SLA Metrics

Flex Insight Historical Reporting enables to report on SLA in multiple ways that can be combined depending on the needs of the organization.

High Level

Insights provides a very basic definition of SLA by default. The SLA definition is the same for all customers and is applied across all queues, channels, etc. The reason for this is that the SLA metric is subject to updates and any custom changes would be overwritten with future updates to the built-in content.

The analytics language offers incredible flexibility in how SLA is defined in an organization or individual organizational units. Thanks to analytics language support for nested metrics it is easy to build SLA calculation from simple building blocks and make it as unified or as specific as needed.

Changing SLA Preferences

Suitable for smaller contact centers focused on single channel and having one SLA standard.

The built-in SLA metric has two preferences that can be adjusted by sending a request to our support team. These changes apply to all conversations and are not specific for individual queues, channels or other attributes.

Pros:

• Very easy to set up
• Does not require creating custom metrics

Cons:

• Applies the same SLA calculation across all conversations

Custom SLA Metrics for an Organization Unit

Suitable for large organizations where individual departments, teams or other organizational units want to watch SLA differently and independently on other units. Each unit can define their own way to calculate SLA.

You can define custom SLAs for individual queues, channels or specific use cases. There are custom metrics that you can create in a business-friendly analytics language.

Pros:

• Requires only basic skills in the analytics language
• Very flexible
• Any built-in metric or attribute can be used in the SLA calculation
• Customers can provide additional metrics and attributes via TaskRouter task attributes that can be used in the SLA calculation

Cons:

• Multiple SLA definitions may exist

Example - Custom Metric for Outbound Dialer

Example of custom SLA used for reporting on a custom implementation of the outbound dialer to ensure our agents handle them within legal constraints.

Sample metric in which we say that all Outbound calls initiated by a dialer are included in the calculation of SLA:

Segments Subject to Outbound SLA = SELECT Segments WHERE Direction = (Outbound) AND Initiated By = Dialer

Metric for segments that are within SLA:

Segments within SLA = SELECT Segments WHERE Waiting Time < 5 AND Direction = (Outbound) AND Initiated By = Dialer

Metric that gives the actual SLA based on the previous metrics:

Outbound SLA = SELECT Segments within SLA / Segments Subject to SLA

Custom SLA Metric for the Entire Organization

Suitable for large organizations where you want to monitor all departments, teams or other organizational units using a single metric.

For convenience in reporting many customers opt for having a single SLA metric that adapts based on what conversations it gets used. One of the examples is

Pros:

• One metric that can be used across the entire organization
• Very flexible
• Any built-in metric or attribute can be used in the SLA calculation
• Customers can provide additional metrics and attributes via TaskRouter task attributes that can be used in the SLA calculation

Cons:

• Definition of SLA is more complex and requires collaboration across organizational units

Example - SLA Based on Queue and Channel

In this example, we use different thresholds for VIP queue where our most important customers land, we use 60 seconds waiting time as a target for all calls and leave 3 minutes (180 seconds) target for any other scenario - typically other channels.

Metrics to adjust target waiting time based on the queue and the channel:

SLA Threshold = SELECT CASE
WHEN Queue = VIP THEN 30,
WHEN Channel = Call THEN 60,
ELSE 180 END

Metrics for adjusting short abandons based on the queue and the channel:

Short Abandoned Threshold = SELECT CASE
WHEN Queue = VIP THEN 5,
WHEN Channel = Call THEN 20,
ELSE 30 END

We can structure the SLA calculation so it is easier to read and maintain. The first metric counting the segments that are subject to SLA:

Segments Subject to SLA = SELECT Segments WHERE Waiting Time > Short Abandoned Threshold OR Abandoned = No

Metric for segments that are within SLA:

Segments within SLA = SELECT Segments WHERE Waiting Time < SLA Threshold

Metric that gives the actual SLA based on the previous metrics:

SLA = SELECT Segments within SLA / Segments Subject to SLA

Programmable Management of Service Level

Programmable Management of Service Level is the most flexible option. The option is suitable for environments where conditions for deciding whether a conversation was handled within SLA or not depends on very dynamic factors.

Pros:

• Extremely flexible
• The SLA calculation can be influenced by dynamic factors and evolve over time
• Metrics definitions in Flex Insights require limited skill and no adjustments once first defined

Cons:

• Requires programmatically set Twilio TaskRouter attributes and thus development skills
• Changes may require development

Pass Information in TaskRouter Attributes

Flex Insights consumes conversation tasks attributes that may contain a wide set of information supplied by the customer programmatically. Attribute Service Level is available for customers to provide SLA-related information.

Customers programmatically set the attribute to the desired value in their implementation. The implementation can use any information at hand to decide what value to set.

"task_attributes": { "conversations" : { "service_level" : "Compliant" } }

The value of the attribute is not restricted and can contain any text. Example values customers use are the following:

The above possible values are just examples. You can name the values differently or use more values for more granularity such as having multiple tiers for SLA targets.

Custom Metrics

You can create custom metrics based on the Service Level attribute. These metrics can be based solely on the Service Level attribute or combine it with any other conditions.

Metric that gives the number of segments that is used as a base for SLA calculation. You can decide which categories you want to exclude:

Segments Subject to SLA = SELECT Segments WHERE Service Level NOT IN (Not Applicable, Short Abandoned)

Metric that gives the number of segments that are within SLA:

Segments within SLA = SELECT Segments WHERE Service Level IN (Compliant)

Metric that gives the actual SLA based on the previous metrics:

SLA = SELECT Segments within SLA / Segments Subject to SLA

Use of the Custom Metrics

The custom metrics can be used as built-in metrics to create reports and dashboards using drag and drop report and dashboard builder.

SLA Metrics in Queues View

QueuesDataTable Columns

SLA metrics feature adds extra columns to Flex.QueuesStats.QueuesDataTable with the following keys:

• sla-30min

• sla-today

• handled-tasks-30min

• handled-tasks-today

• abandoned-tasks-30min

• abandoned-tasks-today

SLA metrics are calculated for two timeframes: the last 30 minute floating window and the current business day.

There are more metrics available than those displayed by default. See below for the full list of metrics and instructions how to add them to the table.

Accessing SLA Metrics Programmatically

The WorkerQueue data object is extended with sla_30_min and sla_today keys that contain all SLA metrics for the queue. WorkerQueueSLA include the following metrics:

• total_tasks_count
• handled_tasks_count
• handled_tasks_within_sl_threshold_count
• handled_tasks_within_sl_threshold_percentage
• short_abandoned_tasks_count
• short_abandoned_tasks_percentage
• abandoned_tasks_count
• abandoned_tasks_percentage
• flow_out_tasks_count
• flow_out_tasks_percentage
• sla_percentage

If a queue handles multiple channels, the SLA data is also available per channel. The channels key on WorkerQueue contains an array of WorkerQueueChannel, which has the following keys:

• sid
• unique_name
• friendly_name
• sla_30_min (WorkerQueueSLA)
• sla_today (WorkerQueueSLA)

workerQueue.channels[0].sla_30_min.total_tasks_count

Example - Add SLA Columns to Queues Data

The following snippet adds two columns to the QueuesDataTable table that show the number of short abandoned tasks in the last 30 minutes and for the day. It uses QueuesDataTableCell in the content render function in order to add extra expandable rows of channel specific data.

const RenderShortAbandoned30Min = (workerQueue: Flex.QueuesStats.WorkerQueue) =>
  // QueuesDataTableCell component helps us render additional expandable rows with channel specific data
  <Flex.QueuesStats.QueuesDataTableCell
    // Pass the queue data down 
    queue={workerQueue}
    // Render the queue level value
    renderQueueData={(queue) => queue.sla_30_min.short_abandoned_tasks_count}
    // Render a value for each active channel in the queue
    renderChannelData={(channel, queue) => channel.sla_30_min.short_abandoned_tasks_count} 
  />

const RenderShortAbandonedToday = (workerQueue: Flex.QueuesStats.WorkerQueue) =>
  <Flex.QueuesStats.QueuesDataTableCell 
    queue={workerQueue}
    renderQueueData={(queue) => queue.sla_today.short_abandoned_tasks_count}
    renderChannelData={(channel, queue) => channel.sla_today.short_abandoned_tasks_count}
  />


Flex.QueuesStats.QueuesDataTable.Content.add(
  <Flex.ColumnDefinition
    key="short-abandoned-30min"
    header="Short Abandoned"
    // Since our columns have the same header, we can set
    // the same headerColSpanKey on both to merge their headers.
    headerColSpanKey="short-abandoned"
    subHeader="30 min"
    content={RenderShortAbandoned30Min}
  />
);

Flex.QueuesStats.QueuesDataTable.Content.add(
  <Flex.ColumnDefinition
    key="short-abandoned-today"
    header="Short Abandoned"
    headerColSpanKey="short-abandoned"
    subHeader="Today"
    content={RenderShortAbandonedToday}
  />
);

Please refer to the Queues View Programmability page for more information.