Data Warehouse Use Case Resource Library

Updated by Emily Shreero

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

Common examples are below, with additional guidance on executing the use case where applicable.

This documentation is updated on an as-needed basis, and is not comprehensive of all use cases or instructions. If you need further guidance, reach out to

General or Executive Dashboards

Use Case

Tables Required


Marketing Performance Report

Buckets Breakdown

Report on conversions, spend, CPA, ROAS, or custom metrics at any level of granularity (ex Channel --> Creative) across all marketing channels.

Time Period Comparisons

Buckets Breakdown

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

Media Pacing or Spend Trends

Buckets Breakdown

Compare actual spend across channels to planned spend to monitor pacing.

Channel Specific Dashboards

Buckets Breakdown

Create channel manager dashboards to monitor campaign, audience, or creative level performance with standard or custom metrics.

Dashboards by Features of Marketing Spend or Conversions

(ex geo, product, or campaign level dashboards)

Buckets Breakdown

Conversions (if order level data like product or DMA is required)

Answer questions like "how does North America's performance compare to Europe's" or "how does performance for product A compare to performance for product B?"

Joining Attribution Data to Internal Data

Use Case

Tables Required


LTV Analysis

Log Level MTA (attributed conversions against each conversion_key)

Internal Customer LTV Data

Calculate LTV per marketing channel by applying a general LTV multiplier to Rockerbox data or more granularly calculate LTV per customer cohort by joining order level attribution data to internal customer data.

Profit Margins

Log Level MTA (attributed conversions against each conversion_key)

Internal Profit Margin Data (ex COGS per product)

Subtract COGS from Rockerbox-tracked Revenue to calculate profit margin (overall, per channel, or any level of granularity)

Product Level Analysis

Conversions (product per order (if passed to Rockerbox on conversion data)

Log Level MTA (attributed conversions against each conversion_key, if you want to join product data to marketing data)

Identify if per channel attribution varies per product purchased, or if users who purchase product A are served ads for product A or if they're beginning their journey by seeing ads for other products.

Comparing Existing Attribution to Rockerbox Attribution

Buckets Breakdown

Platform Performance Data (if comparing to platform-reported performance)

Marketers who are new to Rockerbox might want to compare historical attribution models to Rockerbox to identify channel that see a change in conversion volume or performance with the improved visibility (ex offline, views) Rockerbox provides.

Appending Data not Tracked in Rockerbox to Core Rockerbox Dataset

ex: customer service orders, returns)

Tables depends on type of data

Have conversion or spend data that is not tracked in Rockerbox? Append this data to your core dataset to see complete reporting.

Custom Attribution

Use Case

Tables Required


Measuring performance by date of ad exposure vs conversion date

Buckets Breakdown (Spend)

Log Level MTA (marketing touchpoints)

Instructions: Performance by Date of Ad Exposure

Custom Attribution Windows

Log Level MTA (timestamps of marketing events and conversion events)

Buckets Breakdown (if spend is relevant)

Restrict the attribution window for specific channels as you see fit (ex you want to see Emails credit for only 7 days).

View vs Click Based Touchpoints

Log Level MTA (marketing_type)

Break out performance reporting by clicks vs views for channels where both are reported on by Rockerbox.

Uploading Attribution Data to Ad Platforms (ex Adwords or META)

Conversions (user level order data)

Log Level MTA (attributed conversions and gclid / fbclid)

Uploading attributed conversion data to your ad platforms for stronger in-platform learnings and bidding algorithms.

Instructions: Uploading Attributed Conversion Data to your Ad Platforms


Use Case

Tables Required


Geo-Lift Tests

Buckets Breakdown (spend)

Log Level MTA (marketing touchpoints per order_id)

Conversions (geo-level data per order)

Order-level granularity unlocks the ability to measure geo-lift using Rockerbox data.

Ex: I cut FB retargeting in DMA X. Did the overall conversion volume decrease compared to a lookalike market? Was there a halo impact on other channels?

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