While sending emails through Virtuous CRM+ is an impactful part of a Responsive Fundraising strategy, using Analytics to evaluate engagement creates a real strategic advantage to your next email campaign. Analytics can help you predict giving patterns related to sent emails, identify patterns in donor engagements, and maximize every fundraising opportunity. In this guide, we cover the different tables needed for analyzing email performance in Analytics and some guidance for common reporting scenarios.
What Tables Include Email Performance?
Depending on what you need to include in your report, Analytics has both pre-built aggregate datasets and raw dataset options for email engagement tables.
Pre-Built Aggregate Datasets
These tables provide pre-calculated statistics for quick analysis:
- ag_sent_email_statistics Table: Includes high-level statistics on sent email engagement based on email ID. This table is helpful for understanding overall email performance, but it does not include specific contact engagement, send date data, or email names.
- ag_contact_email_statistics Table: Includes high-level statistics on how contacts are engaging with your email campaigns. This table is helpful for identifying contacts who frequently interact with your emails and general email performance over different time periods, but it does not include individual email open and click rates or specific email performance metrics.
Raw Datasets
Analytics provides one table with raw email performance data, and this can be joined with email dimension tables to create custom analysis:
- ft_contact_email_actions Table: Displays raw email engagement data. This table does not include any pre-built statistics like the aggregate tables, but it can be used to build custom statistics. This allows you more flexibility in building the exact insights your team needs.
- dm_emails Table: Contains raw email information including email names, subject lines, and send dates. This table is essential for joining with the ft_contact_email_actions table (using DM Email IDs) to get complete email performance insights
- dm_email_lists Table: Contains email list information that may be helpful for analysis when joined with the ft_contact_email_actions table.
How Can I Build My Own Email Statistics?
Here are some practical ways your organization can use ft_contact_email_actions to create reports with custom statistics:
Scenario | How You'd Use ft_contact_email_actions |
|---|---|
| Which version of my Giving Tuesday email had a better click rate? | Add dm_emails and ft_contact_email_actions to your workbook. Add Version A Name and Version B Name columns via lookup to the ft_contact_email_actions. Then, filter by Email Version, calculate clicks รท sends to compare performance. |
| Did my mid-year appeal perform better than my year-end appeal? | Add dm_emails and ft_contact_email_actions to your workbook. Add Email Name to ft_contact_email_actions via lookup. Filter by Names of appeal emails, then calculate open and click rates for each appeal. |
| Are supporters opening more emails when sent on weekends? | Using ft_contact_email_actions, filter Contact Action Type to only include Open. Group by Action Date UTC, then truncate to the day to analyze open rates by day of the week. |
| How many people clicked on my May newsletter but didn't donate? | Using ft_contact_email_actions, filter Contact Action Type to only include Click, then use a lookup or join to compare against segment-specific donation data from ft_gift tables. Make sure to group by Contact ID so that Gifts are not double counted. |
| What's my email engagement trend over the past 6 months? | Using ft_contact_email_actions, group by month using Send Date, calculate open rates and click rates over time. |
| Which subject lines generate the highest open rates? | Using ft_contact_email_actions, group by subject lines, then calculate open rates by subject line variations. |
Important Considerations
- Contact-level data: For all email engagement tables, the data is recorded at the Contact level in Analytics. If multiple Individuals within the same Contact record engage with an email, each interaction is recorded separately as its own row. For this reason, you may see multiple actions of the same type for a single email.
- Action-based tracking: For the ft_contact_email_actions Table, each action (open, click, unsubscribe) creates a separate row in the table. So, a single Individual may have multiple actions, or rows, for each email.