your team have any suggestions for work arounds?

Thanks!

-Melanie

Hi @Melanie Williams

we usually recommend 3 months of historical data before the models are able to tune to your environment and for you to see more reflective staffing requirements, in some environments we can see improvements within as little as 3-4 weeks. 

------------------------------
Paul Wood
Product Manager for Genesys Cloud Workforce Management


Original Message:
Sent: 06-06-2023 14:16
From: Melanie Williams
Subject: Belonging to multiple Planning Groups does not drive efficiency

Hi @Paul Wood , 

How long would we need to build the appropriate amount of data to have accurate predictions? We are in a similar boat where Genesys doesn't seem to account for cross training across planning groups - so it doesn't account for efficiencies in staffing resulting in us being overstaffed. 

I will note that we are also using the bullseye routing method, which according to the resource center is not compatible with the forecasting module. 

Thoughts?

-Melanie

------------------------------
Melanie Williams
Camis Inc


Original Message:
Sent: 05-25-2023 11:19
From: Paul Wood
Subject: Belonging to multiple Planning Groups does not drive efficiency

Hi Felicity, 

when balancing/blending requirements over multiple Planning Groups. Genesys uses a complex Contact Centre model that uses both historical demand and performance data:

-Interaction Volumes
-Average Handle Times
-Average Speed of Answer
-Abandonment rates
-Agent time
-Available time
-Shrinkage

when combining the Contact Centre model with our simulation models we can more accurately forecast demand, staffing requirements and resulting service goal predictions.

Depending on your level of historical data, we may not have access to all of the performance data required to build and refine these models. In these instances, we fall back to a more Erlang-based methodology, which would explain your current results.

As your historical data builds the models will learn and improve, more accurately reflecting what is actually happening within your environment.

------------------------------
Paul Wood
Product Manager for Genesys Cloud Workforce Management

@Hitesh Haran can you comment on release expectations of conditional routing?

Thanks again @Jay Langsford . 
When will this solution be available?

New or changed route paths, which are part of the planning groups, are what needs time to have actual historical aggregated data collected by Analytics in order for WFM to more accurately forecast. When we query for historical data we are querying for the individual route paths that have been configured.

As far as bullseye routing, my recommendation is to not use it for queues you want to accurately forecast and staff for. Instead use skills-based routing. Bullseye routing just does not mix well with WFM - a big knock is that simple skills-based routing would route to an available agent whereas bullseye can potentially wait at a ring, or multiple rings, for the entire configured duration unnecessarily. The other issue with bullseye is there simply is no way to tell in aggregate analytics data what the final skill requirements were - only the original set is known.

Conditional routing would be a choice, when available, that would be more WFM friendly than bullseye that would give you some capabilities above stock skills-based routing: https://help.mypurecloud.com/articles/conditional-group-routing-overview/

Thanks @Paul Wood

We have 4.5 years worth of call data. Which historical data are you referring to? Historical data stored in the planning groups or call data?

It seems that with using the bulls eye routing - the system is projecting that we are understaffed since it can't tell that we are routing more efficiently. Therefore it shows we are understaffed with a low SL prediction (usually about 2-3FTE off). Does your team have any suggestions for work arounds?

Thanks!

-Melanie

Hi @Melanie Williams

we usually recommend 3 months of historical data before the models are able to tune to your environment and for you to see more reflective staffing requirements, in some environments we can see improvements within as little as 3-4 weeks. 

------------------------------
Paul Wood
Product Manager for Genesys Cloud Workforce Management


Original Message:
Sent: 06-06-2023 14:16
From: Melanie Williams
Subject: Belonging to multiple Planning Groups does not drive efficiency

Hi @Paul Wood , 

How long would we need to build the appropriate amount of data to have accurate predictions? We are in a similar boat where Genesys doesn't seem to account for cross training across planning groups - so it doesn't account for efficiencies in staffing resulting in us being overstaffed. 

I will note that we are also using the bullseye routing method, which according to the resource center is not compatible with the forecasting module. 

Thoughts?

-Melanie

------------------------------
Melanie Williams
Camis Inc


Original Message:
Sent: 05-25-2023 11:19
From: Paul Wood
Subject: Belonging to multiple Planning Groups does not drive efficiency

Hi Felicity, 

when balancing/blending requirements over multiple Planning Groups. Genesys uses a complex Contact Centre model that uses both historical demand and performance data:

-Interaction Volumes
-Average Handle Times
-Average Speed of Answer
-Abandonment rates
-Agent time
-Available time
-Shrinkage

when combining the Contact Centre model with our simulation models we can more accurately forecast demand, staffing requirements and resulting service goal predictions.

Depending on your level of historical data, we may not have access to all of the performance data required to build and refine these models. In these instances, we fall back to a more Erlang-based methodology, which would explain your current results.

As your historical data builds the models will learn and improve, more accurately reflecting what is actually happening within your environment.

------------------------------
Paul Wood
Product Manager for Genesys Cloud Workforce Management