- 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 Adwords data
After you have connected your Google Adwords account, you can use the Data Warehouse Manager to easily track relevant data fields for analysis.
There, you notice two tables available for replication into your Data Warehouse:
campaigns[account-id]adwords[account-id]
The campaigns table should be used by default, so you can begin by syncing all relevant fields from that table.
The adwords table contains four columns that are not in the campaigns table:
keywordadContentadDestinationUrladGroup
Whenever you are interested in performing an analysis that considers these attributes, you must use the adwords table.
IMPORTANT
This table excludes rows where all four of these columns are null.
Below is a look at the expected schema for both tables.
Campaigns table
The campaigns table contains the following columns:
| Column | Description |
|---|---|
\_id |
The primary key for the table |
accountId |
The account ID |
adClicks |
Total number of clicks for the day |
adCost |
Total cost for the campaign for the day |
adwordsCampaignID |
Adwords Campaign ID |
campaign |
Campaign name (for example, utm_campaign) |
date |
The date the campaign ran |
impressions |
Number of impressions for the day |
profileId |
The profile ID |
profileName |
The profile name |
\_updated\_at |
The date and time of the last update for this row |
AdWords table
The adwords table contains the following columns:
| Column | Description |
|---|---|
\_id |
The primary key for the table |
accountId |
The account ID |
adClicks |
Total number of clicks for the day |
adCost |
Total cost for the campaign for the day |
adwordsCampaignID |
Adwords Campaign ID |
campaign |
Campaign name (for example, utm_campaign) |
date |
The date the campaign ran |
impressions |
Number of impressions for the day |
profileId |
The profile ID |
profileName |
The profile name |
\_updated\_at |
The date and time of the last update for this row |
keyword |
The keyword of the campaign |
adContent |
The first line of the text for the online campaign |
adDestinationUrl |
The URL to which the Adwords ads referred traffic |
adGroup |
The name of the Adwords ad group |
Using this data, you can start creating metrics and reports based on spending data and marry it to your lifetime revenue to calculate ROI.
Consolidated tables
Adobe recommends creating a consolidated ad spend table to combine the data from all of your multiple advertising sources into a single table. This enables you to use a single set of metrics for advertising analysis.
If you do not have a consolidated table and you build a beautiful dashboard on the adwords table, you need to replicate the reporting or create duplicate metrics to compare that data to your Facebook Ads data. Using a consolidated table allows you to seamlessly incorporate Facebook Ads data with your existing Adwords reports. You can segment by ad platform as well.
If you have already synced the fields above, contact us to consolidate your ad spend.
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