Blip is a messaging and chatbot platform that helps businesses create conversational experiences for customer communication. It provides tools for building chatbots, managing messaging channels, and automating customer interactions to improve engagement and support.

Configuring Blip as a Source

In the Sources tab, click on the “Add source” button located on the top right of your screen. Then, select the Blip option from the list of connectors. Click Next and you’ll be prompted to add your access.

1. Add account access

You’ll need the following credentials from your Blip account:

Authorization token: The token to authenticate against the API service - please note it must be a HTTP token. It should be generated using the Connect using HTTP option. For more information on how to generate the token, please check the Blip documentation.
Company identifier (contract ID): The company identifier (also known as contract ID) used to send commands through the API. Its value can be identified as being part of your URL, in the following format: https://{contract_id}.http.msging.net/commands.
Start date: Records created or updated after the start date will be extracted from the source. Format: YYYY-MM-DD.

Once you have all the required credentials, add the account access and click Next.

2. Select streams

Choose which data streams you want to sync - you can select all streams or pick specific ones that matter most to you.

Tip: The stream can be found more easily by typing its name.

Select the streams and click Next.

3. Configure data streams

Customize how you want your data to appear in your catalog. Select the desired layer where the data will be placed, a folder to organize it inside the layer, a name for each table (which will effectively contain the fetched data) and the type of sync.

Layer: choose between the existing layers on your catalog. This is where you will find your new extracted tables as the extraction runs successfully.
Folder: a folder can be created inside the selected layer to group all tables being created from this new data source.
Table name: we suggest a name, but feel free to customize it. You have the option to add a prefix to all tables at once and make this process faster!
Sync Type: you can choose between INCREMENTAL and FULL_TABLE.
- Incremental: every time the extraction happens, we’ll get only the new data - which is good if, for example, you want to keep every record ever fetched.
- Full table: every time the extraction happens, we’ll get the current state of the data - which is good if, for example, you don’t want to have deleted data in your catalog.

Once you are done configuring, click Next.

4. Configure data source

Describe your data source for easy identification within your organization, not exceeding 140 characters. To define your Trigger, consider how often you want data to be extracted from this source. This decision usually depends on how frequently you need the new table data updated (every day, once a week, or only at specific times). Optionally, you can define some additional settings:

Configure Delta Log Retention and determine for how long we should store old states of this table as it gets updated. Read more about this resource here.
Determine when to execute an Additional Full Sync. This will complement the incremental data extractions, ensuring that your data is completely synchronized with your source every once in a while.

Once you are ready, click Next to finalize the setup.

5. Check your new source

You can view your new source on the Sources page. If needed, manually trigger the source extraction by clicking on the arrow button. Once executed, your data will appear in your Catalog.

For you to be able to see it on your Catalog, you need at least one successful source run.

Streams and Fields

Below you’ll find all available data streams from Blip and their corresponding fields:

Active Messages Daily Report

Stream for tracking daily message activity metrics.Key fields:

Field	Type	Description
`id`	String	Unique identifier for the report
`interval_start`	String	Start time of the reporting interval
`interval_end`	String	End time of the reporting interval
`count`	Integer	Number of active messages in the interval

Agent Metrics

Stream for real-time agent performance metrics.Key fields:

Field	Type	Description
`sync_date`	String	When the metrics were collected
`identity`	String	Agent identifier
`status`	String	Current agent status
`is_enabled`	Boolean	Whether the agent is enabled
`opened_tickets`	Integer	Number of open tickets
`agent_name`	String	Name of the agent
`break_duration_in_seconds`	Integer	Duration of break time
`current_status_date_time`	String	When the current status was set
`closed_tickets`	Integer	Number of closed tickets
`average_attendance_time`	String	Average time spent on tickets
`average_response_time`	String	Average time to respond
`tickets_count`	Integer	Total number of tickets handled

Agents Daily Report

Stream for daily agent performance metrics.Key fields:

Field	Type	Description
`id`	String	Unique identifier for the report
`sync_date`	String	Date of the report
`identity`	String	Agent identifier
`status`	String	Agent status
`is_enabled`	Boolean	Whether the agent is enabled
`opened_tickets`	Integer	Number of open tickets
`agent_name`	String	Name of the agent
`break_duration_in_seconds`	Integer	Duration of break time
`current_status_date_time`	String	When the current status was set
`closed_tickets`	Integer	Number of closed tickets
`average_attendance_time`	String	Average time spent on tickets
`average_response_time`	String	Average time to respond
`average_first_response_time`	String	Average time for first response
`average_wait_time`	String	Average wait time for customers
`tickets_count`	Integer	Total number of tickets handled

Ticket Metrics

Stream for overall ticket performance metrics.Key fields:

Field	Type	Description
`sync_date`	String	When the metrics were collected
`max_queue_time`	String	Maximum time in queue
`max_first_response_time`	String	Maximum time for first response
`max_without_first_response_time`	String	Maximum time without first response
`avg_queue_time`	String	Average time in queue
`avg_first_response_time`	String	Average time for first response
`avg_wait_time`	String	Average wait time
`avg_response_time`	String	Average response time
`avg_attendance_time`	String	Average attendance time
`tickets_per_attendant`	Integer	Number of tickets per agent

Tickets

Stream for detailed ticket information.Key fields:

Field	Type	Description
`id`	String	Unique identifier for the ticket
`sequential_id`	Integer	Sequential ticket number
`owner_identity`	String	Identity of the ticket owner
`customer_identity`	String	Identity of the customer
`customer_domain`	String	Domain of the customer
`agent_identity`	String	Identity of the assigned agent
`provider`	String	Service provider
`status`	String	Current ticket status
`storage_date`	String	When the ticket was stored
`open_date`	String	When the ticket was opened
`close_date`	String	When the ticket was closed
`status_date`	String	When the status was last updated
`rating`	Integer	Customer rating
`team`	String	Assigned team
`unread_messages`	Integer	Number of unread messages
`closed`	Boolean	Whether the ticket is closed
`closed_by`	String	Who closed the ticket
`tags`	String	Tags associated with the ticket
`first_response_date`	String	When the first response was sent
`priority`	Integer	Ticket priority level
`is_automatic_distribution`	Boolean	Whether automatically distributed
`distribution_type`	String	Type of distribution

Tickets Daily Report

Stream for daily ticket statistics.Key fields:

Field	Type	Description
`id`	String	Unique identifier for the report
`date`	String	Date of the report
`waiting`	Integer	Number of tickets waiting
`open`	Integer	Number of open tickets
`closed`	Integer	Number of closed tickets
`closed_attendant`	Integer	Tickets closed by attendants
`closed_client`	Integer	Tickets closed by clients
`transferred`	Integer	Number of transferred tickets
`missed`	Integer	Number of missed tickets
`in_attendance`	Integer	Number of tickets in attendance

Use Cases for Data Analysis

Here are some valuable business intelligence use cases when consolidating Blip data, along with ready-to-use SQL queries that you can run on Explorer.

1. Agent Performance Analysis

Track agent productivity and response times. Business Value:

Monitor individual agent performance
Identify training needs
Optimize workload distribution
Improve customer response times

SQL code

WITH
	agent_daily_stats AS (
		SELECT
			"identity",
			DATE_TRUNC ('day', "sync_date") AS "report_date",
			"opened_tickets",
			"closed_tickets",
			"tickets_count",
			CAST(
				SPLIT_PART ("average_response_time", ':', 1) AS DOUBLE
			) * 3600 + CAST(
				SPLIT_PART ("average_response_time", ':', 2) AS DOUBLE
			) * 60 + CAST(
				SPLIT_PART ("average_response_time", ':', 3) AS DOUBLE
			) AS "avg_response_seconds",
			CAST(
				SPLIT_PART ("average_first_response_time", ':', 1) AS DOUBLE
			) * 3600 + CAST(
				SPLIT_PART ("average_first_response_time", ':', 2) AS DOUBLE
			) * 60 + CAST(
				SPLIT_PART ("average_first_response_time", ':', 3) AS DOUBLE
			) AS "avg_first_response_seconds",
			CAST(
				SPLIT_PART ("average_attendance_time", ':', 1) AS DOUBLE
			) * 3600 + CAST(
				SPLIT_PART ("average_attendance_time", ':', 2) AS DOUBLE
			) * 60 + CAST(
				SPLIT_PART ("average_attendance_time", ':', 3) AS DOUBLE
			) AS "avg_attendance_seconds",
			"break_duration_in_seconds"
		FROM
			"nekt_raw"."blip_agents_daily_report"
		WHERE
			"sync_date" >= CURRENT_DATE - INTERVAL '30' DAY
	),
	agent_summary AS (
		SELECT
			"identity",
			COUNT(DISTINCT "report_date") AS "active_days",
			SUM("closed_tickets") AS "total_closed_tickets",
			SUM("tickets_count") AS "total_tickets_handled",
			AVG("avg_response_seconds") AS "avg_response_time_seconds",
			AVG("avg_first_response_seconds") AS "avg_first_response_time_seconds",
			AVG("avg_attendance_seconds") AS "avg_attendance_time_seconds",
			SUM("break_duration_in_seconds") / 3600.0 AS "total_break_hours"
		FROM
			agent_daily_stats
		GROUP BY
			"identity"
	)
SELECT
	"identity",
	"active_days",
	"total_closed_tickets",
	"total_tickets_handled",
	ROUND(
		CAST("total_closed_tickets" AS DOUBLE) / NULLIF("total_tickets_handled", 0) * 100,
		2
	) AS "resolution_rate",
	ROUND(
		"total_tickets_handled" / NULLIF("active_days", 0),
		1
	) AS "avg_daily_tickets",
	ROUND("avg_response_time_seconds" / 60.0, 2) AS "avg_response_time_minutes",
	ROUND("avg_first_response_time_seconds" / 60.0, 2) AS "avg_first_response_time_minutes",
	ROUND("avg_attendance_time_seconds" / 60.0, 2) AS "avg_attendance_time_minutes",
	ROUND("total_break_hours", 2) AS "total_break_hours"
FROM
	agent_summary
ORDER BY
	"total_tickets_handled" DESC

2. Ticket Resolution Analysis

Analyze ticket resolution patterns and identify bottlenecks. Business Value:

Improve ticket resolution efficiency
Reduce customer wait times
Identify common issues
Optimize team allocation

SQL code

WITH daily_metrics AS (
  SELECT
    DATE_TRUNC('day', date) AS report_date,
    waiting,
    open,
    closed,
    closed_attendant,
    closed_client,
    transferred,
    missed,
    in_attendance,
    closed + waiting + open + in_attendance AS total_tickets,
    ROUND(
      CAST(closed AS DOUBLE) / NULLIF(closed + waiting + open + in_attendance, 0) * 100,
      2
    ) AS resolution_rate
  FROM nekt_raw.blip_tickets_daily_report
  WHERE date >= CURRENT_DATE - INTERVAL '30' DAY
),
ticket_timing AS (
  SELECT
    DATE_TRUNC('day', open_date) AS created_date,
    status,
    priority,
    team,
    CASE
      WHEN close_date IS NOT NULL 
      THEN DATE_DIFF('minute', open_date, close_date)
    END AS resolution_time_minutes
  FROM nekt_raw.blip_tickets
  WHERE open_date >= CURRENT_DATE - INTERVAL '30' DAY
)
SELECT
  dm.report_date,
  dm.total_tickets,
  dm.waiting AS tickets_waiting,
  dm.open AS tickets_open,
  dm.closed AS tickets_closed,
  dm.transferred AS tickets_transferred,
  dm.missed AS tickets_missed,
  dm.resolution_rate AS daily_resolution_rate,
  ROUND(AVG(tt.resolution_time_minutes), 2) AS avg_resolution_time_minutes,
  COUNT(DISTINCT CASE WHEN tt.priority >= 2 THEN tt.id END) AS high_priority_tickets,
  COUNT(DISTINCT CASE WHEN tt.resolution_time_minutes > 120 THEN tt.id END) AS long_resolution_tickets
FROM daily_metrics dm
LEFT JOIN ticket_timing tt ON dm.report_date = tt.created_date
GROUP BY
  dm.report_date,
  dm.total_tickets,
  dm.waiting,
  dm.open,
  dm.closed,
  dm.transferred,
  dm.missed,
  dm.resolution_rate
ORDER BY dm.report_date DESC

Implementation Notes

Data Quality Considerations

Monitor agent status changes for accurate reporting
Validate ticket resolution times for outliers
Ensure consistent rating data collection
Track message delivery status

Automation Opportunities

Schedule daily agent performance reports
Set up alerts for long wait times
Automate customer satisfaction reporting
Generate team workload distribution reports

These SQL transformations provide a foundation for comprehensive Blip analytics. Customize the date ranges, filters, and metrics based on your specific business requirements and reporting needs.

Skills for agents

Download Blip skills file

Blip connector documentation as plain markdown, for use in AI agent contexts.

Documentation

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Using Nekt

Workspace

Resources

Blip as a data source

Configuring Blip as a Source

1. Add account access

2. Select streams

3. Configure data streams

4. Configure data source

5. Check your new source

Streams and Fields

Use Cases for Data Analysis

1. Agent Performance Analysis

2. Ticket Resolution Analysis

Implementation Notes

Data Quality Considerations

Automation Opportunities

Skills for agents

Download Blip skills file

​Configuring Blip as a Source

​1. Add account access

​2. Select streams

​3. Configure data streams

​4. Configure data source

​5. Check your new source

​Streams and Fields

​Use Cases for Data Analysis

​1. Agent Performance Analysis

​2. Ticket Resolution Analysis

​Implementation Notes

​Data Quality Considerations

​Automation Opportunities

​Skills for agents

Download Blip skills file

Configuring Blip as a Source

1. Add account access

2. Select streams

3. Configure data streams

4. Configure data source

5. Check your new source

Streams and Fields

Use Cases for Data Analysis

1. Agent Performance Analysis

2. Ticket Resolution Analysis

Implementation Notes

Data Quality Considerations

Automation Opportunities

Skills for agents