Many of us have data from multiple sources and in many different files. We want to answer high-level questions, like what does email engagement look like when broken down by subscription plan? Or, how does email engagement change with different sign up cohorts? This requires pulling data from an email marketing service like MailChimp, and a payment tracking service, like Stripe. Thanks to DataHero’s newest feature, combines, organizations can take the data from multiple sources and merge it into one to answer those encompassing business questions.
Combining datasets allows us to answer important business questions and get a comprehensive view of our data across various sources. Being able to combine datasets in DataHero from a payment service (Stripe in this case) and an email marketing service (MailChimp here), allows us to tie ROI directly to our email marketing efforts. Let’s see how.
Combining two data sets:
1. To begin, ensure both MailChimp and Stripe are connected to the DataHero account. Then import the Customers and Charges report from Stripe and a campaign from Mailchimp.
2. Navigate to Customers and Charges report in Stripe then click “Combine Your Data” in the left column. Click “Combine With Another Dataset”.
3. DataHero then suggests keys that you can combine on, in this case select Email.
4. Select the next dataset. Click on MailChimp in the left column, then select the campaign you want to combine on.
5. Drag the common key (email) onto the drag and drop area.
6. Check the total records in each dataset and verify the combined number is what you would expect.
7. Click Combine and retitle the new dataset.
After we’ve laid the groundwork for data analysis by importing the correct reports, we’ll start making the charts that answer these key business questions. The charting process is very similar to the charting process with an un-combined dataset. You can choose to use one of DataHero’s suggested charts, or create a new chart.
In this post we’ll create a new chart so I can outline exactly which attributes to add to each chart to get the answers to the two questions mentioned above.
What does email engagement look like broken down by subscription plan?
From the Combined dataset page click “Create New Chart”
Drag on the following attributes:
Once you drag these two attributes onto the chart canvas, DataHero will automatically create the following stacked column chart:
Above we can see when email recipients clicked on the link in the email campaign, based on their subscription plan. Perhaps there are more Platinum members clicking at a certain time, providing insight into when to send each email based on customer plan segments.
How does email engagement change with different sign up cohorts?
A sign up cohort is a group of individuals who registered for a service around the same time. We can easily chart cohorts of sign ups as they relate to email engagement by combining Stripe and MailChimp data.
Drag on the following attributes:
Ensure that the time grouping under Charge Created is Month
Ensure that the time grouping under First Click is Hour
DataHero then generates the following chart of time of first click broken down by sign up cohort:
We can see in the chart above that the July 2013 cohort is the most active in the email campaign but there are no cohorts before April of 2013. It may be time to send out a campaign specifically to re-engage these users from older cohorts. It’s also interesting to note that in this example the email was sent out at 6 AM but most clicks (from all cohorts) occurred between 12 PM and 2 PM. Perhaps it would make more sense to send out campaigns around this time.
We can now make better data-driven decisions about email marketing based on payment data with DataHero combining. Get the answers you need today, by merging datasets more easily than ever before. Sign up for a DataHero account and give it a try with your data today.
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