She is a former User Group Leader for the Atlanta area and has lectured on customer service topics at various webinars and personal events. A service level agreement (SLA) is an agreed measure of the response and resolution times provided by your support team to your customers. Providing support based on service levels ensures that you provide a measured and predictable service. It also offers greater visibility in case of problems. We also have 2 ticketing charges available: developer.zendesk.com/rest_api/docs/core/side_loading#supported-endpoints We have an incremental endpoint for ALS violations and we respond to events: developer.zendesk.com/rest_api/docs/core/ticket_metric_events denial-of-service attacks, natural disasters, changes resulting from state measures, policies or regulations or court decisions, strikes or labor disputes, acts of civil disobedience, acts of war, and other events beyond the proper control of Zendesk Sunshine Conversations. To determine how best to integrate SLA policies into your customer service, we take a moment to look at some of the most important types of SLA structures: a service level agreement (SLA) is a contract between you and your customers that establishes performance codes for support based on ticket priority. For example, we react to urgent tickets in ten minutes and solve them in two hours. Your SLA policies are applied to posts in the order they are displayed on this page, so drag them to rearrange them if necessary We`ve started introducing SLA metrics (and a dashboard) into Insights – you should write the report by the end of today`s article! See my listing for more information: support.zendesk.com/hc/en-us/articles/207964937-SLA-reporting-available-on-Insights-rolling-release- Or did we get it backwards? Without ALS monitoring, it`s hard to provide customer service and support – your team has to ask themselves, “What tickets are we going to make next?” Monitoring SLA metrics with an SLA dashboard should display aggregated and segmented ticket lists in a countdown time series, with the ability to dynamically filter tickets to make informed operational decisions.