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Configuring RD Station as a Source

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1. Add account access

1. 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.
2. 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.
   • Extract Custom Fields as JSON: When enabled, all contact custom fields are extracted as a single JSON string column (custom_fields) instead of one column per field. This keeps the schema stable when custom fields are added or changed in RD Station, and prevents failures caused by inconsistent or out-of-range custom field values.
   • 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.
   • Enable Contact Funnels: Extract the funnel of each contact (whether it is marked as an opportunity, its lifecycle stage and its fit and interest scores) into the contact_funnels stream. RD Station only exposes this per contact, so it costs one extra request per contact, capped at about 120 requests per minute on most plans (500 on Advanced). A good estimate of the extra time is the number of contacts divided by 120, in minutes.
     • Contact Funnels Segmentation List ID: Restrict the stream to the contacts of a single segmentation list. Leave it empty to read every contact in your lead base, which is much slower. Creating a segmentation list in RD Station filtered by “is an opportunity” and setting it here is the fastest way to track opportunities.
     • Contact Funnels Parallel Workers: Number of funnel requests fetched in parallel (1 to 8, default 1). The gain is bounded by RD Station’s rate limit per account, so only raise it on an Advanced account.
   • Enable Contact Opportunity Events: Extract the history of when each contact was marked as an opportunity into the contact_opportunity_events stream. It also costs one extra request per contact under the same rate limit. RD Station records no event when a contact stops being an opportunity, so this stream shows when contacts became opportunities and never when they left.
     • Contact Opportunity Events Segmentation List ID: Restrict the stream to the contacts of a single segmentation list. Leave it empty to read every contact in your lead base.
     • Contact Opportunity Events Parallel Workers: Number of event requests fetched in parallel (1 to 8, default 1), bounded by the same rate limit.
   • Analytics Backfill Months: How many months of history the analytics streams extract (default 12). RD Station limits these reports to 120 requests per hour, and the streams that must query day by day spend one request per day of history, so an unbounded backfill turns into a run that never finishes. Set 0 to remove the ceiling and crawl from August 2021, when RD Station analytics data begins.
3. Click Next.

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2. Select streams

1. 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 campaigns stream is only available for RD accounts with access to the Campaign Manager (Gerenciador de Campanhas) feature. When the account cannot consume the campaigns API, the stream is skipped with a warning and the other streams still run.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.The contact_funnels and contact_opportunity_events streams are only discovered when the corresponding Enable contact funnels / Enable contact opportunity events setting is turned on.
2. Click Next.

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3. Configure data streams

• 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.

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4. Configure data source

• 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.

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5. Check your new source

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Streams and Fields

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Available streams

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Fields by stream

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Data Model

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Use Cases for Data Analysis

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Lead Generation Analysis

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1. Marketing Funnel Performance

• 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

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

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2. Lead Source Attribution

• 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

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

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Email Marketing Performance

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3. Campaign Email Performance

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

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

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4. Workflow Automation Performance

• 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

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

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Conversion Asset Analysis

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5. Landing Page and Form Performance

• 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

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

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6. Contact Base Analysis

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

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
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
FROM
    contact_stats
ORDER BY
    acquisition_month DESC