Ready to get the most from your Mailchimp email marketing data? Get started by setting up a connection between PowerMetrics and your Mailchimp data and adding one (or more) modelled data sources.
When you're finished adding Mailchimp modelled data sources, you'll use them to power your custom metrics.
This article includes:
Creating a Mailchimp modelled data source
When you create a modelled data source, you:
- Connect PowerMetrics to your Mailchimp data.
- Choose the data you want to retrieve.
- Refine the data in the modeller (optional).
Connecting PowerMetrics to your Mailchimp data
The first step in creating a Mailchimp data source is to connect PowerMetrics to your data. The first time you connect PowerMetrics to your Mailchimp data, you'll be asked to enter your Mailchimp API Key.
How do I find my Mailchimp API Key?
- Click the following link: Your Mailchimp API Key.
- Enter your Mailchimp login credentials.
- Copy the number that displays in the API Key field. This is your Mailchimp API Key.
Note: If, at some point, you need to know your Data Center Name, it is the characters following the dash at the end of your API Key, for example, us12.
You can use the same account connection every time you connect to Mailchimp. You can also edit the connection (rename or enter a different API Key) by clicking the 3-dot menu for the connection. (See below.)
To connect PowerMetrics to your Mailchimp data:
- In the left navigation sidebar, click your Account Name > Data Sources.
- Click the Create a New Data Source button.
- On the Where is your data? page, select Mailchimp.
- If this is your first time connecting to Mailchimp, click Add account settings.
- Enter your Mailchimp API Key and click Use account.
You're connected! Next step – choose the data you want to retrieve.
Note: The procedure above describes the first time you connect to Mailchimp to create a data source. The next time you create a data source, we assume you want to connect to the same account as last time and take you directly to the next step (choosing the data you want to retrieve). If you want to connect to a different account, you can do so from there by clicking the account connection at the top of the data preview window (see below).
Choosing the data you want to retrieve
This is where you tell us what data you want to retrieve for your modelled data source. Later, when you create custom metrics, you’ll choose which pieces of data from the data source you want to display for each metric.
Using the data view you select, we’ll run a query to get a list of available columns (fields) from within the data view. You’ll then choose from those columns to specify what data to include in your modelled data source.
Tip: Use filtering to pinpoint the data you want to retrieve and include in your modelled data source. This reduces the size of the query, improving performance and ensuring you only get the data you actually need. You can apply filters to the data view and to columns in the left sidebar and in the data preview. Learn more about filtering.
To choose the data you want to retrieve:
- Under Data view, click the drop-down and choose the subset of data you want to query.
Tip: Find the data view you’re looking for by entering its name in the search box or by entering the name of a column/field to find the data view that includes it.
- Under Data view filters, add required filters (if needed) and optional filters (if desired).
Tip: APIs for some services include "StartDate" and "EndDate" as optional data view filters. Applying filters here, at the query (source) level, rather than later at the query results level can improve efficiency and simplify data handling.
- Under Columns, select the checkboxes beside the columns you want to include in your modelled data source.
Tip: Columns represent the fields that are available for the data view you selected above. Each item you choose will display as a column in the data preview table. If there are a lot of columns to choose from and you know what you’re looking for, start typing the column name into the search box. You can add up to 30 columns for each data view.
- Optionally, apply filters to applicable columns to narrow down your data before previewing.
Tip: You may find it easier to apply filters to columns in the next step (in the data preview table) as you can see your data more clearly there. However, if you know your data set well, applying filters in the sidebar can be beneficial because it removes excess data before previewing. It may also be necessary when your data is too large to query all at once.
- When you’re done selecting (and filtering) columns, click Preview data. The data preview window is populated with the results of the query for the columns you chose.
Note: The data preview table can show up to the first 1000 rows of your data. As a result, if you have a large data set, some rows may not display. Using the drop-down, you can select the number of rows to display (50, 100, 500, or 1000). You can also see how long it took for your query to complete. (See below.)
- If this isn’t the data you want for your modelled data source, choose a different data view and/or columns. To preview your most recent choices, either click Preview data again or Reload data (in the data preview window).
Note: At the top of the data preview window, you can see the Mailchimp account you’re currently connected to (see below). By clicking it, you can change to a different account. If you change accounts, your recent choices are removed and you start over by choosing a data view and columns.
- In the data preview window, you can narrow down the data you want to include in your modelled data source by applying filters to applicable columns.
- When you’re satisfied with your choices, click Save and continue.
You've chosen your data. Next step - refine the data.
Refining the data for your modelled data source (optional)
You’ve arrived in the modeller where you can choose to either accept the automatic settings or make changes.
If you’re happy with the automatic settings:
- Click Save and exit.
If you want to make changes:
- Some common adjustments include changing the data format or names for columns, updating the modelled data source name, and combining columns using formulas - there are many customization options available.
- If you want to adjust your query choices (for example, add or remove columns or filters) click the Back button to return to the previous screen.
- When you’re finished modelling your data, click Save and exit.
There’s lots of helpful information in this article: Learn how to model a data source. However, there are a few exceptions to note for modelled data sources that will be used for custom metrics:
- The columns in the modeller that were generated from the columns/fields chosen in the previous step are locked. You can rename and move them but you can’t enter new formulas for them or duplicate or delete them.
- If you add new columns in the modeller, they are editable (not locked) and can include formulas that refer to the locked columns or other added columns.
- Formulas that use results references are supported. Formulas that use datasource references are not supported.
Now that you've created one or more modelled Mailchimp data sources, you can return to them and use them to make custom metrics. A single modelled data source can be used to create a single or multiple custom metrics. To see your list of modelled data sources, in the left navigation sidebar, click your Account Name > Data Sources. Select the modelled data source from the list to open its details page. Click Create metrics.
View your metrics from multiple perspectives. Explore and analyze your data. Gather your metrics onto dashboards and share them with your colleagues. Track your progress as your data grows and changes over time. Most importantly - use the insights you get from PowerMetrics to make the right decisions at the right time.