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Email Analytics Proves Success
Posted On October 3, 2019
Email was one of the major outbound marketing channels for this brand. The email list was large and mature with open and click through rates that satisfied industry benchmarks. However, there were issues determining if users were buying products on the email or at all! Through the use of email analytics tools such a Google Analytics and Data Studio, it was determined to be a successful medium. Also, a weekly dynamic reporting system put in place.
The Email Marketing Specialist and Chief Marketing Officer wanted to track when emails are viewed, whether these users are buying the products from the email. So, first I thought of tracking clicks from the emails to get a sense of the actual data coming to the website. I used the Campaign URL Builder to start to segment out the traffic. I measured data from campaigns and even specific content within the email!
I parsed through data with the Email Marketing Specialist who had partially tracked some campaigns, so that I could recover some data. Then, I created relevant names to each image, link and icon in each email. In addition to this, I created segments in Google Analytics based on these parameters and the email medium. These insights helped to remove the need the of the Webmaster creating separate landing pages for specific campaigns. As a result of this email workflow speed was increased and future email would have proper tracking.
Challenge 2: Created a System for Dynamic Email Analytics
As data started to populate from subsequent campaigns, I realized that there should be a template of reporting key performance indicators. That would be consistent and could change dynamically week over week. The solution would be to implement the use of Data Studio. Above is the template I would use to do weekly reports on the email performance. Above all, this clarified the question of whether email users were buying the products on each email. The majority of the time they would not, but they did use the email as a reminder to buy.
I segmented out the email data into sessions and users. This is because even though the marketing team would like users to purchase after clicking the email, they did not always. Specially, I analyzed that users that had open emails from weeks ago would continue to buy different products. For this reason, we got a better idea of our customer and the length of the buying cycle. To emphasize this I would have a blue data of the past week campaign next to all email users in the past 30 days.
Moreover, I imported data pie chart on new users vs returning user. This would give us an idea of customer retention and loyalty. Another, insight I developed was a pivot table that showed the specific campaign, product, revenue and quantity purchased. Certainly, this provided a wealth of information to guide our marketing decisions that we didn’t have before!
d Even though users did not always buy the products suggested in the email directly from the web. However, the email was used as a reminder to buy for many users who know their favorite products.
Also, many users who had sales representatives would forward these emails to them to complete the purchase of those items! We also realized by expanding the data that users who have viewed email come back to the website at multiple touch points such a direct, google/organic, google/ppc and social.