PowerMetrics: Adding Mailchimp metrics

Visualize, discover, and act on your Mailchimp email marketing data with metrics and metric dashboards!

Get started by connecting to your Mailchimp data and adding some instant or custom metrics.

This article includes:

Connecting to your Mailchimp data

The first time you connect Klipfolio to your Mailchimp data, you'll be asked to enter your Mailchimp API Key.

How do I find my Mailchimp API Key?

  1. Click the following link: Your Mailchimp API Key.
  2. Enter your Mailchimp login credentials.
  3. 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.)

Adding Mailchimp instant metrics

PowerMetrics includes a wide variety of instant metrics for Mailchimp. Created for you by industry experts and based on best practices, our instant metrics will get you tracking your Mailchimp data in no time!

Note: The first time you connect to Mailchimp, you’ll need to enter your Mailchimp API Key.

To add Mailchimp instant metrics:

  1. In the left navigation sidebar click the + button beside Metrics.

Note: You can also add instant metrics from your Metric List page or from an open dashboard in edit mode.

  1. On the Where is your data? page, select Mailchimp.
  2. Select one or more instant metrics.

You can select up to 5 metrics each time you visit this page. If you want to learn more about a metric, click the information button (see below) to see a brief metric definition, a list of available dimensions, and the metric formula. For more detailed information, click the link to go to its information page in MetricHQ.

  1. Click Add metrics.
  2. If this is your first time adding metrics for Mailchimp, click Add account settings.

  1. Enter your Mailchimp API Key and click Use account.

Note: The next time you add Mailchimp instant metrics, you won’t need to enter your Mailchimp API Key. That’s because we assume you want to use the same account as before and skip that step. However, if you want to connect to a different account, before clicking “Add metrics”, click the account connection in the top right of the window (see below) and either select an alternate, existing account or click “Add new account”.

 

That's all there is to it! You just added Mailchimp instant metrics to your account. If you added them from the navigation sidebar or from your Metric List page, they're added to your list of metrics. If you added them from an open dashboard, they're added to the dashboard and to your list of metrics.

You're ready to start analyzing and tracking your data as it changes over time. Learn more about exploring your data.

Adding Mailchimp custom metrics

If our Mailchimp instant metrics don’t get you to the data you’re looking for, you can create a custom metric instead. Custom metrics put you in control. Pinpoint the exact data you’re looking for with queries and filters, further refine it by modelling (if needed), and, finally, choose your metric display properties.

To add Mailchimp custom metrics:

  1. Connect PowerMetrics to your Mailchimp account.
  2. Create a modelled data source.
  3. Choose settings for your custom metric.

Connecting PowerMetrics to your Mailchimp data

When you connect PowerMetrics to your Mailchimp data, you enter your Mailchimp API Key.

When you create custom metrics next time, you can use the same account connection or add new account settings (using a different API Key) to point to different data.

To connect PowerMetrics to your Mailchimp data:

  1. In the left navigation sidebar click the + button beside Metrics.

Note: You can also add a custom metric from your Metric List page or from an open dashboard in edit mode.

  1. On the Where is your data? page, select Mailchimp.
  2. Click Create a custom metric.
  3. If this is your first time connecting to Mailchimp, click Add account settings.

  1. Enter your Mailchimp API Key and click Use account.

Note: The next time you connect to Mailchimp to create a custom metric, you won’t need to enter your Mailchimp API Key. That’s because we assume you want to use the same account as before and take you directly to the next step (creating a Mailchimp modelled data source). However, 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) and either selecting an alternate, existing account or clicking “Add new account”.

You're connected! Next step – create a modelled data source.

Creating a Mailchimp modelled data source

Now that you’ve connected to your data, you’re ready to create a modelled data source. A single modelled data source can be used to create a single or multiple custom metrics.

As you create a Mailchimp modelled data source, you will:

Choosing data to include in your modelled data source

As a first step, you’ll 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: You can apply filters to the data view and to columns in the left sidebar and in the data preview. Applying filters helps narrow down the data you’re looking for, ensuring you only query the data you need. By pinpointing your desired data, you optimize the volume of data being queried, which not only makes your data easier to understand and handle, it often makes the query run faster! Learn more about filtering.

To choose data to include in your modelled data source:

  1. In the Choose data for your metric page, under Data view, click the drop-down and choose the subset of data you want to query.

  1. 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.

  1. 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.

  1. Optionally, apply filters to applicable columns to narrow down your data before previewing.

Note: 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.

  1. 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 display up to the first 1000 rows of your data. As a result, if you have a large data set, some rows may not display.

  1. 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.

  1. 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.
  2. 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 Mailchimp modelled data source

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:

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 continue and proceed to Choosing settings for your Mailchimp custom metric.

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 that were generated from the 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.

Note: After you’re finished creating a custom metric with this modelled data source, you can return to it and use it to make more custom metrics. 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.

You've created a modelled Mailchimp data source. Next step - choose settings for your custom metric.

Choosing settings for your Mailchimp custom metric

Great! You’ve created your first Mailchimp modelled data source. It’s time to use it to make your first custom metric.

To choose settings for your Mailchimp custom metric:

  1. In the Metric value step, choose which column from your modelled data source that you want to track in your metric. Click Next.

Once you've chosen a metric value, its default aggregation type displays. For numeric columns, the default aggregation type is “sum”. For text columns, it's “count”. The default aggregation type will be used wherever there are no configuration options available. For example, on the Metric List page, metrics will display using a default aggregation type based on your selection here.

  1. In the Segmentation step, select the columns by which you want to be able to segment and filter your metric value. Click Next.

You can select up to 5 columns. Each selection shows a unique view of your business, for example, you might choose to segment your sales data based on region, sales rep, or product type. The segments you select when creating a metric become filters in the finished metric. You can filter on the individual metric level or at the dashboard level (when you add your metric to a dashboard).

If you want to create a metric without allowing filtering or segmentation, click the toggle button at Segment values for this metric to turn off this step in the setup wizard.

When choosing segmentation, you can only select columns that have a text format. If the modelled data source for your metric doesn’t include any text columns (or has only one text column) there will be no available categories by which to segment. This step will be marked as complete and you will move on to the next step in the wizard.

  1. In the Date & time step, either select a timestamp column from your modelled data source or Use the current date (if your modelled data source doesn’t include a timestamp column). Click Next.
  2. In the Date shape step, select an appropriate aggregation method: Transactional values, Current values, or Periodic summary. Click Next.

Your choice determines the data points your metric will use when calculating the metric value for a time period. If you’re unsure which option is best, that's okay, you can change your selection later by editing the metric:

  • Transactional values: Select this option if you want to include every value for a time period to create the total of values. For example, you may have a spreadsheet that includes all sales transactions for the month of January. In this case, you want to sum all values to get the total values for the month.
  • Current values: Select this option if your data includes current value totals for specific points in time and new values replace older values. For example, if you are tracking social media followers, each import gives you the current value, which is the total of all values for the time period.
  • Periodic summary: (Hour, Day, Week, Month, Year) Select this option if each value in your data is a summary of a time period. For example, with Google Analytics "sessions by day" data, the value at any point in time is the summary up to that point in the day. Your data is collected again on the following day, beginning at zero. In such cases, the last value is the complete summary of the day. To calculate the metric's value for a month, you sum the last value for each day in the month. When you select this option, you also select a time period from the drop-down list below it (Hour, Day, Week, Month, or Year).
  1. In the Display settings step:
  • Enter a Metric name.
  • Choose a data format: Numeric, Currency, Percentage, or Duration.
  • Optionally, set your data to Show as cumulative by default for all of the metric's view modes.
  • Optionally, set the Favourable trend for your metric. Choose whether you want ascending or descending values to indicate a positive trend. For example, if your metric includes sales totals, a higher (ascending) number demonstrates a positive trend but, if your metric includes cost to acquire customers, a lower (descending) number indicates a positive trend. Favourable trends are indicated by a green arrow. Note: This option is not applicable to all visualization types.
  1. Click Save.

 

If you added the custom metric from the navigation sidebar or from your Metric List page, it's added to your list of metrics. If you added the custom metric from an open dashboard, it's added to the dashboard and to your list of metrics.

You’re ready to start analyzing and tracking your data as it changes over time. Learn more about exploring your data.

Next steps

Gather your metrics into dashboards, track and analyze your data as it grows and changes over time, share with your colleagues, explore your data from multiple perspectives - but above all - use the insights you get from PowerMetrics to make the right decisions at the right time.

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