Data Warehousing Query Library

Updated 1 month ago by Patrick Reed

Rockerbox customers leverage Rockerbox's shared tables in their data warehouse to build custom dashboards against specific use cases.

Contents

1. Log Level MTA and Conversions
2. Buckets Breakdown and Conversions
3. Conversions and (Shopify) order tables
4. Marketing Performance
5. Time Period Comparison
6. Media Pacing
7. To Report on Revenue, Spend, CPA & ROAS
8. Customer Life Time Value
9. Customer Life Time Value (QoQ)
10. Customer Life Time Value Q1, Q2, Q3 after first purchase
11. Attributed Sessions
12. Monitoring Site Traffic and Events over time

Here are a few select base queries to get started with building reports.

Table Joins

Log Level MTA and Conversions

SELECT *
FROM [Log Level MTA]
LEFT JOIN [Conversions]
ON [Log Level MTA].conversion_key = [Conversions].conversion_key

Notes:

• Join on conversion_key which is in both tables
• There is a many to one relationship between Log Level MTA and Conversion. Think “each conversion can have multiple marketing touch points”
• Where a touchpoint has multiple conversions, the touchpoint is duplicated in Log Level MTA with a unique conversion and revenue credit for each associated conversion.

Buckets Breakdown and Conversions

SELECT *
FROM [Buckets Breakdown]
LEFT JOIN [Conversions]
ON [Buckets Breakdown].date = [Conversions].date
AND [Buckets Breakdown].tier_1 = [Conversions].tier_1
AND [Buckets Breakdown].tier_2 = [Conversions].tier_2

Notes:

• Join on date and tier 1 to 5 which is in both tables to get conversions per day and reporting tier
• There is a many to many relationship between the tables so will require grouping to aggregate

Conversions and (Shopify) order tables

• Join on conversion and Order ID in each table respectively
• There is a one-to-one relationship Conversions and Shopify order table. Except for purchase events that Rockerbox doesn’t account for such as returns

Marketing Report Sample Queries

Cross-Channel Attribution report

This SQL query replicates the Cross-Channel Attribution report. This shows for a given time frame, all marketing spend and conversions mapped back to each marketing placement. This report enables you to see weighted CPA/ROAS against marketing placements for new and existing users.

To use this query:

1. Input the date range you want to report on - To do this change "Start date YYYY-MM-DD" and "End date YYYY-MM-DD" into your intended report date range and replace with date ranges in for the form of 'YYYY-MM-DD' eg: '2024-01-01'. Dont forget the single quotation marks
2. Input your database and table names. - To do this replace each instance of "database name"."buckets breakdown table" with your Rockerbox database name and Bucket Breakdown table name.
3. Select the reporting tiers you want to analyze - This is done by uncommenting the 2 thru 5 reporting tiers in two places in the query (one at the towards the top and one at the bottom).

Next hit run and your Cross-Channel Attribution report will be output.

-- CROSS CHANNEL ATTRIBUTION REPORT
WITH date_range AS (
    -- 1. INPUT DATE RANGE
    SELECT 
        "Start date YYYY-MM-DD" AS start_date,
        "End date YYYY-MM-DD" AS end_date
),
totals AS (
    SELECT 
        SUM(even) AS conversion_count,
        SUM(revenue_even) AS revenue_count,
        SUM(ntf_even) AS ntf_conversion_count,
        SUM(ntf_revenue_even) AS ntf_revenue_count
    FROM 
        "database name"."buckets breakdown table"
    WHERE 
        date BETWEEN (SELECT start_date from date_range) AND (SELECT end_date from date_range)
)
SELECT
    --- 3.A SELECT REPORTING TIERS
    tier_1 AS RBX_tier_1,
    -- tier_2 AS RBX_tier_2,
    -- tier_3 AS RBX_tier_3,
    -- tier_4 AS RBX_tier_4,
    -- tier_5 AS RBX_tier_5,

    -- ALL CUSTOMERS
    SUM(last_touch) AS conversions_last_touch,
    SUM(first_touch) AS conversions_first_touch,
    SUM(even) AS conversions_even,
    SUM(normalized) AS conversions_normalized,
    SUM(revenue_last_touch) AS revenue_last_touch,
    SUM(revenue_first_touch) AS revenue_first_touch,
    SUM(revenue_even) AS revenue_even,
    SUM(revenue_normalized) AS revenue_normalized,
    SUM(last_touch) * 100.0 / NULLIF((SELECT conversion_count FROM totals), 0) AS percentage_conversions_last_touch,
    SUM(first_touch) * 100.0 / NULLIF((SELECT conversion_count FROM totals), 0) AS percentage_conversions_first_touch,
    SUM(even) * 100.0 / NULLIF((SELECT conversion_count FROM totals), 0) AS percentage_conversions_even,
    SUM(normalized) * 100.0 / NULLIF((SELECT conversion_count FROM totals), 0) AS percentage_conversions_normalized,    
    SUM(revenue_last_touch) * 100.0 / NULLIF((SELECT revenue_count FROM totals), 0) AS percentage_revenue_last_touch,
    SUM(revenue_first_touch) * 100.0 / NULLIF((SELECT revenue_count FROM totals), 0) AS percentage_revenue_first_touch,
    SUM(revenue_even) * 100.0 / NULLIF((SELECT revenue_count FROM totals), 0) AS percentage_revenue_even,
    SUM(revenue_normalized) * 100.0 / NULLIF((SELECT revenue_count FROM totals), 0) AS percentage_revenue_normalized,

    -- NEW CUSTOMERS ONLY
    SUM(ntf_last_touch) AS ntf_conversions_last_touch,
    SUM(ntf_first_touch) AS ntf_conversions_first_touch,
    SUM(ntf_even) AS ntf_conversions_even,
    SUM(ntf_normalized) AS ntf_conversions_normalized,
    SUM(ntf_revenue_last_touch) AS ntf_revenue_last_touch,
    SUM(ntf_revenue_first_touch) AS ntf_revenue_first_touch,
    SUM(ntf_revenue_even) AS ntf_revenue_even,
    SUM(ntf_revenue_normalized) AS ntf_revenue_normalized,
    SUM(ntf_last_touch) * 100.0 / NULLIF((SELECT ntf_conversion_count FROM totals), 0) AS ntf_percentage_conversions_last_touch,
    SUM(ntf_first_touch) * 100.0 / NULLIF((SELECT ntf_conversion_count FROM totals), 0) AS ntf_percentage_conversions_first_touch,
    SUM(ntf_even) * 100.0 / NULLIF((SELECT ntf_conversion_count FROM totals), 0) AS ntf_percentage_conversions_even,
    SUM(ntf_normalized) * 100.0 / NULLIF((SELECT ntf_conversion_count FROM totals), 0) AS ntf_percentage_conversions_normalized,    
    SUM(ntf_last_touch) * 100.0 / NULLIF((SELECT ntf_revenue_count FROM totals), 0) AS ntf_percentage_revenue_last_touch,
    SUM(ntf_first_touch) * 100.0 / NULLIF((SELECT ntf_revenue_count FROM totals), 0) AS ntf_percentage_revenue_first_touch,
    SUM(ntf_even) * 100.0 / NULLIF((SELECT ntf_revenue_count FROM totals), 0) AS ntf_percentage_revenue_even,
    SUM(ntf_normalized) * 100.0 / NULLIF((SELECT ntf_revenue_count FROM totals), 0) AS ntf_percentage_revenue_normalized,

    -- TOTAL SPEND
    SUM(spend) AS spend,

    -- ROAS
    SUM(revenue_last_touch) / NULLIF(SUM(spend), 0) AS roas_last_touch,
    SUM(revenue_first_touch) / NULLIF(SUM(spend), 0) AS roas_first_touch,
    SUM(revenue_even) / NULLIF(SUM(spend), 0) AS roas_even,
    SUM(revenue_normalized) / NULLIF(SUM(spend), 0) AS roas_normalized,

    -- CPA
    SUM(last_touch) / NULLIF(SUM(spend), 0) AS cpa_last_touch,
    SUM(first_touch) / NULLIF(SUM(spend), 0) AS cpa_first_touch,
    SUM(even) / NULLIF(SUM(spend), 0) AS cpa_even,
    SUM(normalized) / NULLIF(SUM(spend), 0) AS cpa_normalized
    
FROM
    "database name"."buckets breakdown table",
    date_range
WHERE
    date BETWEEN date_range.start_date AND date_range.end_date
GROUP BY
    --- 3.A SELECT REPORTING TIERS
    tier_1
    -- tier_2,
    -- tier_3,
    -- tier_4,
    -- tier_5;

Example results from the Cross-Channel Attribution report in Snowflake

Time Period Comparison (Compare performance across key time frames (ex MoM, WoW, QoQ, YoY) to assess trending changes in performance or spend)

SET DEFAULT ROLE TO CS;
USE ROLE CS;
USE WAREHOUSE CS;

-- WEEK OVER WEEK TIME PERIOD COMPARISON
SELECT
 WEEK1.DATE AS TP1_DATE
 , WEEK1.CONVERSIONS AS TP1_CONVERSIONS
 , WEEK2.DATE AS TP2_DATE
 , WEEK2.CONVERSIONS AS TP2_CONVERSIONS
 , (WEEK2.CONVERSIONS - WEEK1.CONVERSIONS)/ WEEK1.CONVERSIONS * 100 AS TP1_TP2_CONVERSIONS_DETLA
 , WEEK1.REVENUE AS TP1_REVENUE
 , WEEK2.REVENUE AS TP2_REVENUE
 , (WEEK2.REVENUE - WEEK1.REVENUE)/ WEEK1.REVENUE * 100 AS TP1_TP2_REVENUE_DETLA
FROM (
 -- TIME PERIOD 1
 SELECT
 DATE
 , DAYOFWEEK(DATE) AS DAYOFWEEK
 , SUM(NORMALIZED) AS CONVERSIONS
 , SUM(REVENUE_NORMALIZED) AS REVENUE
 FROM [Customer name].PUBLIC.[Log level MTA]
 WHERE (DATE >= '2023-11-27' AND DATE <= '2023-12-03')
 GROUP BY DATE
 ORDER BY DATE
) AS WEEK1
JOIN (
 -- TIME PERIOD 2
 SELECT
 DATE
 , DAYOFWEEK(DATE) AS DAYOFWEEK
 , SUM(NORMALIZED) AS CONVERSIONS
 , SUM(REVENUE_NORMALIZED) AS REVENUE
 FROM [Customer name].PUBLIC.[Log level MTA]
 WHERE (DATE >= '2023-11-20' AND DATE <= '2023-11-26')
 GROUP BY DATE
 ORDER BY DATE
) AS WEEK2
-- JOINED ON DAYOFWEEK
ON WEEK1.DAYOFWEEK = WEEK2.DAYOFWEEKsql

Media Pacing (compare actual spend across channels to planned spend to monitor pacing)

SET DEFAULT ROLE TO CS;
USE ROLE CS;
USE WAREHOUSE CS;
​
SELECT
 BUDGET.DATE_VALUE AS DATE
 , BUDGET.PLATFORM_BUDGET_DAILY
 , SPEND.SPEND
 , SPEND.SPEND - BUDGET.PLATFORM_BUDGET_DAILY AS DELTA_ABS
 , (SPEND.SPEND - BUDGET.PLATFORM_BUDGET_DAILY)/ SPEND.SPEND * 100 AS DELTA_PER
FROM (
-- PLANNED SPEND DUMMY DATA
WITH RECURSIVE DATE_RANGE AS (
 SELECT '2023-12-01'::DATE AS DATE_VALUE
 UNION ALL
 SELECT DATE_VALUE + 1
 FROM DATE_RANGE
 WHERE DATE_VALUE < '2023-12-30'::DATE
)
SELECT DATE_VALUE, 15000 AS PLATFORM_BUDGET_DAILY FROM DATE_RANGE ) AS BUDGET
LEFT JOIN (
 SELECT
 DATE
 , SUM(SPEND) AS SPEND
 FROM [Customer name].PUBLIC.FACEBOOK_PLATFORM_DATA
 WHERE (DATE >= '2023-12-01' AND DATE <= '2023-12-30')
 GROUP BY DATE
 ORDER BY DATE
) AS SPEND
ON BUDGET.DATE_VALUE = SPEND.DATE

To Report on Revenue, Spend, CPA & ROAS at Tier 1 Granularity Over a Custom Date Range

--CPA ROAS tier1 across all dates
select
 tier_1,
 NULLIF(SUM(normalized),0) as normalized,
 NULLIF(SUM(revenue_normalized),0) as revenue_normalized,
 NULLIF(SUM(spend),0) as spend,
 NULLIF(SUM(spend),0) / NULLIF(SUM(normalized),0) as CPA,
 SUM(revenue_normalized) / NULLIF(SUM(spend),0) as ROAS,
 NULLIF(SUM(ntf_normalized),0) as ntf_normalized,
 NULLIF(SUM(ntf_revenue_normalized),0) as ntf_revenue_normalized,
 NULLIF(SUM(spend),0) / NULLIF(SUM(ntf_normalized),0) as NTF_CPA,
 SUM(ntf_revenue_normalized) / NULLIF(SUM(spend),0) as NTF_ROAS,
from Buckets_Breakdown_Purchase_Batch
where
 date >= [Enter Start Date]
 and date <= [End End Date]
group by 1
order by 1;

Customer Life Time Value

 WITH 
-- Define date values
DateParameters AS (
    SELECT 
        CAST('2023-01-01' AS DATE) AS start_date,
        CAST('2023-12-31' AS DATE) AS end_date,
),
-- Calculate the Average Order Value
AverageOrderValue AS (
    SELECT 
        AVG(REVENUE) AS average_order_value
    FROM 
        "RB_DATABASE"."RB_CONVERSION_DATA",
        DateParameters
    WHERE 
        DATE BETWEEN DateParameters.start_date AND DateParameters.end_date
),
-- Calculate the Average Purchasing Frequency
AveragePurchasingFrequency AS (
    SELECT 
        AVG(purchase_count) AS average_purchasing_frequency
    FROM (
        SELECT 
            EMAIL,
            COUNT(*) AS purchase_count
        FROM 
            "RB_DATABASE"."RB_CONVERSION_DATA",
            DateParameters
        WHERE 
            DATE BETWEEN DateParameters.start_date AND DateParameters.end_date
        GROUP BY 
            EMAIL
    ) AS customer_purchases
),
-- Calculate the Average Customer Lifetime
AverageCustomerLifetime AS (
    SELECT 
        AVG(customer_lifetime) AS average_customer_lifetime
    FROM (
        SELECT 
            EMAIL,
            DATEDIFF(month, MIN(DATE), MAX(DATE)) AS customer_lifetime
        FROM 
            "RB_DATABASE"."RB_CONVERSION_DATA",
            DateParameters
        WHERE 
            DATE BETWEEN DateParameters.start_date AND DateParameters.end_date
        GROUP BY 
            EMAIL
    ) AS customer_lifetimes
)
-- Combine results to get Lifetime Value
SELECT 
    AOV.average_order_value,
    APF.average_purchasing_frequency,
    ACL.average_customer_lifetime,
    (AOV.average_order_value * APF.average_purchasing_frequency * ACL.average_customer_lifetime) AS lifetime_value
FROM 
    AverageOrderValue AOV,
    AveragePurchasingFrequency APF,
    AverageCustomerLifetime ACL;

Customer Life Time Value (QoQ)

WITH 
-- Calculate the first purchase quarter for each customer
CustomerFirstPurchase AS (
    SELECT 
        EMAIL,
        MIN(DATE) AS first_purchase_date,
        CONCAT(EXTRACT(YEAR FROM MIN(DATE)), '-Q', EXTRACT(QUARTER FROM MIN(DATE))) AS first_purchase_quarter
    FROM 
        "RB_DATABASE"."RB_CONVERSION_DATA"
    GROUP BY 
        EMAIL
),
-- Calculate the Average Order Value for each quarter
AverageOrderValue AS (
    SELECT 
        CFP.first_purchase_quarter,
        AVG(T_CONVERSIONS.REVENUE) AS average_order_value
    FROM 
        "RB_DATABASE"."RB_CONVERSION_DATA" AS T_CONVERSIONS
        JOIN CustomerFirstPurchase CFP ON T_CONVERSIONS.EMAIL = CFP.EMAIL
    WHERE 
        CFP.first_purchase_date = T_CONVERSIONS.DATE
    GROUP BY 
        CFP.first_purchase_quarter
),
-- Calculate the Average Purchasing Frequency
AveragePurchasingFrequency AS (
    SELECT 
        customer_purchases.first_purchase_quarter,
        AVG(purchase_count) AS average_purchasing_frequency
    FROM (
        SELECT 
            CFP.EMAIL,
            CFP.first_purchase_quarter,
            COUNT(*) AS purchase_count
        FROM 
            "RB_DATABASE"."RB_CONVERSION_DATA" AS T_CONVERSIONS
            JOIN CustomerFirstPurchase CFP ON T_CONVERSIONS.EMAIL = CFP.EMAIL
        GROUP BY 
            CFP.EMAIL, CFP.first_purchase_quarter
    ) AS customer_purchases
    GROUP BY 
        first_purchase_quarter
),
-- Calculate the Average Customer Lifetime
AverageCustomerLifetime AS (
    SELECT 
        first_purchase_quarter, 
        AVG(customer_lifetime) AS average_customer_lifetime
    FROM (
        SELECT 
            CFP.EMAIL,
            first_purchase_quarter,
            DATEDIFF(month, MIN(DATE), MAX(DATE)) AS customer_lifetime
        FROM 
            "RB_DATABASE"."RB_CONVERSION_DATA" AS T_CONVERSIONS
            JOIN CustomerFirstPurchase CFP ON T_CONVERSIONS.EMAIL = CFP.EMAIL
        GROUP BY 
            CFP.EMAIL, CFP.first_purchase_quarter
    ) AS customer_lifetimes
    GROUP BY 
        first_purchase_quarter
)
-- Combine results to get Lifetime Value
SELECT 
    AOV.first_purchase_quarter,
    AOV.average_order_value,
    APF.average_purchasing_frequency,
    ACL.average_customer_lifetime,
    (AOV.average_order_value * APF.average_purchasing_frequency * ACL.average_customer_lifetime) AS lifetime_value
FROM 
    AverageOrderValue AOV
    JOIN AveragePurchasingFrequency APF ON AOV.first_purchase_quarter = APF.first_purchase_quarter
    JOIN AverageCustomerLifetime ACL ON AOV.first_purchase_quarter = ACL.first_purchase_quarter
ORDER BY
    first_purchase_quarter

Customer Life Time Value Q1, Q2, Q3 after first purchase

WITH 
CustomerFirstPurchase AS (
    SELECT 
        EMAIL,
        MIN(DATE) AS first_purchase_date,
        CONCAT(EXTRACT(YEAR FROM MIN(DATE)), '-Q', EXTRACT(QUARTER FROM MIN(DATE))) AS first_purchase_quarter
    FROM 
        "RB_DATABASE"."RB_CONVERSION_DATA"
    GROUP BY 
        EMAIL
),
RevenueByPeriod AS (
    SELECT
        CFP.EMAIL,
        CFP.first_purchase_quarter,
        SUM(CASE 
                WHEN DATEDIFF(day, CFP.first_purchase_date, T_CONVERSIONS.DATE) = 0 
                THEN T_CONVERSIONS.REVENUE 
                ELSE 0 
            END) AS first_purchase_revenue,
        SUM(CASE 
                WHEN DATEDIFF(day, CFP.first_purchase_date, T_CONVERSIONS.DATE) <= 90 
                THEN T_CONVERSIONS.REVENUE 
                ELSE 0 
            END) AS three_month_revenue,
        SUM(CASE 
                WHEN DATEDIFF(day, CFP.first_purchase_date, T_CONVERSIONS.DATE) <= 180 
                THEN T_CONVERSIONS.REVENUE 
                ELSE 0 
            END) AS six_month_revenue,
        SUM(CASE 
                WHEN DATEDIFF(day, CFP.first_purchase_date, T_CONVERSIONS.DATE) <= 270 
                THEN T_CONVERSIONS.REVENUE 
                ELSE 0 
            END) AS nine_month_revenue
    FROM 
        "RB_DATABASE"."RB_CONVERSION_DATA"Customer Life Time Value Q1, Q2, Q3 after first purchase AS T_CONVERSIONS
        JOIN CustomerFirstPurchase CFP ON T_CONVERSIONS.EMAIL = CFP.EMAIL
    GROUP BY 
        CFP.EMAIL, CFP.first_purchase_quarter
),
AverageRevenuePerCustomer AS (
    SELECT
        first_purchase_quarter,
        AVG(first_purchase_revenue) AS avg_first_purchase_revenue,
        AVG(three_month_revenue) AS avg_three_month_revenue,
        AVG(six_month_revenue) AS avg_six_month_revenue,
        AVG(nine_month_revenue) AS avg_nine_month_revenue
    FROM
        RevenueByPeriod
    GROUP BY
        first_purchase_quarter
)
SELECT
    first_purchase_quarter,
    avg_first_purchase_revenue,
    avg_three_month_revenue,
    avg_six_month_revenue,
    avg_nine_month_revenue
FROM
    AverageRevenuePerCustomer
ORDER BY
    first_purchase_quarter

Attributed Sessions

The count of unique sessions attributed to click-based marketing touchpoints.

SELECT 
    COUNT(DISTINCT CONCAT(session_id, '_', uid)) AS session_count,
    date,
    tier_1, 
    tier_2,
    tier_3,
    tier_4,
    tier_5
FROM `database.schema.table`
WHERE 
     date BETWEEN 'YYYY-MM-DD' AND 'YYYY-MM-DD'
     AND session_start = 1
GROUP BY date, tier_1, tier_2, tier_3, tier_4, tier_5;

Monitoring Site Traffic and Events over time

SELECT 
    date,
    COUNT(DISTINCT uid) AS users,
    SUM(CASE WHEN session_start = '1' THEN 1 ELSE 0 END) AS sessions,
    COUNT(*) AS event_count
FROM `database.schema.table`
WHERE 
    AND date BETWEEN 'YYYY-MM-DD' AND 'YYYY-MM-DD'
GROUP BY 
    date
ORDER BY 
    date;

How did we do?

Related Articles

• What is Data Warehousing?
• Getting started with Rockerbox Data Warehousing
• Data Warehouse Use Case Resource Library