In this post, we’re going to see how to setup a Google Sheets and Mailchimp integration, using Apps Script to access the Mailchimp API.
The end goal is to import campaign and list data into Google Sheets so we can analyze our Mailchimp data and create visualizations, like this one:
Mailchimp is a popular email service provider for small businesses. Google Sheets is popular with small businesses, digital marketers and other online folks. So let’s connect the two to build a Mailchimp data analysis tool in Google Sheets!
Once you have the data from Mailchimp in a Google Sheet, you can do all sorts of customized reporting, thereby saving you time in the long run.
I use Mailchimp myself to manage my own email list and send out campaigns, such as this beginner API guide (Interested?), so I was keen to create this Mailchimp integration so I can include Mailchimp KPI’s and visualizations in my business dashboards.
For this tutorial I collaborated with another data-obsessed marketer, Julian from Measure School, to create a video lesson. High quality video tutorials are hard to create but thankfully Julian is a master, so I hope you enjoy this one:
(Be sure to check out Julian’s YouTube channel for lots more data-driven marketing videos.)
In the steps outlined below, I walk through creating a CRUD app in Rails that saves images to, and serves images from, Amazon S3, using the paperclip gem. The images are displayed in a grid using Flexbox with some Ruby fun to change the grid layout each time the page is refreshed.
I’m working my way through this book, Data Analysis Using SQL and Excel, at the moment and chapter 6 is all about survival and retention modelling. I learn best when I can attack real life problems, so I took some of the lessons from this chapter and applied them to the MailChimp email data I already had in a MySQL database.
This post takes it a step further by looking at subscriber behaviour within the email campaign data. What can we say about how long people remain active subscribers? For a subscriber who has been active for a given length of time, how likely are they to continue being active?
This post digs deeper into the MailChimp data which I analyzed in my previous post. In this analysis, I’ve focused on the distribution of subscribers and what was the most recent date they opened an email.
Specifically, I wanted to answer:
What is the distribution of subscribers by campaign date that opened an email in 2014?
And what are their email addresses?
The way I approached this problem was to break it down into its constituent parts, tackle each of those and then build that back up into a single query.
This post shows how I analyzed a MailChimp email list, including all the data from weekly newsletters for 2014, using MySQL. It was inspired by this excellent tutorial: Performing cohort analysis by Micheal Herman. The email list I’m working with consists of about 18,000 subscribers.
I wanted to answer questions such as:
How many emails do active subscribers open on average?
How active are the subset of users who bought a product during the digital flash sale in March of this year?