Data Warehouse Use Case Resource Library
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.
General or Executive Dashboards
Use Case | Tables Required | Description |
Marketing Performance Report | Report on conversions, spend, CPA, ROAS, or custom metrics at any level of granularity (ex Channel --> Creative) across all marketing channels. | |
Time Period Comparisons | Compare performance across key time frames (ex MoM, WoW, QoQ, YoY) to assess trending changes in performance or spend. | |
Media Pacing or Spend Trends | Compare actual spend across channels to planned spend to monitor pacing. | |
Channel Specific Dashboards | 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) | 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?" |
Traffic, Visitor, or Sessions Analysis | Measure channel impact of driving sessions, traffic, or visitors. |
Joining Attribution Data to Internal Data
Use Case | Tables Required | Description |
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 | 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 | Description |
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 |
Miscellaneous
Use Case | Tables Required | Description |
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? |
Audience segmentation | Log Level MTA (user level order data, geo data and marketing touchpoints per order_id) | Clustering algorithms can be used to segment audiences into distinct groups of users based on their purchase history, location, tenure as a customer and other factors. Uniquely, Rockerbox can help you understand which channels drive purchase for each audience segment and which channels introduced each segment to your brand for their first purchase. |