Data in Marketing: Advanced Ways to Use Your Email Data

April 05, 2023

This blog is part of our new series “Data in Marketing.” Each month, we’ll bring you fresh tips on leveraging your data to enhance and optimize your marketing work. This month, we’re focusing on your email data.

In February, the JCA Arts Marketing team launched its newest offering: Email & Marketing Automation Services consulting and, to celebrate, we released a blog post “Quick Wins Using Your Email Data”. This blog post is the twin to that one—but these analytics prompts are not quick. This post is intended to give organizations who want to understand their email data more deeply some ideas of what can be possible once you get past the basics. Beyond simply looking at open and click rates for individual emails, these are more in-depth questions designed to probe deeper into the treasure trove of data in your email marketing system.

1. What words yield the best subject lines?

Here’s one, potentially fun, analysis you can try out with your comprehensive email analysis—look for patterns in successful subject lines. Oftentimes, the power of subject lines is overlooked, as we spend so much time on the content of the email, making sure it’s both visually appealing and engaging in its writing. Even if you are being thoughtful with subject lines, it can be hard to thread a line between all of your emails as to what makes your subject lines successful.

My suggestion? Break your last year’s worth of emails into deciles, according to open rate. (Note: While open rate numbers are hard to trust now because of Apple’s email policies, we’re just using these numbers to rank emails, assuming Apple’s effect will be proportional.) For each of your 10 deciles, aggregate the words from those subject lines and create a word cloud. (See? I told you this was a fun one!)

Using those word clouds, you can try to find patterns in the kinds of words that are creating email opens. Do emojis only appear in the top deciles? Does buoyant language appear to have most success, or is it critic’s quotes? Breaking your year’s worth of subject lines into the kind of visual a word cloud gives you can the key to knowing! (Bonus points: break your customers up by segments, like long term subscribers vs once-a-year attendees, and see if those word clouds change!)

2. What kind of migration patterns are happening in your email engagement?

To start this kind of analysis, create engagement segments for your database. In the same way that you segment your customers by purchase data, including behaviors like frequency, subscription purchases, donor status, and recency, you can think about your customers through a similar lens when it comes to how they interact with your emails.

Pre-purchase data, which can range from email clicks to webpage visits to video click-to-plays, is generally underutilized in the arts field. In addition to having thoughtful strategies to segment your customers by their purchase history, the wider world of customer engagement data can be an incredibly useful tool to add to your decision making.

In this case, break up your email database into distinct segments based on behavior like frequency of interactions (how often do they open and click), recency of engagement (when was the last time they engaged with one of your emails), and email type (within your email genres, which kinds of emails do they interact with most often). This will allow us to monitor if their engagement is falling off, a clear indicator that their relationship with your organization might be changing. Because this email behavior data happens earlier in the purchase process, we can intervene before an unopened email becomes a lapsed ticket buyer.

3. What links are most popular?

Click through rate (CTR) is one of, if not the most, crucial of email metrics. When it comes to marketing (especially digital marketing), we always want to track whether our customers took the next step we wanted them to take. For emails that contain a call-to-action (CTA), this usually means clicking through to your website.

However, all links are not created equal and a straightforward CTR can only tell you, of all the emails that were sent out, how often did someone click on any link in the email. To deepen your analysis, you can start bucketing your links according to your website sections. If you’ve worked with Content Groupings in Google Analytics, you’ve done this work before—essentially  you’re categorizing your webpages according to distinct categories like the pages’ goals (sales page vs. informational page) or topics (music events vs. dance events).

For example, for JCA Arts Marketing, this page “Future-Focused Performing Arts Consultants (” could be categorized as a “Verticals Page”, whereas this page “When is Interim Staffing right for your team? (“, would fall under “Blog Page”. If we were categorizing by topic, the former could be “Clients” and the latter “Interim Staffing”.

By doing the same thing with our email link clicks, compiling that data over a large period of time, we can start breaking down what piques people’s curiosity enough to click. Are discount pages the most popular? Or do people click on artist profiles most of all? Maybe it’s the customer service email provided? Looking at the larger data set of links over time can start giving us these answers.

Make Data-driven Decisions

If you want to enhance your email strategy and processes, we’re here to help. We’ll develop a roadmap for your email usage that supports your digital marketing initiatives and makes effective use of your people and technology.

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