- Commerce Intelligence User Guide
- Commerce Intelligence Introduction
- Getting Started
- Administrator
- Analyze Data
- Data Analyst
- Data Warehouse Manager
- Introduction
- Advanced Calculated Column Types
- Building Google Ecommerce dimensions
- Calculated Column Types
- Configuring Replication Methods
- Configuring Data Rechecks
- Changing a metric’s operational table
- Creating and Using Data Warehouse Views
- Creating / Deleting paths for calculated columns
- Creating / Using a SQL Calculated Column
- Creating calculated columns
- Data and Updates Information
- Guest orders
- How Commerce Stores Data
- Entity Relationship Diagrams
- Managing data dimensions in metrics
- MongoDB data modeling guide
- Replicating Google Analytics channels
- Standardizing data with mapping tables
- Translating SQL queries into Commerce Intelligence reports
- Understanding and Evaluating Table Relationships
- Using the Date Difference Calculated
- Using Dashboard Wide Filtering
- Using the Event Number Calculated Column
- Using the Sequential Comparison Calculated Column
- Common Commerce Tables
- SQL Report Builder
- Using the Cohort Report Builder
- Using the Cohort Report Builder for Non-Date Based Cohorts
- Creating a qualitative cohort analysis
- Explore special filter operators
- Export the results of my query
- Using Formulas in the [Report Builder]
- Create Google Analytics charts
- Importance of the Lifetime Revenue Cohort Analysis
- Ordering data using the Show Top/Bottom feature
- Using the SQL Report Builder
- First purchase report
- Understanding the Repeat Order Probability Report
- Auditing Metrics using the SQL Report Builder
- Differences in Columns Between SQL and Data Warehouse Manager
- Connecting Data
- SaaS Integrations
- SaaS Integrations
- Understand Results Between Database and SQL Editor
- Connecting Adobe Analytics
- Expected Adobe Analytics Data
- Connecting Facebook Ads
- Expected Facebook Ads data
- Connecting Google Adwords
- Expected Google Adword data
- Auditing Google Adwords data
- Connecting Google Analytics Warehoused
- Expected Google Analytics Warehoused Data
- Connecting Google Analytics
- Expected Google Analytics data
- Connecting Google ECommerce
- Expected Google ECommerce data
- Connecting Mixpanel
- Expected Mixpanel data
- Data Validation in Mixpanel
- Connecting PrestaShop
- Connecting Quickbooks
- Expected Quickbooks data
- Connecting Salesforce
- Expected Salesforce data
- Connecting Spree
- Expected Spree Data
- Connecting Stripe
- Expected Stripe data
- Connecting WooCommerce
- Connecting Zendesk
- Expected Zendesk data
- Analyzing Zendesk data
- Auditing Zendesk data
- Database Integrations
- Connecting Amazon RDS
- Connecting Databases via VPN
- Connect Your MySQL Database to Commerce Intelligence
- Connecting Adobe Commerce
- Expected Commerce Data
- Connecting Microsoft SQL Server
- Connecting MongoDB via SSH Tunnel
- Connecting MySQL via a direct connection
- Connecting MySQL via cPanel
- Connecting MySQL via SSH Tunnel
- Connecting PostgreSQ
- Analyze Campaigns
- Analyze Customers
- Calculating Commerce churn rates
- Defining customer concentration
- Defining customer churn
- Expected Lifetime Value (LTV) analysis (basic)
- Expected Lifetime Value (LTV) analysis (advanced)
- Track User Acquisition Source Data Overview
- Track User Device and Browser Data in your Database
- Analyzing customer repurchasing behavior
- Analyzing Website Activity and Customer Conversion Rates
- Recency, frequency, monetary (RFM) analysis
- Analyze Business Performance
- Tracking goals against actual metrics
- Analyzing returned orders
- Year-over-year, month-over-month, week-over-week
- Analyzing holiday season performance
- Analyzing repeat probability decay and churn
- Understanding and building a basic analytics
- Identifying your most valuable marketing sources and channels
- Understanding Google Analytics UTM attribution
- Analyzing inventory levels
- Reporting a retail calendar
- Forecasting
- Build Reports and Share Data
- Data User
- Reports
- Dashboards
- Create Dashboards
- Out-of-the-box dashboards
- Dashboard Pro
- Importing charts from another user
- Permanently deleting a chart
- Using Dashboard Groups
- Managing Dashboards
- Deleting Dashboards
- Renaming Dashboards
- Setting a default Dashboard
- Adding charts to Dashboards
- Removing charts from Dashboards
- Sizing and arranging charts in a Dashboard
- Bulk-editing charts in Dashboards
- Cloning Dashboards
- Searching for Dashboards
- Sharing Dashboards with other users
- Sharing Dashboards organization-wide
- Accessing shared Dashboards
- Changing access to shared Dashboards
- Leaving (unsharing) a Dashboard
- Sharing Data
- Best Practices
- Working with Data
- Working with data
- UTM tagging in Google Analytics
- Formatting and Importing Financial Data
- Recommended Data Dimensions for Segmentation and Filtering
- Checking the Update Cycle Status
- Reducing Your Update Cycle Time
- Modifying Your Database to Support Incremental Replication
- Optimizing your Database for Analysis
- Optimizing Your SQL Queries
- Understanding your Commerce Intelligence Environment
- Project Organization
- Working with Dashboards
- Working with Data
- Tutorials
Expected Google Analytics Warehoused Data
Requires Admin permissions.
Some information was used with permission from your friends at Stitch.
Google Analytics Warehoused integration in Commerce Intelligence uses the Google Analytics Core Reporting API.
To avoid unexpected or nonsensical results, confirm that any dimensions you use are compatible with one or more metrics you use in the Report Builder.
A single table - called report - is created in your Data Warehouse.
The schema of this table is composed of the Metrics and Dimensions that you selected during the setup process and two other columns: start-date and end-date.
If for example, you selected the following Metrics and Dimensions during setup:
Metrics:ga:usersDimensions:ga:month
The table would look like the example below.
| Column Name | Description |
|---|---|
\_id |
This column is the primary key. |
\_rjm\_record\_hash |
Commerce Intelligence unique identifier. This column is created by Commerce Intelligence. |
\_updated\_at |
This column contains the last time that the data row was updated. This column is created by Commerce Intelligence. |
start-date |
Identification of what day the row is for. |
end-date |
Identification of what day the row is for. |
month |
Selected dimension: Month of the session, a two-digit integer from 01 to 12. |
users |
Selected metric: The total number of users for the requested time period. |
What’s the difference between Google Analytics Warehoused and Live Integration
The main differentiator is that one integration is stored (Google Analytics Warehoused), and the other is not (Google Analytics Live). In cases of Google Analytics Warehoused, this allows for manipulation of your Google Analytics data and gives you the ability to combine Google Analytics and other data sources to create insightful reporting.
Look at Google Analytics ad campaigns for an example of what can be done from a manipulation standpoint. Suppose you had multiple ad campaigns for Q4 with different names. The campaigns were a result of a specific marketing initiative. With warehoused data, you can create a column that finds the campaign names in question and returns the Q4 initiative name of Operation Dumbo.
The combination aspect allows Google Analytics data to be joined to other data in order to conduct analyses. For example, take Total Time On Site By Ad Campaign data from Google Analytics and join it up against Total Spent Per Campaign data from Facebook Ads to get a complete picture of how much engagement is costing you.
With the Google Analytics Live integration on the other hand, every Google Analytics chart is like a little silo that is not stored in your Commerce Intelligence Data Warehouse.
Related:
Resources
Adobe Account
