SLA metrics are becoming a more crucial reporting mechanism for Intercom customers, particularly with how customer success teams evaluate the effectiveness of their teammates.
This feature would add the following data:
Add median_next_response_time metric to intercom_conversations model, would need to be calculated upstream in intercom_conversation_parts model. (This could be the heaviest lift, so open to discussing).
Configurable variables for target_first_response_time_minutes and target_next_response_time_minutes for the customer and set, to allow us to define whether SLAs get hit or not in our models.
Add is_sla_hit or is_sla_miss boolean fields based on target_first_response_time_minutes and target_next_response_time_minutes configurable variables in `conversation_metrics.
Add sla_hits and sla_misses to eventually calculate SLA hits and misses by admin and company, to admin_metrics and company_metrics.
Describe alternatives you've considered
The SLA metrics we get directly from Intercom do have 'hit', 'miss', 'cancelled' values according to documentation. So we could technically get aggregate SLA hit and miss rate over a particular time period if we want to scope down.
However, we do not know what the actual SLA targets are, customer-by-customer, as they are configurable goals that could very much differ. It'd be better if we set SLA goal metrics on our end based on targets.
If median_next_response_time is too complicated to calculate, it might be worth discussing whether we parse down this to request to only the first_response SLA targets.
Are you interested in contributing this feature?
[X] Yes.
[ ] Yes, but I will need assistance and will schedule time during your office hours for guidance.
Is there an existing feature request for this?
Describe the Feature
SLA metrics are becoming a more crucial reporting mechanism for Intercom customers, particularly with how customer success teams evaluate the effectiveness of their teammates.
This feature would add the following data:
median_next_response_timemetric tointercom_conversationsmodel, would need to be calculated upstream inintercom_conversation_partsmodel. (This could be the heaviest lift, so open to discussing).target_first_response_time_minutesandtarget_next_response_time_minutesfor the customer and set, to allow us to define whether SLAs get hit or not in our models.is_sla_hitoris_sla_missboolean fields based ontarget_first_response_time_minutesandtarget_next_response_time_minutesconfigurable variables in `conversation_metrics.sla_hitsandsla_missesto eventually calculate SLA hits and misses by admin and company, toadmin_metricsandcompany_metrics.Describe alternatives you've considered
The SLA metrics we get directly from Intercom do have 'hit', 'miss', 'cancelled' values according to documentation. So we could technically get aggregate SLA hit and miss rate over a particular time period if we want to scope down.
However, we do not know what the actual SLA targets are, customer-by-customer, as they are configurable goals that could very much differ. It'd be better if we set SLA goal metrics on our end based on targets.
If
median_next_response_timeis too complicated to calculate, it might be worth discussing whether we parse down this to request to only thefirst_responseSLA targets.Are you interested in contributing this feature?
Anything else?
Read more on Intercom SLA performance here.