How to Create a Trended Funnel Report in Google Data Studio for Shopping and Checkout Behaviour Analysis

by Tara McClinchey

Visualising funnels in Google Data Studio takes some creativity without a default widget available. You may have built a funnel using scorecard widgets and rectangles or an uploaded image of an inverted triangle. We have used scorecard widgets with arrows between them. Scorecards work well to show the static performance of each step in your funnel.  This can be taken a step further, though, by creating a trended funnel report. Showing each step in a Time Series widget will not only provide a visualisation for funnel data; it will also provide a trendline for each funnel step. In the following post, I will explain how to achieve this to visualise Shopping and Checkout Behaviour funnels.

Note that the following instructions require enhanced ecommerce. My next post will show you how Data Studio can be used for funnel reporting without having access to enhanced ecommerce in Google Analytics.

How to create a trended funnel report for shopping and checkout behavior analysis

1. Add a Time Series widget  to the report. On a page that is sized by default, it is best if it can fill most of the width and approximately half of the length of the page so that the dates and trend lines can be displayed clearly.

2. Make sure the Time Series Properties panel is displayed to the right of the report (see image below). If not, click on the Time Series widget to access its Properties options.

3. In the Properties panel, select your preferred Time Dimension for the X-axis. ‘Date’ is the default dimension, displaying the date in the ‘mmm d’ or ‘mmm dd’ format (see image below).

Other Time Dimension options include:

    • Hour (e.g., 10 AM)
    • Day of the month (e.g., 1, 15, 31)
    • ISO Week of the Year (e.g., Week 40)
    • Day of Week (e.g., Sunday)
    • Minute (e.g., 4, 36, 56)
    • Year (e.g., 2017)
    • Hour of Day (e.g., Sep 19, 12 AM)
    • Month of Year (e.g., Sep 2017)
    • Month of the Year (e.g., September)
    • ISO Week of ISO Year (e.g., Sep 25, 2017)

4. In the Properties panel, select ‘Shopping Stage’ as the Breakdown Dimension, and use ‘Sessions’ as the Metric. Keep the default Breakdown Dimension Sort options: Sessions in Descending order.

5. In the Properties panel, select the Default Date Range. The ‘Auto’ range is set to the last 28 days by default, or can be customised using the date widget. Custom options include the last 7, 14, 28, or 30 days; today or yesterday; this week or last week; this month or last month; this quarter or last quarter; this year or last year; or a customised fixed period.

6. Plan which filters to apply to the Time Series widget. The filters will differ slightly between Shopping and Checkout Behavior, and will depend on your reporting requirements.

a. Decide which Shopping Stages to include in each trended funnel.  In the Google Analytics View you have as your Data Source in the report, navigate to the Shopping Behavior report, and take note of the stages it includes (see image below using Google Merchandise Store Data).

Navigate to the Checkout Behavior report, and take note of the stages it includes (see image below using Google Merchandise Store Data).

b. Create a Custom Report and select ‘Shopping Stage’ as the dimension and ’Sessions’ as the metric. The report will provide you with all the stages available (see image below using Google Merchandise Store data).

c. Take note of the stages to include in the Shopping Behavior Funnel (e.g., ALL_VISITS, PRODUCT_VIEW, ADD_TO_CART, CHECKOUT, TRANSACTION), and repeat for the Checkout Behavior Funnel (e.g., CHECKOUT_1, CHECKOUT_2, CHECKOUT_3, TRANSACTION).

d. Similarly, take note of the stages to exclude from each funnel. Neither funnel will include the following:

1. Stages containing NO (NO_SHOPPING_ACTIVITY, NO_CART_ADDITION, NO_PRODUCT_VIEW);

2. Stages containing WITH (ADD_TO_CART_WITH_VIEW, CHECKOUT_WITH_CART_ADDITION, ADD_TO_CART_WITHOUT_VIEW, CHECKOUT_WITHOUT_CART_ADDITION, TRANSACTION_WITHOUT_CHECKOUT, CHECKOUT_3_WITHOUT_CHECKOUT_2, CHECKOUT_2_WITHOUT_CHECKOUT_1);

3. Stages containing ABANDON (CART_ABANDONMENT, CHECKOUT_ABANDONMENT, CHECKOUT_1_ABANDONMENT, CHECKOUT_2_ABANDONMENT, CHECKOUT_3_ABANDONMENT).

7. Finally, at the bottom of the Properties panel, click on  and give the filter a descriptive name (e.g., Exclude ‘NO’ steps). Then use the information gathered in Step 7 to develop the filters. Apply the following to both Shopping and Checkout Behavior trend funnels:

a. Exclude ‘NO’ steps:

b. Exclude WITH steps (will exclude both WITH and WITHOUT):

c. Exclude Abandons:

Apply the following to the Shopping Behavior trend funnel:

d. Exclude CHECKOUT_ (to exclude CHECKOUT_1, CHECKOUT_2, CHECKOUT_3):

Apply the following to the Checkout Behavior trend funnel:

e. Include Checkouts and Transaction:

Click  to apply filters to the Time Series widget.

Below is an example of the Shopping Behavior trended funnel, including the static funnel steps on top (using Google Merchandise Store Data). 

Have any questions or comments? We would love to hear from you.

Tara McClinchey

Analyst, and Data Studio Practice Lead

As a member of Napkyn's Analyst Team and Practice Lead for Google Data Studio, Tara is an expert in telling the story behind the data through data analysis, rich visualizations, and client consultation and training.

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