There is an overwhelming amount of information related to email marketing analytics, but a lack of information related to the details of the data munging required to make it usable. Avinash Kaushik again provides an incredible resource for KPIs related to Email Marketing: Campaign Analysis, Metrics, Best Practices. This article briefly explains an effective email data warehouse for measuring performance and how to connect it with additional data sources.
Some of the real insights that need to be achieved through email analytics are questions like:
- What % of our newsletter subscribers have opened or clicked on an email within the last 90 days?
- Has that % improved compared to a trailing 180 days or trailing 12 months?
- Does email marketing increase r
Frequently email data is stored by an email service provider like Exact Target or Lyris and only accessible via an API. The data that is in-turn downloaded needs to be normalized and stored in relational tables that provide a foundation for later analysis. The foundation should consist of four major components that is shown in below image and described accordingly, which in turn can be used for aggregates that can be used for analyzing engagement.
Message : any meta data related to an email, i.e. subject, sentdate
List: a subscriber list that is the starting point for a email newsletter, this list may be segmented for the purpose of a specific send
Member: the meta data for a subscriber that is used for segmentation…meta data frequently has to be updated from a CRM and uploaded to email marketing solution to in-turn perform segmentation; therefore, a unique id to the CRM should be included for later connection of data.
Transaction: every event that is able to be logged at an individual user and message level, i.e. open, click, send, or unsubscribe
Summarizing individual campaigns will provide understanding of how a campaign performed and compare to other campaigns. This can provide answers on :
- What day of the week results in most opens?
- Do shock value subject lines result in better open rates without causing drastic increases in unsubscribes or decreased opens over time
- Does a different sender result in more opens?
- Does using a person’s first name in subject?
- Does a different layout result in more clicks?
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|John Smith, 3-D Visualizations
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|3-D Visualizations with rotating
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1. Does a test in subject line have less impact then a date of an email send in causing more opens?
2. Does using a person’s first name improve open rate?
3. Do factors other than the individual email and individual person have an impact that I as a sender can consider?
To assess these hypothesises we will need to look at transactional data and then assess if these hypotheses can be assessed across multiple emails. The next post will address these questions using the transactional level data and comparison of similar vairables.