RD Station is a marketing automation and lead generation platform that helps businesses attract, convert, and nurture leads. It provides tools for email marketing, landing page creation, lead scoring, and marketing analytics to improve conversion rates and customer acquisition.

Configuring RD Station as a Source

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

1. Add account access

Click Next and you’ll be prompted to login with your account to grant permissions for Nekt to extract data from your RD Station Marketing account.
After successful authentication, you’ll be prompted to configure additional settings:
- Start Date: The earliest record date to sync. This defines the starting point for data extraction - only records created or modified after this date will be synchronized.
- Enable Contact Custom Fields: Define whether the user should have access to details about contacts or not. Should be used with caution since the extraction time can increase significantly when enabled, as it requires an additional request for each contact.
- Contacts Extraction Mode: Define which segmentation lists the connector should extract. Each segmentation will be extracted to a separate table in your Lakehouse.
  - Entire Lead Base: Extract all contacts from your account.
  - Segmentation Lists: Extract only contacts from specific segmentation lists.
  - Entire Lead Base and Segmentation Lists: Extract contacts from both the entire base and from specified segmentation lists.
Please note contacts who are in different segmentation lists may be extracted multiple times.
- Segmentation List IDs: The IDs of the segmentation lists to extract contacts from if you want to filter only specific lists to speed up extraction. If not provided, contacts from all segmentation lists will be extracted.
Click Next.

2. Select streams

The next step is letting us know which streams you want to bring. You can select entire groups of streams or only a subset of them.
Tip: The stream can be found more easily by typing its name.

The analytics_funnel stream is only available for RD accounts with access to the Advanced plan.The analytics_email_stats, analytics_workflow_email_stats and analytics_conversion_assets streams are only available for RD accounts with access to the Professional plan.The contact_events and contact_details streams are only available if you extract the entire lead base, either by selecting Entire Lead Base or Entire Lead Base and Segmentation Lists.
Click Next.

3. Configure data streams

Customize how you want your data to appear in your catalog. Select the layer, a name for each table (which will contain the fetched data) and the type of sync.

Layer: companies in the Growth plan can choose in which layer the tables with the extracted data will be placed.
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: depending on the data you are bringing to the lake, you can choose between INCREMENTAL and FULL_TABLE. Read more about Sync Types here.

Click Next.

4. Configure data source

Describe your data source for easy identification within your organization. You can inform things like what data it brings, to which team it belongs, etc. 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 (if available).

Configure Delta Log Retention and determine for how log 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.

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

Available streams

The table below lists every stream, its slug (the exact identifier to pass when creating the source via API) and a short description. Streams marked optional are only discovered when the corresponding setting is enabled or the RD Station plan grants access.

Stream	Slug	Description
Segmentations	`segmentations`	Segmentation lists defined in the account.
Workflows	`workflows`	Automated marketing workflows.
Campaigns	`campaigns`	Marketing campaigns.
Emails	`emails`	Email marketing sends and their metadata.
Forms	`embeddables`	Embeddable forms configured in the account.
Landing Pages	`landing_pages`	Landing pages published in the account.
Popups	`popups`	Popups configured in the account.
Segmentation Contacts	`segmentation_contacts`	Optional. Contacts of the entire lead base; discovered when the contacts extraction mode includes the entire base. Contacts from specific segmentation lists are exposed as additional `segmentation_contacts_{id}` streams.
Contact Events	`contact_events`	Optional. Conversion events for each contact; skipped when extracting only by segmentation list.
Contacts Details	`contacts_details`	Optional. Detailed contact records including custom fields; discovered only when contact custom fields are enabled.
Analytics Funnel	`analytics_funnel`	Optional. Funnel analytics; available only on the Advanced plan.
Analytics Email Stats	`analytics_email_stats`	Optional. Email delivery and engagement statistics; available only on the Professional plan.
Analytics Workflow Email Stats	`analytics_workflow_email_stats`	Optional. Email statistics for workflow-triggered emails; available only on the Professional plan.
Analytics Conversion Assets	`analytics_conversion_assets`	Optional. Conversion metrics broken down by asset; available only on the Professional plan.

Fields by stream

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

Segmentations

Stream for managing segmentation lists in your RD Station account. Segmentations allow you to group contacts based on specific criteria for targeted marketing campaigns.Key Fields:

Field	Type	Description
`id`	String	Unique identifier for the segmentation
`name`	String	Name of the segmentation list
`standard`	Boolean	Whether this is a standard/default segmentation
`process_status`	String	Current processing status of the segmentation
`created_at`	Timestamp	When the segmentation was created
`updated_at`	Timestamp	When the segmentation was last updated
`links`	Array	Array of API links related to the segmentation

Segmentation Contacts

Stream for extracting contacts from segmentation lists. When extracting from the entire lead base, a single segmentation_contacts table is created. When extracting from specific segmentation lists, separate tables are created with the naming pattern segmentation_contacts_{segmentation_id}.Key Fields:

Field	Type	Description
`uuid`	String	Unique identifier for the contact
`name`	String	Full name of the contact
`email`	String	Email address of the contact
`last_conversion_date`	Timestamp	Date of the last conversion event
`created_at`	Timestamp	When the contact was created
`updated_at`	Timestamp	When the contact was last updated
`links`	Array	Array of API links related to the contact

Contact Details

Detailed contact information including custom fields. This stream is only available when extracting the entire lead base and requires enabling the “Contact Custom Fields” option.

Note: Enabling this stream significantly increases extraction time as it requires an additional API request for each contact.

Key Fields:

Field	Type	Description
`uuid`	String	Unique identifier for the contact
`name`	String	Full name of the contact
`email`	String	Primary email address
`job_title`	String	Job title of the contact
`birthdate`	String	Date of birth
`bio`	String	Biography or description
`website`	String	Personal or company website
`personal_phone`	String	Personal phone number
`mobile_phone`	String	Mobile phone number
`city`	String	City of residence
`state`	String	State or province
`country`	String	Country
`twitter`	String	Twitter handle
`facebook`	String	Facebook profile
`linkedin`	String	LinkedIn profile
`tags`	Array	Array of tags assigned to the contact
`extra_emails`	Array	Additional email addresses
`legal_bases`	Array	Array of legal consent information
`legal_bases.category`	String	Category of the legal base
`legal_bases.type`	String	Type of consent
`legal_bases.status`	String	Consent status
`links`	Array	Array of API links related to the contact
`updated_at`	Timestamp	When the contact was last updated
`+ Custom Fields`	Varies	Any custom fields defined in your RD Station account

Contact Events

Stream for extracting conversion events associated with contacts. This stream is only available when extracting the entire lead base.Key Fields:

Field	Type	Description
`id`	String	Unique identifier for the event
`contact_uuid`	String	UUID of the associated contact
`event_type`	String	Type of event (e.g., CONVERSION)
`event_family`	String	Family/category of the event
`event_identifier`	String	Identifier of the conversion asset
`event_timestamp`	Timestamp	When the event occurred
`payload.conversion_identifier`	String	Identifier of the conversion point
`payload.traffic_source`	String	Source of the traffic (e.g., organic, paid)
`payload.traffic_medium`	String	Medium of the traffic (e.g., cpc, email)
`payload.traffic_campaign`	String	Campaign associated with the traffic
`payload.+ Custom Fields`	Varies	Any custom fields captured during conversion

Campaigns

Stream for managing email marketing campaigns and their settings.Key Fields:

Field	Type	Description
`id`	String	Unique identifier for the campaign
`name`	String	Name of the campaign
`status`	String	Current status of the campaign
`total_items`	Integer	Number of items/emails in the campaign
`created_at`	Timestamp	When the campaign was created
`updated_at`	Timestamp	When the campaign was last updated
`user`	Object	Object containing information about the campaign creator
`user.email`	String	Email of the user who created the campaign
`user.links`	Array	API links related to the user

Emails

Stream for managing individual email communications within campaigns.Key Fields:

Field	Type	Description
`id`	String	Unique identifier for the email
`name`	String	Name of the email
`status`	String	Current status of the email
`type`	String	Type of email
`leads_count`	Integer	Number of leads targeted by this email
`send_at`	Timestamp	Scheduled send time
`created_at`	Timestamp	When the email was created
`updated_at`	Timestamp	When the email was last updated
`campaign_id`	String	ID of the parent campaign
`component_template_id`	String	ID of the template used
`is_predictive_sending`	Boolean	Whether predictive sending is enabled
`sending_is_imminent`	Boolean	Whether sending is about to happen
`behavior_score_info`	Object	Object containing behavior score settings
`behavior_score_info.disengaged`	Boolean	Whether to include disengaged contacts
`behavior_score_info.engaged`	Boolean	Whether to include engaged contacts
`behavior_score_info.indeterminate`	Boolean	Whether to include indeterminate contacts

Workflows (Automated Workflows)

Stream for managing marketing automation workflows.Key Fields:

Field	Type	Description
`id`	String	Unique identifier for the workflow
`name`	String	Name of the workflow
`user_email_created`	String	Email of the user who created the workflow
`user_email_updated`	String	Email of the user who last updated the workflow
`created_at`	Timestamp	When the workflow was created
`updated_at`	Timestamp	When the workflow was last updated
`configurations`	Object	Object containing workflow settings
`configurations.status`	String	Current status of the workflow

Landing Pages

Stream for managing landing pages used for lead capture.Key Fields:

Field	Type	Description
`id`	String	Unique identifier for the landing page
`title`	String	Title of the landing page
`status`	String	Current status (e.g., published, draft)
`conversion_identifier`	String	Identifier used for tracking conversions
`has_active_experiment`	Boolean	Whether an A/B test is currently active
`had_experiment`	Boolean	Whether the page has had A/B tests in the past
`created_at`	Timestamp	When the landing page was created
`updated_at`	Timestamp	When the landing page was last updated

Embeddables (Forms)

Stream for managing embeddable forms that can be placed on external websites.Key Fields:

Field	Type	Description
`id`	String	Unique identifier for the form
`title`	String	Title of the form
`status`	String	Current status of the form
`conversion_identifier`	String	Identifier used for tracking conversions
`created_at`	Timestamp	When the form was created
`updated_at`	Timestamp	When the form was last updated

Popups

Stream for managing popup forms and lead capture overlays.Key Fields:

Field	Type	Description
`id`	String	Unique identifier for the popup
`title`	String	Title of the popup
`status`	String	Current status of the popup
`conversion_identifier`	String	Identifier used for tracking conversions
`trigger`	String	Trigger condition for displaying the popup
`created_at`	Timestamp	When the popup was created
`updated_at`	Timestamp	When the popup was last updated

Analytics Funnel (Advanced Plan)

Daily funnel analytics showing lead progression through marketing stages. Only available for accounts with the Advanced plan.Key Fields:

Field	Type	Description
`reference_day`	Timestamp	Date of the analytics data
`visitors_count`	Integer	Number of website visitors
`contacts_count`	Integer	Number of new contacts generated
`qualified_contacts_count`	Integer	Number of qualified contacts (MQLs)
`opportunities_count`	Integer	Number of opportunities created
`sales_count`	Integer	Number of closed sales

Analytics Email Stats (Professional Plan)

Email campaign performance statistics. Only available for accounts with the Professional plan.Key Fields:

Field	Type	Description
`query_date_start`	Timestamp	Start date of the query period
`query_date_end`	Timestamp	End date of the query period
`campaign_id`	String	ID of the campaign
`campaign_name`	String	Name of the campaign
`send_at`	Timestamp	When the email was sent
`contacts_count`	Integer	Number of contacts targeted
`email_dropped_count`	Integer	Number of emails dropped
`email_delivered_count`	Integer	Number of emails delivered
`email_bounced_count`	Integer	Number of emails bounced
`email_opened_count`	Integer	Number of emails opened
`email_clicked_count`	Integer	Number of emails clicked
`email_unsubscribed_count`	Integer	Number of unsubscribes
`email_spam_reported_count`	Integer	Number of spam reports
`email_delivered_rate`	Float	Delivery rate
`email_opened_rate`	Float	Open rate
`email_clicked_rate`	Float	Click rate
`email_spam_reported_rate`	Float	Spam report rate

Analytics Workflow Email Stats (Professional Plan)

Email performance statistics for workflow automation emails. Only available for accounts with the Professional plan.Key Fields:

Field	Type	Description
`query_date_start`	Timestamp	Start date of the query period
`query_date_end`	Timestamp	End date of the query period
`workflow_id`	String	ID of the workflow
`workflow_name`	String	Name of the workflow
`workflow_action_id`	String	ID of the workflow action
`email_name`	String	Name of the email
`created_at`	Timestamp	When the workflow was created
`updated_at`	Timestamp	When the workflow was last updated
`contacts_count`	Integer	Number of contacts processed
`count_processed`	Integer	Total processed count
`email_dropped_count`	Integer	Number of emails dropped
`email_delivered_count`	Integer	Number of emails delivered
`email_hard_bounced_unique_count`	Integer	Unique hard bounces
`email_soft_bounced_unique_count`	Integer	Unique soft bounces
`email_bounced_unique_count`	Integer	Total unique bounces
`email_opened_unique_count`	Integer	Unique opens
`email_clicked_unique_count`	Integer	Unique clicks
`email_unsubscribed_count`	Integer	Number of unsubscribes
`email_spam_reported_count`	Integer	Number of spam reports
`email_delivered_rate`	Float	Delivery rate
`email_opened_rate`	Float	Open rate
`email_clicked_rate`	Float	Click rate
`email_spam_reported_rate`	Float	Spam report rate

Analytics Conversion Assets (Professional Plan)

Performance statistics for conversion assets (landing pages, forms, popups). Only available for accounts with the Professional plan.Key Fields:

Field	Type	Description
`query_date_start`	Timestamp	Start date of the query period
`query_date_end`	Timestamp	End date of the query period
`asset_id`	String	Unique identifier of the asset
`asset_identifier`	String	Conversion identifier of the asset
`asset_type`	String	Type of asset (landing_page, form, popup)
`asset_created_at`	Timestamp	When the asset was created
`asset_updated_at`	Timestamp	When the asset was last updated
`visits_count`	Integer	Number of visits to the asset
`conversion_count`	Integer	Number of conversions
`conversion_rate`	Float	Conversion rate (conversions/visits)

Use Cases for Data Analysis

This guide outlines valuable business intelligence use cases when consolidating RD Station data, along with ready-to-use SQL queries that you can run on Explorer.

Lead Generation Analysis

1. Marketing Funnel Performance

Track the progression of leads through your marketing funnel over time to identify bottlenecks and opportunities. Business Value:

Identify which funnel stages have the highest drop-off rates
Track conversion rates between funnel stages
Monitor trends in lead generation and sales performance
Optimize marketing strategies based on funnel insights

SQL query

WITH
    daily_funnel AS (
        SELECT
            DATE(reference_day) AS reference_date,
            visitors_count,
            contacts_count,
            qualified_contacts_count,
            opportunities_count,
            sales_count,
            CAST(contacts_count AS DOUBLE) * 100.0 / NULLIF(visitors_count, 0) AS visitor_to_contact_rate,
            CAST(qualified_contacts_count AS DOUBLE) * 100.0 / NULLIF(contacts_count, 0) AS contact_to_mql_rate,
            CAST(opportunities_count AS DOUBLE) * 100.0 / NULLIF(qualified_contacts_count, 0) AS mql_to_opportunity_rate,
            CAST(sales_count AS DOUBLE) * 100.0 / NULLIF(opportunities_count, 0) AS opportunity_to_sale_rate
        FROM
            nekt_raw.rd_station_analytics_funnel
        WHERE
            DATE(reference_day) >= CURRENT_DATE - INTERVAL '30' DAY
    )
SELECT
    reference_date,
    visitors_count,
    contacts_count,
    qualified_contacts_count,
    opportunities_count,
    sales_count,
    ROUND(visitor_to_contact_rate, 2) AS visitor_to_contact_pct,
    ROUND(contact_to_mql_rate, 2) AS contact_to_mql_pct,
    ROUND(mql_to_opportunity_rate, 2) AS mql_to_opportunity_pct,
    ROUND(opportunity_to_sale_rate, 2) AS opportunity_to_sale_pct
FROM
    daily_funnel
ORDER BY
    reference_date DESC

WITH
    daily_funnel AS (
        SELECT
            DATE(reference_day) AS reference_date,
            visitors_count,
            contacts_count,
            qualified_contacts_count,
            opportunities_count,
            sales_count,
            SAFE_DIVIDE(CAST(contacts_count AS FLOAT64) * 100.0, visitors_count) AS visitor_to_contact_rate,
            SAFE_DIVIDE(CAST(qualified_contacts_count AS FLOAT64) * 100.0, contacts_count) AS contact_to_mql_rate,
            SAFE_DIVIDE(CAST(opportunities_count AS FLOAT64) * 100.0, qualified_contacts_count) AS mql_to_opportunity_rate,
            SAFE_DIVIDE(CAST(sales_count AS FLOAT64) * 100.0, opportunities_count) AS opportunity_to_sale_rate
        FROM
            `nekt_raw.rd_station_analytics_funnel`
        WHERE
            DATE(reference_day) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
    )
SELECT
    reference_date,
    visitors_count,
    contacts_count,
    qualified_contacts_count,
    opportunities_count,
    sales_count,
    ROUND(visitor_to_contact_rate, 2) AS visitor_to_contact_pct,
    ROUND(contact_to_mql_rate, 2) AS contact_to_mql_pct,
    ROUND(mql_to_opportunity_rate, 2) AS mql_to_opportunity_pct,
    ROUND(opportunity_to_sale_rate, 2) AS opportunity_to_sale_pct
FROM
    daily_funnel
ORDER BY
    reference_date DESC

Sample Result

reference_date	visitors_count	contacts_count	qualified_contacts_count	opportunities_count	sales_count	visitor_to_contact_pct	contact_to_mql_pct	mql_to_opportunity_pct	opportunity_to_sale_pct
2024-11-27	12,450	234	45	12	3	1.88	19.23	26.67	25.00
2024-11-26	11,890	212	38	10	2	1.78	17.92	26.32	20.00
2024-11-25	10,234	189	32	8	2	1.85	16.93	25.00	25.00
2024-11-24	8,920	145	28	6	1	1.63	19.31	21.43	16.67
2024-11-23	9,456	167	31	7	2	1.77	18.56	22.58	28.57
2024-11-22	14,230	289	52	14	4	2.03	17.99	26.92	28.57

2. Lead Source Attribution

Analyze which traffic sources and campaigns generate the most conversions to optimize marketing spend. Business Value:

Identify the most effective traffic sources for lead generation
Understand which campaigns drive the highest conversion rates
Allocate marketing budget more effectively
Track ROI by marketing channel

SQL query

WITH
    source_conversions AS (
        SELECT
            COALESCE(payload.traffic_source, 'Direct') AS traffic_source,
            COALESCE(payload.traffic_medium, 'None') AS traffic_medium,
            COALESCE(payload.traffic_campaign, 'None') AS traffic_campaign,
            COUNT(DISTINCT contact_uuid) AS unique_leads,
            COUNT(*) AS total_conversions
        FROM
            nekt_raw.rd_station_contact_events
        WHERE
            event_type = 'CONVERSION'
            AND DATE(event_timestamp) >= CURRENT_DATE - INTERVAL '30' DAY
        GROUP BY
            payload.traffic_source,
            payload.traffic_medium,
            payload.traffic_campaign
    )
SELECT
    traffic_source,
    traffic_medium,
    traffic_campaign,
    unique_leads,
    total_conversions,
    ROUND(CAST(total_conversions AS FLOAT64) / unique_leads, 2) AS conversions_per_lead
FROM
    source_conversions
ORDER BY
    unique_leads DESC

WITH
    source_conversions AS (
        SELECT
            COALESCE(payload.traffic_source, 'Direct') AS traffic_source,
            COALESCE(payload.traffic_medium, 'None') AS traffic_medium,
            COALESCE(payload.traffic_campaign, 'None') AS traffic_campaign,
            COUNT(DISTINCT contact_uuid) AS unique_leads,
            COUNT(*) AS total_conversions
        FROM
            `nekt_raw.rd_station_contact_events`
        WHERE
            event_type = 'CONVERSION'
            AND DATE(event_timestamp) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
        GROUP BY
            payload.traffic_source,
            payload.traffic_medium,
            payload.traffic_campaign
    )
SELECT
    traffic_source,
    traffic_medium,
    traffic_campaign,
    unique_leads,
    total_conversions,
    ROUND(SAFE_DIVIDE(CAST(total_conversions AS FLOAT64), unique_leads), 2) AS conversions_per_lead
FROM
    source_conversions
ORDER BY
    unique_leads DESC

Sample Result

traffic_source	traffic_medium	traffic_campaign	unique_leads	total_conversions	conversions_per_lead
google	cpc	black_friday_2024	456	892	1.96
google	organic	None	312	423	1.36
facebook	paid	product_launch	234	345	1.47
Direct	None	None	189	234	1.24
instagram	paid	brand_awareness	156	189	1.21
linkedin	cpc	b2b_campaign	89	112	1.26
email	newsletter	weekly_digest	78	145	1.86

Email Marketing Performance

3. Campaign Email Performance

Analyze the performance of email campaigns to optimize email marketing strategies. Business Value:

Identify top-performing email campaigns
Track engagement metrics over time
Reduce unsubscribe and spam report rates
Improve email deliverability and click-through rates

SQL query

WITH
    campaign_metrics AS (
        SELECT
            campaign_name,
            campaign_id,
            SUM(contacts_count) AS total_contacts,
            SUM(email_delivered_count) AS total_delivered,
            SUM(email_opened_count) AS total_opens,
            SUM(email_clicked_count) AS total_clicks,
            SUM(email_bounced_count) AS total_bounces,
            SUM(email_unsubscribed_count) AS total_unsubscribes,
            SUM(email_spam_reported_count) AS total_spam_reports,
            AVG(email_delivered_rate) AS avg_delivery_rate,
            AVG(email_opened_rate) AS avg_open_rate,
            AVG(email_clicked_rate) AS avg_click_rate
        FROM
            nekt_raw.rd_station_analytics_email_stats
        WHERE
            DATE(query_date_start) >= CURRENT_DATE - INTERVAL '30' DAY
        GROUP BY
            campaign_name,
            campaign_id
    )
SELECT
    campaign_name,
    total_contacts,
    total_delivered,
    total_opens,
    total_clicks,
    ROUND(avg_delivery_rate * 100, 2) AS delivery_rate_pct,
    ROUND(avg_open_rate * 100, 2) AS open_rate_pct,
    ROUND(avg_click_rate * 100, 2) AS click_rate_pct,
    total_bounces,
    total_unsubscribes,
    ROUND(
        CAST(total_clicks AS DOUBLE) * 100.0 / NULLIF(total_opens, 0),
        2
    ) AS click_to_open_rate
FROM
    campaign_metrics
ORDER BY
    total_delivered DESC

WITH
    campaign_metrics AS (
        SELECT
            campaign_name,
            campaign_id,
            SUM(contacts_count) AS total_contacts,
            SUM(email_delivered_count) AS total_delivered,
            SUM(email_opened_count) AS total_opens,
            SUM(email_clicked_count) AS total_clicks,
            SUM(email_bounced_count) AS total_bounces,
            SUM(email_unsubscribed_count) AS total_unsubscribes,
            SUM(email_spam_reported_count) AS total_spam_reports,
            AVG(email_delivered_rate) AS avg_delivery_rate,
            AVG(email_opened_rate) AS avg_open_rate,
            AVG(email_clicked_rate) AS avg_click_rate
        FROM
            `nekt_raw.rd_station_analytics_email_stats`
        WHERE
            DATE(query_date_start) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
        GROUP BY
            campaign_name,
            campaign_id
    )
SELECT
    campaign_name,
    total_contacts,
    total_delivered,
    total_opens,
    total_clicks,
    ROUND(avg_delivery_rate * 100, 2) AS delivery_rate_pct,
    ROUND(avg_open_rate * 100, 2) AS open_rate_pct,
    ROUND(avg_click_rate * 100, 2) AS click_rate_pct,
    total_bounces,
    total_unsubscribes,
    ROUND(
        SAFE_DIVIDE(CAST(total_clicks AS FLOAT64) * 100.0, total_opens),
        2
    ) AS click_to_open_rate
FROM
    campaign_metrics
ORDER BY
    total_delivered DESC

Sample Result

campaign_name	total_contacts	total_delivered	total_opens	total_clicks	delivery_rate_pct	open_rate_pct	click_rate_pct	total_bounces	total_unsubscribes	click_to_open_rate
Black Friday 2024	45,230	43,890	18,920	4,560	97.04	43.11	10.39	1,340	89	24.10
Product Launch	28,450	27,560	12,340	3,890	96.87	44.77	14.12	890	56	31.53
Weekly Newsletter	34,560	33,450	8,920	1,234	96.79	26.67	3.69	1,110	123	13.83
Nurturing Sequence	12,890	12,450	5,670	1,890	96.58	45.54	15.18	440	34	33.33
Re-engagement	8,450	7,890	2,340	456	93.37	29.66	5.78	560	78	19.49

4. Workflow Automation Performance

Track the effectiveness of automated email workflows to optimize nurturing sequences. Business Value:

Identify high-performing automation workflows
Compare email performance across different workflow steps
Optimize workflow sequences based on engagement data
Reduce drop-off in nurturing campaigns

SQL query

WITH
    workflow_metrics AS (
        SELECT
            workflow_name,
            workflow_id,
            email_name,
            SUM(contacts_count) AS total_contacts,
            SUM(email_delivered_count) AS total_delivered,
            SUM(email_opened_unique_count) AS unique_opens,
            SUM(email_clicked_unique_count) AS unique_clicks,
            SUM(email_hard_bounced_unique_count) AS hard_bounces,
            SUM(email_soft_bounced_unique_count) AS soft_bounces,
            SUM(email_unsubscribed_count) AS total_unsubscribes,
            AVG(email_opened_rate) AS avg_open_rate,
            AVG(email_clicked_rate) AS avg_click_rate
        FROM
            nekt_raw.rd_station_analytics_workflow_email_stats
        WHERE
            DATE(query_date_start) >= CURRENT_DATE - INTERVAL '30' DAY
        GROUP BY
            workflow_name,
            workflow_id,
            email_name
    )
SELECT
    workflow_name,
    email_name,
    total_contacts,
    total_delivered,
    unique_opens,
    unique_clicks,
    ROUND(avg_open_rate * 100, 2) AS open_rate_pct,
    ROUND(avg_click_rate * 100, 2) AS click_rate_pct,
    ROUND(
        CAST(unique_clicks AS DOUBLE) * 100.0 / NULLIF(unique_opens, 0),
        2
    ) AS click_to_open_rate,
    hard_bounces + soft_bounces AS total_bounces,
    total_unsubscribes
FROM
    workflow_metrics
ORDER BY
    workflow_name,
    total_delivered DESC

WITH
    workflow_metrics AS (
        SELECT
            workflow_name,
            workflow_id,
            email_name,
            SUM(contacts_count) AS total_contacts,
            SUM(email_delivered_count) AS total_delivered,
            SUM(email_opened_unique_count) AS unique_opens,
            SUM(email_clicked_unique_count) AS unique_clicks,
            SUM(email_hard_bounced_unique_count) AS hard_bounces,
            SUM(email_soft_bounced_unique_count) AS soft_bounces,
            SUM(email_unsubscribed_count) AS total_unsubscribes,
            AVG(email_opened_rate) AS avg_open_rate,
            AVG(email_clicked_rate) AS avg_click_rate
        FROM
            `nekt_raw.rd_station_analytics_workflow_email_stats`
        WHERE
            DATE(query_date_start) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
        GROUP BY
            workflow_name,
            workflow_id,
            email_name
    )
SELECT
    workflow_name,
    email_name,
    total_contacts,
    total_delivered,
    unique_opens,
    unique_clicks,
    ROUND(avg_open_rate * 100, 2) AS open_rate_pct,
    ROUND(avg_click_rate * 100, 2) AS click_rate_pct,
    ROUND(
        SAFE_DIVIDE(CAST(unique_clicks AS FLOAT64) * 100.0, unique_opens),
        2
    ) AS click_to_open_rate,
    hard_bounces + soft_bounces AS total_bounces,
    total_unsubscribes
FROM
    workflow_metrics
ORDER BY
    workflow_name,
    total_delivered DESC

Sample Result

workflow_name	email_name	total_contacts	total_delivered	unique_opens	unique_clicks	open_rate_pct	click_rate_pct	click_to_open_rate	total_bounces	total_unsubscribes
Onboarding Flow	Welcome Email	8,920	8,670	5,230	1,890	60.32	21.80	36.14	250	12
Onboarding Flow	Getting Started	7,450	7,230	3,890	1,234	53.81	17.07	31.72	220	18
Onboarding Flow	Pro Tips	6,120	5,980	2,890	890	48.33	14.88	30.80	140	23
Nurturing Campaign	Case Study	4,560	4,430	1,890	567	42.66	12.80	30.00	130	8
Nurturing Campaign	Product Demo	3,890	3,780	1,670	489	44.18	12.94	29.28	110	11
Re-activation	We Miss You	2,340	2,120	567	123	26.75	5.80	21.69	220	45

Conversion Asset Analysis

5. Landing Page and Form Performance

Analyze the performance of landing pages, forms, and popups to optimize conversion rates. Business Value:

Identify top-performing conversion assets
Track conversion rates across different asset types
Prioritize optimization efforts on high-traffic, low-conversion assets
Compare performance across different time periods

SQL query

WITH
    asset_performance AS (
        SELECT
            asset_type,
            asset_identifier,
            asset_id,
            SUM(visits_count) AS total_visits,
            SUM(conversion_count) AS total_conversions,
            AVG(conversion_rate) AS avg_conversion_rate
        FROM
            nekt_raw.rd_station_analytics_conversion_assets
        WHERE
            DATE(query_date_start) >= CURRENT_DATE - INTERVAL '30' DAY
        GROUP BY
            asset_type,
            asset_identifier,
            asset_id
    )
SELECT
    asset_type,
    asset_identifier,
    total_visits,
    total_conversions,
    ROUND(avg_conversion_rate * 100, 2) AS conversion_rate_pct,
    CASE
        WHEN total_visits > 100 AND avg_conversion_rate < 0.02 THEN 'High traffic, low conversion - optimize!'
        WHEN total_visits > 100 AND avg_conversion_rate >= 0.05 THEN 'High performer'
        WHEN total_visits <= 100 AND avg_conversion_rate >= 0.05 THEN 'Good conversion, needs more traffic'
        ELSE 'Monitor'
    END AS recommendation
FROM
    asset_performance
ORDER BY
    total_visits DESC

WITH
    asset_performance AS (
        SELECT
            asset_type,
            asset_identifier,
            asset_id,
            SUM(visits_count) AS total_visits,
            SUM(conversion_count) AS total_conversions,
            AVG(conversion_rate) AS avg_conversion_rate
        FROM
            `nekt_raw.rd_station_analytics_conversion_assets`
        WHERE
            DATE(query_date_start) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
        GROUP BY
            asset_type,
            asset_identifier,
            asset_id
    )
SELECT
    asset_type,
    asset_identifier,
    total_visits,
    total_conversions,
    ROUND(avg_conversion_rate * 100, 2) AS conversion_rate_pct,
    CASE
        WHEN total_visits > 100 AND avg_conversion_rate < 0.02 THEN 'High traffic, low conversion - optimize!'
        WHEN total_visits > 100 AND avg_conversion_rate >= 0.05 THEN 'High performer'
        WHEN total_visits <= 100 AND avg_conversion_rate >= 0.05 THEN 'Good conversion, needs more traffic'
        ELSE 'Monitor'
    END AS recommendation
FROM
    asset_performance
ORDER BY
    total_visits DESC

Sample Result

asset_type	asset_identifier	total_visits	total_conversions	conversion_rate_pct	recommendation
landing_page	black-friday-2024	12,450	1,890	15.18	High performer
landing_page	product-demo-request	8,920	567	6.36	High performer
form	newsletter-signup	5,670	456	8.04	High performer
landing_page	ebook-download	4,230	78	1.84	High traffic, low conversion - optimize!
popup	exit-intent-offer	3,890	234	6.01	High performer
form	contact-us	2,340	189	8.08	Good conversion, needs more traffic
landing_page	webinar-registration	1,890	145	7.67	Good conversion, needs more traffic

6. Contact Base Analysis

Analyze your contact base to understand lead acquisition trends and engagement patterns. Business Value:

Understand lead acquisition trends over time
Identify periods of high contact growth
Track conversion activity patterns
Monitor contact database health

SQL query

WITH
    contact_stats AS (
        SELECT
            DATE_TRUNC('month', created_at) AS acquisition_month,
            COUNT(DISTINCT uuid) AS new_contacts,
            COUNT(DISTINCT CASE 
                WHEN last_conversion_date IS NOT NULL 
                THEN uuid 
            END) AS contacts_with_conversion,
            MIN(created_at) AS earliest_contact,
            MAX(last_conversion_date) AS latest_conversion
        FROM
            nekt_raw.rd_station_segmentation_contacts
        WHERE
            created_at IS NOT NULL
        GROUP BY
            DATE_TRUNC('month', created_at)
    )
SELECT
    acquisition_month,
    new_contacts,
    contacts_with_conversion,
    ROUND(
        CAST(contacts_with_conversion AS DOUBLE) * 100.0 / NULLIF(new_contacts, 0),
        2
    ) AS conversion_rate_pct,
    SUM(new_contacts) OVER (ORDER BY acquisition_month) AS cumulative_contacts
FROM
    contact_stats
ORDER BY
    acquisition_month DESC

WITH
    contact_stats AS (
        SELECT
            DATE_TRUNC(created_at, MONTH) AS acquisition_month,
            COUNT(DISTINCT uuid) AS new_contacts,
            COUNT(DISTINCT CASE 
                WHEN last_conversion_date IS NOT NULL 
                THEN uuid 
            END) AS contacts_with_conversion,
            MIN(created_at) AS earliest_contact,
            MAX(last_conversion_date) AS latest_conversion
        FROM
            `nekt_raw.rd_station_segmentation_contacts`
        WHERE
            created_at IS NOT NULL
        GROUP BY
            DATE_TRUNC(created_at, MONTH)
    )
SELECT
    acquisition_month,
    new_contacts,
    contacts_with_conversion,
    ROUND(
        SAFE_DIVIDE(CAST(contacts_with_conversion AS FLOAT64) * 100.0, new_contacts),
        2
    ) AS conversion_rate_pct,
    SUM(new_contacts) OVER (ORDER BY acquisition_month) AS cumulative_contacts
FROM
    contact_stats
ORDER BY
    acquisition_month DESC

Sample Result

acquisition_month	new_contacts	contacts_with_conversion	conversion_rate_pct	cumulative_contacts
2024-11-01	2,890	2,450	84.78	45,670
2024-10-01	3,120	2,780	89.10	42,780
2024-09-01	2,780	2,340	84.17	39,660
2024-08-01	2,450	2,120	86.53	36,880
2024-07-01	2,670	2,230	83.52	34,430
2024-06-01	2,340	1,980	84.62	31,760

Time-Based Analysis

7. Conversion Trends by Day of Week

Identify patterns in lead generation and conversion activity throughout the week. Business Value:

Optimize campaign scheduling based on engagement patterns
Identify best days for email sends
Plan content publication strategy
Allocate resources effectively based on expected activity

SQL query

WITH
    daily_conversions AS (
        SELECT
            DATE(event_timestamp) AS conversion_date,
            DATE_FORMAT(DATE(event_timestamp), '%W') AS day_of_week,
            COUNT(*) AS total_conversions,
            COUNT(DISTINCT contact_uuid) AS unique_leads,
            COUNT(DISTINCT payload.conversion_identifier) AS unique_assets
        FROM
            nekt_raw.rd_station_contact_events
        WHERE
            event_type = 'CONVERSION'
            AND DATE(event_timestamp) >= CURRENT_DATE - INTERVAL '30' DAY
        GROUP BY
            DATE(event_timestamp),
            DATE_FORMAT(DATE(event_timestamp), '%W')
    ),
    weekly_averages AS (
        SELECT
            day_of_week,
            AVG(total_conversions) AS avg_conversions,
            AVG(unique_leads) AS avg_leads,
            AVG(unique_assets) AS avg_assets,
            COUNT(*) AS days_counted
        FROM
            daily_conversions
        GROUP BY
            day_of_week
    )
SELECT
    day_of_week,
    ROUND(avg_conversions, 1) AS avg_daily_conversions,
    ROUND(avg_leads, 1) AS avg_daily_leads,
    ROUND(avg_assets, 1) AS avg_assets_used,
    days_counted,
    CASE
        WHEN avg_conversions >= (SELECT MAX(avg_conversions) * 0.8 FROM weekly_averages) THEN 'Peak Day'
        WHEN avg_conversions <= (SELECT MIN(avg_conversions) * 1.2 FROM weekly_averages) THEN 'Low Day'
        ELSE 'Average'
    END AS day_classification
FROM
    weekly_averages
ORDER BY
    CASE day_of_week
        WHEN 'Monday' THEN 1
        WHEN 'Tuesday' THEN 2
        WHEN 'Wednesday' THEN 3
        WHEN 'Thursday' THEN 4
        WHEN 'Friday' THEN 5
        WHEN 'Saturday' THEN 6
        WHEN 'Sunday' THEN 7
    END

WITH
    daily_conversions AS (
        SELECT
            DATE(event_timestamp) AS conversion_date,
            FORMAT_DATE('%A', DATE(event_timestamp)) AS day_of_week,
            COUNT(*) AS total_conversions,
            COUNT(DISTINCT contact_uuid) AS unique_leads,
            COUNT(DISTINCT payload.conversion_identifier) AS unique_assets
        FROM
            `nekt_raw.rd_station_contact_events`
        WHERE
            event_type = 'CONVERSION'
            AND DATE(event_timestamp) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
        GROUP BY
            DATE(event_timestamp),
            FORMAT_DATE('%A', DATE(event_timestamp))
    ),
    weekly_averages AS (
        SELECT
            day_of_week,
            AVG(total_conversions) AS avg_conversions,
            AVG(unique_leads) AS avg_leads,
            AVG(unique_assets) AS avg_assets,
            COUNT(*) AS days_counted
        FROM
            daily_conversions
        GROUP BY
            day_of_week
    )
SELECT
    day_of_week,
    ROUND(avg_conversions, 1) AS avg_daily_conversions,
    ROUND(avg_leads, 1) AS avg_daily_leads,
    ROUND(avg_assets, 1) AS avg_assets_used,
    days_counted,
    CASE
        WHEN avg_conversions >= (SELECT MAX(avg_conversions) * 0.8 FROM weekly_averages) THEN 'Peak Day'
        WHEN avg_conversions <= (SELECT MIN(avg_conversions) * 1.2 FROM weekly_averages) THEN 'Low Day'
        ELSE 'Average'
    END AS day_classification
FROM
    weekly_averages
ORDER BY
    CASE day_of_week
        WHEN 'Monday' THEN 1
        WHEN 'Tuesday' THEN 2
        WHEN 'Wednesday' THEN 3
        WHEN 'Thursday' THEN 4
        WHEN 'Friday' THEN 5
        WHEN 'Saturday' THEN 6
        WHEN 'Sunday' THEN 7
    END

Sample Result

day_of_week	avg_daily_conversions	avg_daily_leads	avg_assets_used	days_counted	day_classification
Monday	45.2	38.4	8.2	4	Average
Tuesday	52.8	44.6	9.1	4	Peak Day
Wednesday	56.4	47.2	9.4	4	Peak Day
Thursday	48.6	41.8	8.6	4	Average
Friday	42.1	36.2	7.8	4	Average
Saturday	18.4	15.6	4.2	4	Low Day
Sunday	12.8	10.4	3.1	4	Low Day

Implementation Notes

API Behaviors & Limits

The connector automatically handles API rate limits (HTTP 429) and server errors (HTTP 5xx) by retrying failed requests up to 5 times. It intelligently respects the Retry-After-Quotas and Retry-After headers provided by the RD Station API to pause extractions when limits are reached, falling back to exponential backoff for other transient network errors.

Data Quality Considerations

The contact_events and contacts_details streams require extracting the entire lead base. Plan for longer extraction times if your database is large.
Analytics streams (funnel, email stats, workflow stats, conversion assets) are only available with Professional or Advanced plans.
When using the “Enable Contact Custom Fields” option, extraction time increases significantly due to individual API calls per contact.

Best Practices

Start with the core streams (segmentations, segmentation_contacts, campaigns, emails) before enabling advanced analytics.
Use segmentation list filtering if you only need specific segments to reduce extraction time.
Schedule extractions during off-peak hours if you have a large contact database.
For real-time reporting needs, consider using incremental syncs with daily triggers.

Troubleshooting

Issue	Possible cause	Solution
`Error getting access token` in logs	Invalid or expired credentials, or network issue	Re-authenticate your RD Station account. If the issue persists, check the exact error message logged by Nekt to identify HTTP or connection failures.
`Error getting segmentation lists` in logs	Insufficient permissions or API downtime	Ensure your connected account has the correct permissions to fetch segmentations. Review the detailed error logged in Nekt for specific API responses.

Skills for agents

Download RD Station skills file

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

​Configuring RD Station 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

​Available streams

​Fields by stream

​Use Cases for Data Analysis

​Lead Generation Analysis

​1. Marketing Funnel Performance

​2. Lead Source Attribution

​Email Marketing Performance

​3. Campaign Email Performance

​4. Workflow Automation Performance

​Conversion Asset Analysis

​5. Landing Page and Form Performance

​6. Contact Base Analysis

​Time-Based Analysis

​7. Conversion Trends by Day of Week

​Implementation Notes

​API Behaviors & Limits

​Data Quality Considerations

​Best Practices

​Troubleshooting

​Skills for agents

Download RD Station skills file

Configuring RD Station 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

Available streams

Fields by stream

Use Cases for Data Analysis

Lead Generation Analysis

1. Marketing Funnel Performance

2. Lead Source Attribution

Email Marketing Performance

3. Campaign Email Performance

4. Workflow Automation Performance

Conversion Asset Analysis

5. Landing Page and Form Performance

6. Contact Base Analysis

Time-Based Analysis

7. Conversion Trends by Day of Week

Implementation Notes

API Behaviors & Limits

Data Quality Considerations

Best Practices

Troubleshooting

Skills for agents